[DISCUSS] FLIP-107: Reading table columns from different parts of source records

[Jark Wu-2]

Hi Timo,

1.  "`Map<String, DataType> listReadableMetadata()` only allows one
possible DataType for a metadata key."
I think the main purpose of the metadata feature is to access the Kafka
timestamp and use it as a rowtime attribute.
If we force users to use the specific type, then this feature might be
tricky to use,
e.g. rowtime AS CAST(CAST(SYSTEM_METADATA("timestamp") AS BIGINT) AS
TIMESTAMP(3) WITH LOCAL TIME ZONE). It will be super long.

My suggestion would be either we use "TIMESTAMP(3) WITH LOCAL TIME ZONE" as
the defined type of Kafka timestamp,
or allow different types in the CAST iff the defined type can be casted to
the cast type, for example,
CAST(SYSTEM_METADATA("partition") AS BIGINT) can be valid, because the
defined type "INT" can be casted to the cast type "BIGINT".

The former one is more concise, the later one is more flexible. What do you
think?

2. "I don't see a reason why `DecodingFormat#applyReadableMetadata` needs a
DataType argument."
Do you mean the output TypeInformation of the `DeserializationSchema` can
be calculated via producedDataType + metadata columns?
Then maybe we also don't need the outputDataType argument for
`SupportsReadingMetadata#applyReadableMetadata`.
I guess the outputDataType is needed here because
of SupportsComputedColumnPushDown?  Btw, Shengkai started a discussion in
the
mailing list to merge `SupportsComputedColumnPushDown` and
`SupportsWatermarkPushdown` interfaces [1].

3. "list the metadata keys"
LGTM. Maybe we can expand the properties in the "source", e.g. allow
"source.ts_ms" metadata, this is the most commonly used one.


Best,
Jark

[1]:
http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/Merge-SupportsComputedColumnPushDown-and-SupportsWatermarkPushDown-td44387.html



On Mon, 7 Sep 2020 at 23:51, Timo Walther <[hidden email]> wrote:

> Hi Jark,
>
> 1. "`Map<String, DataType> listReadableMetadata()` only allows one
> possible DataType for a metadata key."
> I was thinking about this topic a lot today. My conclusion is: yes, we
> should force users to specify the type as documented. Users can further
> cast or compute using expressions to more specific types. I decided for
> BIGINT instead of TIMESTAMP(3) for Kafka timestamps, I think for
> metadata we should directly forward the underlying atomic type of the
> external system. And for a Kafka consumer record this is BIGINT without
> any timezone interpretation. Users can further cast to TIMESTAMP(3) if
> necessary. I wouldn't introduce too much magic here. What do you think?
>
> 2. I don't see a reason why `DecodingFormat#applyReadableMetadata` needs
> a DataType argument. This argument would need to be created by the
> source then. Do you have an example in mind? In any case the format
> could also calculate it later via: producedDataType + metadata columns
>
> 3. "list the metadata keys"
> I went through the list of current connectors and formats. I updated the
> FLIP for the Kafka and Debezium. For the key design, I used the FLIP-122
> naming schema. For HBase, Elasticsearch and others I could not find
> metadata that might be important for users.
>
> 4. "sub-expression"
> Yes, sub-expression like the ones you mentioned would be allowed.
> We will push down only one "headers" metadata.
>
> Regards,
> Timo
>
>
> On 07.09.20 14:41, Jark Wu wrote:
> > Sorry, I forgot to ask one more question.
> >
> > 4. Do we allow to use the SYSTEM_METADATA as a sub-expression? For
> example,
> >
> > checksum AS CAST(CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
> > BYTES>)['checksum'] AS STRING),
> > myvalue AS CAST(CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
> > BYTES>)['mykey'] AS BIGINT)
> >
> > And we will push down only one "headers" metadata, right?
> >
> > Best,
> > Jark
> >
> >
> >
> > On Mon, 7 Sep 2020 at 19:55, Jark Wu <[hidden email]> wrote:
> >
> >> Thanks Timo,
> >>
> >> I think this FLIP is already in great shape!
> >>
> >> I have following questions:
> >>
> >> 1. `Map<String, DataType> listReadableMetadata()` only allows one
> possible
> >> DataType for a metadata key.
> >> However, users may expect to use different types, e.g. for "timestamp"
> >> metadata, users may use it as BIGINT, or TIMESTAMP(6) WITH LOCAL TIME
> ZONE
> >>   or TIMESTAMP(3) WITH LOCAL TIME ZONE.
> >> Do we force users to use the specific types or can use several types in
> >> the CAST?
> >>
> >> 2. Why does the `DecodingFormat#applyReadableMetadata(List<String>
> >> metadataKeys)` don't need the `DataType outputDataType` parameter?
> >>
> >> 3. I think it would be great if we can list the metadata keys (and
> >> readable/writable) we want to expose in the first version. I think they
> are
> >> also important public APIs, like connector options?
> >>
> >> Best,
> >> Jark
> >>
> >> On Mon, 7 Sep 2020 at 18:28, Timo Walther <[hidden email]> wrote:
> >>
> >>> Hi Leonard,
> >>>
> >>> thanks for your feedback.
> >>>
> >>> (1) Actually, I discuss this already in the FLIP. But let me summarize
> >>> our options again if it was not clear enough in the FLIP:
> >>>
> >>> a) CREATE TABLE t (a AS CAST(SYSTEM_METADATA("offset") AS INT))
> >>> pro: readable, complex arithmetic possible, more SQL compliant, SQL
> >>> Server compliant
> >>> con: long
> >>>
> >>> b) CREATE TABLE t (a INT AS SYSTEM_METADATA("offset"))
> >>> pro: shorter, not SQL nor SQL Server compliant
> >>> con: requires parser changes, no complex arithmetic like
> >>> `computeSomeThing(SYSTEM_METADATA("offset"))` possible
> >>>
> >>> c) CREATE TABLE t (a AS SYSTEM_METADATA("offset", INT))
> >>> pro: shorter, very readable, complex arithmetic possible
> >>> con: non SQL expression, requires parser changes
> >>>
> >>> So I decided for a) with less disadvantages.
> >>>
> >>> 2) Yes, a format can expose its metadata through the mentioned
> >>> interfaces in the FLIP. I added an example to the FLIP.
> >>>
> >>> 3) The concept of a key or value format is connector specific. And
> since
> >>> the table source/table sinks are responsible for returning the metadata
> >>> columns. We can allow this in the future due to the flexibility of the
> >>> design. But I also don't think that we need this case for now. I think
> >>> we can focus on the value format and ignore metadata from the key.
> >>>
> >>> Regards,
> >>> Timo
> >>>
> >>>
> >>> On 07.09.20 11:03, Leonard Xu wrote:
> >>>> Ignore  my question(4), I’ve  found the answer in the doc :
> >>> 'value.fields-include' = ‘EXCEPT_KEY' (all fields of the schema minus
> >>> fields of the key)
> >>>>
> >>>>> 在 2020年9月7日，16:33，Leonard Xu <[hidden email]> 写道：
> >>>>>
> >>>>> (4) About Reading and writing from key and value section, we bind
> that
> >>> the fields of key part must belong to the fields of value part
> according to
> >>> the options 'key.fields' = 'id, name' and 'value.fields-include' =
> 'ALL',
> >>> Is this by design? I think the key fields and value fields are
> independent
> >>> each other in Kafka.
> >>>>>
> >>>>
> >>>>
> >>>
> >>>
> >
>
>

[Danny Chan]

Thanks Timo ~

The FLIP was already in pretty good shape, I have only 2 questions here:


1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid read-only computed column for Kafka and can be extracted by the planner.”


What is the pros we follow the SQL-SERVER syntax here ? Usually an expression return type can be inferred automatically. But I guess SQL-SERVER does not have function like SYSTEM_METADATA which actually does not have a specific return type.

And why not use the Oracle or MySQL syntax there ?

column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
Which is more straight-forward.

2. “SYSTEM_METADATA("offset")` returns the NULL type by default”

The default type should not be NULL because only NULL literal does that. Usually we use ANY as the type if we do not know the specific type in the SQL context. ANY means the physical value can be any java object.

[1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
[2] https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html

Best,
Danny Chan
在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：

> Hi everyone,
>
> I completely reworked FLIP-107. It now covers the full story how to read
> and write metadata from/to connectors and formats. It considers all of
> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It introduces
> the concept of PERSISTED computed columns and leaves out partitioning
> for now.
>
> Looking forward to your feedback.
>
> Regards,
> Timo
>
>
> On 04.03.20 09:45, Kurt Young wrote:
> > Sorry, forgot one question.
> >
> > 4. Can we make the value.fields-include more orthogonal? Like one can
> > specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> > With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not config to
> > just ignore timestamp but keep key.
> >
> > Best,
> > Kurt
> >
> >
> > On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]> wrote:
> >
> > > Hi Dawid,
> > >
> > > I have a couple of questions around key fields, actually I also have some
> > > other questions but want to be focused on key fields first.
> > >
> > > 1. I don't fully understand the usage of "key.fields". Is this option only
> > > valid during write operation? Because for
> > > reading, I can't imagine how such options can be applied. I would expect
> > > that there might be a SYSTEM_METADATA("key")
> > > to read and assign the key to a normal field?
> > >
> > > 2. If "key.fields" is only valid in write operation, I want to propose we
> > > can simplify the options to not introducing key.format.type and
> > > other related options. I think a single "key.field" (not fields) would be
> > > enough, users can use UDF to calculate whatever key they
> > > want before sink.
> > >
> > > 3. Also I don't want to introduce "value.format.type" and
> > > "value.format.xxx" with the "value" prefix. Not every connector has a
> > > concept
> > > of key and values. The old parameter "format.type" already good enough to
> > > use.
> > >
> > > Best,
> > > Kurt
> > >
> > >
> > > On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]> wrote:
> > >
> > > > Thanks Dawid,
> > > >
> > > > I have two more questions.
> > > >
> > > > > SupportsMetadata
> > > > Introducing SupportsMetadata sounds good to me. But I have some questions
> > > > regarding to this interface.
> > > > 1) How do the source know what the expected return type of each metadata?
> > > > 2) Where to put the metadata fields? Append to the existing physical
> > > > fields?
> > > > If yes, I would suggest to change the signature to `TableSource
> > > > appendMetadataFields(String[] metadataNames, DataType[] metadataTypes)`
> > > >
> > > > > SYSTEM_METADATA("partition")
> > > > Can SYSTEM_METADATA() function be used nested in a computed column
> > > > expression? If yes, how to specify the return type of SYSTEM_METADATA?
> > > >
> > > > Best,
> > > > Jark
> > > >
> > > > On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <[hidden email]>
> > > > wrote:
> > > >
> > > > > Hi,
> > > > >
> > > > > 1. I thought a bit more on how the source would emit the columns and I
> > > > > now see its not exactly the same as regular columns. I see a need to
> > > > > elaborate a bit more on that in the FLIP as you asked, Jark.
> > > > >
> > > > > I do agree mostly with Danny on how we should do that. One additional
> > > > > things I would introduce is an
> > > > >
> > > > > interface SupportsMetadata {
> > > > >
> > > > > boolean supportsMetadata(Set<String> metadataFields);
> > > > >
> > > > > TableSource generateMetadataFields(Set<String> metadataFields);
> > > > >
> > > > > }
> > > > >
> > > > > This way the source would have to declare/emit only the requested
> > > > > metadata fields. In order not to clash with user defined fields. When
> > > > > emitting the metadata field I would prepend the column name with
> > > > > __system_{property_name}. Therefore when requested
> > > > > SYSTEM_METADATA("partition") the source would append a field
> > > > > __system_partition to the schema. This would be never visible to the
> > > > > user as it would be used only for the subsequent computed columns. If
> > > > > that makes sense to you, I will update the FLIP with this description.
> > > > >
> > > > > 2. CAST vs explicit type in computed columns
> > > > >
> > > > > Here I agree with Danny. It is also the current state of the proposal.
> > > > >
> > > > > 3. Partitioning on computed column vs function
> > > > >
> > > > > Here I also agree with Danny. I also think those are orthogonal. I would
> > > > > leave out the STORED computed columns out of the discussion. I don't see
> > > > > how do they relate to the partitioning. I already put both of those
> > > > > cases in the document. We can either partition on a computed column or
> > > > > use a udf in a partioned by clause. I am fine with leaving out the
> > > > > partitioning by udf in the first version if you still have some
> > > > concerns.
> > > > >
> > > > > As for your question Danny. It depends which partitioning strategy you
> > > > use.
> > > > >
> > > > > For the HASH partitioning strategy I thought it would work as you
> > > > > explained. It would be N = MOD(expr, num). I am not sure though if we
> > > > > should introduce the PARTITIONS clause. Usually Flink does not own the
> > > > > data and the partitions are already an intrinsic property of the
> > > > > underlying source e.g. for kafka we do not create topics, but we just
> > > > > describe pre-existing pre-partitioned topic.
> > > > >
> > > > > 4. timestamp vs timestamp.field vs connector.field vs ...
> > > > >
> > > > > I am fine with changing it to timestamp.field to be consistent with
> > > > > other value.fields and key.fields. Actually that was also my initial
> > > > > proposal in a first draft I prepared. I changed it afterwards to shorten
> > > > > the key.
> > > > >
> > > > > Best,
> > > > >
> > > > > Dawid
> > > > >
> > > > > On 03/03/2020 09:00, Danny Chan wrote:
> > > > > > Thanks Dawid for bringing up this discussion, I think it is a useful
> > > > > feature ~
> > > > > >
> > > > > > About how the metadata outputs from source
> > > > > >
> > > > > > I think it is completely orthogonal, computed column push down is
> > > > > another topic, this should not be a blocker but a promotion, if we do
> > > > not
> > > > > have any filters on the computed column, there is no need to do any
> > > > > pushings; the source node just emit the complete record with full
> > > > metadata
> > > > > with the declared physical schema, then when generating the virtual
> > > > > columns, we would extract the metadata info and output as full
> > > > columns(with
> > > > > full schema).
> > > > > >
> > > > > > About the type of metadata column
> > > > > >
> > > > > > Personally i prefer explicit type instead of CAST, they are symantic
> > > > > equivalent though, explict type is more straight-forward and we can
> > > > declare
> > > > > the nullable attribute there.
> > > > > >
> > > > > > About option A: partitioning based on acomputed column VS option B:
> > > > > partitioning with just a function
> > > > > >
> > > > > > From the FLIP, it seems that B's partitioning is just a strategy when
> > > > > writing data, the partiton column is not included in the table schema,
> > > > so
> > > > > it's just useless when reading from that.
> > > > > >
> > > > > > - Compared to A, we do not need to generate the partition column when
> > > > > selecting from the table(but insert into)
> > > > > > - For A we can also mark the column as STORED when we want to persist
> > > > > that
> > > > > >
> > > > > > So in my opition they are orthogonal, we can support both, i saw that
> > > > > MySQL/Oracle[1][2] would suggest to also define the PARTITIONS num, and
> > > > the
> > > > > partitions are managed under a "tablenamespace", the partition in which
> > > > the
> > > > > record is stored is partition number N, where N = MOD(expr, num), for
> > > > your
> > > > > design, which partiton the record would persist ?
> > > > > >
> > > > > > [1] https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> > > > > > [2]
> > > > >
> > > > https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> > > > > >
> > > > > > Best,
> > > > > > Danny Chan
> > > > > > 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <[hidden email]
> > > > > ，写道：
> > > > > > > Hi Jark,
> > > > > > > Ad. 2 I added a section to discuss relation to FLIP-63
> > > > > > > Ad. 3 Yes, I also tried to somewhat keep hierarchy of properties.
> > > > > Therefore you have the key.format.type.
> > > > > > > I also considered exactly what you are suggesting (prefixing with
> > > > > connector or kafka). I should've put that into an Option/Rejected
> > > > > alternatives.
> > > > > > > I agree timestamp, key.*, value.* are connector properties. Why I
> > > > > wanted to suggest not adding that prefix in the first version is that
> > > > > actually all the properties in the WITH section are connector
> > > > properties.
> > > > > Even format is in the end a connector property as some of the sources
> > > > might
> > > > > not have a format, imo. The benefit of not adding the prefix is that it
> > > > > makes the keys a bit shorter. Imagine prefixing all the properties with
> > > > > connector (or if we go with FLINK-12557: elasticsearch):
> > > > > > > elasticsearch.key.format.type: csv
> > > > > > > elasticsearch.key.format.field: ....
> > > > > > > elasticsearch.key.format.delimiter: ....
> > > > > > > elasticsearch.key.format.*: ....
> > > > > > > I am fine with doing it though if this is a preferred approach in the
> > > > > community.
> > > > > > > Ad in-line comments:
> > > > > > > I forgot to update the `value.fields.include` property. It should be
> > > > > value.fields-include. Which I think you also suggested in the comment,
> > > > > right?
> > > > > > > As for the cast vs declaring output type of computed column. I think
> > > > > it's better not to use CAST, but declare a type of an expression and
> > > > later
> > > > > on infer the output type of SYSTEM_METADATA. The reason is I think this
> > > > way
> > > > > it will be easier to implement e.g. filter push downs when working with
> > > > the
> > > > > native types of the source, e.g. in case of Kafka's offset, i think it's
> > > > > better to pushdown long rather than string. This could let us push
> > > > > expression like e.g. offset > 12345 & offset < 59382. Otherwise we would
> > > > > have to push down cast(offset, long) > 12345 && cast(offset, long) <
> > > > 59382.
> > > > > Moreover I think we need to introduce the type for computed columns
> > > > anyway
> > > > > to support functions that infer output type based on expected return
> > > > type.
> > > > > > > As for the computed column push down. Yes, SYSTEM_METADATA would have
> > > > > to be pushed down to the source. If it is not possible the planner
> > > > should
> > > > > fail. As far as I know computed columns push down will be part of source
> > > > > rework, won't it? ;)
> > > > > > > As for the persisted computed column. I think it is completely
> > > > > orthogonal. In my current proposal you can also partition by a computed
> > > > > column. The difference between using a udf in partitioned by vs
> > > > partitioned
> > > > > by a computed column is that when you partition by a computed column
> > > > this
> > > > > column must be also computed when reading the table. If you use a udf in
> > > > > the partitioned by, the expression is computed only when inserting into
> > > > the
> > > > > table.
> > > > > > > Hope this answers some of your questions. Looking forward for further
> > > > > suggestions.
> > > > > > > Best,
> > > > > > > Dawid
> > > > > > >
> > > > > > >
> > > > > > > On 02/03/2020 05:18, Jark Wu wrote:
> > > > > > > > Hi,
> > > > > > > >
> > > > > > > > Thanks Dawid for starting such a great discussion. Reaing metadata
> > > > and
> > > > > > > > key-part information from source is an important feature for
> > > > streaming
> > > > > > > > users.
> > > > > > > >
> > > > > > > > In general, I agree with the proposal of the FLIP.
> > > > > > > > I will leave my thoughts and comments here:
> > > > > > > >
> > > > > > > > 1) +1 to use connector properties instead of introducing HEADER
> > > > > keyword as
> > > > > > > > the reason you mentioned in the FLIP.
> > > > > > > > 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we should
> > > > > add a
> > > > > > > > section to explain what's the relationship between them.
> > > > > > > > Do their concepts conflict? Could INSERT PARTITION be used on the
> > > > > > > > PARTITIONED table in this FLIP?
> > > > > > > > 3) Currently, properties are hierarchical in Flink SQL. Shall we
> > > > make
> > > > > the
> > > > > > > > new introduced properties more hierarchical?
> > > > > > > > For example, "timestamp" => "connector.timestamp"? (actually, I
> > > > > prefer
> > > > > > > > "kafka.timestamp" which is another improvement for properties
> > > > > FLINK-12557)
> > > > > > > > A single "timestamp" in properties may mislead users that the
> > > > field
> > > > > is
> > > > > > > > a rowtime attribute.
> > > > > > > >
> > > > > > > > I also left some minor comments in the FLIP.
> > > > > > > >
> > > > > > > > Thanks,
> > > > > > > > Jark
> > > > > > > >
> > > > > > > >
> > > > > > > >
> > > > > > > > On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> > > > [hidden email]>
> > > > > > > > wrote:
> > > > > > > >
> > > > > > > > > Hi,
> > > > > > > > >
> > > > > > > > > I would like to propose an improvement that would enable reading
> > > > table
> > > > > > > > > columns from different parts of source records. Besides the main
> > > > > payload
> > > > > > > > > majority (if not all of the sources) expose additional
> > > > information. It
> > > > > > > > > can be simply a read-only metadata such as offset, ingestion time
> > > > or a
> > > > > > > > > read and write parts of the record that contain data but
> > > > additionally
> > > > > > > > > serve different purposes (partitioning, compaction etc.), e.g. key
> > > > or
> > > > > > > > > timestamp in Kafka.
> > > > > > > > >
> > > > > > > > > We should make it possible to read and write data from all of those
> > > > > > > > > locations. In this proposal I discuss reading partitioning data,
> > > > for
> > > > > > > > > completeness this proposal discusses also the partitioning when
> > > > > writing
> > > > > > > > > data out.
> > > > > > > > >
> > > > > > > > > I am looking forward to your comments.
> > > > > > > > >
> > > > > > > > > You can access the FLIP here:
> > > > > > > > >
> > > > > > > > >
> > > > >
> > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> > > > > > > > >
> > > > > > > > > Best,
> > > > > > > > >
> > > > > > > > > Dawid
> > > > > > > > >
> > > > > > > > >
> > > > > > > > >
> > > > >
> > > > >
> > > >
> > >
> >
>

[Leonard Xu]

Hi, Timo

Thanks for you explanation and update,  I have only one question  for the latest FLIP.

About the MAP<STRING, STRING> DataType of key 'debezium-json.source', if user want to use the table name metadata, they need to write:
tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS MAP<STRING, STRING>)['table']

the expression is a little complex for user, Could we only support necessary metas with simple DataType as following?
tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source.table') AS STRING),
transactionTime LONG AS CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),

In this way, we can simplify the expression, the mainly used metadata in changelog format may include 'database','table','source.ts_ms','ts_ms' from my side,
maybe we could only support them at first version.

Both Debezium and Canal have above four metadata, and I‘m willing to take some subtasks in next development if necessary.

Debezium:
{
  "before": null,
  "after": {  "id": 101,"name": "scooter"},
  "source": {
    "db": "inventory",                  # 1. database name the changelog belongs to.
    "table": "products",                # 2. table name the changelog belongs to.
    "ts_ms": 1589355504100,             # 3. timestamp of the change happened in database system, i.e.: transaction time in database.
    "connector": "mysql",
    ….
  },
  "ts_ms": 1589355606100,              # 4. timestamp when the debezium processed the changelog.
  "op": "c",
  "transaction": null
}

Canal:
{
  "data": [{  "id": "102", "name": "car battery" }],
  "database": "inventory",      # 1. database name the changelog belongs to.
  "table": "products",          # 2. table name the changelog belongs to.
  "es": 1589374013000,          # 3. execution time of the change in database system, i.e.: transaction time in database.
  "ts": 1589374013680,          # 4. timestamp when the cannal processed the changelog.
  "isDdl": false,
  "mysqlType": {},
  ....
}


Best
Leonard

> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>
> Thanks Timo ~
>
> The FLIP was already in pretty good shape, I have only 2 questions here:
>
>
> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid read-only computed column for Kafka and can be extracted by the planner.”
>
>
> What is the pros we follow the SQL-SERVER syntax here ? Usually an expression return type can be inferred automatically. But I guess SQL-SERVER does not have function like SYSTEM_METADATA which actually does not have a specific return type.
>
> And why not use the Oracle or MySQL syntax there ?
>
> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
> Which is more straight-forward.
>
> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
>
> The default type should not be NULL because only NULL literal does that. Usually we use ANY as the type if we do not know the specific type in the SQL context. ANY means the physical value can be any java object.
>
> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
> [2] https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>
> Best,
> Danny Chan
> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
>> Hi everyone,
>>
>> I completely reworked FLIP-107. It now covers the full story how to read
>> and write metadata from/to connectors and formats. It considers all of
>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It introduces
>> the concept of PERSISTED computed columns and leaves out partitioning
>> for now.
>>
>> Looking forward to your feedback.
>>
>> Regards,
>> Timo
>>
>>
>> On 04.03.20 09:45, Kurt Young wrote:
>>> Sorry, forgot one question.
>>>
>>> 4. Can we make the value.fields-include more orthogonal? Like one can
>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not config to
>>> just ignore timestamp but keep key.
>>>
>>> Best,
>>> Kurt
>>>
>>>
>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]> wrote:
>>>
>>>> Hi Dawid,
>>>>
>>>> I have a couple of questions around key fields, actually I also have some
>>>> other questions but want to be focused on key fields first.
>>>>
>>>> 1. I don't fully understand the usage of "key.fields". Is this option only
>>>> valid during write operation? Because for
>>>> reading, I can't imagine how such options can be applied. I would expect
>>>> that there might be a SYSTEM_METADATA("key")
>>>> to read and assign the key to a normal field?
>>>>
>>>> 2. If "key.fields" is only valid in write operation, I want to propose we
>>>> can simplify the options to not introducing key.format.type and
>>>> other related options. I think a single "key.field" (not fields) would be
>>>> enough, users can use UDF to calculate whatever key they
>>>> want before sink.
>>>>
>>>> 3. Also I don't want to introduce "value.format.type" and
>>>> "value.format.xxx" with the "value" prefix. Not every connector has a
>>>> concept
>>>> of key and values. The old parameter "format.type" already good enough to
>>>> use.
>>>>
>>>> Best,
>>>> Kurt
>>>>
>>>>
>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]> wrote:
>>>>
>>>>> Thanks Dawid,
>>>>>
>>>>> I have two more questions.
>>>>>
>>>>>> SupportsMetadata
>>>>> Introducing SupportsMetadata sounds good to me. But I have some questions
>>>>> regarding to this interface.
>>>>> 1) How do the source know what the expected return type of each metadata?
>>>>> 2) Where to put the metadata fields? Append to the existing physical
>>>>> fields?
>>>>> If yes, I would suggest to change the signature to `TableSource
>>>>> appendMetadataFields(String[] metadataNames, DataType[] metadataTypes)`
>>>>>
>>>>>> SYSTEM_METADATA("partition")
>>>>> Can SYSTEM_METADATA() function be used nested in a computed column
>>>>> expression? If yes, how to specify the return type of SYSTEM_METADATA?
>>>>>
>>>>> Best,
>>>>> Jark
>>>>>
>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <[hidden email]>
>>>>> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> 1. I thought a bit more on how the source would emit the columns and I
>>>>>> now see its not exactly the same as regular columns. I see a need to
>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
>>>>>>
>>>>>> I do agree mostly with Danny on how we should do that. One additional
>>>>>> things I would introduce is an
>>>>>>
>>>>>> interface SupportsMetadata {
>>>>>>
>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>
>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
>>>>>>
>>>>>> }
>>>>>>
>>>>>> This way the source would have to declare/emit only the requested
>>>>>> metadata fields. In order not to clash with user defined fields. When
>>>>>> emitting the metadata field I would prepend the column name with
>>>>>> __system_{property_name}. Therefore when requested
>>>>>> SYSTEM_METADATA("partition") the source would append a field
>>>>>> __system_partition to the schema. This would be never visible to the
>>>>>> user as it would be used only for the subsequent computed columns. If
>>>>>> that makes sense to you, I will update the FLIP with this description.
>>>>>>
>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>
>>>>>> Here I agree with Danny. It is also the current state of the proposal.
>>>>>>
>>>>>> 3. Partitioning on computed column vs function
>>>>>>
>>>>>> Here I also agree with Danny. I also think those are orthogonal. I would
>>>>>> leave out the STORED computed columns out of the discussion. I don't see
>>>>>> how do they relate to the partitioning. I already put both of those
>>>>>> cases in the document. We can either partition on a computed column or
>>>>>> use a udf in a partioned by clause. I am fine with leaving out the
>>>>>> partitioning by udf in the first version if you still have some
>>>>> concerns.
>>>>>>
>>>>>> As for your question Danny. It depends which partitioning strategy you
>>>>> use.
>>>>>>
>>>>>> For the HASH partitioning strategy I thought it would work as you
>>>>>> explained. It would be N = MOD(expr, num). I am not sure though if we
>>>>>> should introduce the PARTITIONS clause. Usually Flink does not own the
>>>>>> data and the partitions are already an intrinsic property of the
>>>>>> underlying source e.g. for kafka we do not create topics, but we just
>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>
>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
>>>>>>
>>>>>> I am fine with changing it to timestamp.field to be consistent with
>>>>>> other value.fields and key.fields. Actually that was also my initial
>>>>>> proposal in a first draft I prepared. I changed it afterwards to shorten
>>>>>> the key.
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Dawid
>>>>>>
>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a useful
>>>>>> feature ~
>>>>>>>
>>>>>>> About how the metadata outputs from source
>>>>>>>
>>>>>>> I think it is completely orthogonal, computed column push down is
>>>>>> another topic, this should not be a blocker but a promotion, if we do
>>>>> not
>>>>>> have any filters on the computed column, there is no need to do any
>>>>>> pushings; the source node just emit the complete record with full
>>>>> metadata
>>>>>> with the declared physical schema, then when generating the virtual
>>>>>> columns, we would extract the metadata info and output as full
>>>>> columns(with
>>>>>> full schema).
>>>>>>>
>>>>>>> About the type of metadata column
>>>>>>>
>>>>>>> Personally i prefer explicit type instead of CAST, they are symantic
>>>>>> equivalent though, explict type is more straight-forward and we can
>>>>> declare
>>>>>> the nullable attribute there.
>>>>>>>
>>>>>>> About option A: partitioning based on acomputed column VS option B:
>>>>>> partitioning with just a function
>>>>>>>
>>>>>>> From the FLIP, it seems that B's partitioning is just a strategy when
>>>>>> writing data, the partiton column is not included in the table schema,
>>>>> so
>>>>>> it's just useless when reading from that.
>>>>>>>
>>>>>>> - Compared to A, we do not need to generate the partition column when
>>>>>> selecting from the table(but insert into)
>>>>>>> - For A we can also mark the column as STORED when we want to persist
>>>>>> that
>>>>>>>
>>>>>>> So in my opition they are orthogonal, we can support both, i saw that
>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS num, and
>>>>> the
>>>>>> partitions are managed under a "tablenamespace", the partition in which
>>>>> the
>>>>>> record is stored is partition number N, where N = MOD(expr, num), for
>>>>> your
>>>>>> design, which partiton the record would persist ?
>>>>>>>
>>>>>>> [1] https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>> [2]
>>>>>>
>>>>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>
>>>>>>> Best,
>>>>>>> Danny Chan
>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <[hidden email]
>>>>>> ，写道：
>>>>>>>> Hi Jark,
>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of properties.
>>>>>> Therefore you have the key.format.type.
>>>>>>>> I also considered exactly what you are suggesting (prefixing with
>>>>>> connector or kafka). I should've put that into an Option/Rejected
>>>>>> alternatives.
>>>>>>>> I agree timestamp, key.*, value.* are connector properties. Why I
>>>>>> wanted to suggest not adding that prefix in the first version is that
>>>>>> actually all the properties in the WITH section are connector
>>>>> properties.
>>>>>> Even format is in the end a connector property as some of the sources
>>>>> might
>>>>>> not have a format, imo. The benefit of not adding the prefix is that it
>>>>>> makes the keys a bit shorter. Imagine prefixing all the properties with
>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>> I am fine with doing it though if this is a preferred approach in the
>>>>>> community.
>>>>>>>> Ad in-line comments:
>>>>>>>> I forgot to update the `value.fields.include` property. It should be
>>>>>> value.fields-include. Which I think you also suggested in the comment,
>>>>>> right?
>>>>>>>> As for the cast vs declaring output type of computed column. I think
>>>>>> it's better not to use CAST, but declare a type of an expression and
>>>>> later
>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I think this
>>>>> way
>>>>>> it will be easier to implement e.g. filter push downs when working with
>>>>> the
>>>>>> native types of the source, e.g. in case of Kafka's offset, i think it's
>>>>>> better to pushdown long rather than string. This could let us push
>>>>>> expression like e.g. offset > 12345 & offset < 59382. Otherwise we would
>>>>>> have to push down cast(offset, long) > 12345 && cast(offset, long) <
>>>>> 59382.
>>>>>> Moreover I think we need to introduce the type for computed columns
>>>>> anyway
>>>>>> to support functions that infer output type based on expected return
>>>>> type.
>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA would have
>>>>>> to be pushed down to the source. If it is not possible the planner
>>>>> should
>>>>>> fail. As far as I know computed columns push down will be part of source
>>>>>> rework, won't it? ;)
>>>>>>>> As for the persisted computed column. I think it is completely
>>>>>> orthogonal. In my current proposal you can also partition by a computed
>>>>>> column. The difference between using a udf in partitioned by vs
>>>>> partitioned
>>>>>> by a computed column is that when you partition by a computed column
>>>>> this
>>>>>> column must be also computed when reading the table. If you use a udf in
>>>>>> the partitioned by, the expression is computed only when inserting into
>>>>> the
>>>>>> table.
>>>>>>>> Hope this answers some of your questions. Looking forward for further
>>>>>> suggestions.
>>>>>>>> Best,
>>>>>>>> Dawid
>>>>>>>>
>>>>>>>>
>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>> Hi,
>>>>>>>>>
>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing metadata
>>>>> and
>>>>>>>>> key-part information from source is an important feature for
>>>>> streaming
>>>>>>>>> users.
>>>>>>>>>
>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>
>>>>>>>>> 1) +1 to use connector properties instead of introducing HEADER
>>>>>> keyword as
>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we should
>>>>>> add a
>>>>>>>>> section to explain what's the relationship between them.
>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used on the
>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL. Shall we
>>>>> make
>>>>>> the
>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>> For example, "timestamp" => "connector.timestamp"? (actually, I
>>>>>> prefer
>>>>>>>>> "kafka.timestamp" which is another improvement for properties
>>>>>> FLINK-12557)
>>>>>>>>> A single "timestamp" in properties may mislead users that the
>>>>> field
>>>>>> is
>>>>>>>>> a rowtime attribute.
>>>>>>>>>
>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Jark
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>> [hidden email]>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi,
>>>>>>>>>>
>>>>>>>>>> I would like to propose an improvement that would enable reading
>>>>> table
>>>>>>>>>> columns from different parts of source records. Besides the main
>>>>>> payload
>>>>>>>>>> majority (if not all of the sources) expose additional
>>>>> information. It
>>>>>>>>>> can be simply a read-only metadata such as offset, ingestion time
>>>>> or a
>>>>>>>>>> read and write parts of the record that contain data but
>>>>> additionally
>>>>>>>>>> serve different purposes (partitioning, compaction etc.), e.g. key
>>>>> or
>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>
>>>>>>>>>> We should make it possible to read and write data from all of those
>>>>>>>>>> locations. In this proposal I discuss reading partitioning data,
>>>>> for
>>>>>>>>>> completeness this proposal discusses also the partitioning when
>>>>>> writing
>>>>>>>>>> data out.
>>>>>>>>>>
>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>
>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>
>>>>>>>>>>
>>>>>>
>>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>>
>>>>>>>>>> Dawid
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>

[Timo Walther-2]

Hi everyone,

I updated the FLIP again and hope that I could address the mentioned
concerns.

@Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
source.ts_ms have different semantics. I updated the FLIP and expose the
most commonly used properties separately. So frequently used properties
are not hidden in the MAP anymore:

debezium-json.ingestion-timestamp
debezium-json.source.timestamp
debezium-json.source.database
debezium-json.source.schema
debezium-json.source.table

However, since other properties depend on the used connector/vendor, the
remaining options are stored in:

debezium-json.source.properties

And accessed with:

CAST(SYSTEM_METADATA('debezium-json.source.properties') AS MAP<STRING,
STRING>)['table']

Otherwise it is not possible to figure out the value and column type
during validation.

@Jark: You convinced me in relaxing the CAST constraints. I added a
dedicacated sub-section to the FLIP:

For making the use of SYSTEM_METADATA easier and avoid nested casting we
allow explicit casting to a target data type:

rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH LOCAL
TIME ZONE)

A connector still produces and consumes the data type returned by
`listMetadata()`. The planner will insert necessary explicit casts.

In any case, the user must provide a CAST such that the computed column
receives a valid data type when constructing the table schema.

"I don't see a reason why `DecodingFormat#applyReadableMetadata` needs a
DataType argument."

Correct he DeserializationSchema doesn't need TypeInfo, it is always
executed locally. It is the source that needs TypeInfo for serializing
the record to the next operator. And that's this is what we provide.

@Danny:

“SYSTEM_METADATA("offset")` returns the NULL type by default”

We can also use some other means to represent an UNKNOWN data type. In
the Flink type system, we use the NullType for it. The important part is
that the final data type is known for the entire computed column. As I
mentioned before, I would avoid the suggested option b) that would be
similar to your suggestion. The CAST should be enough and allows for
complex expressions in the computed column. Option b) would need parser
changes.

Regards,
Timo



On 08.09.20 06:21, Leonard Xu wrote:

> Hi, Timo
>
> Thanks for you explanation and update,  I have only one question  for the latest FLIP.
>
> About the MAP<STRING, STRING> DataType of key 'debezium-json.source', if user want to use the table name metadata, they need to write:
> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS MAP<STRING, STRING>)['table']
>
> the expression is a little complex for user, Could we only support necessary metas with simple DataType as following?
> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source.table') AS STRING),
> transactionTime LONG AS CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>
> In this way, we can simplify the expression, the mainly used metadata in changelog format may include 'database','table','source.ts_ms','ts_ms' from my side,
> maybe we could only support them at first version.
>
> Both Debezium and Canal have above four metadata, and I‘m willing to take some subtasks in next development if necessary.
>
> Debezium:
> {
>    "before": null,
>    "after": {  "id": 101,"name": "scooter"},
>    "source": {
>      "db": "inventory",                  # 1. database name the changelog belongs to.
>      "table": "products",                # 2. table name the changelog belongs to.
>      "ts_ms": 1589355504100,             # 3. timestamp of the change happened in database system, i.e.: transaction time in database.
>      "connector": "mysql",
>      ….
>    },
>    "ts_ms": 1589355606100,              # 4. timestamp when the debezium processed the changelog.
>    "op": "c",
>    "transaction": null
> }
>
> Canal:
> {
>    "data": [{  "id": "102", "name": "car battery" }],
>    "database": "inventory",      # 1. database name the changelog belongs to.
>    "table": "products",          # 2. table name the changelog belongs to.
>    "es": 1589374013000,          # 3. execution time of the change in database system, i.e.: transaction time in database.
>    "ts": 1589374013680,          # 4. timestamp when the cannal processed the changelog.
>    "isDdl": false,
>    "mysqlType": {},
>    ....
> }
>
>
> Best
> Leonard
>
>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>
>> Thanks Timo ~
>>
>> The FLIP was already in pretty good shape, I have only 2 questions here:
>>
>>
>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid read-only computed column for Kafka and can be extracted by the planner.”
>>
>>
>> What is the pros we follow the SQL-SERVER syntax here ? Usually an expression return type can be inferred automatically. But I guess SQL-SERVER does not have function like SYSTEM_METADATA which actually does not have a specific return type.
>>
>> And why not use the Oracle or MySQL syntax there ?
>>
>> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
>> Which is more straight-forward.
>>
>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>
>> The default type should not be NULL because only NULL literal does that. Usually we use ANY as the type if we do not know the specific type in the SQL context. ANY means the physical value can be any java object.
>>
>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
>> [2] https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>
>> Best,
>> Danny Chan
>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
>>> Hi everyone,
>>>
>>> I completely reworked FLIP-107. It now covers the full story how to read
>>> and write metadata from/to connectors and formats. It considers all of
>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It introduces
>>> the concept of PERSISTED computed columns and leaves out partitioning
>>> for now.
>>>
>>> Looking forward to your feedback.
>>>
>>> Regards,
>>> Timo
>>>
>>>
>>> On 04.03.20 09:45, Kurt Young wrote:
>>>> Sorry, forgot one question.
>>>>
>>>> 4. Can we make the value.fields-include more orthogonal? Like one can
>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not config to
>>>> just ignore timestamp but keep key.
>>>>
>>>> Best,
>>>> Kurt
>>>>
>>>>
>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]> wrote:
>>>>
>>>>> Hi Dawid,
>>>>>
>>>>> I have a couple of questions around key fields, actually I also have some
>>>>> other questions but want to be focused on key fields first.
>>>>>
>>>>> 1. I don't fully understand the usage of "key.fields". Is this option only
>>>>> valid during write operation? Because for
>>>>> reading, I can't imagine how such options can be applied. I would expect
>>>>> that there might be a SYSTEM_METADATA("key")
>>>>> to read and assign the key to a normal field?
>>>>>
>>>>> 2. If "key.fields" is only valid in write operation, I want to propose we
>>>>> can simplify the options to not introducing key.format.type and
>>>>> other related options. I think a single "key.field" (not fields) would be
>>>>> enough, users can use UDF to calculate whatever key they
>>>>> want before sink.
>>>>>
>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>> "value.format.xxx" with the "value" prefix. Not every connector has a
>>>>> concept
>>>>> of key and values. The old parameter "format.type" already good enough to
>>>>> use.
>>>>>
>>>>> Best,
>>>>> Kurt
>>>>>
>>>>>
>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]> wrote:
>>>>>
>>>>>> Thanks Dawid,
>>>>>>
>>>>>> I have two more questions.
>>>>>>
>>>>>>> SupportsMetadata
>>>>>> Introducing SupportsMetadata sounds good to me. But I have some questions
>>>>>> regarding to this interface.
>>>>>> 1) How do the source know what the expected return type of each metadata?
>>>>>> 2) Where to put the metadata fields? Append to the existing physical
>>>>>> fields?
>>>>>> If yes, I would suggest to change the signature to `TableSource
>>>>>> appendMetadataFields(String[] metadataNames, DataType[] metadataTypes)`
>>>>>>
>>>>>>> SYSTEM_METADATA("partition")
>>>>>> Can SYSTEM_METADATA() function be used nested in a computed column
>>>>>> expression? If yes, how to specify the return type of SYSTEM_METADATA?
>>>>>>
>>>>>> Best,
>>>>>> Jark
>>>>>>
>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <[hidden email]>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> 1. I thought a bit more on how the source would emit the columns and I
>>>>>>> now see its not exactly the same as regular columns. I see a need to
>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
>>>>>>>
>>>>>>> I do agree mostly with Danny on how we should do that. One additional
>>>>>>> things I would introduce is an
>>>>>>>
>>>>>>> interface SupportsMetadata {
>>>>>>>
>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>
>>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
>>>>>>>
>>>>>>> }
>>>>>>>
>>>>>>> This way the source would have to declare/emit only the requested
>>>>>>> metadata fields. In order not to clash with user defined fields. When
>>>>>>> emitting the metadata field I would prepend the column name with
>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>> SYSTEM_METADATA("partition") the source would append a field
>>>>>>> __system_partition to the schema. This would be never visible to the
>>>>>>> user as it would be used only for the subsequent computed columns. If
>>>>>>> that makes sense to you, I will update the FLIP with this description.
>>>>>>>
>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>
>>>>>>> Here I agree with Danny. It is also the current state of the proposal.
>>>>>>>
>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>
>>>>>>> Here I also agree with Danny. I also think those are orthogonal. I would
>>>>>>> leave out the STORED computed columns out of the discussion. I don't see
>>>>>>> how do they relate to the partitioning. I already put both of those
>>>>>>> cases in the document. We can either partition on a computed column or
>>>>>>> use a udf in a partioned by clause. I am fine with leaving out the
>>>>>>> partitioning by udf in the first version if you still have some
>>>>>> concerns.
>>>>>>>
>>>>>>> As for your question Danny. It depends which partitioning strategy you
>>>>>> use.
>>>>>>>
>>>>>>> For the HASH partitioning strategy I thought it would work as you
>>>>>>> explained. It would be N = MOD(expr, num). I am not sure though if we
>>>>>>> should introduce the PARTITIONS clause. Usually Flink does not own the
>>>>>>> data and the partitions are already an intrinsic property of the
>>>>>>> underlying source e.g. for kafka we do not create topics, but we just
>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>
>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
>>>>>>>
>>>>>>> I am fine with changing it to timestamp.field to be consistent with
>>>>>>> other value.fields and key.fields. Actually that was also my initial
>>>>>>> proposal in a first draft I prepared. I changed it afterwards to shorten
>>>>>>> the key.
>>>>>>>
>>>>>>> Best,
>>>>>>>
>>>>>>> Dawid
>>>>>>>
>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a useful
>>>>>>> feature ~
>>>>>>>>
>>>>>>>> About how the metadata outputs from source
>>>>>>>>
>>>>>>>> I think it is completely orthogonal, computed column push down is
>>>>>>> another topic, this should not be a blocker but a promotion, if we do
>>>>>> not
>>>>>>> have any filters on the computed column, there is no need to do any
>>>>>>> pushings; the source node just emit the complete record with full
>>>>>> metadata
>>>>>>> with the declared physical schema, then when generating the virtual
>>>>>>> columns, we would extract the metadata info and output as full
>>>>>> columns(with
>>>>>>> full schema).
>>>>>>>>
>>>>>>>> About the type of metadata column
>>>>>>>>
>>>>>>>> Personally i prefer explicit type instead of CAST, they are symantic
>>>>>>> equivalent though, explict type is more straight-forward and we can
>>>>>> declare
>>>>>>> the nullable attribute there.
>>>>>>>>
>>>>>>>> About option A: partitioning based on acomputed column VS option B:
>>>>>>> partitioning with just a function
>>>>>>>>
>>>>>>>>  From the FLIP, it seems that B's partitioning is just a strategy when
>>>>>>> writing data, the partiton column is not included in the table schema,
>>>>>> so
>>>>>>> it's just useless when reading from that.
>>>>>>>>
>>>>>>>> - Compared to A, we do not need to generate the partition column when
>>>>>>> selecting from the table(but insert into)
>>>>>>>> - For A we can also mark the column as STORED when we want to persist
>>>>>>> that
>>>>>>>>
>>>>>>>> So in my opition they are orthogonal, we can support both, i saw that
>>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS num, and
>>>>>> the
>>>>>>> partitions are managed under a "tablenamespace", the partition in which
>>>>>> the
>>>>>>> record is stored is partition number N, where N = MOD(expr, num), for
>>>>>> your
>>>>>>> design, which partiton the record would persist ?
>>>>>>>>
>>>>>>>> [1] https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>> [2]
>>>>>>>
>>>>>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Danny Chan
>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <[hidden email]
>>>>>>> ，写道：
>>>>>>>>> Hi Jark,
>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of properties.
>>>>>>> Therefore you have the key.format.type.
>>>>>>>>> I also considered exactly what you are suggesting (prefixing with
>>>>>>> connector or kafka). I should've put that into an Option/Rejected
>>>>>>> alternatives.
>>>>>>>>> I agree timestamp, key.*, value.* are connector properties. Why I
>>>>>>> wanted to suggest not adding that prefix in the first version is that
>>>>>>> actually all the properties in the WITH section are connector
>>>>>> properties.
>>>>>>> Even format is in the end a connector property as some of the sources
>>>>>> might
>>>>>>> not have a format, imo. The benefit of not adding the prefix is that it
>>>>>>> makes the keys a bit shorter. Imagine prefixing all the properties with
>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>> I am fine with doing it though if this is a preferred approach in the
>>>>>>> community.
>>>>>>>>> Ad in-line comments:
>>>>>>>>> I forgot to update the `value.fields.include` property. It should be
>>>>>>> value.fields-include. Which I think you also suggested in the comment,
>>>>>>> right?
>>>>>>>>> As for the cast vs declaring output type of computed column. I think
>>>>>>> it's better not to use CAST, but declare a type of an expression and
>>>>>> later
>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I think this
>>>>>> way
>>>>>>> it will be easier to implement e.g. filter push downs when working with
>>>>>> the
>>>>>>> native types of the source, e.g. in case of Kafka's offset, i think it's
>>>>>>> better to pushdown long rather than string. This could let us push
>>>>>>> expression like e.g. offset > 12345 & offset < 59382. Otherwise we would
>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset, long) <
>>>>>> 59382.
>>>>>>> Moreover I think we need to introduce the type for computed columns
>>>>>> anyway
>>>>>>> to support functions that infer output type based on expected return
>>>>>> type.
>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA would have
>>>>>>> to be pushed down to the source. If it is not possible the planner
>>>>>> should
>>>>>>> fail. As far as I know computed columns push down will be part of source
>>>>>>> rework, won't it? ;)
>>>>>>>>> As for the persisted computed column. I think it is completely
>>>>>>> orthogonal. In my current proposal you can also partition by a computed
>>>>>>> column. The difference between using a udf in partitioned by vs
>>>>>> partitioned
>>>>>>> by a computed column is that when you partition by a computed column
>>>>>> this
>>>>>>> column must be also computed when reading the table. If you use a udf in
>>>>>>> the partitioned by, the expression is computed only when inserting into
>>>>>> the
>>>>>>> table.
>>>>>>>>> Hope this answers some of your questions. Looking forward for further
>>>>>>> suggestions.
>>>>>>>>> Best,
>>>>>>>>> Dawid
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>> Hi,
>>>>>>>>>>
>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing metadata
>>>>>> and
>>>>>>>>>> key-part information from source is an important feature for
>>>>>> streaming
>>>>>>>>>> users.
>>>>>>>>>>
>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>
>>>>>>>>>> 1) +1 to use connector properties instead of introducing HEADER
>>>>>>> keyword as
>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we should
>>>>>>> add a
>>>>>>>>>> section to explain what's the relationship between them.
>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used on the
>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL. Shall we
>>>>>> make
>>>>>>> the
>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>> For example, "timestamp" => "connector.timestamp"? (actually, I
>>>>>>> prefer
>>>>>>>>>> "kafka.timestamp" which is another improvement for properties
>>>>>>> FLINK-12557)
>>>>>>>>>> A single "timestamp" in properties may mislead users that the
>>>>>> field
>>>>>>> is
>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>
>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Jark
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>> [hidden email]>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi,
>>>>>>>>>>>
>>>>>>>>>>> I would like to propose an improvement that would enable reading
>>>>>> table
>>>>>>>>>>> columns from different parts of source records. Besides the main
>>>>>>> payload
>>>>>>>>>>> majority (if not all of the sources) expose additional
>>>>>> information. It
>>>>>>>>>>> can be simply a read-only metadata such as offset, ingestion time
>>>>>> or a
>>>>>>>>>>> read and write parts of the record that contain data but
>>>>>> additionally
>>>>>>>>>>> serve different purposes (partitioning, compaction etc.), e.g. key
>>>>>> or
>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>
>>>>>>>>>>> We should make it possible to read and write data from all of those
>>>>>>>>>>> locations. In this proposal I discuss reading partitioning data,
>>>>>> for
>>>>>>>>>>> completeness this proposal discusses also the partitioning when
>>>>>>> writing
>>>>>>>>>>> data out.
>>>>>>>>>>>
>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>
>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>
>>>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>>
>>>>>>>>>>> Dawid
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>
>

[Jark Wu-2]

Hi Timo,

The updated CAST SYSTEM_METADATA behavior sounds good to me. I just noticed
that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME ZONE".
So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL TIME
ZONE" as the defined type of Kafka timestamp? I think this makes sense,
because it represents the milli-seconds since epoch.

Regarding "DeserializationSchema doesn't need TypeInfo", I don't think so.
The DeserializationSchema implements ResultTypeQueryable, thus the
implementation needs to return an output TypeInfo.
Besides, FlinkKafkaConsumer also
calls DeserializationSchema.getProducedType as the produced type of the
source function [1].

Best,
Jark

[1]:
https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066

On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]> wrote:

> Hi everyone,
>
> I updated the FLIP again and hope that I could address the mentioned
> concerns.
>
> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
> source.ts_ms have different semantics. I updated the FLIP and expose the
> most commonly used properties separately. So frequently used properties
> are not hidden in the MAP anymore:
>
> debezium-json.ingestion-timestamp
> debezium-json.source.timestamp
> debezium-json.source.database
> debezium-json.source.schema
> debezium-json.source.table
>
> However, since other properties depend on the used connector/vendor, the
> remaining options are stored in:
>
> debezium-json.source.properties
>
> And accessed with:
>
> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS MAP<STRING,
> STRING>)['table']
>
> Otherwise it is not possible to figure out the value and column type
> during validation.
>
> @Jark: You convinced me in relaxing the CAST constraints. I added a
> dedicacated sub-section to the FLIP:
>
> For making the use of SYSTEM_METADATA easier and avoid nested casting we
> allow explicit casting to a target data type:
>
> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH LOCAL
> TIME ZONE)
>
> A connector still produces and consumes the data type returned by
> `listMetadata()`. The planner will insert necessary explicit casts.
>
> In any case, the user must provide a CAST such that the computed column
> receives a valid data type when constructing the table schema.
>
> "I don't see a reason why `DecodingFormat#applyReadableMetadata` needs a
> DataType argument."
>
> Correct he DeserializationSchema doesn't need TypeInfo, it is always
> executed locally. It is the source that needs TypeInfo for serializing
> the record to the next operator. And that's this is what we provide.
>
> @Danny:
>
> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>
> We can also use some other means to represent an UNKNOWN data type. In
> the Flink type system, we use the NullType for it. The important part is
> that the final data type is known for the entire computed column. As I
> mentioned before, I would avoid the suggested option b) that would be
> similar to your suggestion. The CAST should be enough and allows for
> complex expressions in the computed column. Option b) would need parser
> changes.
>
> Regards,
> Timo
>
>
>
> On 08.09.20 06:21, Leonard Xu wrote:
> > Hi, Timo
> >
> > Thanks for you explanation and update,  I have only one question  for
> the latest FLIP.
> >
> > About the MAP<STRING, STRING> DataType of key 'debezium-json.source', if
> user want to use the table name metadata, they need to write:
> > tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
> MAP<STRING, STRING>)['table']
> >
> > the expression is a little complex for user, Could we only support
> necessary metas with simple DataType as following?
> > tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
> STRING),
> > transactionTime LONG AS
> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
> >
> > In this way, we can simplify the expression, the mainly used metadata in
> changelog format may include 'database','table','source.ts_ms','ts_ms' from
> my side,
> > maybe we could only support them at first version.
> >
> > Both Debezium and Canal have above four metadata, and I‘m willing to
> take some subtasks in next development if necessary.
> >
> > Debezium:
> > {
> >    "before": null,
> >    "after": {  "id": 101,"name": "scooter"},
> >    "source": {
> >      "db": "inventory",                  # 1. database name the
> changelog belongs to.
> >      "table": "products",                # 2. table name the changelog
> belongs to.
> >      "ts_ms": 1589355504100,             # 3. timestamp of the change
> happened in database system, i.e.: transaction time in database.
> >      "connector": "mysql",
> >      ….
> >    },
> >    "ts_ms": 1589355606100,              # 4. timestamp when the debezium
> processed the changelog.
> >    "op": "c",
> >    "transaction": null
> > }
> >
> > Canal:
> > {
> >    "data": [{  "id": "102", "name": "car battery" }],
> >    "database": "inventory",      # 1. database name the changelog
> belongs to.
> >    "table": "products",          # 2. table name the changelog belongs
> to.
> >    "es": 1589374013000,          # 3. execution time of the change in
> database system, i.e.: transaction time in database.
> >    "ts": 1589374013680,          # 4. timestamp when the cannal
> processed the changelog.
> >    "isDdl": false,
> >    "mysqlType": {},
> >    ....
> > }
> >
> >
> > Best
> > Leonard
> >
> >> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
> >>
> >> Thanks Timo ~
> >>
> >> The FLIP was already in pretty good shape, I have only 2 questions here:
> >>
> >>
> >> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid read-only
> computed column for Kafka and can be extracted by the planner.”
> >>
> >>
> >> What is the pros we follow the SQL-SERVER syntax here ? Usually an
> expression return type can be inferred automatically. But I guess
> SQL-SERVER does not have function like SYSTEM_METADATA which actually does
> not have a specific return type.
> >>
> >> And why not use the Oracle or MySQL syntax there ?
> >>
> >> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
> >> Which is more straight-forward.
> >>
> >> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>
> >> The default type should not be NULL because only NULL literal does
> that. Usually we use ANY as the type if we do not know the specific type in
> the SQL context. ANY means the physical value can be any java object.
> >>
> >> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
> >> [2]
> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
> >>
> >> Best,
> >> Danny Chan
> >> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
> >>> Hi everyone,
> >>>
> >>> I completely reworked FLIP-107. It now covers the full story how to
> read
> >>> and write metadata from/to connectors and formats. It considers all of
> >>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It introduces
> >>> the concept of PERSISTED computed columns and leaves out partitioning
> >>> for now.
> >>>
> >>> Looking forward to your feedback.
> >>>
> >>> Regards,
> >>> Timo
> >>>
> >>>
> >>> On 04.03.20 09:45, Kurt Young wrote:
> >>>> Sorry, forgot one question.
> >>>>
> >>>> 4. Can we make the value.fields-include more orthogonal? Like one can
> >>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> >>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
> config to
> >>>> just ignore timestamp but keep key.
> >>>>
> >>>> Best,
> >>>> Kurt
> >>>>
> >>>>
> >>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]> wrote:
> >>>>
> >>>>> Hi Dawid,
> >>>>>
> >>>>> I have a couple of questions around key fields, actually I also have
> some
> >>>>> other questions but want to be focused on key fields first.
> >>>>>
> >>>>> 1. I don't fully understand the usage of "key.fields". Is this
> option only
> >>>>> valid during write operation? Because for
> >>>>> reading, I can't imagine how such options can be applied. I would
> expect
> >>>>> that there might be a SYSTEM_METADATA("key")
> >>>>> to read and assign the key to a normal field?
> >>>>>
> >>>>> 2. If "key.fields" is only valid in write operation, I want to
> propose we
> >>>>> can simplify the options to not introducing key.format.type and
> >>>>> other related options. I think a single "key.field" (not fields)
> would be
> >>>>> enough, users can use UDF to calculate whatever key they
> >>>>> want before sink.
> >>>>>
> >>>>> 3. Also I don't want to introduce "value.format.type" and
> >>>>> "value.format.xxx" with the "value" prefix. Not every connector has a
> >>>>> concept
> >>>>> of key and values. The old parameter "format.type" already good
> enough to
> >>>>> use.
> >>>>>
> >>>>> Best,
> >>>>> Kurt
> >>>>>
> >>>>>
> >>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]> wrote:
> >>>>>
> >>>>>> Thanks Dawid,
> >>>>>>
> >>>>>> I have two more questions.
> >>>>>>
> >>>>>>> SupportsMetadata
> >>>>>> Introducing SupportsMetadata sounds good to me. But I have some
> questions
> >>>>>> regarding to this interface.
> >>>>>> 1) How do the source know what the expected return type of each
> metadata?
> >>>>>> 2) Where to put the metadata fields? Append to the existing physical
> >>>>>> fields?
> >>>>>> If yes, I would suggest to change the signature to `TableSource
> >>>>>> appendMetadataFields(String[] metadataNames, DataType[]
> metadataTypes)`
> >>>>>>
> >>>>>>> SYSTEM_METADATA("partition")
> >>>>>> Can SYSTEM_METADATA() function be used nested in a computed column
> >>>>>> expression? If yes, how to specify the return type of
> SYSTEM_METADATA?
> >>>>>>
> >>>>>> Best,
> >>>>>> Jark
> >>>>>>
> >>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
> [hidden email]>
> >>>>>> wrote:
> >>>>>>
> >>>>>>> Hi,
> >>>>>>>
> >>>>>>> 1. I thought a bit more on how the source would emit the columns
> and I
> >>>>>>> now see its not exactly the same as regular columns. I see a need
> to
> >>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
> >>>>>>>
> >>>>>>> I do agree mostly with Danny on how we should do that. One
> additional
> >>>>>>> things I would introduce is an
> >>>>>>>
> >>>>>>> interface SupportsMetadata {
> >>>>>>>
> >>>>>>> boolean supportsMetadata(Set<String> metadataFields);
> >>>>>>>
> >>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
> >>>>>>>
> >>>>>>> }
> >>>>>>>
> >>>>>>> This way the source would have to declare/emit only the requested
> >>>>>>> metadata fields. In order not to clash with user defined fields.
> When
> >>>>>>> emitting the metadata field I would prepend the column name with
> >>>>>>> __system_{property_name}. Therefore when requested
> >>>>>>> SYSTEM_METADATA("partition") the source would append a field
> >>>>>>> __system_partition to the schema. This would be never visible to
> the
> >>>>>>> user as it would be used only for the subsequent computed columns.
> If
> >>>>>>> that makes sense to you, I will update the FLIP with this
> description.
> >>>>>>>
> >>>>>>> 2. CAST vs explicit type in computed columns
> >>>>>>>
> >>>>>>> Here I agree with Danny. It is also the current state of the
> proposal.
> >>>>>>>
> >>>>>>> 3. Partitioning on computed column vs function
> >>>>>>>
> >>>>>>> Here I also agree with Danny. I also think those are orthogonal. I
> would
> >>>>>>> leave out the STORED computed columns out of the discussion. I
> don't see
> >>>>>>> how do they relate to the partitioning. I already put both of those
> >>>>>>> cases in the document. We can either partition on a computed
> column or
> >>>>>>> use a udf in a partioned by clause. I am fine with leaving out the
> >>>>>>> partitioning by udf in the first version if you still have some
> >>>>>> concerns.
> >>>>>>>
> >>>>>>> As for your question Danny. It depends which partitioning strategy
> you
> >>>>>> use.
> >>>>>>>
> >>>>>>> For the HASH partitioning strategy I thought it would work as you
> >>>>>>> explained. It would be N = MOD(expr, num). I am not sure though if
> we
> >>>>>>> should introduce the PARTITIONS clause. Usually Flink does not own
> the
> >>>>>>> data and the partitions are already an intrinsic property of the
> >>>>>>> underlying source e.g. for kafka we do not create topics, but we
> just
> >>>>>>> describe pre-existing pre-partitioned topic.
> >>>>>>>
> >>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
> >>>>>>>
> >>>>>>> I am fine with changing it to timestamp.field to be consistent with
> >>>>>>> other value.fields and key.fields. Actually that was also my
> initial
> >>>>>>> proposal in a first draft I prepared. I changed it afterwards to
> shorten
> >>>>>>> the key.
> >>>>>>>
> >>>>>>> Best,
> >>>>>>>
> >>>>>>> Dawid
> >>>>>>>
> >>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
> >>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a
> useful
> >>>>>>> feature ~
> >>>>>>>>
> >>>>>>>> About how the metadata outputs from source
> >>>>>>>>
> >>>>>>>> I think it is completely orthogonal, computed column push down is
> >>>>>>> another topic, this should not be a blocker but a promotion, if we
> do
> >>>>>> not
> >>>>>>> have any filters on the computed column, there is no need to do any
> >>>>>>> pushings; the source node just emit the complete record with full
> >>>>>> metadata
> >>>>>>> with the declared physical schema, then when generating the virtual
> >>>>>>> columns, we would extract the metadata info and output as full
> >>>>>> columns(with
> >>>>>>> full schema).
> >>>>>>>>
> >>>>>>>> About the type of metadata column
> >>>>>>>>
> >>>>>>>> Personally i prefer explicit type instead of CAST, they are
> symantic
> >>>>>>> equivalent though, explict type is more straight-forward and we can
> >>>>>> declare
> >>>>>>> the nullable attribute there.
> >>>>>>>>
> >>>>>>>> About option A: partitioning based on acomputed column VS option
> B:
> >>>>>>> partitioning with just a function
> >>>>>>>>
> >>>>>>>>  From the FLIP, it seems that B's partitioning is just a strategy
> when
> >>>>>>> writing data, the partiton column is not included in the table
> schema,
> >>>>>> so
> >>>>>>> it's just useless when reading from that.
> >>>>>>>>
> >>>>>>>> - Compared to A, we do not need to generate the partition column
> when
> >>>>>>> selecting from the table(but insert into)
> >>>>>>>> - For A we can also mark the column as STORED when we want to
> persist
> >>>>>>> that
> >>>>>>>>
> >>>>>>>> So in my opition they are orthogonal, we can support both, i saw
> that
> >>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS
> num, and
> >>>>>> the
> >>>>>>> partitions are managed under a "tablenamespace", the partition in
> which
> >>>>>> the
> >>>>>>> record is stored is partition number N, where N = MOD(expr, num),
> for
> >>>>>> your
> >>>>>>> design, which partiton the record would persist ?
> >>>>>>>>
> >>>>>>>> [1]
> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> >>>>>>>> [2]
> >>>>>>>
> >>>>>>
> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> >>>>>>>>
> >>>>>>>> Best,
> >>>>>>>> Danny Chan
> >>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <[hidden email]
> >>>>>>> ，写道：
> >>>>>>>>> Hi Jark,
> >>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
> >>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of properties.
> >>>>>>> Therefore you have the key.format.type.
> >>>>>>>>> I also considered exactly what you are suggesting (prefixing with
> >>>>>>> connector or kafka). I should've put that into an Option/Rejected
> >>>>>>> alternatives.
> >>>>>>>>> I agree timestamp, key.*, value.* are connector properties. Why I
> >>>>>>> wanted to suggest not adding that prefix in the first version is
> that
> >>>>>>> actually all the properties in the WITH section are connector
> >>>>>> properties.
> >>>>>>> Even format is in the end a connector property as some of the
> sources
> >>>>>> might
> >>>>>>> not have a format, imo. The benefit of not adding the prefix is
> that it
> >>>>>>> makes the keys a bit shorter. Imagine prefixing all the properties
> with
> >>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
> >>>>>>>>> elasticsearch.key.format.type: csv
> >>>>>>>>> elasticsearch.key.format.field: ....
> >>>>>>>>> elasticsearch.key.format.delimiter: ....
> >>>>>>>>> elasticsearch.key.format.*: ....
> >>>>>>>>> I am fine with doing it though if this is a preferred approach
> in the
> >>>>>>> community.
> >>>>>>>>> Ad in-line comments:
> >>>>>>>>> I forgot to update the `value.fields.include` property. It
> should be
> >>>>>>> value.fields-include. Which I think you also suggested in the
> comment,
> >>>>>>> right?
> >>>>>>>>> As for the cast vs declaring output type of computed column. I
> think
> >>>>>>> it's better not to use CAST, but declare a type of an expression
> and
> >>>>>> later
> >>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I think
> this
> >>>>>> way
> >>>>>>> it will be easier to implement e.g. filter push downs when working
> with
> >>>>>> the
> >>>>>>> native types of the source, e.g. in case of Kafka's offset, i
> think it's
> >>>>>>> better to pushdown long rather than string. This could let us push
> >>>>>>> expression like e.g. offset > 12345 & offset < 59382. Otherwise we
> would
> >>>>>>> have to push down cast(offset, long) > 12345 && cast(offset, long)
> <
> >>>>>> 59382.
> >>>>>>> Moreover I think we need to introduce the type for computed columns
> >>>>>> anyway
> >>>>>>> to support functions that infer output type based on expected
> return
> >>>>>> type.
> >>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA would
> have
> >>>>>>> to be pushed down to the source. If it is not possible the planner
> >>>>>> should
> >>>>>>> fail. As far as I know computed columns push down will be part of
> source
> >>>>>>> rework, won't it? ;)
> >>>>>>>>> As for the persisted computed column. I think it is completely
> >>>>>>> orthogonal. In my current proposal you can also partition by a
> computed
> >>>>>>> column. The difference between using a udf in partitioned by vs
> >>>>>> partitioned
> >>>>>>> by a computed column is that when you partition by a computed
> column
> >>>>>> this
> >>>>>>> column must be also computed when reading the table. If you use a
> udf in
> >>>>>>> the partitioned by, the expression is computed only when inserting
> into
> >>>>>> the
> >>>>>>> table.
> >>>>>>>>> Hope this answers some of your questions. Looking forward for
> further
> >>>>>>> suggestions.
> >>>>>>>>> Best,
> >>>>>>>>> Dawid
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
> >>>>>>>>>> Hi,
> >>>>>>>>>>
> >>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
> metadata
> >>>>>> and
> >>>>>>>>>> key-part information from source is an important feature for
> >>>>>> streaming
> >>>>>>>>>> users.
> >>>>>>>>>>
> >>>>>>>>>> In general, I agree with the proposal of the FLIP.
> >>>>>>>>>> I will leave my thoughts and comments here:
> >>>>>>>>>>
> >>>>>>>>>> 1) +1 to use connector properties instead of introducing HEADER
> >>>>>>> keyword as
> >>>>>>>>>> the reason you mentioned in the FLIP.
> >>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we
> should
> >>>>>>> add a
> >>>>>>>>>> section to explain what's the relationship between them.
> >>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used on
> the
> >>>>>>>>>> PARTITIONED table in this FLIP?
> >>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL. Shall we
> >>>>>> make
> >>>>>>> the
> >>>>>>>>>> new introduced properties more hierarchical?
> >>>>>>>>>> For example, "timestamp" => "connector.timestamp"? (actually, I
> >>>>>>> prefer
> >>>>>>>>>> "kafka.timestamp" which is another improvement for properties
> >>>>>>> FLINK-12557)
> >>>>>>>>>> A single "timestamp" in properties may mislead users that the
> >>>>>> field
> >>>>>>> is
> >>>>>>>>>> a rowtime attribute.
> >>>>>>>>>>
> >>>>>>>>>> I also left some minor comments in the FLIP.
> >>>>>>>>>>
> >>>>>>>>>> Thanks,
> >>>>>>>>>> Jark
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> >>>>>> [hidden email]>
> >>>>>>>>>> wrote:
> >>>>>>>>>>
> >>>>>>>>>>> Hi,
> >>>>>>>>>>>
> >>>>>>>>>>> I would like to propose an improvement that would enable
> reading
> >>>>>> table
> >>>>>>>>>>> columns from different parts of source records. Besides the
> main
> >>>>>>> payload
> >>>>>>>>>>> majority (if not all of the sources) expose additional
> >>>>>> information. It
> >>>>>>>>>>> can be simply a read-only metadata such as offset, ingestion
> time
> >>>>>> or a
> >>>>>>>>>>> read and write parts of the record that contain data but
> >>>>>> additionally
> >>>>>>>>>>> serve different purposes (partitioning, compaction etc.), e.g.
> key
> >>>>>> or
> >>>>>>>>>>> timestamp in Kafka.
> >>>>>>>>>>>
> >>>>>>>>>>> We should make it possible to read and write data from all of
> those
> >>>>>>>>>>> locations. In this proposal I discuss reading partitioning
> data,
> >>>>>> for
> >>>>>>>>>>> completeness this proposal discusses also the partitioning when
> >>>>>>> writing
> >>>>>>>>>>> data out.
> >>>>>>>>>>>
> >>>>>>>>>>> I am looking forward to your comments.
> >>>>>>>>>>>
> >>>>>>>>>>> You can access the FLIP here:
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>
> >>>>>>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> >>>>>>>>>>>
> >>>>>>>>>>> Best,
> >>>>>>>>>>>
> >>>>>>>>>>> Dawid
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>
> >
> >
>
>

[Timo Walther-2]

Hi Jark,

according to Flink's and Calcite's casting definition in [1][2]
TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not,
we will make it possible ;-)

I'm aware of DeserializationSchema.getProducedType but I think that this
method is actually misplaced. The type should rather be passed to the
source itself.

For our Kafka SQL source, we will also not use this method because the
Kafka source will add own metadata in addition to the
DeserializationSchema. So DeserializationSchema.getProducedType will
never be read.

For now I suggest to leave out the `DataType` from
DecodingFormat.applyReadableMetadata. Also because the format's physical
type is passed later in `createRuntimeDecoder`. If necessary, it can be
computed manually by consumedType + metadata types. We will provide a
metadata utility class for that.

Regards,
Timo


[1]
https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
[2]
https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254


On 08.09.20 10:52, Jark Wu wrote:

> Hi Timo,
>
> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just noticed
> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME ZONE".
> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL TIME
> ZONE" as the defined type of Kafka timestamp? I think this makes sense,
> because it represents the milli-seconds since epoch.
>
> Regarding "DeserializationSchema doesn't need TypeInfo", I don't think so.
> The DeserializationSchema implements ResultTypeQueryable, thus the
> implementation needs to return an output TypeInfo.
> Besides, FlinkKafkaConsumer also
> calls DeserializationSchema.getProducedType as the produced type of the
> source function [1].
>
> Best,
> Jark
>
> [1]:
> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>
> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]> wrote:
>
>> Hi everyone,
>>
>> I updated the FLIP again and hope that I could address the mentioned
>> concerns.
>>
>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
>> source.ts_ms have different semantics. I updated the FLIP and expose the
>> most commonly used properties separately. So frequently used properties
>> are not hidden in the MAP anymore:
>>
>> debezium-json.ingestion-timestamp
>> debezium-json.source.timestamp
>> debezium-json.source.database
>> debezium-json.source.schema
>> debezium-json.source.table
>>
>> However, since other properties depend on the used connector/vendor, the
>> remaining options are stored in:
>>
>> debezium-json.source.properties
>>
>> And accessed with:
>>
>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS MAP<STRING,
>> STRING>)['table']
>>
>> Otherwise it is not possible to figure out the value and column type
>> during validation.
>>
>> @Jark: You convinced me in relaxing the CAST constraints. I added a
>> dedicacated sub-section to the FLIP:
>>
>> For making the use of SYSTEM_METADATA easier and avoid nested casting we
>> allow explicit casting to a target data type:
>>
>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH LOCAL
>> TIME ZONE)
>>
>> A connector still produces and consumes the data type returned by
>> `listMetadata()`. The planner will insert necessary explicit casts.
>>
>> In any case, the user must provide a CAST such that the computed column
>> receives a valid data type when constructing the table schema.
>>
>> "I don't see a reason why `DecodingFormat#applyReadableMetadata` needs a
>> DataType argument."
>>
>> Correct he DeserializationSchema doesn't need TypeInfo, it is always
>> executed locally. It is the source that needs TypeInfo for serializing
>> the record to the next operator. And that's this is what we provide.
>>
>> @Danny:
>>
>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>
>> We can also use some other means to represent an UNKNOWN data type. In
>> the Flink type system, we use the NullType for it. The important part is
>> that the final data type is known for the entire computed column. As I
>> mentioned before, I would avoid the suggested option b) that would be
>> similar to your suggestion. The CAST should be enough and allows for
>> complex expressions in the computed column. Option b) would need parser
>> changes.
>>
>> Regards,
>> Timo
>>
>>
>>
>> On 08.09.20 06:21, Leonard Xu wrote:
>>> Hi, Timo
>>>
>>> Thanks for you explanation and update,  I have only one question  for
>> the latest FLIP.
>>>
>>> About the MAP<STRING, STRING> DataType of key 'debezium-json.source', if
>> user want to use the table name metadata, they need to write:
>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
>> MAP<STRING, STRING>)['table']
>>>
>>> the expression is a little complex for user, Could we only support
>> necessary metas with simple DataType as following?
>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>> STRING),
>>> transactionTime LONG AS
>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>
>>> In this way, we can simplify the expression, the mainly used metadata in
>> changelog format may include 'database','table','source.ts_ms','ts_ms' from
>> my side,
>>> maybe we could only support them at first version.
>>>
>>> Both Debezium and Canal have above four metadata, and I‘m willing to
>> take some subtasks in next development if necessary.
>>>
>>> Debezium:
>>> {
>>>     "before": null,
>>>     "after": {  "id": 101,"name": "scooter"},
>>>     "source": {
>>>       "db": "inventory",                  # 1. database name the
>> changelog belongs to.
>>>       "table": "products",                # 2. table name the changelog
>> belongs to.
>>>       "ts_ms": 1589355504100,             # 3. timestamp of the change
>> happened in database system, i.e.: transaction time in database.
>>>       "connector": "mysql",
>>>       ….
>>>     },
>>>     "ts_ms": 1589355606100,              # 4. timestamp when the debezium
>> processed the changelog.
>>>     "op": "c",
>>>     "transaction": null
>>> }
>>>
>>> Canal:
>>> {
>>>     "data": [{  "id": "102", "name": "car battery" }],
>>>     "database": "inventory",      # 1. database name the changelog
>> belongs to.
>>>     "table": "products",          # 2. table name the changelog belongs
>> to.
>>>     "es": 1589374013000,          # 3. execution time of the change in
>> database system, i.e.: transaction time in database.
>>>     "ts": 1589374013680,          # 4. timestamp when the cannal
>> processed the changelog.
>>>     "isDdl": false,
>>>     "mysqlType": {},
>>>     ....
>>> }
>>>
>>>
>>> Best
>>> Leonard
>>>
>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>
>>>> Thanks Timo ~
>>>>
>>>> The FLIP was already in pretty good shape, I have only 2 questions here:
>>>>
>>>>
>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid read-only
>> computed column for Kafka and can be extracted by the planner.”
>>>>
>>>>
>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually an
>> expression return type can be inferred automatically. But I guess
>> SQL-SERVER does not have function like SYSTEM_METADATA which actually does
>> not have a specific return type.
>>>>
>>>> And why not use the Oracle or MySQL syntax there ?
>>>>
>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
>>>> Which is more straight-forward.
>>>>
>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>
>>>> The default type should not be NULL because only NULL literal does
>> that. Usually we use ANY as the type if we do not know the specific type in
>> the SQL context. ANY means the physical value can be any java object.
>>>>
>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>> [2]
>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>
>>>> Best,
>>>> Danny Chan
>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
>>>>> Hi everyone,
>>>>>
>>>>> I completely reworked FLIP-107. It now covers the full story how to
>> read
>>>>> and write metadata from/to connectors and formats. It considers all of
>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It introduces
>>>>> the concept of PERSISTED computed columns and leaves out partitioning
>>>>> for now.
>>>>>
>>>>> Looking forward to your feedback.
>>>>>
>>>>> Regards,
>>>>> Timo
>>>>>
>>>>>
>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>> Sorry, forgot one question.
>>>>>>
>>>>>> 4. Can we make the value.fields-include more orthogonal? Like one can
>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
>> config to
>>>>>> just ignore timestamp but keep key.
>>>>>>
>>>>>> Best,
>>>>>> Kurt
>>>>>>
>>>>>>
>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]> wrote:
>>>>>>
>>>>>>> Hi Dawid,
>>>>>>>
>>>>>>> I have a couple of questions around key fields, actually I also have
>> some
>>>>>>> other questions but want to be focused on key fields first.
>>>>>>>
>>>>>>> 1. I don't fully understand the usage of "key.fields". Is this
>> option only
>>>>>>> valid during write operation? Because for
>>>>>>> reading, I can't imagine how such options can be applied. I would
>> expect
>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>> to read and assign the key to a normal field?
>>>>>>>
>>>>>>> 2. If "key.fields" is only valid in write operation, I want to
>> propose we
>>>>>>> can simplify the options to not introducing key.format.type and
>>>>>>> other related options. I think a single "key.field" (not fields)
>> would be
>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>> want before sink.
>>>>>>>
>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>> "value.format.xxx" with the "value" prefix. Not every connector has a
>>>>>>> concept
>>>>>>> of key and values. The old parameter "format.type" already good
>> enough to
>>>>>>> use.
>>>>>>>
>>>>>>> Best,
>>>>>>> Kurt
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]> wrote:
>>>>>>>
>>>>>>>> Thanks Dawid,
>>>>>>>>
>>>>>>>> I have two more questions.
>>>>>>>>
>>>>>>>>> SupportsMetadata
>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have some
>> questions
>>>>>>>> regarding to this interface.
>>>>>>>> 1) How do the source know what the expected return type of each
>> metadata?
>>>>>>>> 2) Where to put the metadata fields? Append to the existing physical
>>>>>>>> fields?
>>>>>>>> If yes, I would suggest to change the signature to `TableSource
>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>> metadataTypes)`
>>>>>>>>
>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed column
>>>>>>>> expression? If yes, how to specify the return type of
>> SYSTEM_METADATA?
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Jark
>>>>>>>>
>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>> [hidden email]>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi,
>>>>>>>>>
>>>>>>>>> 1. I thought a bit more on how the source would emit the columns
>> and I
>>>>>>>>> now see its not exactly the same as regular columns. I see a need
>> to
>>>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
>>>>>>>>>
>>>>>>>>> I do agree mostly with Danny on how we should do that. One
>> additional
>>>>>>>>> things I would introduce is an
>>>>>>>>>
>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>
>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>
>>>>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
>>>>>>>>>
>>>>>>>>> }
>>>>>>>>>
>>>>>>>>> This way the source would have to declare/emit only the requested
>>>>>>>>> metadata fields. In order not to clash with user defined fields.
>> When
>>>>>>>>> emitting the metadata field I would prepend the column name with
>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>> SYSTEM_METADATA("partition") the source would append a field
>>>>>>>>> __system_partition to the schema. This would be never visible to
>> the
>>>>>>>>> user as it would be used only for the subsequent computed columns.
>> If
>>>>>>>>> that makes sense to you, I will update the FLIP with this
>> description.
>>>>>>>>>
>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>
>>>>>>>>> Here I agree with Danny. It is also the current state of the
>> proposal.
>>>>>>>>>
>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>
>>>>>>>>> Here I also agree with Danny. I also think those are orthogonal. I
>> would
>>>>>>>>> leave out the STORED computed columns out of the discussion. I
>> don't see
>>>>>>>>> how do they relate to the partitioning. I already put both of those
>>>>>>>>> cases in the document. We can either partition on a computed
>> column or
>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving out the
>>>>>>>>> partitioning by udf in the first version if you still have some
>>>>>>>> concerns.
>>>>>>>>>
>>>>>>>>> As for your question Danny. It depends which partitioning strategy
>> you
>>>>>>>> use.
>>>>>>>>>
>>>>>>>>> For the HASH partitioning strategy I thought it would work as you
>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure though if
>> we
>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does not own
>> the
>>>>>>>>> data and the partitions are already an intrinsic property of the
>>>>>>>>> underlying source e.g. for kafka we do not create topics, but we
>> just
>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>
>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
>>>>>>>>>
>>>>>>>>> I am fine with changing it to timestamp.field to be consistent with
>>>>>>>>> other value.fields and key.fields. Actually that was also my
>> initial
>>>>>>>>> proposal in a first draft I prepared. I changed it afterwards to
>> shorten
>>>>>>>>> the key.
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>>
>>>>>>>>> Dawid
>>>>>>>>>
>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a
>> useful
>>>>>>>>> feature ~
>>>>>>>>>>
>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>
>>>>>>>>>> I think it is completely orthogonal, computed column push down is
>>>>>>>>> another topic, this should not be a blocker but a promotion, if we
>> do
>>>>>>>> not
>>>>>>>>> have any filters on the computed column, there is no need to do any
>>>>>>>>> pushings; the source node just emit the complete record with full
>>>>>>>> metadata
>>>>>>>>> with the declared physical schema, then when generating the virtual
>>>>>>>>> columns, we would extract the metadata info and output as full
>>>>>>>> columns(with
>>>>>>>>> full schema).
>>>>>>>>>>
>>>>>>>>>> About the type of metadata column
>>>>>>>>>>
>>>>>>>>>> Personally i prefer explicit type instead of CAST, they are
>> symantic
>>>>>>>>> equivalent though, explict type is more straight-forward and we can
>>>>>>>> declare
>>>>>>>>> the nullable attribute there.
>>>>>>>>>>
>>>>>>>>>> About option A: partitioning based on acomputed column VS option
>> B:
>>>>>>>>> partitioning with just a function
>>>>>>>>>>
>>>>>>>>>>   From the FLIP, it seems that B's partitioning is just a strategy
>> when
>>>>>>>>> writing data, the partiton column is not included in the table
>> schema,
>>>>>>>> so
>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>
>>>>>>>>>> - Compared to A, we do not need to generate the partition column
>> when
>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>> - For A we can also mark the column as STORED when we want to
>> persist
>>>>>>>>> that
>>>>>>>>>>
>>>>>>>>>> So in my opition they are orthogonal, we can support both, i saw
>> that
>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS
>> num, and
>>>>>>>> the
>>>>>>>>> partitions are managed under a "tablenamespace", the partition in
>> which
>>>>>>>> the
>>>>>>>>> record is stored is partition number N, where N = MOD(expr, num),
>> for
>>>>>>>> your
>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>
>>>>>>>>>> [1]
>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>> [2]
>>>>>>>>>
>>>>>>>>
>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>> Danny Chan
>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <[hidden email]
>>>>>>>>> ，写道：
>>>>>>>>>>> Hi Jark,
>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of properties.
>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>> I also considered exactly what you are suggesting (prefixing with
>>>>>>>>> connector or kafka). I should've put that into an Option/Rejected
>>>>>>>>> alternatives.
>>>>>>>>>>> I agree timestamp, key.*, value.* are connector properties. Why I
>>>>>>>>> wanted to suggest not adding that prefix in the first version is
>> that
>>>>>>>>> actually all the properties in the WITH section are connector
>>>>>>>> properties.
>>>>>>>>> Even format is in the end a connector property as some of the
>> sources
>>>>>>>> might
>>>>>>>>> not have a format, imo. The benefit of not adding the prefix is
>> that it
>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the properties
>> with
>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>> I am fine with doing it though if this is a preferred approach
>> in the
>>>>>>>>> community.
>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>> I forgot to update the `value.fields.include` property. It
>> should be
>>>>>>>>> value.fields-include. Which I think you also suggested in the
>> comment,
>>>>>>>>> right?
>>>>>>>>>>> As for the cast vs declaring output type of computed column. I
>> think
>>>>>>>>> it's better not to use CAST, but declare a type of an expression
>> and
>>>>>>>> later
>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I think
>> this
>>>>>>>> way
>>>>>>>>> it will be easier to implement e.g. filter push downs when working
>> with
>>>>>>>> the
>>>>>>>>> native types of the source, e.g. in case of Kafka's offset, i
>> think it's
>>>>>>>>> better to pushdown long rather than string. This could let us push
>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382. Otherwise we
>> would
>>>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset, long)
>> <
>>>>>>>> 59382.
>>>>>>>>> Moreover I think we need to introduce the type for computed columns
>>>>>>>> anyway
>>>>>>>>> to support functions that infer output type based on expected
>> return
>>>>>>>> type.
>>>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA would
>> have
>>>>>>>>> to be pushed down to the source. If it is not possible the planner
>>>>>>>> should
>>>>>>>>> fail. As far as I know computed columns push down will be part of
>> source
>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>> As for the persisted computed column. I think it is completely
>>>>>>>>> orthogonal. In my current proposal you can also partition by a
>> computed
>>>>>>>>> column. The difference between using a udf in partitioned by vs
>>>>>>>> partitioned
>>>>>>>>> by a computed column is that when you partition by a computed
>> column
>>>>>>>> this
>>>>>>>>> column must be also computed when reading the table. If you use a
>> udf in
>>>>>>>>> the partitioned by, the expression is computed only when inserting
>> into
>>>>>>>> the
>>>>>>>>> table.
>>>>>>>>>>> Hope this answers some of your questions. Looking forward for
>> further
>>>>>>>>> suggestions.
>>>>>>>>>>> Best,
>>>>>>>>>>> Dawid
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>> Hi,
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
>> metadata
>>>>>>>> and
>>>>>>>>>>>> key-part information from source is an important feature for
>>>>>>>> streaming
>>>>>>>>>>>> users.
>>>>>>>>>>>>
>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>
>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing HEADER
>>>>>>>>> keyword as
>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we
>> should
>>>>>>>>> add a
>>>>>>>>>>>> section to explain what's the relationship between them.
>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used on
>> the
>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL. Shall we
>>>>>>>> make
>>>>>>>>> the
>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"? (actually, I
>>>>>>>>> prefer
>>>>>>>>>>>> "kafka.timestamp" which is another improvement for properties
>>>>>>>>> FLINK-12557)
>>>>>>>>>>>> A single "timestamp" in properties may mislead users that the
>>>>>>>> field
>>>>>>>>> is
>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>
>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Jark
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>> [hidden email]>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>
>>>>>>>>>>>>> I would like to propose an improvement that would enable
>> reading
>>>>>>>> table
>>>>>>>>>>>>> columns from different parts of source records. Besides the
>> main
>>>>>>>>> payload
>>>>>>>>>>>>> majority (if not all of the sources) expose additional
>>>>>>>> information. It
>>>>>>>>>>>>> can be simply a read-only metadata such as offset, ingestion
>> time
>>>>>>>> or a
>>>>>>>>>>>>> read and write parts of the record that contain data but
>>>>>>>> additionally
>>>>>>>>>>>>> serve different purposes (partitioning, compaction etc.), e.g.
>> key
>>>>>>>> or
>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>
>>>>>>>>>>>>> We should make it possible to read and write data from all of
>> those
>>>>>>>>>>>>> locations. In this proposal I discuss reading partitioning
>> data,
>>>>>>>> for
>>>>>>>>>>>>> completeness this proposal discusses also the partitioning when
>>>>>>>>> writing
>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>
>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>
>>>
>>
>>
>

[Leonard Xu]

HI, Timo

Thanks for driving this FLIP.

Sorry but I have a concern about Writing metadata via DynamicTableSink section:

CREATE TABLE kafka_table (
  id BIGINT,
  name STRING,
  timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT) PERSISTED,
  headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING, BYTES>) PERSISTED
) WITH (
  ...
)
An insert statement could look like:

INSERT INTO kafka_table VALUES (
  (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
)

The proposed INERT syntax does not make sense to me, because it contains computed(generated) column.
Both SQL server and Postgresql do not allow to insert value to computed columns even they are persisted, this boke the generated column semantics and may confuse user much.

For SQL server computed column[1]:
> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ] ]...  
> NOTE: A computed column cannot be the target of an INSERT or UPDATE statement.

For Postgresql generated column[2]:
>  height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
> NOTE: A generated column cannot be written to directly. In INSERT or UPDATE commands, a value cannot be specified for a generated column, but the keyword DEFAULT may be specified.

It shouldn't be allowed to set/update value for generated column after lookup the SQL 2016:
> <insert statement> ::=
> INSERT INTO <insertion target> <insert columns and source>
>
> If <contextually typed table value constructor> CTTVC is specified, then every <contextually typed row
> value constructor element> simply contained in CTTVC whose positionally corresponding <column name>
> in <insert column list> references a column of which some underlying column is a generated column shall
> be a <default specification>.
> A <default specification> specifies the default value of some associated item.


[1] https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15 <https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15>
[2] https://www.postgresql.org/docs/12/ddl-generated-columns.html <https://www.postgresql.org/docs/12/ddl-generated-columns.html>

> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>
> Hi Jark,
>
> according to Flink's and Calcite's casting definition in [1][2] TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not, we will make it possible ;-)
>
> I'm aware of DeserializationSchema.getProducedType but I think that this method is actually misplaced. The type should rather be passed to the source itself.
>
> For our Kafka SQL source, we will also not use this method because the Kafka source will add own metadata in addition to the DeserializationSchema. So DeserializationSchema.getProducedType will never be read.
>
> For now I suggest to leave out the `DataType` from DecodingFormat.applyReadableMetadata. Also because the format's physical type is passed later in `createRuntimeDecoder`. If necessary, it can be computed manually by consumedType + metadata types. We will provide a metadata utility class for that.
>
> Regards,
> Timo
>
>
> [1] https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
> [2] https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>
>
> On 08.09.20 10:52, Jark Wu wrote:
>> Hi Timo,
>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just noticed
>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME ZONE".
>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL TIME
>> ZONE" as the defined type of Kafka timestamp? I think this makes sense,
>> because it represents the milli-seconds since epoch.
>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't think so.
>> The DeserializationSchema implements ResultTypeQueryable, thus the
>> implementation needs to return an output TypeInfo.
>> Besides, FlinkKafkaConsumer also
>> calls DeserializationSchema.getProducedType as the produced type of the
>> source function [1].
>> Best,
>> Jark
>> [1]:
>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]> wrote:
>>> Hi everyone,
>>>
>>> I updated the FLIP again and hope that I could address the mentioned
>>> concerns.
>>>
>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
>>> source.ts_ms have different semantics. I updated the FLIP and expose the
>>> most commonly used properties separately. So frequently used properties
>>> are not hidden in the MAP anymore:
>>>
>>> debezium-json.ingestion-timestamp
>>> debezium-json.source.timestamp
>>> debezium-json.source.database
>>> debezium-json.source.schema
>>> debezium-json.source.table
>>>
>>> However, since other properties depend on the used connector/vendor, the
>>> remaining options are stored in:
>>>
>>> debezium-json.source.properties
>>>
>>> And accessed with:
>>>
>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS MAP<STRING,
>>> STRING>)['table']
>>>
>>> Otherwise it is not possible to figure out the value and column type
>>> during validation.
>>>
>>> @Jark: You convinced me in relaxing the CAST constraints. I added a
>>> dedicacated sub-section to the FLIP:
>>>
>>> For making the use of SYSTEM_METADATA easier and avoid nested casting we
>>> allow explicit casting to a target data type:
>>>
>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH LOCAL
>>> TIME ZONE)
>>>
>>> A connector still produces and consumes the data type returned by
>>> `listMetadata()`. The planner will insert necessary explicit casts.
>>>
>>> In any case, the user must provide a CAST such that the computed column
>>> receives a valid data type when constructing the table schema.
>>>
>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata` needs a
>>> DataType argument."
>>>
>>> Correct he DeserializationSchema doesn't need TypeInfo, it is always
>>> executed locally. It is the source that needs TypeInfo for serializing
>>> the record to the next operator. And that's this is what we provide.
>>>
>>> @Danny:
>>>
>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>
>>> We can also use some other means to represent an UNKNOWN data type. In
>>> the Flink type system, we use the NullType for it. The important part is
>>> that the final data type is known for the entire computed column. As I
>>> mentioned before, I would avoid the suggested option b) that would be
>>> similar to your suggestion. The CAST should be enough and allows for
>>> complex expressions in the computed column. Option b) would need parser
>>> changes.
>>>
>>> Regards,
>>> Timo
>>>
>>>
>>>
>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>> Hi, Timo
>>>>
>>>> Thanks for you explanation and update,  I have only one question  for
>>> the latest FLIP.
>>>>
>>>> About the MAP<STRING, STRING> DataType of key 'debezium-json.source', if
>>> user want to use the table name metadata, they need to write:
>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
>>> MAP<STRING, STRING>)['table']
>>>>
>>>> the expression is a little complex for user, Could we only support
>>> necessary metas with simple DataType as following?
>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>> STRING),
>>>> transactionTime LONG AS
>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>
>>>> In this way, we can simplify the expression, the mainly used metadata in
>>> changelog format may include 'database','table','source.ts_ms','ts_ms' from
>>> my side,
>>>> maybe we could only support them at first version.
>>>>
>>>> Both Debezium and Canal have above four metadata, and I‘m willing to
>>> take some subtasks in next development if necessary.
>>>>
>>>> Debezium:
>>>> {
>>>>    "before": null,
>>>>    "after": {  "id": 101,"name": "scooter"},
>>>>    "source": {
>>>>      "db": "inventory",                  # 1. database name the
>>> changelog belongs to.
>>>>      "table": "products",                # 2. table name the changelog
>>> belongs to.
>>>>      "ts_ms": 1589355504100,             # 3. timestamp of the change
>>> happened in database system, i.e.: transaction time in database.
>>>>      "connector": "mysql",
>>>>      ….
>>>>    },
>>>>    "ts_ms": 1589355606100,              # 4. timestamp when the debezium
>>> processed the changelog.
>>>>    "op": "c",
>>>>    "transaction": null
>>>> }
>>>>
>>>> Canal:
>>>> {
>>>>    "data": [{  "id": "102", "name": "car battery" }],
>>>>    "database": "inventory",      # 1. database name the changelog
>>> belongs to.
>>>>    "table": "products",          # 2. table name the changelog belongs
>>> to.
>>>>    "es": 1589374013000,          # 3. execution time of the change in
>>> database system, i.e.: transaction time in database.
>>>>    "ts": 1589374013680,          # 4. timestamp when the cannal
>>> processed the changelog.
>>>>    "isDdl": false,
>>>>    "mysqlType": {},
>>>>    ....
>>>> }
>>>>
>>>>
>>>> Best
>>>> Leonard
>>>>
>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>>
>>>>> Thanks Timo ~
>>>>>
>>>>> The FLIP was already in pretty good shape, I have only 2 questions here:
>>>>>
>>>>>
>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid read-only
>>> computed column for Kafka and can be extracted by the planner.”
>>>>>
>>>>>
>>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually an
>>> expression return type can be inferred automatically. But I guess
>>> SQL-SERVER does not have function like SYSTEM_METADATA which actually does
>>> not have a specific return type.
>>>>>
>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>
>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
>>>>> Which is more straight-forward.
>>>>>
>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>
>>>>> The default type should not be NULL because only NULL literal does
>>> that. Usually we use ANY as the type if we do not know the specific type in
>>> the SQL context. ANY means the physical value can be any java object.
>>>>>
>>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>> [2]
>>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>>
>>>>> Best,
>>>>> Danny Chan
>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
>>>>>> Hi everyone,
>>>>>>
>>>>>> I completely reworked FLIP-107. It now covers the full story how to
>>> read
>>>>>> and write metadata from/to connectors and formats. It considers all of
>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It introduces
>>>>>> the concept of PERSISTED computed columns and leaves out partitioning
>>>>>> for now.
>>>>>>
>>>>>> Looking forward to your feedback.
>>>>>>
>>>>>> Regards,
>>>>>> Timo
>>>>>>
>>>>>>
>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>> Sorry, forgot one question.
>>>>>>>
>>>>>>> 4. Can we make the value.fields-include more orthogonal? Like one can
>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
>>> config to
>>>>>>> just ignore timestamp but keep key.
>>>>>>>
>>>>>>> Best,
>>>>>>> Kurt
>>>>>>>
>>>>>>>
>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]> wrote:
>>>>>>>
>>>>>>>> Hi Dawid,
>>>>>>>>
>>>>>>>> I have a couple of questions around key fields, actually I also have
>>> some
>>>>>>>> other questions but want to be focused on key fields first.
>>>>>>>>
>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is this
>>> option only
>>>>>>>> valid during write operation? Because for
>>>>>>>> reading, I can't imagine how such options can be applied. I would
>>> expect
>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>
>>>>>>>> 2. If "key.fields" is only valid in write operation, I want to
>>> propose we
>>>>>>>> can simplify the options to not introducing key.format.type and
>>>>>>>> other related options. I think a single "key.field" (not fields)
>>> would be
>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>> want before sink.
>>>>>>>>
>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>>> "value.format.xxx" with the "value" prefix. Not every connector has a
>>>>>>>> concept
>>>>>>>> of key and values. The old parameter "format.type" already good
>>> enough to
>>>>>>>> use.
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Kurt
>>>>>>>>
>>>>>>>>
>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]> wrote:
>>>>>>>>
>>>>>>>>> Thanks Dawid,
>>>>>>>>>
>>>>>>>>> I have two more questions.
>>>>>>>>>
>>>>>>>>>> SupportsMetadata
>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have some
>>> questions
>>>>>>>>> regarding to this interface.
>>>>>>>>> 1) How do the source know what the expected return type of each
>>> metadata?
>>>>>>>>> 2) Where to put the metadata fields? Append to the existing physical
>>>>>>>>> fields?
>>>>>>>>> If yes, I would suggest to change the signature to `TableSource
>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>> metadataTypes)`
>>>>>>>>>
>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed column
>>>>>>>>> expression? If yes, how to specify the return type of
>>> SYSTEM_METADATA?
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>> Jark
>>>>>>>>>
>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>> [hidden email]>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi,
>>>>>>>>>>
>>>>>>>>>> 1. I thought a bit more on how the source would emit the columns
>>> and I
>>>>>>>>>> now see its not exactly the same as regular columns. I see a need
>>> to
>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
>>>>>>>>>>
>>>>>>>>>> I do agree mostly with Danny on how we should do that. One
>>> additional
>>>>>>>>>> things I would introduce is an
>>>>>>>>>>
>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>
>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>
>>>>>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
>>>>>>>>>>
>>>>>>>>>> }
>>>>>>>>>>
>>>>>>>>>> This way the source would have to declare/emit only the requested
>>>>>>>>>> metadata fields. In order not to clash with user defined fields.
>>> When
>>>>>>>>>> emitting the metadata field I would prepend the column name with
>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a field
>>>>>>>>>> __system_partition to the schema. This would be never visible to
>>> the
>>>>>>>>>> user as it would be used only for the subsequent computed columns.
>>> If
>>>>>>>>>> that makes sense to you, I will update the FLIP with this
>>> description.
>>>>>>>>>>
>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>
>>>>>>>>>> Here I agree with Danny. It is also the current state of the
>>> proposal.
>>>>>>>>>>
>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>
>>>>>>>>>> Here I also agree with Danny. I also think those are orthogonal. I
>>> would
>>>>>>>>>> leave out the STORED computed columns out of the discussion. I
>>> don't see
>>>>>>>>>> how do they relate to the partitioning. I already put both of those
>>>>>>>>>> cases in the document. We can either partition on a computed
>>> column or
>>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving out the
>>>>>>>>>> partitioning by udf in the first version if you still have some
>>>>>>>>> concerns.
>>>>>>>>>>
>>>>>>>>>> As for your question Danny. It depends which partitioning strategy
>>> you
>>>>>>>>> use.
>>>>>>>>>>
>>>>>>>>>> For the HASH partitioning strategy I thought it would work as you
>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure though if
>>> we
>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does not own
>>> the
>>>>>>>>>> data and the partitions are already an intrinsic property of the
>>>>>>>>>> underlying source e.g. for kafka we do not create topics, but we
>>> just
>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>
>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
>>>>>>>>>>
>>>>>>>>>> I am fine with changing it to timestamp.field to be consistent with
>>>>>>>>>> other value.fields and key.fields. Actually that was also my
>>> initial
>>>>>>>>>> proposal in a first draft I prepared. I changed it afterwards to
>>> shorten
>>>>>>>>>> the key.
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>>
>>>>>>>>>> Dawid
>>>>>>>>>>
>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a
>>> useful
>>>>>>>>>> feature ~
>>>>>>>>>>>
>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>
>>>>>>>>>>> I think it is completely orthogonal, computed column push down is
>>>>>>>>>> another topic, this should not be a blocker but a promotion, if we
>>> do
>>>>>>>>> not
>>>>>>>>>> have any filters on the computed column, there is no need to do any
>>>>>>>>>> pushings; the source node just emit the complete record with full
>>>>>>>>> metadata
>>>>>>>>>> with the declared physical schema, then when generating the virtual
>>>>>>>>>> columns, we would extract the metadata info and output as full
>>>>>>>>> columns(with
>>>>>>>>>> full schema).
>>>>>>>>>>>
>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>
>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they are
>>> symantic
>>>>>>>>>> equivalent though, explict type is more straight-forward and we can
>>>>>>>>> declare
>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>
>>>>>>>>>>> About option A: partitioning based on acomputed column VS option
>>> B:
>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>
>>>>>>>>>>>  From the FLIP, it seems that B's partitioning is just a strategy
>>> when
>>>>>>>>>> writing data, the partiton column is not included in the table
>>> schema,
>>>>>>>>> so
>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>
>>>>>>>>>>> - Compared to A, we do not need to generate the partition column
>>> when
>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>> - For A we can also mark the column as STORED when we want to
>>> persist
>>>>>>>>>> that
>>>>>>>>>>>
>>>>>>>>>>> So in my opition they are orthogonal, we can support both, i saw
>>> that
>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS
>>> num, and
>>>>>>>>> the
>>>>>>>>>> partitions are managed under a "tablenamespace", the partition in
>>> which
>>>>>>>>> the
>>>>>>>>>> record is stored is partition number N, where N = MOD(expr, num),
>>> for
>>>>>>>>> your
>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>
>>>>>>>>>>> [1]
>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>> [2]
>>>>>>>>>>
>>>>>>>>>
>>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>> Danny Chan
>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <[hidden email]
>>>>>>>>>> ，写道：
>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of properties.
>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>> I also considered exactly what you are suggesting (prefixing with
>>>>>>>>>> connector or kafka). I should've put that into an Option/Rejected
>>>>>>>>>> alternatives.
>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector properties. Why I
>>>>>>>>>> wanted to suggest not adding that prefix in the first version is
>>> that
>>>>>>>>>> actually all the properties in the WITH section are connector
>>>>>>>>> properties.
>>>>>>>>>> Even format is in the end a connector property as some of the
>>> sources
>>>>>>>>> might
>>>>>>>>>> not have a format, imo. The benefit of not adding the prefix is
>>> that it
>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the properties
>>> with
>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>> I am fine with doing it though if this is a preferred approach
>>> in the
>>>>>>>>>> community.
>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>> I forgot to update the `value.fields.include` property. It
>>> should be
>>>>>>>>>> value.fields-include. Which I think you also suggested in the
>>> comment,
>>>>>>>>>> right?
>>>>>>>>>>>> As for the cast vs declaring output type of computed column. I
>>> think
>>>>>>>>>> it's better not to use CAST, but declare a type of an expression
>>> and
>>>>>>>>> later
>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I think
>>> this
>>>>>>>>> way
>>>>>>>>>> it will be easier to implement e.g. filter push downs when working
>>> with
>>>>>>>>> the
>>>>>>>>>> native types of the source, e.g. in case of Kafka's offset, i
>>> think it's
>>>>>>>>>> better to pushdown long rather than string. This could let us push
>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382. Otherwise we
>>> would
>>>>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset, long)
>>> <
>>>>>>>>> 59382.
>>>>>>>>>> Moreover I think we need to introduce the type for computed columns
>>>>>>>>> anyway
>>>>>>>>>> to support functions that infer output type based on expected
>>> return
>>>>>>>>> type.
>>>>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA would
>>> have
>>>>>>>>>> to be pushed down to the source. If it is not possible the planner
>>>>>>>>> should
>>>>>>>>>> fail. As far as I know computed columns push down will be part of
>>> source
>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>> As for the persisted computed column. I think it is completely
>>>>>>>>>> orthogonal. In my current proposal you can also partition by a
>>> computed
>>>>>>>>>> column. The difference between using a udf in partitioned by vs
>>>>>>>>> partitioned
>>>>>>>>>> by a computed column is that when you partition by a computed
>>> column
>>>>>>>>> this
>>>>>>>>>> column must be also computed when reading the table. If you use a
>>> udf in
>>>>>>>>>> the partitioned by, the expression is computed only when inserting
>>> into
>>>>>>>>> the
>>>>>>>>>> table.
>>>>>>>>>>>> Hope this answers some of your questions. Looking forward for
>>> further
>>>>>>>>>> suggestions.
>>>>>>>>>>>> Best,
>>>>>>>>>>>> Dawid
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
>>> metadata
>>>>>>>>> and
>>>>>>>>>>>>> key-part information from source is an important feature for
>>>>>>>>> streaming
>>>>>>>>>>>>> users.
>>>>>>>>>>>>>
>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>
>>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing HEADER
>>>>>>>>>> keyword as
>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we
>>> should
>>>>>>>>>> add a
>>>>>>>>>>>>> section to explain what's the relationship between them.
>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used on
>>> the
>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL. Shall we
>>>>>>>>> make
>>>>>>>>>> the
>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"? (actually, I
>>>>>>>>>> prefer
>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for properties
>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>> A single "timestamp" in properties may mislead users that the
>>>>>>>>> field
>>>>>>>>>> is
>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>> [hidden email]>
>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I would like to propose an improvement that would enable
>>> reading
>>>>>>>>> table
>>>>>>>>>>>>>> columns from different parts of source records. Besides the
>>> main
>>>>>>>>>> payload
>>>>>>>>>>>>>> majority (if not all of the sources) expose additional
>>>>>>>>> information. It
>>>>>>>>>>>>>> can be simply a read-only metadata such as offset, ingestion
>>> time
>>>>>>>>> or a
>>>>>>>>>>>>>> read and write parts of the record that contain data but
>>>>>>>>> additionally
>>>>>>>>>>>>>> serve different purposes (partitioning, compaction etc.), e.g.
>>> key
>>>>>>>>> or
>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> We should make it possible to read and write data from all of
>>> those
>>>>>>>>>>>>>> locations. In this proposal I discuss reading partitioning
>>> data,
>>>>>>>>> for
>>>>>>>>>>>>>> completeness this proposal discusses also the partitioning when
>>>>>>>>>> writing
>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>
>>>>
>>>
>>>
>

[Timo Walther-2]

Hi Leonard,

the only alternative I see is that we introduce a concept that is
completely different to computed columns. This is also mentioned in the
rejected alternative section of the FLIP. Something like:

CREATE TABLE kafka_table (
     id BIGINT,
     name STRING,
     timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
     headers MAP<STRING, BYTES> SYSTEM_METADATA("headers") PERSISTED
) WITH (
    ...
)

This way we would avoid confusion at all and can easily map columns to
metadata columns. The disadvantage is that users cannot call UDFs or
parse timestamps. This would need to be done in a real computed column.

I'm happy about better alternatives.

Regards,
Timo


On 08.09.20 15:37, Leonard Xu wrote:

> HI, Timo
>
> Thanks for driving this FLIP.
>
> Sorry but I have a concern about Writing metadata via DynamicTableSink section:
>
> CREATE TABLE kafka_table (
>    id BIGINT,
>    name STRING,
>    timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT) PERSISTED,
>    headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING, BYTES>) PERSISTED
> ) WITH (
>    ...
> )
> An insert statement could look like:
>
> INSERT INTO kafka_table VALUES (
>    (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
> )
>
> The proposed INERT syntax does not make sense to me, because it contains computed(generated) column.
> Both SQL server and Postgresql do not allow to insert value to computed columns even they are persisted, this boke the generated column semantics and may confuse user much.
>
> For SQL server computed column[1]:
>> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ] ]...
>> NOTE: A computed column cannot be the target of an INSERT or UPDATE statement.
>
> For Postgresql generated column[2]:
>>   height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
>> NOTE: A generated column cannot be written to directly. In INSERT or UPDATE commands, a value cannot be specified for a generated column, but the keyword DEFAULT may be specified.
>
> It shouldn't be allowed to set/update value for generated column after lookup the SQL 2016:
>> <insert statement> ::=
>> INSERT INTO <insertion target> <insert columns and source>
>>
>> If <contextually typed table value constructor> CTTVC is specified, then every <contextually typed row
>> value constructor element> simply contained in CTTVC whose positionally corresponding <column name>
>> in <insert column list> references a column of which some underlying column is a generated column shall
>> be a <default specification>.
>> A <default specification> specifies the default value of some associated item.
>
>
> [1] https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15 <https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15>
> [2] https://www.postgresql.org/docs/12/ddl-generated-columns.html <https://www.postgresql.org/docs/12/ddl-generated-columns.html>
>
>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>>
>> Hi Jark,
>>
>> according to Flink's and Calcite's casting definition in [1][2] TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not, we will make it possible ;-)
>>
>> I'm aware of DeserializationSchema.getProducedType but I think that this method is actually misplaced. The type should rather be passed to the source itself.
>>
>> For our Kafka SQL source, we will also not use this method because the Kafka source will add own metadata in addition to the DeserializationSchema. So DeserializationSchema.getProducedType will never be read.
>>
>> For now I suggest to leave out the `DataType` from DecodingFormat.applyReadableMetadata. Also because the format's physical type is passed later in `createRuntimeDecoder`. If necessary, it can be computed manually by consumedType + metadata types. We will provide a metadata utility class for that.
>>
>> Regards,
>> Timo
>>
>>
>> [1] https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
>> [2] https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>>
>>
>> On 08.09.20 10:52, Jark Wu wrote:
>>> Hi Timo,
>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just noticed
>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME ZONE".
>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL TIME
>>> ZONE" as the defined type of Kafka timestamp? I think this makes sense,
>>> because it represents the milli-seconds since epoch.
>>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't think so.
>>> The DeserializationSchema implements ResultTypeQueryable, thus the
>>> implementation needs to return an output TypeInfo.
>>> Besides, FlinkKafkaConsumer also
>>> calls DeserializationSchema.getProducedType as the produced type of the
>>> source function [1].
>>> Best,
>>> Jark
>>> [1]:
>>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]> wrote:
>>>> Hi everyone,
>>>>
>>>> I updated the FLIP again and hope that I could address the mentioned
>>>> concerns.
>>>>
>>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
>>>> source.ts_ms have different semantics. I updated the FLIP and expose the
>>>> most commonly used properties separately. So frequently used properties
>>>> are not hidden in the MAP anymore:
>>>>
>>>> debezium-json.ingestion-timestamp
>>>> debezium-json.source.timestamp
>>>> debezium-json.source.database
>>>> debezium-json.source.schema
>>>> debezium-json.source.table
>>>>
>>>> However, since other properties depend on the used connector/vendor, the
>>>> remaining options are stored in:
>>>>
>>>> debezium-json.source.properties
>>>>
>>>> And accessed with:
>>>>
>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS MAP<STRING,
>>>> STRING>)['table']
>>>>
>>>> Otherwise it is not possible to figure out the value and column type
>>>> during validation.
>>>>
>>>> @Jark: You convinced me in relaxing the CAST constraints. I added a
>>>> dedicacated sub-section to the FLIP:
>>>>
>>>> For making the use of SYSTEM_METADATA easier and avoid nested casting we
>>>> allow explicit casting to a target data type:
>>>>
>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH LOCAL
>>>> TIME ZONE)
>>>>
>>>> A connector still produces and consumes the data type returned by
>>>> `listMetadata()`. The planner will insert necessary explicit casts.
>>>>
>>>> In any case, the user must provide a CAST such that the computed column
>>>> receives a valid data type when constructing the table schema.
>>>>
>>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata` needs a
>>>> DataType argument."
>>>>
>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is always
>>>> executed locally. It is the source that needs TypeInfo for serializing
>>>> the record to the next operator. And that's this is what we provide.
>>>>
>>>> @Danny:
>>>>
>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>
>>>> We can also use some other means to represent an UNKNOWN data type. In
>>>> the Flink type system, we use the NullType for it. The important part is
>>>> that the final data type is known for the entire computed column. As I
>>>> mentioned before, I would avoid the suggested option b) that would be
>>>> similar to your suggestion. The CAST should be enough and allows for
>>>> complex expressions in the computed column. Option b) would need parser
>>>> changes.
>>>>
>>>> Regards,
>>>> Timo
>>>>
>>>>
>>>>
>>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>>> Hi, Timo
>>>>>
>>>>> Thanks for you explanation and update,  I have only one question  for
>>>> the latest FLIP.
>>>>>
>>>>> About the MAP<STRING, STRING> DataType of key 'debezium-json.source', if
>>>> user want to use the table name metadata, they need to write:
>>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
>>>> MAP<STRING, STRING>)['table']
>>>>>
>>>>> the expression is a little complex for user, Could we only support
>>>> necessary metas with simple DataType as following?
>>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>>> STRING),
>>>>> transactionTime LONG AS
>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>>
>>>>> In this way, we can simplify the expression, the mainly used metadata in
>>>> changelog format may include 'database','table','source.ts_ms','ts_ms' from
>>>> my side,
>>>>> maybe we could only support them at first version.
>>>>>
>>>>> Both Debezium and Canal have above four metadata, and I‘m willing to
>>>> take some subtasks in next development if necessary.
>>>>>
>>>>> Debezium:
>>>>> {
>>>>>     "before": null,
>>>>>     "after": {  "id": 101,"name": "scooter"},
>>>>>     "source": {
>>>>>       "db": "inventory",                  # 1. database name the
>>>> changelog belongs to.
>>>>>       "table": "products",                # 2. table name the changelog
>>>> belongs to.
>>>>>       "ts_ms": 1589355504100,             # 3. timestamp of the change
>>>> happened in database system, i.e.: transaction time in database.
>>>>>       "connector": "mysql",
>>>>>       ….
>>>>>     },
>>>>>     "ts_ms": 1589355606100,              # 4. timestamp when the debezium
>>>> processed the changelog.
>>>>>     "op": "c",
>>>>>     "transaction": null
>>>>> }
>>>>>
>>>>> Canal:
>>>>> {
>>>>>     "data": [{  "id": "102", "name": "car battery" }],
>>>>>     "database": "inventory",      # 1. database name the changelog
>>>> belongs to.
>>>>>     "table": "products",          # 2. table name the changelog belongs
>>>> to.
>>>>>     "es": 1589374013000,          # 3. execution time of the change in
>>>> database system, i.e.: transaction time in database.
>>>>>     "ts": 1589374013680,          # 4. timestamp when the cannal
>>>> processed the changelog.
>>>>>     "isDdl": false,
>>>>>     "mysqlType": {},
>>>>>     ....
>>>>> }
>>>>>
>>>>>
>>>>> Best
>>>>> Leonard
>>>>>
>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>>>
>>>>>> Thanks Timo ~
>>>>>>
>>>>>> The FLIP was already in pretty good shape, I have only 2 questions here:
>>>>>>
>>>>>>
>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid read-only
>>>> computed column for Kafka and can be extracted by the planner.”
>>>>>>
>>>>>>
>>>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually an
>>>> expression return type can be inferred automatically. But I guess
>>>> SQL-SERVER does not have function like SYSTEM_METADATA which actually does
>>>> not have a specific return type.
>>>>>>
>>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>>
>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
>>>>>> Which is more straight-forward.
>>>>>>
>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>
>>>>>> The default type should not be NULL because only NULL literal does
>>>> that. Usually we use ANY as the type if we do not know the specific type in
>>>> the SQL context. ANY means the physical value can be any java object.
>>>>>>
>>>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>>> [2]
>>>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>>>
>>>>>> Best,
>>>>>> Danny Chan
>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
>>>>>>> Hi everyone,
>>>>>>>
>>>>>>> I completely reworked FLIP-107. It now covers the full story how to
>>>> read
>>>>>>> and write metadata from/to connectors and formats. It considers all of
>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It introduces
>>>>>>> the concept of PERSISTED computed columns and leaves out partitioning
>>>>>>> for now.
>>>>>>>
>>>>>>> Looking forward to your feedback.
>>>>>>>
>>>>>>> Regards,
>>>>>>> Timo
>>>>>>>
>>>>>>>
>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>>> Sorry, forgot one question.
>>>>>>>>
>>>>>>>> 4. Can we make the value.fields-include more orthogonal? Like one can
>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
>>>> config to
>>>>>>>> just ignore timestamp but keep key.
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Kurt
>>>>>>>>
>>>>>>>>
>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]> wrote:
>>>>>>>>
>>>>>>>>> Hi Dawid,
>>>>>>>>>
>>>>>>>>> I have a couple of questions around key fields, actually I also have
>>>> some
>>>>>>>>> other questions but want to be focused on key fields first.
>>>>>>>>>
>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is this
>>>> option only
>>>>>>>>> valid during write operation? Because for
>>>>>>>>> reading, I can't imagine how such options can be applied. I would
>>>> expect
>>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>>
>>>>>>>>> 2. If "key.fields" is only valid in write operation, I want to
>>>> propose we
>>>>>>>>> can simplify the options to not introducing key.format.type and
>>>>>>>>> other related options. I think a single "key.field" (not fields)
>>>> would be
>>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>>> want before sink.
>>>>>>>>>
>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every connector has a
>>>>>>>>> concept
>>>>>>>>> of key and values. The old parameter "format.type" already good
>>>> enough to
>>>>>>>>> use.
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>> Kurt
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]> wrote:
>>>>>>>>>
>>>>>>>>>> Thanks Dawid,
>>>>>>>>>>
>>>>>>>>>> I have two more questions.
>>>>>>>>>>
>>>>>>>>>>> SupportsMetadata
>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have some
>>>> questions
>>>>>>>>>> regarding to this interface.
>>>>>>>>>> 1) How do the source know what the expected return type of each
>>>> metadata?
>>>>>>>>>> 2) Where to put the metadata fields? Append to the existing physical
>>>>>>>>>> fields?
>>>>>>>>>> If yes, I would suggest to change the signature to `TableSource
>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>>> metadataTypes)`
>>>>>>>>>>
>>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed column
>>>>>>>>>> expression? If yes, how to specify the return type of
>>>> SYSTEM_METADATA?
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>> Jark
>>>>>>>>>>
>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>>> [hidden email]>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi,
>>>>>>>>>>>
>>>>>>>>>>> 1. I thought a bit more on how the source would emit the columns
>>>> and I
>>>>>>>>>>> now see its not exactly the same as regular columns. I see a need
>>>> to
>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
>>>>>>>>>>>
>>>>>>>>>>> I do agree mostly with Danny on how we should do that. One
>>>> additional
>>>>>>>>>>> things I would introduce is an
>>>>>>>>>>>
>>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>>
>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>>
>>>>>>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
>>>>>>>>>>>
>>>>>>>>>>> }
>>>>>>>>>>>
>>>>>>>>>>> This way the source would have to declare/emit only the requested
>>>>>>>>>>> metadata fields. In order not to clash with user defined fields.
>>>> When
>>>>>>>>>>> emitting the metadata field I would prepend the column name with
>>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a field
>>>>>>>>>>> __system_partition to the schema. This would be never visible to
>>>> the
>>>>>>>>>>> user as it would be used only for the subsequent computed columns.
>>>> If
>>>>>>>>>>> that makes sense to you, I will update the FLIP with this
>>>> description.
>>>>>>>>>>>
>>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>>
>>>>>>>>>>> Here I agree with Danny. It is also the current state of the
>>>> proposal.
>>>>>>>>>>>
>>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>>
>>>>>>>>>>> Here I also agree with Danny. I also think those are orthogonal. I
>>>> would
>>>>>>>>>>> leave out the STORED computed columns out of the discussion. I
>>>> don't see
>>>>>>>>>>> how do they relate to the partitioning. I already put both of those
>>>>>>>>>>> cases in the document. We can either partition on a computed
>>>> column or
>>>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving out the
>>>>>>>>>>> partitioning by udf in the first version if you still have some
>>>>>>>>>> concerns.
>>>>>>>>>>>
>>>>>>>>>>> As for your question Danny. It depends which partitioning strategy
>>>> you
>>>>>>>>>> use.
>>>>>>>>>>>
>>>>>>>>>>> For the HASH partitioning strategy I thought it would work as you
>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure though if
>>>> we
>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does not own
>>>> the
>>>>>>>>>>> data and the partitions are already an intrinsic property of the
>>>>>>>>>>> underlying source e.g. for kafka we do not create topics, but we
>>>> just
>>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>>
>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
>>>>>>>>>>>
>>>>>>>>>>> I am fine with changing it to timestamp.field to be consistent with
>>>>>>>>>>> other value.fields and key.fields. Actually that was also my
>>>> initial
>>>>>>>>>>> proposal in a first draft I prepared. I changed it afterwards to
>>>> shorten
>>>>>>>>>>> the key.
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>>
>>>>>>>>>>> Dawid
>>>>>>>>>>>
>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a
>>>> useful
>>>>>>>>>>> feature ~
>>>>>>>>>>>>
>>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>>
>>>>>>>>>>>> I think it is completely orthogonal, computed column push down is
>>>>>>>>>>> another topic, this should not be a blocker but a promotion, if we
>>>> do
>>>>>>>>>> not
>>>>>>>>>>> have any filters on the computed column, there is no need to do any
>>>>>>>>>>> pushings; the source node just emit the complete record with full
>>>>>>>>>> metadata
>>>>>>>>>>> with the declared physical schema, then when generating the virtual
>>>>>>>>>>> columns, we would extract the metadata info and output as full
>>>>>>>>>> columns(with
>>>>>>>>>>> full schema).
>>>>>>>>>>>>
>>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>>
>>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they are
>>>> symantic
>>>>>>>>>>> equivalent though, explict type is more straight-forward and we can
>>>>>>>>>> declare
>>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>>
>>>>>>>>>>>> About option A: partitioning based on acomputed column VS option
>>>> B:
>>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>>
>>>>>>>>>>>>   From the FLIP, it seems that B's partitioning is just a strategy
>>>> when
>>>>>>>>>>> writing data, the partiton column is not included in the table
>>>> schema,
>>>>>>>>>> so
>>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>>
>>>>>>>>>>>> - Compared to A, we do not need to generate the partition column
>>>> when
>>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>>> - For A we can also mark the column as STORED when we want to
>>>> persist
>>>>>>>>>>> that
>>>>>>>>>>>>
>>>>>>>>>>>> So in my opition they are orthogonal, we can support both, i saw
>>>> that
>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS
>>>> num, and
>>>>>>>>>> the
>>>>>>>>>>> partitions are managed under a "tablenamespace", the partition in
>>>> which
>>>>>>>>>> the
>>>>>>>>>>> record is stored is partition number N, where N = MOD(expr, num),
>>>> for
>>>>>>>>>> your
>>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>>
>>>>>>>>>>>> [1]
>>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>>> [2]
>>>>>>>>>>>
>>>>>>>>>>
>>>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>>>
>>>>>>>>>>>> Best,
>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <[hidden email]
>>>>>>>>>>> ，写道：
>>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of properties.
>>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>>> I also considered exactly what you are suggesting (prefixing with
>>>>>>>>>>> connector or kafka). I should've put that into an Option/Rejected
>>>>>>>>>>> alternatives.
>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector properties. Why I
>>>>>>>>>>> wanted to suggest not adding that prefix in the first version is
>>>> that
>>>>>>>>>>> actually all the properties in the WITH section are connector
>>>>>>>>>> properties.
>>>>>>>>>>> Even format is in the end a connector property as some of the
>>>> sources
>>>>>>>>>> might
>>>>>>>>>>> not have a format, imo. The benefit of not adding the prefix is
>>>> that it
>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the properties
>>>> with
>>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
>>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>>> I am fine with doing it though if this is a preferred approach
>>>> in the
>>>>>>>>>>> community.
>>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>>> I forgot to update the `value.fields.include` property. It
>>>> should be
>>>>>>>>>>> value.fields-include. Which I think you also suggested in the
>>>> comment,
>>>>>>>>>>> right?
>>>>>>>>>>>>> As for the cast vs declaring output type of computed column. I
>>>> think
>>>>>>>>>>> it's better not to use CAST, but declare a type of an expression
>>>> and
>>>>>>>>>> later
>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I think
>>>> this
>>>>>>>>>> way
>>>>>>>>>>> it will be easier to implement e.g. filter push downs when working
>>>> with
>>>>>>>>>> the
>>>>>>>>>>> native types of the source, e.g. in case of Kafka's offset, i
>>>> think it's
>>>>>>>>>>> better to pushdown long rather than string. This could let us push
>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382. Otherwise we
>>>> would
>>>>>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset, long)
>>>> <
>>>>>>>>>> 59382.
>>>>>>>>>>> Moreover I think we need to introduce the type for computed columns
>>>>>>>>>> anyway
>>>>>>>>>>> to support functions that infer output type based on expected
>>>> return
>>>>>>>>>> type.
>>>>>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA would
>>>> have
>>>>>>>>>>> to be pushed down to the source. If it is not possible the planner
>>>>>>>>>> should
>>>>>>>>>>> fail. As far as I know computed columns push down will be part of
>>>> source
>>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>>> As for the persisted computed column. I think it is completely
>>>>>>>>>>> orthogonal. In my current proposal you can also partition by a
>>>> computed
>>>>>>>>>>> column. The difference between using a udf in partitioned by vs
>>>>>>>>>> partitioned
>>>>>>>>>>> by a computed column is that when you partition by a computed
>>>> column
>>>>>>>>>> this
>>>>>>>>>>> column must be also computed when reading the table. If you use a
>>>> udf in
>>>>>>>>>>> the partitioned by, the expression is computed only when inserting
>>>> into
>>>>>>>>>> the
>>>>>>>>>>> table.
>>>>>>>>>>>>> Hope this answers some of your questions. Looking forward for
>>>> further
>>>>>>>>>>> suggestions.
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
>>>> metadata
>>>>>>>>>> and
>>>>>>>>>>>>>> key-part information from source is an important feature for
>>>>>>>>>> streaming
>>>>>>>>>>>>>> users.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing HEADER
>>>>>>>>>>> keyword as
>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we
>>>> should
>>>>>>>>>>> add a
>>>>>>>>>>>>>> section to explain what's the relationship between them.
>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used on
>>>> the
>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL. Shall we
>>>>>>>>>> make
>>>>>>>>>>> the
>>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"? (actually, I
>>>>>>>>>>> prefer
>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for properties
>>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users that the
>>>>>>>>>> field
>>>>>>>>>>> is
>>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I would like to propose an improvement that would enable
>>>> reading
>>>>>>>>>> table
>>>>>>>>>>>>>>> columns from different parts of source records. Besides the
>>>> main
>>>>>>>>>>> payload
>>>>>>>>>>>>>>> majority (if not all of the sources) expose additional
>>>>>>>>>> information. It
>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset, ingestion
>>>> time
>>>>>>>>>> or a
>>>>>>>>>>>>>>> read and write parts of the record that contain data but
>>>>>>>>>> additionally
>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction etc.), e.g.
>>>> key
>>>>>>>>>> or
>>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> We should make it possible to read and write data from all of
>>>> those
>>>>>>>>>>>>>>> locations. In this proposal I discuss reading partitioning
>>>> data,
>>>>>>>>>> for
>>>>>>>>>>>>>>> completeness this proposal discusses also the partitioning when
>>>>>>>>>>> writing
>>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>
>
>

[Kurt Young]

I also share the concern that reusing the computed column syntax but have
different semantics
would confuse users a lot.

Besides, I think metadata fields are conceptually not the same with
computed columns. The metadata
field is a connector specific thing and it only contains the information
that where does the field come
from (during source) or where does the field need to write to (during
sink). It's more similar with normal
fields, with assumption that all these fields need going to the data part.

Thus I'm more lean to the rejected alternative that Timo mentioned. And I
think we don't need the
PERSISTED keyword, SYSTEM_METADATA should be enough.

During implementation, the framework only needs to pass such <field,
metadata field> information to the
connector, and the logic of handling such fields inside the connector
should be straightforward.

Regarding the downside Timo mentioned:

> The disadvantage is that users cannot call UDFs or parse timestamps.

I think this is fairly simple to solve. Since the metadata field isn't a
computed column anymore, we can support
referencing such fields in the computed column. For example:

CREATE TABLE kafka_table (
     id BIGINT,
     name STRING,
     timestamp STRING SYSTEM_METADATA("timestamp"),  // get the timestamp
field from metadata
     ts AS to_timestamp(timestamp) // normal computed column, parse the
string to TIMESTAMP type by using the metadata field
) WITH (
    ...
)

Best,
Kurt


On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]> wrote:

> Hi Leonard,
>
> the only alternative I see is that we introduce a concept that is
> completely different to computed columns. This is also mentioned in the
> rejected alternative section of the FLIP. Something like:
>
> CREATE TABLE kafka_table (
>      id BIGINT,
>      name STRING,
>      timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
>      headers MAP<STRING, BYTES> SYSTEM_METADATA("headers") PERSISTED
> ) WITH (
>     ...
> )
>
> This way we would avoid confusion at all and can easily map columns to
> metadata columns. The disadvantage is that users cannot call UDFs or
> parse timestamps. This would need to be done in a real computed column.
>
> I'm happy about better alternatives.
>
> Regards,
> Timo
>
>
> On 08.09.20 15:37, Leonard Xu wrote:
> > HI, Timo
> >
> > Thanks for driving this FLIP.
> >
> > Sorry but I have a concern about Writing metadata via DynamicTableSink
> section:
> >
> > CREATE TABLE kafka_table (
> >    id BIGINT,
> >    name STRING,
> >    timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT) PERSISTED,
> >    headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING, BYTES>)
> PERSISTED
> > ) WITH (
> >    ...
> > )
> > An insert statement could look like:
> >
> > INSERT INTO kafka_table VALUES (
> >    (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
> > )
> >
> > The proposed INERT syntax does not make sense to me, because it contains
> computed(generated) column.
> > Both SQL server and Postgresql do not allow to insert value to computed
> columns even they are persisted, this boke the generated column semantics
> and may confuse user much.
> >
> > For SQL server computed column[1]:
> >> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ] ]...
> >> NOTE: A computed column cannot be the target of an INSERT or UPDATE
> statement.
> >
> > For Postgresql generated column[2]:
> >>   height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
> >> NOTE: A generated column cannot be written to directly. In INSERT or
> UPDATE commands, a value cannot be specified for a generated column, but
> the keyword DEFAULT may be specified.
> >
> > It shouldn't be allowed to set/update value for generated column after
> lookup the SQL 2016:
> >> <insert statement> ::=
> >> INSERT INTO <insertion target> <insert columns and source>
> >>
> >> If <contextually typed table value constructor> CTTVC is specified,
> then every <contextually typed row
> >> value constructor element> simply contained in CTTVC whose positionally
> corresponding <column name>
> >> in <insert column list> references a column of which some underlying
> column is a generated column shall
> >> be a <default specification>.
> >> A <default specification> specifies the default value of some
> associated item.
> >
> >
> > [1]
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> <
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> >
> > [2] https://www.postgresql.org/docs/12/ddl-generated-columns.html <
> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
> >
> >> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
> >>
> >> Hi Jark,
> >>
> >> according to Flink's and Calcite's casting definition in [1][2]
> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not, we
> will make it possible ;-)
> >>
> >> I'm aware of DeserializationSchema.getProducedType but I think that
> this method is actually misplaced. The type should rather be passed to the
> source itself.
> >>
> >> For our Kafka SQL source, we will also not use this method because the
> Kafka source will add own metadata in addition to the
> DeserializationSchema. So DeserializationSchema.getProducedType will never
> be read.
> >>
> >> For now I suggest to leave out the `DataType` from
> DecodingFormat.applyReadableMetadata. Also because the format's physical
> type is passed later in `createRuntimeDecoder`. If necessary, it can be
> computed manually by consumedType + metadata types. We will provide a
> metadata utility class for that.
> >>
> >> Regards,
> >> Timo
> >>
> >>
> >> [1]
> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
> >> [2]
> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
> >>
> >>
> >> On 08.09.20 10:52, Jark Wu wrote:
> >>> Hi Timo,
> >>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just
> noticed
> >>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME
> ZONE".
> >>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL TIME
> >>> ZONE" as the defined type of Kafka timestamp? I think this makes sense,
> >>> because it represents the milli-seconds since epoch.
> >>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't think
> so.
> >>> The DeserializationSchema implements ResultTypeQueryable, thus the
> >>> implementation needs to return an output TypeInfo.
> >>> Besides, FlinkKafkaConsumer also
> >>> calls DeserializationSchema.getProducedType as the produced type of the
> >>> source function [1].
> >>> Best,
> >>> Jark
> >>> [1]:
> >>>
> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
> >>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]> wrote:
> >>>> Hi everyone,
> >>>>
> >>>> I updated the FLIP again and hope that I could address the mentioned
> >>>> concerns.
> >>>>
> >>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
> >>>> source.ts_ms have different semantics. I updated the FLIP and expose
> the
> >>>> most commonly used properties separately. So frequently used
> properties
> >>>> are not hidden in the MAP anymore:
> >>>>
> >>>> debezium-json.ingestion-timestamp
> >>>> debezium-json.source.timestamp
> >>>> debezium-json.source.database
> >>>> debezium-json.source.schema
> >>>> debezium-json.source.table
> >>>>
> >>>> However, since other properties depend on the used connector/vendor,
> the
> >>>> remaining options are stored in:
> >>>>
> >>>> debezium-json.source.properties
> >>>>
> >>>> And accessed with:
> >>>>
> >>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS MAP<STRING,
> >>>> STRING>)['table']
> >>>>
> >>>> Otherwise it is not possible to figure out the value and column type
> >>>> during validation.
> >>>>
> >>>> @Jark: You convinced me in relaxing the CAST constraints. I added a
> >>>> dedicacated sub-section to the FLIP:
> >>>>
> >>>> For making the use of SYSTEM_METADATA easier and avoid nested casting
> we
> >>>> allow explicit casting to a target data type:
> >>>>
> >>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH
> LOCAL
> >>>> TIME ZONE)
> >>>>
> >>>> A connector still produces and consumes the data type returned by
> >>>> `listMetadata()`. The planner will insert necessary explicit casts.
> >>>>
> >>>> In any case, the user must provide a CAST such that the computed
> column
> >>>> receives a valid data type when constructing the table schema.
> >>>>
> >>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata`
> needs a
> >>>> DataType argument."
> >>>>
> >>>> Correct he DeserializationSchema doesn't need TypeInfo, it is always
> >>>> executed locally. It is the source that needs TypeInfo for serializing
> >>>> the record to the next operator. And that's this is what we provide.
> >>>>
> >>>> @Danny:
> >>>>
> >>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>>>
> >>>> We can also use some other means to represent an UNKNOWN data type. In
> >>>> the Flink type system, we use the NullType for it. The important part
> is
> >>>> that the final data type is known for the entire computed column. As I
> >>>> mentioned before, I would avoid the suggested option b) that would be
> >>>> similar to your suggestion. The CAST should be enough and allows for
> >>>> complex expressions in the computed column. Option b) would need
> parser
> >>>> changes.
> >>>>
> >>>> Regards,
> >>>> Timo
> >>>>
> >>>>
> >>>>
> >>>> On 08.09.20 06:21, Leonard Xu wrote:
> >>>>> Hi, Timo
> >>>>>
> >>>>> Thanks for you explanation and update,  I have only one question  for
> >>>> the latest FLIP.
> >>>>>
> >>>>> About the MAP<STRING, STRING> DataType of key
> 'debezium-json.source', if
> >>>> user want to use the table name metadata, they need to write:
> >>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
> >>>> MAP<STRING, STRING>)['table']
> >>>>>
> >>>>> the expression is a little complex for user, Could we only support
> >>>> necessary metas with simple DataType as following?
> >>>>> tableName STRING AS
> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
> >>>> STRING),
> >>>>> transactionTime LONG AS
> >>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
> >>>>>
> >>>>> In this way, we can simplify the expression, the mainly used
> metadata in
> >>>> changelog format may include
> 'database','table','source.ts_ms','ts_ms' from
> >>>> my side,
> >>>>> maybe we could only support them at first version.
> >>>>>
> >>>>> Both Debezium and Canal have above four metadata, and I‘m willing to
> >>>> take some subtasks in next development if necessary.
> >>>>>
> >>>>> Debezium:
> >>>>> {
> >>>>>     "before": null,
> >>>>>     "after": {  "id": 101,"name": "scooter"},
> >>>>>     "source": {
> >>>>>       "db": "inventory",                  # 1. database name the
> >>>> changelog belongs to.
> >>>>>       "table": "products",                # 2. table name the
> changelog
> >>>> belongs to.
> >>>>>       "ts_ms": 1589355504100,             # 3. timestamp of the
> change
> >>>> happened in database system, i.e.: transaction time in database.
> >>>>>       "connector": "mysql",
> >>>>>       ….
> >>>>>     },
> >>>>>     "ts_ms": 1589355606100,              # 4. timestamp when the
> debezium
> >>>> processed the changelog.
> >>>>>     "op": "c",
> >>>>>     "transaction": null
> >>>>> }
> >>>>>
> >>>>> Canal:
> >>>>> {
> >>>>>     "data": [{  "id": "102", "name": "car battery" }],
> >>>>>     "database": "inventory",      # 1. database name the changelog
> >>>> belongs to.
> >>>>>     "table": "products",          # 2. table name the changelog
> belongs
> >>>> to.
> >>>>>     "es": 1589374013000,          # 3. execution time of the change
> in
> >>>> database system, i.e.: transaction time in database.
> >>>>>     "ts": 1589374013680,          # 4. timestamp when the cannal
> >>>> processed the changelog.
> >>>>>     "isDdl": false,
> >>>>>     "mysqlType": {},
> >>>>>     ....
> >>>>> }
> >>>>>
> >>>>>
> >>>>> Best
> >>>>> Leonard
> >>>>>
> >>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
> >>>>>>
> >>>>>> Thanks Timo ~
> >>>>>>
> >>>>>> The FLIP was already in pretty good shape, I have only 2 questions
> here:
> >>>>>>
> >>>>>>
> >>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid
> read-only
> >>>> computed column for Kafka and can be extracted by the planner.”
> >>>>>>
> >>>>>>
> >>>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually an
> >>>> expression return type can be inferred automatically. But I guess
> >>>> SQL-SERVER does not have function like SYSTEM_METADATA which actually
> does
> >>>> not have a specific return type.
> >>>>>>
> >>>>>> And why not use the Oracle or MySQL syntax there ?
> >>>>>>
> >>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
> >>>>>> Which is more straight-forward.
> >>>>>>
> >>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>>>>>
> >>>>>> The default type should not be NULL because only NULL literal does
> >>>> that. Usually we use ANY as the type if we do not know the specific
> type in
> >>>> the SQL context. ANY means the physical value can be any java object.
> >>>>>>
> >>>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
> >>>>>> [2]
> >>>>
> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
> >>>>>>
> >>>>>> Best,
> >>>>>> Danny Chan
> >>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
> >>>>>>> Hi everyone,
> >>>>>>>
> >>>>>>> I completely reworked FLIP-107. It now covers the full story how to
> >>>> read
> >>>>>>> and write metadata from/to connectors and formats. It considers
> all of
> >>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
> introduces
> >>>>>>> the concept of PERSISTED computed columns and leaves out
> partitioning
> >>>>>>> for now.
> >>>>>>>
> >>>>>>> Looking forward to your feedback.
> >>>>>>>
> >>>>>>> Regards,
> >>>>>>> Timo
> >>>>>>>
> >>>>>>>
> >>>>>>> On 04.03.20 09:45, Kurt Young wrote:
> >>>>>>>> Sorry, forgot one question.
> >>>>>>>>
> >>>>>>>> 4. Can we make the value.fields-include more orthogonal? Like one
> can
> >>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> >>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
> >>>> config to
> >>>>>>>> just ignore timestamp but keep key.
> >>>>>>>>
> >>>>>>>> Best,
> >>>>>>>> Kurt
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]>
> wrote:
> >>>>>>>>
> >>>>>>>>> Hi Dawid,
> >>>>>>>>>
> >>>>>>>>> I have a couple of questions around key fields, actually I also
> have
> >>>> some
> >>>>>>>>> other questions but want to be focused on key fields first.
> >>>>>>>>>
> >>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is this
> >>>> option only
> >>>>>>>>> valid during write operation? Because for
> >>>>>>>>> reading, I can't imagine how such options can be applied. I would
> >>>> expect
> >>>>>>>>> that there might be a SYSTEM_METADATA("key")
> >>>>>>>>> to read and assign the key to a normal field?
> >>>>>>>>>
> >>>>>>>>> 2. If "key.fields" is only valid in write operation, I want to
> >>>> propose we
> >>>>>>>>> can simplify the options to not introducing key.format.type and
> >>>>>>>>> other related options. I think a single "key.field" (not fields)
> >>>> would be
> >>>>>>>>> enough, users can use UDF to calculate whatever key they
> >>>>>>>>> want before sink.
> >>>>>>>>>
> >>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
> >>>>>>>>> "value.format.xxx" with the "value" prefix. Not every connector
> has a
> >>>>>>>>> concept
> >>>>>>>>> of key and values. The old parameter "format.type" already good
> >>>> enough to
> >>>>>>>>> use.
> >>>>>>>>>
> >>>>>>>>> Best,
> >>>>>>>>> Kurt
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]>
> wrote:
> >>>>>>>>>
> >>>>>>>>>> Thanks Dawid,
> >>>>>>>>>>
> >>>>>>>>>> I have two more questions.
> >>>>>>>>>>
> >>>>>>>>>>> SupportsMetadata
> >>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have some
> >>>> questions
> >>>>>>>>>> regarding to this interface.
> >>>>>>>>>> 1) How do the source know what the expected return type of each
> >>>> metadata?
> >>>>>>>>>> 2) Where to put the metadata fields? Append to the existing
> physical
> >>>>>>>>>> fields?
> >>>>>>>>>> If yes, I would suggest to change the signature to `TableSource
> >>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
> >>>> metadataTypes)`
> >>>>>>>>>>
> >>>>>>>>>>> SYSTEM_METADATA("partition")
> >>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed
> column
> >>>>>>>>>> expression? If yes, how to specify the return type of
> >>>> SYSTEM_METADATA?
> >>>>>>>>>>
> >>>>>>>>>> Best,
> >>>>>>>>>> Jark
> >>>>>>>>>>
> >>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
> >>>> [hidden email]>
> >>>>>>>>>> wrote:
> >>>>>>>>>>
> >>>>>>>>>>> Hi,
> >>>>>>>>>>>
> >>>>>>>>>>> 1. I thought a bit more on how the source would emit the
> columns
> >>>> and I
> >>>>>>>>>>> now see its not exactly the same as regular columns. I see a
> need
> >>>> to
> >>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
> >>>>>>>>>>>
> >>>>>>>>>>> I do agree mostly with Danny on how we should do that. One
> >>>> additional
> >>>>>>>>>>> things I would introduce is an
> >>>>>>>>>>>
> >>>>>>>>>>> interface SupportsMetadata {
> >>>>>>>>>>>
> >>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
> >>>>>>>>>>>
> >>>>>>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
> >>>>>>>>>>>
> >>>>>>>>>>> }
> >>>>>>>>>>>
> >>>>>>>>>>> This way the source would have to declare/emit only the
> requested
> >>>>>>>>>>> metadata fields. In order not to clash with user defined
> fields.
> >>>> When
> >>>>>>>>>>> emitting the metadata field I would prepend the column name
> with
> >>>>>>>>>>> __system_{property_name}. Therefore when requested
> >>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a field
> >>>>>>>>>>> __system_partition to the schema. This would be never visible
> to
> >>>> the
> >>>>>>>>>>> user as it would be used only for the subsequent computed
> columns.
> >>>> If
> >>>>>>>>>>> that makes sense to you, I will update the FLIP with this
> >>>> description.
> >>>>>>>>>>>
> >>>>>>>>>>> 2. CAST vs explicit type in computed columns
> >>>>>>>>>>>
> >>>>>>>>>>> Here I agree with Danny. It is also the current state of the
> >>>> proposal.
> >>>>>>>>>>>
> >>>>>>>>>>> 3. Partitioning on computed column vs function
> >>>>>>>>>>>
> >>>>>>>>>>> Here I also agree with Danny. I also think those are
> orthogonal. I
> >>>> would
> >>>>>>>>>>> leave out the STORED computed columns out of the discussion. I
> >>>> don't see
> >>>>>>>>>>> how do they relate to the partitioning. I already put both of
> those
> >>>>>>>>>>> cases in the document. We can either partition on a computed
> >>>> column or
> >>>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving out
> the
> >>>>>>>>>>> partitioning by udf in the first version if you still have some
> >>>>>>>>>> concerns.
> >>>>>>>>>>>
> >>>>>>>>>>> As for your question Danny. It depends which partitioning
> strategy
> >>>> you
> >>>>>>>>>> use.
> >>>>>>>>>>>
> >>>>>>>>>>> For the HASH partitioning strategy I thought it would work as
> you
> >>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure
> though if
> >>>> we
> >>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does not
> own
> >>>> the
> >>>>>>>>>>> data and the partitions are already an intrinsic property of
> the
> >>>>>>>>>>> underlying source e.g. for kafka we do not create topics, but
> we
> >>>> just
> >>>>>>>>>>> describe pre-existing pre-partitioned topic.
> >>>>>>>>>>>
> >>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
> >>>>>>>>>>>
> >>>>>>>>>>> I am fine with changing it to timestamp.field to be consistent
> with
> >>>>>>>>>>> other value.fields and key.fields. Actually that was also my
> >>>> initial
> >>>>>>>>>>> proposal in a first draft I prepared. I changed it afterwards
> to
> >>>> shorten
> >>>>>>>>>>> the key.
> >>>>>>>>>>>
> >>>>>>>>>>> Best,
> >>>>>>>>>>>
> >>>>>>>>>>> Dawid
> >>>>>>>>>>>
> >>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
> >>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a
> >>>> useful
> >>>>>>>>>>> feature ~
> >>>>>>>>>>>>
> >>>>>>>>>>>> About how the metadata outputs from source
> >>>>>>>>>>>>
> >>>>>>>>>>>> I think it is completely orthogonal, computed column push
> down is
> >>>>>>>>>>> another topic, this should not be a blocker but a promotion,
> if we
> >>>> do
> >>>>>>>>>> not
> >>>>>>>>>>> have any filters on the computed column, there is no need to
> do any
> >>>>>>>>>>> pushings; the source node just emit the complete record with
> full
> >>>>>>>>>> metadata
> >>>>>>>>>>> with the declared physical schema, then when generating the
> virtual
> >>>>>>>>>>> columns, we would extract the metadata info and output as full
> >>>>>>>>>> columns(with
> >>>>>>>>>>> full schema).
> >>>>>>>>>>>>
> >>>>>>>>>>>> About the type of metadata column
> >>>>>>>>>>>>
> >>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they are
> >>>> symantic
> >>>>>>>>>>> equivalent though, explict type is more straight-forward and
> we can
> >>>>>>>>>> declare
> >>>>>>>>>>> the nullable attribute there.
> >>>>>>>>>>>>
> >>>>>>>>>>>> About option A: partitioning based on acomputed column VS
> option
> >>>> B:
> >>>>>>>>>>> partitioning with just a function
> >>>>>>>>>>>>
> >>>>>>>>>>>>   From the FLIP, it seems that B's partitioning is just a
> strategy
> >>>> when
> >>>>>>>>>>> writing data, the partiton column is not included in the table
> >>>> schema,
> >>>>>>>>>> so
> >>>>>>>>>>> it's just useless when reading from that.
> >>>>>>>>>>>>
> >>>>>>>>>>>> - Compared to A, we do not need to generate the partition
> column
> >>>> when
> >>>>>>>>>>> selecting from the table(but insert into)
> >>>>>>>>>>>> - For A we can also mark the column as STORED when we want to
> >>>> persist
> >>>>>>>>>>> that
> >>>>>>>>>>>>
> >>>>>>>>>>>> So in my opition they are orthogonal, we can support both, i
> saw
> >>>> that
> >>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS
> >>>> num, and
> >>>>>>>>>> the
> >>>>>>>>>>> partitions are managed under a "tablenamespace", the partition
> in
> >>>> which
> >>>>>>>>>> the
> >>>>>>>>>>> record is stored is partition number N, where N = MOD(expr,
> num),
> >>>> for
> >>>>>>>>>> your
> >>>>>>>>>>> design, which partiton the record would persist ?
> >>>>>>>>>>>>
> >>>>>>>>>>>> [1]
> >>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> >>>>>>>>>>>> [2]
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>
> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> >>>>>>>>>>>>
> >>>>>>>>>>>> Best,
> >>>>>>>>>>>> Danny Chan
> >>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
> [hidden email]
> >>>>>>>>>>> ，写道：
> >>>>>>>>>>>>> Hi Jark,
> >>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
> >>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
> properties.
> >>>>>>>>>>> Therefore you have the key.format.type.
> >>>>>>>>>>>>> I also considered exactly what you are suggesting (prefixing
> with
> >>>>>>>>>>> connector or kafka). I should've put that into an
> Option/Rejected
> >>>>>>>>>>> alternatives.
> >>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector properties.
> Why I
> >>>>>>>>>>> wanted to suggest not adding that prefix in the first version
> is
> >>>> that
> >>>>>>>>>>> actually all the properties in the WITH section are connector
> >>>>>>>>>> properties.
> >>>>>>>>>>> Even format is in the end a connector property as some of the
> >>>> sources
> >>>>>>>>>> might
> >>>>>>>>>>> not have a format, imo. The benefit of not adding the prefix is
> >>>> that it
> >>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
> properties
> >>>> with
> >>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
> >>>>>>>>>>>>> elasticsearch.key.format.type: csv
> >>>>>>>>>>>>> elasticsearch.key.format.field: ....
> >>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
> >>>>>>>>>>>>> elasticsearch.key.format.*: ....
> >>>>>>>>>>>>> I am fine with doing it though if this is a preferred
> approach
> >>>> in the
> >>>>>>>>>>> community.
> >>>>>>>>>>>>> Ad in-line comments:
> >>>>>>>>>>>>> I forgot to update the `value.fields.include` property. It
> >>>> should be
> >>>>>>>>>>> value.fields-include. Which I think you also suggested in the
> >>>> comment,
> >>>>>>>>>>> right?
> >>>>>>>>>>>>> As for the cast vs declaring output type of computed column.
> I
> >>>> think
> >>>>>>>>>>> it's better not to use CAST, but declare a type of an
> expression
> >>>> and
> >>>>>>>>>> later
> >>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I
> think
> >>>> this
> >>>>>>>>>> way
> >>>>>>>>>>> it will be easier to implement e.g. filter push downs when
> working
> >>>> with
> >>>>>>>>>> the
> >>>>>>>>>>> native types of the source, e.g. in case of Kafka's offset, i
> >>>> think it's
> >>>>>>>>>>> better to pushdown long rather than string. This could let us
> push
> >>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
> Otherwise we
> >>>> would
> >>>>>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset,
> long)
> >>>> <
> >>>>>>>>>> 59382.
> >>>>>>>>>>> Moreover I think we need to introduce the type for computed
> columns
> >>>>>>>>>> anyway
> >>>>>>>>>>> to support functions that infer output type based on expected
> >>>> return
> >>>>>>>>>> type.
> >>>>>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA
> would
> >>>> have
> >>>>>>>>>>> to be pushed down to the source. If it is not possible the
> planner
> >>>>>>>>>> should
> >>>>>>>>>>> fail. As far as I know computed columns push down will be part
> of
> >>>> source
> >>>>>>>>>>> rework, won't it? ;)
> >>>>>>>>>>>>> As for the persisted computed column. I think it is
> completely
> >>>>>>>>>>> orthogonal. In my current proposal you can also partition by a
> >>>> computed
> >>>>>>>>>>> column. The difference between using a udf in partitioned by vs
> >>>>>>>>>> partitioned
> >>>>>>>>>>> by a computed column is that when you partition by a computed
> >>>> column
> >>>>>>>>>> this
> >>>>>>>>>>> column must be also computed when reading the table. If you
> use a
> >>>> udf in
> >>>>>>>>>>> the partitioned by, the expression is computed only when
> inserting
> >>>> into
> >>>>>>>>>> the
> >>>>>>>>>>> table.
> >>>>>>>>>>>>> Hope this answers some of your questions. Looking forward for
> >>>> further
> >>>>>>>>>>> suggestions.
> >>>>>>>>>>>>> Best,
> >>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
> >>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
> >>>> metadata
> >>>>>>>>>> and
> >>>>>>>>>>>>>> key-part information from source is an important feature for
> >>>>>>>>>> streaming
> >>>>>>>>>>>>>> users.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
> >>>>>>>>>>>>>> I will leave my thoughts and comments here:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing
> HEADER
> >>>>>>>>>>> keyword as
> >>>>>>>>>>>>>> the reason you mentioned in the FLIP.
> >>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we
> >>>> should
> >>>>>>>>>>> add a
> >>>>>>>>>>>>>> section to explain what's the relationship between them.
> >>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used
> on
> >>>> the
> >>>>>>>>>>>>>> PARTITIONED table in this FLIP?
> >>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL.
> Shall we
> >>>>>>>>>> make
> >>>>>>>>>>> the
> >>>>>>>>>>>>>> new introduced properties more hierarchical?
> >>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
> (actually, I
> >>>>>>>>>>> prefer
> >>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
> properties
> >>>>>>>>>>> FLINK-12557)
> >>>>>>>>>>>>>> A single "timestamp" in properties may mislead users that
> the
> >>>>>>>>>> field
> >>>>>>>>>>> is
> >>>>>>>>>>>>>> a rowtime attribute.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> I also left some minor comments in the FLIP.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Thanks,
> >>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> >>>>>>>>>> [hidden email]>
> >>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> I would like to propose an improvement that would enable
> >>>> reading
> >>>>>>>>>> table
> >>>>>>>>>>>>>>> columns from different parts of source records. Besides the
> >>>> main
> >>>>>>>>>>> payload
> >>>>>>>>>>>>>>> majority (if not all of the sources) expose additional
> >>>>>>>>>> information. It
> >>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
> ingestion
> >>>> time
> >>>>>>>>>> or a
> >>>>>>>>>>>>>>> read and write parts of the record that contain data but
> >>>>>>>>>> additionally
> >>>>>>>>>>>>>>> serve different purposes (partitioning, compaction etc.),
> e.g.
> >>>> key
> >>>>>>>>>> or
> >>>>>>>>>>>>>>> timestamp in Kafka.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> We should make it possible to read and write data from all
> of
> >>>> those
> >>>>>>>>>>>>>>> locations. In this proposal I discuss reading partitioning
> >>>> data,
> >>>>>>>>>> for
> >>>>>>>>>>>>>>> completeness this proposal discusses also the partitioning
> when
> >>>>>>>>>>> writing
> >>>>>>>>>>>>>>> data out.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> I am looking forward to your comments.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> You can access the FLIP here:
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>>
> >>>>>
> >>>>>
> >>>>
> >>>>
> >>
> >
> >
>
>

[Timo Walther-2]

Hi Kurt,

thanks for sharing your opinion. I'm totally up for not reusing computed
columns. I think Jark was a big supporter of this syntax, @Jark are you
fine with this as well? The non-computed column approach was only a
"slightly rejected alternative".

Furthermore, we would need to think about how such a new design
influences the LIKE clause though.

However, we should still keep the `PERSISTED` keyword as it influences
the query->sink schema. If you look at the list of metadata for existing
connectors and formats, we currently offer only two writable metadata
fields. Otherwise, one would need to declare two tables whenever a
metadata columns is read (one for the source, one for the sink). This
can be quite inconvientient e.g. for just reading the topic.

Regards,
Timo


On 09.09.20 08:52, Kurt Young wrote:

> I also share the concern that reusing the computed column syntax but have
> different semantics
> would confuse users a lot.
>
> Besides, I think metadata fields are conceptually not the same with
> computed columns. The metadata
> field is a connector specific thing and it only contains the information
> that where does the field come
> from (during source) or where does the field need to write to (during
> sink). It's more similar with normal
> fields, with assumption that all these fields need going to the data part.
>
> Thus I'm more lean to the rejected alternative that Timo mentioned. And I
> think we don't need the
> PERSISTED keyword, SYSTEM_METADATA should be enough.
>
> During implementation, the framework only needs to pass such <field,
> metadata field> information to the
> connector, and the logic of handling such fields inside the connector
> should be straightforward.
>
> Regarding the downside Timo mentioned:
>
>> The disadvantage is that users cannot call UDFs or parse timestamps.
>
> I think this is fairly simple to solve. Since the metadata field isn't a
> computed column anymore, we can support
> referencing such fields in the computed column. For example:
>
> CREATE TABLE kafka_table (
>       id BIGINT,
>       name STRING,
>       timestamp STRING SYSTEM_METADATA("timestamp"),  // get the timestamp
> field from metadata
>       ts AS to_timestamp(timestamp) // normal computed column, parse the
> string to TIMESTAMP type by using the metadata field
> ) WITH (
>      ...
> )
>
> Best,
> Kurt
>
>
> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]> wrote:
>
>> Hi Leonard,
>>
>> the only alternative I see is that we introduce a concept that is
>> completely different to computed columns. This is also mentioned in the
>> rejected alternative section of the FLIP. Something like:
>>
>> CREATE TABLE kafka_table (
>>       id BIGINT,
>>       name STRING,
>>       timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
>>       headers MAP<STRING, BYTES> SYSTEM_METADATA("headers") PERSISTED
>> ) WITH (
>>      ...
>> )
>>
>> This way we would avoid confusion at all and can easily map columns to
>> metadata columns. The disadvantage is that users cannot call UDFs or
>> parse timestamps. This would need to be done in a real computed column.
>>
>> I'm happy about better alternatives.
>>
>> Regards,
>> Timo
>>
>>
>> On 08.09.20 15:37, Leonard Xu wrote:
>>> HI, Timo
>>>
>>> Thanks for driving this FLIP.
>>>
>>> Sorry but I have a concern about Writing metadata via DynamicTableSink
>> section:
>>>
>>> CREATE TABLE kafka_table (
>>>     id BIGINT,
>>>     name STRING,
>>>     timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT) PERSISTED,
>>>     headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING, BYTES>)
>> PERSISTED
>>> ) WITH (
>>>     ...
>>> )
>>> An insert statement could look like:
>>>
>>> INSERT INTO kafka_table VALUES (
>>>     (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
>>> )
>>>
>>> The proposed INERT syntax does not make sense to me, because it contains
>> computed(generated) column.
>>> Both SQL server and Postgresql do not allow to insert value to computed
>> columns even they are persisted, this boke the generated column semantics
>> and may confuse user much.
>>>
>>> For SQL server computed column[1]:
>>>> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ] ]...
>>>> NOTE: A computed column cannot be the target of an INSERT or UPDATE
>> statement.
>>>
>>> For Postgresql generated column[2]:
>>>>    height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
>>>> NOTE: A generated column cannot be written to directly. In INSERT or
>> UPDATE commands, a value cannot be specified for a generated column, but
>> the keyword DEFAULT may be specified.
>>>
>>> It shouldn't be allowed to set/update value for generated column after
>> lookup the SQL 2016:
>>>> <insert statement> ::=
>>>> INSERT INTO <insertion target> <insert columns and source>
>>>>
>>>> If <contextually typed table value constructor> CTTVC is specified,
>> then every <contextually typed row
>>>> value constructor element> simply contained in CTTVC whose positionally
>> corresponding <column name>
>>>> in <insert column list> references a column of which some underlying
>> column is a generated column shall
>>>> be a <default specification>.
>>>> A <default specification> specifies the default value of some
>> associated item.
>>>
>>>
>>> [1]
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>> <
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>
>>> [2] https://www.postgresql.org/docs/12/ddl-generated-columns.html <
>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
>>>
>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>>>>
>>>> Hi Jark,
>>>>
>>>> according to Flink's and Calcite's casting definition in [1][2]
>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not, we
>> will make it possible ;-)
>>>>
>>>> I'm aware of DeserializationSchema.getProducedType but I think that
>> this method is actually misplaced. The type should rather be passed to the
>> source itself.
>>>>
>>>> For our Kafka SQL source, we will also not use this method because the
>> Kafka source will add own metadata in addition to the
>> DeserializationSchema. So DeserializationSchema.getProducedType will never
>> be read.
>>>>
>>>> For now I suggest to leave out the `DataType` from
>> DecodingFormat.applyReadableMetadata. Also because the format's physical
>> type is passed later in `createRuntimeDecoder`. If necessary, it can be
>> computed manually by consumedType + metadata types. We will provide a
>> metadata utility class for that.
>>>>
>>>> Regards,
>>>> Timo
>>>>
>>>>
>>>> [1]
>> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
>>>> [2]
>> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>>>>
>>>>
>>>> On 08.09.20 10:52, Jark Wu wrote:
>>>>> Hi Timo,
>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just
>> noticed
>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME
>> ZONE".
>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL TIME
>>>>> ZONE" as the defined type of Kafka timestamp? I think this makes sense,
>>>>> because it represents the milli-seconds since epoch.
>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't think
>> so.
>>>>> The DeserializationSchema implements ResultTypeQueryable, thus the
>>>>> implementation needs to return an output TypeInfo.
>>>>> Besides, FlinkKafkaConsumer also
>>>>> calls DeserializationSchema.getProducedType as the produced type of the
>>>>> source function [1].
>>>>> Best,
>>>>> Jark
>>>>> [1]:
>>>>>
>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]> wrote:
>>>>>> Hi everyone,
>>>>>>
>>>>>> I updated the FLIP again and hope that I could address the mentioned
>>>>>> concerns.
>>>>>>
>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
>>>>>> source.ts_ms have different semantics. I updated the FLIP and expose
>> the
>>>>>> most commonly used properties separately. So frequently used
>> properties
>>>>>> are not hidden in the MAP anymore:
>>>>>>
>>>>>> debezium-json.ingestion-timestamp
>>>>>> debezium-json.source.timestamp
>>>>>> debezium-json.source.database
>>>>>> debezium-json.source.schema
>>>>>> debezium-json.source.table
>>>>>>
>>>>>> However, since other properties depend on the used connector/vendor,
>> the
>>>>>> remaining options are stored in:
>>>>>>
>>>>>> debezium-json.source.properties
>>>>>>
>>>>>> And accessed with:
>>>>>>
>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS MAP<STRING,
>>>>>> STRING>)['table']
>>>>>>
>>>>>> Otherwise it is not possible to figure out the value and column type
>>>>>> during validation.
>>>>>>
>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I added a
>>>>>> dedicacated sub-section to the FLIP:
>>>>>>
>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested casting
>> we
>>>>>> allow explicit casting to a target data type:
>>>>>>
>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH
>> LOCAL
>>>>>> TIME ZONE)
>>>>>>
>>>>>> A connector still produces and consumes the data type returned by
>>>>>> `listMetadata()`. The planner will insert necessary explicit casts.
>>>>>>
>>>>>> In any case, the user must provide a CAST such that the computed
>> column
>>>>>> receives a valid data type when constructing the table schema.
>>>>>>
>>>>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata`
>> needs a
>>>>>> DataType argument."
>>>>>>
>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is always
>>>>>> executed locally. It is the source that needs TypeInfo for serializing
>>>>>> the record to the next operator. And that's this is what we provide.
>>>>>>
>>>>>> @Danny:
>>>>>>
>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>
>>>>>> We can also use some other means to represent an UNKNOWN data type. In
>>>>>> the Flink type system, we use the NullType for it. The important part
>> is
>>>>>> that the final data type is known for the entire computed column. As I
>>>>>> mentioned before, I would avoid the suggested option b) that would be
>>>>>> similar to your suggestion. The CAST should be enough and allows for
>>>>>> complex expressions in the computed column. Option b) would need
>> parser
>>>>>> changes.
>>>>>>
>>>>>> Regards,
>>>>>> Timo
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>>>>> Hi, Timo
>>>>>>>
>>>>>>> Thanks for you explanation and update,  I have only one question  for
>>>>>> the latest FLIP.
>>>>>>>
>>>>>>> About the MAP<STRING, STRING> DataType of key
>> 'debezium-json.source', if
>>>>>> user want to use the table name metadata, they need to write:
>>>>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
>>>>>> MAP<STRING, STRING>)['table']
>>>>>>>
>>>>>>> the expression is a little complex for user, Could we only support
>>>>>> necessary metas with simple DataType as following?
>>>>>>> tableName STRING AS
>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>>>>> STRING),
>>>>>>> transactionTime LONG AS
>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>>>>
>>>>>>> In this way, we can simplify the expression, the mainly used
>> metadata in
>>>>>> changelog format may include
>> 'database','table','source.ts_ms','ts_ms' from
>>>>>> my side,
>>>>>>> maybe we could only support them at first version.
>>>>>>>
>>>>>>> Both Debezium and Canal have above four metadata, and I‘m willing to
>>>>>> take some subtasks in next development if necessary.
>>>>>>>
>>>>>>> Debezium:
>>>>>>> {
>>>>>>>      "before": null,
>>>>>>>      "after": {  "id": 101,"name": "scooter"},
>>>>>>>      "source": {
>>>>>>>        "db": "inventory",                  # 1. database name the
>>>>>> changelog belongs to.
>>>>>>>        "table": "products",                # 2. table name the
>> changelog
>>>>>> belongs to.
>>>>>>>        "ts_ms": 1589355504100,             # 3. timestamp of the
>> change
>>>>>> happened in database system, i.e.: transaction time in database.
>>>>>>>        "connector": "mysql",
>>>>>>>        ….
>>>>>>>      },
>>>>>>>      "ts_ms": 1589355606100,              # 4. timestamp when the
>> debezium
>>>>>> processed the changelog.
>>>>>>>      "op": "c",
>>>>>>>      "transaction": null
>>>>>>> }
>>>>>>>
>>>>>>> Canal:
>>>>>>> {
>>>>>>>      "data": [{  "id": "102", "name": "car battery" }],
>>>>>>>      "database": "inventory",      # 1. database name the changelog
>>>>>> belongs to.
>>>>>>>      "table": "products",          # 2. table name the changelog
>> belongs
>>>>>> to.
>>>>>>>      "es": 1589374013000,          # 3. execution time of the change
>> in
>>>>>> database system, i.e.: transaction time in database.
>>>>>>>      "ts": 1589374013680,          # 4. timestamp when the cannal
>>>>>> processed the changelog.
>>>>>>>      "isDdl": false,
>>>>>>>      "mysqlType": {},
>>>>>>>      ....
>>>>>>> }
>>>>>>>
>>>>>>>
>>>>>>> Best
>>>>>>> Leonard
>>>>>>>
>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>>>>>
>>>>>>>> Thanks Timo ~
>>>>>>>>
>>>>>>>> The FLIP was already in pretty good shape, I have only 2 questions
>> here:
>>>>>>>>
>>>>>>>>
>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid
>> read-only
>>>>>> computed column for Kafka and can be extracted by the planner.”
>>>>>>>>
>>>>>>>>
>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually an
>>>>>> expression return type can be inferred automatically. But I guess
>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which actually
>> does
>>>>>> not have a specific return type.
>>>>>>>>
>>>>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>>>>
>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression) [VIRTUAL]
>>>>>>>> Which is more straight-forward.
>>>>>>>>
>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>>>
>>>>>>>> The default type should not be NULL because only NULL literal does
>>>>>> that. Usually we use ANY as the type if we do not know the specific
>> type in
>>>>>> the SQL context. ANY means the physical value can be any java object.
>>>>>>>>
>>>>>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>>>>> [2]
>>>>>>
>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Danny Chan
>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
>>>>>>>>> Hi everyone,
>>>>>>>>>
>>>>>>>>> I completely reworked FLIP-107. It now covers the full story how to
>>>>>> read
>>>>>>>>> and write metadata from/to connectors and formats. It considers
>> all of
>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
>> introduces
>>>>>>>>> the concept of PERSISTED computed columns and leaves out
>> partitioning
>>>>>>>>> for now.
>>>>>>>>>
>>>>>>>>> Looking forward to your feedback.
>>>>>>>>>
>>>>>>>>> Regards,
>>>>>>>>> Timo
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>>>>> Sorry, forgot one question.
>>>>>>>>>>
>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal? Like one
>> can
>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
>>>>>> config to
>>>>>>>>>> just ignore timestamp but keep key.
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>> Kurt
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]>
>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi Dawid,
>>>>>>>>>>>
>>>>>>>>>>> I have a couple of questions around key fields, actually I also
>> have
>>>>>> some
>>>>>>>>>>> other questions but want to be focused on key fields first.
>>>>>>>>>>>
>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is this
>>>>>> option only
>>>>>>>>>>> valid during write operation? Because for
>>>>>>>>>>> reading, I can't imagine how such options can be applied. I would
>>>>>> expect
>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>>>>
>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I want to
>>>>>> propose we
>>>>>>>>>>> can simplify the options to not introducing key.format.type and
>>>>>>>>>>> other related options. I think a single "key.field" (not fields)
>>>>>> would be
>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>>>>> want before sink.
>>>>>>>>>>>
>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every connector
>> has a
>>>>>>>>>>> concept
>>>>>>>>>>> of key and values. The old parameter "format.type" already good
>>>>>> enough to
>>>>>>>>>>> use.
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>> Kurt
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]>
>> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Thanks Dawid,
>>>>>>>>>>>>
>>>>>>>>>>>> I have two more questions.
>>>>>>>>>>>>
>>>>>>>>>>>>> SupportsMetadata
>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have some
>>>>>> questions
>>>>>>>>>>>> regarding to this interface.
>>>>>>>>>>>> 1) How do the source know what the expected return type of each
>>>>>> metadata?
>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the existing
>> physical
>>>>>>>>>>>> fields?
>>>>>>>>>>>> If yes, I would suggest to change the signature to `TableSource
>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>>>>> metadataTypes)`
>>>>>>>>>>>>
>>>>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed
>> column
>>>>>>>>>>>> expression? If yes, how to specify the return type of
>>>>>> SYSTEM_METADATA?
>>>>>>>>>>>>
>>>>>>>>>>>> Best,
>>>>>>>>>>>> Jark
>>>>>>>>>>>>
>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>>>>> [hidden email]>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>
>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit the
>> columns
>>>>>> and I
>>>>>>>>>>>>> now see its not exactly the same as regular columns. I see a
>> need
>>>>>> to
>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that. One
>>>>>> additional
>>>>>>>>>>>>> things I would introduce is an
>>>>>>>>>>>>>
>>>>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>>>>
>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>>>>
>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String> metadataFields);
>>>>>>>>>>>>>
>>>>>>>>>>>>> }
>>>>>>>>>>>>>
>>>>>>>>>>>>> This way the source would have to declare/emit only the
>> requested
>>>>>>>>>>>>> metadata fields. In order not to clash with user defined
>> fields.
>>>>>> When
>>>>>>>>>>>>> emitting the metadata field I would prepend the column name
>> with
>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a field
>>>>>>>>>>>>> __system_partition to the schema. This would be never visible
>> to
>>>>>> the
>>>>>>>>>>>>> user as it would be used only for the subsequent computed
>> columns.
>>>>>> If
>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with this
>>>>>> description.
>>>>>>>>>>>>>
>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>>>>
>>>>>>>>>>>>> Here I agree with Danny. It is also the current state of the
>>>>>> proposal.
>>>>>>>>>>>>>
>>>>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>>>>
>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
>> orthogonal. I
>>>>>> would
>>>>>>>>>>>>> leave out the STORED computed columns out of the discussion. I
>>>>>> don't see
>>>>>>>>>>>>> how do they relate to the partitioning. I already put both of
>> those
>>>>>>>>>>>>> cases in the document. We can either partition on a computed
>>>>>> column or
>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving out
>> the
>>>>>>>>>>>>> partitioning by udf in the first version if you still have some
>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>
>>>>>>>>>>>>> As for your question Danny. It depends which partitioning
>> strategy
>>>>>> you
>>>>>>>>>>>> use.
>>>>>>>>>>>>>
>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would work as
>> you
>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure
>> though if
>>>>>> we
>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does not
>> own
>>>>>> the
>>>>>>>>>>>>> data and the partitions are already an intrinsic property of
>> the
>>>>>>>>>>>>> underlying source e.g. for kafka we do not create topics, but
>> we
>>>>>> just
>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>>>>
>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
>>>>>>>>>>>>>
>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be consistent
>> with
>>>>>>>>>>>>> other value.fields and key.fields. Actually that was also my
>>>>>> initial
>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it afterwards
>> to
>>>>>> shorten
>>>>>>>>>>>>> the key.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is a
>>>>>> useful
>>>>>>>>>>>>> feature ~
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I think it is completely orthogonal, computed column push
>> down is
>>>>>>>>>>>>> another topic, this should not be a blocker but a promotion,
>> if we
>>>>>> do
>>>>>>>>>>>> not
>>>>>>>>>>>>> have any filters on the computed column, there is no need to
>> do any
>>>>>>>>>>>>> pushings; the source node just emit the complete record with
>> full
>>>>>>>>>>>> metadata
>>>>>>>>>>>>> with the declared physical schema, then when generating the
>> virtual
>>>>>>>>>>>>> columns, we would extract the metadata info and output as full
>>>>>>>>>>>> columns(with
>>>>>>>>>>>>> full schema).
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they are
>>>>>> symantic
>>>>>>>>>>>>> equivalent though, explict type is more straight-forward and
>> we can
>>>>>>>>>>>> declare
>>>>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> About option A: partitioning based on acomputed column VS
>> option
>>>>>> B:
>>>>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>    From the FLIP, it seems that B's partitioning is just a
>> strategy
>>>>>> when
>>>>>>>>>>>>> writing data, the partiton column is not included in the table
>>>>>> schema,
>>>>>>>>>>>> so
>>>>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> - Compared to A, we do not need to generate the partition
>> column
>>>>>> when
>>>>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we want to
>>>>>> persist
>>>>>>>>>>>>> that
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support both, i
>> saw
>>>>>> that
>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the PARTITIONS
>>>>>> num, and
>>>>>>>>>>>> the
>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the partition
>> in
>>>>>> which
>>>>>>>>>>>> the
>>>>>>>>>>>>> record is stored is partition number N, where N = MOD(expr,
>> num),
>>>>>> for
>>>>>>>>>>>> your
>>>>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [1]
>>>>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>
>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
>> [hidden email]
>>>>>>>>>>>>> ，写道：
>>>>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
>> properties.
>>>>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>>>>> I also considered exactly what you are suggesting (prefixing
>> with
>>>>>>>>>>>>> connector or kafka). I should've put that into an
>> Option/Rejected
>>>>>>>>>>>>> alternatives.
>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector properties.
>> Why I
>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first version
>> is
>>>>>> that
>>>>>>>>>>>>> actually all the properties in the WITH section are connector
>>>>>>>>>>>> properties.
>>>>>>>>>>>>> Even format is in the end a connector property as some of the
>>>>>> sources
>>>>>>>>>>>> might
>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the prefix is
>>>>>> that it
>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
>> properties
>>>>>> with
>>>>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
>> approach
>>>>>> in the
>>>>>>>>>>>>> community.
>>>>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>>>>> I forgot to update the `value.fields.include` property. It
>>>>>> should be
>>>>>>>>>>>>> value.fields-include. Which I think you also suggested in the
>>>>>> comment,
>>>>>>>>>>>>> right?
>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed column.
>> I
>>>>>> think
>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
>> expression
>>>>>> and
>>>>>>>>>>>> later
>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I
>> think
>>>>>> this
>>>>>>>>>>>> way
>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs when
>> working
>>>>>> with
>>>>>>>>>>>> the
>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's offset, i
>>>>>> think it's
>>>>>>>>>>>>> better to pushdown long rather than string. This could let us
>> push
>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
>> Otherwise we
>>>>>> would
>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset,
>> long)
>>>>>> <
>>>>>>>>>>>> 59382.
>>>>>>>>>>>>> Moreover I think we need to introduce the type for computed
>> columns
>>>>>>>>>>>> anyway
>>>>>>>>>>>>> to support functions that infer output type based on expected
>>>>>> return
>>>>>>>>>>>> type.
>>>>>>>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA
>> would
>>>>>> have
>>>>>>>>>>>>> to be pushed down to the source. If it is not possible the
>> planner
>>>>>>>>>>>> should
>>>>>>>>>>>>> fail. As far as I know computed columns push down will be part
>> of
>>>>>> source
>>>>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
>> completely
>>>>>>>>>>>>> orthogonal. In my current proposal you can also partition by a
>>>>>> computed
>>>>>>>>>>>>> column. The difference between using a udf in partitioned by vs
>>>>>>>>>>>> partitioned
>>>>>>>>>>>>> by a computed column is that when you partition by a computed
>>>>>> column
>>>>>>>>>>>> this
>>>>>>>>>>>>> column must be also computed when reading the table. If you
>> use a
>>>>>> udf in
>>>>>>>>>>>>> the partitioned by, the expression is computed only when
>> inserting
>>>>>> into
>>>>>>>>>>>> the
>>>>>>>>>>>>> table.
>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking forward for
>>>>>> further
>>>>>>>>>>>>> suggestions.
>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
>>>>>> metadata
>>>>>>>>>>>> and
>>>>>>>>>>>>>>>> key-part information from source is an important feature for
>>>>>>>>>>>> streaming
>>>>>>>>>>>>>>>> users.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing
>> HEADER
>>>>>>>>>>>>> keyword as
>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe we
>>>>>> should
>>>>>>>>>>>>> add a
>>>>>>>>>>>>>>>> section to explain what's the relationship between them.
>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used
>> on
>>>>>> the
>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL.
>> Shall we
>>>>>>>>>>>> make
>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
>> (actually, I
>>>>>>>>>>>>> prefer
>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
>> properties
>>>>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users that
>> the
>>>>>>>>>>>> field
>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I would like to propose an improvement that would enable
>>>>>> reading
>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>> columns from different parts of source records. Besides the
>>>>>> main
>>>>>>>>>>>>> payload
>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose additional
>>>>>>>>>>>> information. It
>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
>> ingestion
>>>>>> time
>>>>>>>>>>>> or a
>>>>>>>>>>>>>>>>> read and write parts of the record that contain data but
>>>>>>>>>>>> additionally
>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction etc.),
>> e.g.
>>>>>> key
>>>>>>>>>>>> or
>>>>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> We should make it possible to read and write data from all
>> of
>>>>>> those
>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading partitioning
>>>>>> data,
>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>> completeness this proposal discusses also the partitioning
>> when
>>>>>>>>>>>>> writing
>>>>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>
>>>
>>>
>>
>>
>

[Jark Wu-2]

Hi everyone,

I think we have a conclusion that the writable metadata shouldn't be
defined as a computed column, but a normal column.

"timestamp STRING SYSTEM_METADATA('timestamp')" is one of the approaches.
However, it is not SQL standard compliant, we need to be cautious enough
when adding new syntax.
Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
resolve the query-sink schema problem if it is read-only metadata. That
adds more stuff to learn for users.

From my point of view, the "timestamp", "headers" are something like "key"
and "value" that stores with the real data. So why not define the
"timestamp" in the same way with "key" by using a "timestamp.field"
connector option?
On the other side, the read-only metadata, such as "offset", shouldn't be
defined as a normal column. So why not use the existing computed column
syntax for such metadata? Then we don't have the query-sink schema problem.
So here is my proposal:

CREATE TABLE kafka_table (
  id BIGINT,
  name STRING,
  col1 STRING,
  col2 STRING,
  ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field, so can
be read and written.
  offset AS SYSTEM_METADATA("offset")
) WITH (
  'connector' = 'kafka',
  'topic' = 'test-topic',
  'key.fields' = 'id, name',
  'key.format' = 'csv',
  'value.format' = 'avro',
  'timestamp.field' = 'ts'    -- define the mapping of Kafka timestamp
);

INSERT INTO kafka_table
SELECT id, name, col1, col2, rowtime FROM another_table;

I think this can solve all the problems without introducing any new syntax.
The only minor disadvantage is that we separate the definition way/syntax
of read-only metadata and read-write fields.
However, I don't think this is a big problem.

Best,
Jark


On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]> wrote:

> Hi Kurt,
>
> thanks for sharing your opinion. I'm totally up for not reusing computed
> columns. I think Jark was a big supporter of this syntax, @Jark are you
> fine with this as well? The non-computed column approach was only a
> "slightly rejected alternative".
>
> Furthermore, we would need to think about how such a new design
> influences the LIKE clause though.
>
> However, we should still keep the `PERSISTED` keyword as it influences
> the query->sink schema. If you look at the list of metadata for existing
> connectors and formats, we currently offer only two writable metadata
> fields. Otherwise, one would need to declare two tables whenever a
> metadata columns is read (one for the source, one for the sink). This
> can be quite inconvientient e.g. for just reading the topic.
>
> Regards,
> Timo
>
>
> On 09.09.20 08:52, Kurt Young wrote:
> > I also share the concern that reusing the computed column syntax but have
> > different semantics
> > would confuse users a lot.
> >
> > Besides, I think metadata fields are conceptually not the same with
> > computed columns. The metadata
> > field is a connector specific thing and it only contains the information
> > that where does the field come
> > from (during source) or where does the field need to write to (during
> > sink). It's more similar with normal
> > fields, with assumption that all these fields need going to the data
> part.
> >
> > Thus I'm more lean to the rejected alternative that Timo mentioned. And I
> > think we don't need the
> > PERSISTED keyword, SYSTEM_METADATA should be enough.
> >
> > During implementation, the framework only needs to pass such <field,
> > metadata field> information to the
> > connector, and the logic of handling such fields inside the connector
> > should be straightforward.
> >
> > Regarding the downside Timo mentioned:
> >
> >> The disadvantage is that users cannot call UDFs or parse timestamps.
> >
> > I think this is fairly simple to solve. Since the metadata field isn't a
> > computed column anymore, we can support
> > referencing such fields in the computed column. For example:
> >
> > CREATE TABLE kafka_table (
> >       id BIGINT,
> >       name STRING,
> >       timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
> timestamp
> > field from metadata
> >       ts AS to_timestamp(timestamp) // normal computed column, parse the
> > string to TIMESTAMP type by using the metadata field
> > ) WITH (
> >      ...
> > )
> >
> > Best,
> > Kurt
> >
> >
> > On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]> wrote:
> >
> >> Hi Leonard,
> >>
> >> the only alternative I see is that we introduce a concept that is
> >> completely different to computed columns. This is also mentioned in the
> >> rejected alternative section of the FLIP. Something like:
> >>
> >> CREATE TABLE kafka_table (
> >>       id BIGINT,
> >>       name STRING,
> >>       timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
> >>       headers MAP<STRING, BYTES> SYSTEM_METADATA("headers") PERSISTED
> >> ) WITH (
> >>      ...
> >> )
> >>
> >> This way we would avoid confusion at all and can easily map columns to
> >> metadata columns. The disadvantage is that users cannot call UDFs or
> >> parse timestamps. This would need to be done in a real computed column.
> >>
> >> I'm happy about better alternatives.
> >>
> >> Regards,
> >> Timo
> >>
> >>
> >> On 08.09.20 15:37, Leonard Xu wrote:
> >>> HI, Timo
> >>>
> >>> Thanks for driving this FLIP.
> >>>
> >>> Sorry but I have a concern about Writing metadata via DynamicTableSink
> >> section:
> >>>
> >>> CREATE TABLE kafka_table (
> >>>     id BIGINT,
> >>>     name STRING,
> >>>     timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
> PERSISTED,
> >>>     headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING, BYTES>)
> >> PERSISTED
> >>> ) WITH (
> >>>     ...
> >>> )
> >>> An insert statement could look like:
> >>>
> >>> INSERT INTO kafka_table VALUES (
> >>>     (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
> >>> )
> >>>
> >>> The proposed INERT syntax does not make sense to me, because it
> contains
> >> computed(generated) column.
> >>> Both SQL server and Postgresql do not allow to insert value to computed
> >> columns even they are persisted, this boke the generated column
> semantics
> >> and may confuse user much.
> >>>
> >>> For SQL server computed column[1]:
> >>>> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ]
> ]...
> >>>> NOTE: A computed column cannot be the target of an INSERT or UPDATE
> >> statement.
> >>>
> >>> For Postgresql generated column[2]:
> >>>>    height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
> >>>> NOTE: A generated column cannot be written to directly. In INSERT or
> >> UPDATE commands, a value cannot be specified for a generated column, but
> >> the keyword DEFAULT may be specified.
> >>>
> >>> It shouldn't be allowed to set/update value for generated column after
> >> lookup the SQL 2016:
> >>>> <insert statement> ::=
> >>>> INSERT INTO <insertion target> <insert columns and source>
> >>>>
> >>>> If <contextually typed table value constructor> CTTVC is specified,
> >> then every <contextually typed row
> >>>> value constructor element> simply contained in CTTVC whose
> positionally
> >> corresponding <column name>
> >>>> in <insert column list> references a column of which some underlying
> >> column is a generated column shall
> >>>> be a <default specification>.
> >>>> A <default specification> specifies the default value of some
> >> associated item.
> >>>
> >>>
> >>> [1]
> >>
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> >> <
> >>
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> >>>
> >>> [2] https://www.postgresql.org/docs/12/ddl-generated-columns.html <
> >> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
> >>>
> >>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
> >>>>
> >>>> Hi Jark,
> >>>>
> >>>> according to Flink's and Calcite's casting definition in [1][2]
> >> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not,
> we
> >> will make it possible ;-)
> >>>>
> >>>> I'm aware of DeserializationSchema.getProducedType but I think that
> >> this method is actually misplaced. The type should rather be passed to
> the
> >> source itself.
> >>>>
> >>>> For our Kafka SQL source, we will also not use this method because the
> >> Kafka source will add own metadata in addition to the
> >> DeserializationSchema. So DeserializationSchema.getProducedType will
> never
> >> be read.
> >>>>
> >>>> For now I suggest to leave out the `DataType` from
> >> DecodingFormat.applyReadableMetadata. Also because the format's physical
> >> type is passed later in `createRuntimeDecoder`. If necessary, it can be
> >> computed manually by consumedType + metadata types. We will provide a
> >> metadata utility class for that.
> >>>>
> >>>> Regards,
> >>>> Timo
> >>>>
> >>>>
> >>>> [1]
> >>
> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
> >>>> [2]
> >>
> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
> >>>>
> >>>>
> >>>> On 08.09.20 10:52, Jark Wu wrote:
> >>>>> Hi Timo,
> >>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just
> >> noticed
> >>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME
> >> ZONE".
> >>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL
> TIME
> >>>>> ZONE" as the defined type of Kafka timestamp? I think this makes
> sense,
> >>>>> because it represents the milli-seconds since epoch.
> >>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't
> think
> >> so.
> >>>>> The DeserializationSchema implements ResultTypeQueryable, thus the
> >>>>> implementation needs to return an output TypeInfo.
> >>>>> Besides, FlinkKafkaConsumer also
> >>>>> calls DeserializationSchema.getProducedType as the produced type of
> the
> >>>>> source function [1].
> >>>>> Best,
> >>>>> Jark
> >>>>> [1]:
> >>>>>
> >>
> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
> >>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
> wrote:
> >>>>>> Hi everyone,
> >>>>>>
> >>>>>> I updated the FLIP again and hope that I could address the mentioned
> >>>>>> concerns.
> >>>>>>
> >>>>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
> >>>>>> source.ts_ms have different semantics. I updated the FLIP and expose
> >> the
> >>>>>> most commonly used properties separately. So frequently used
> >> properties
> >>>>>> are not hidden in the MAP anymore:
> >>>>>>
> >>>>>> debezium-json.ingestion-timestamp
> >>>>>> debezium-json.source.timestamp
> >>>>>> debezium-json.source.database
> >>>>>> debezium-json.source.schema
> >>>>>> debezium-json.source.table
> >>>>>>
> >>>>>> However, since other properties depend on the used connector/vendor,
> >> the
> >>>>>> remaining options are stored in:
> >>>>>>
> >>>>>> debezium-json.source.properties
> >>>>>>
> >>>>>> And accessed with:
> >>>>>>
> >>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
> MAP<STRING,
> >>>>>> STRING>)['table']
> >>>>>>
> >>>>>> Otherwise it is not possible to figure out the value and column type
> >>>>>> during validation.
> >>>>>>
> >>>>>> @Jark: You convinced me in relaxing the CAST constraints. I added a
> >>>>>> dedicacated sub-section to the FLIP:
> >>>>>>
> >>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
> casting
> >> we
> >>>>>> allow explicit casting to a target data type:
> >>>>>>
> >>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH
> >> LOCAL
> >>>>>> TIME ZONE)
> >>>>>>
> >>>>>> A connector still produces and consumes the data type returned by
> >>>>>> `listMetadata()`. The planner will insert necessary explicit casts.
> >>>>>>
> >>>>>> In any case, the user must provide a CAST such that the computed
> >> column
> >>>>>> receives a valid data type when constructing the table schema.
> >>>>>>
> >>>>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata`
> >> needs a
> >>>>>> DataType argument."
> >>>>>>
> >>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is always
> >>>>>> executed locally. It is the source that needs TypeInfo for
> serializing
> >>>>>> the record to the next operator. And that's this is what we provide.
> >>>>>>
> >>>>>> @Danny:
> >>>>>>
> >>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>>>>>
> >>>>>> We can also use some other means to represent an UNKNOWN data type.
> In
> >>>>>> the Flink type system, we use the NullType for it. The important
> part
> >> is
> >>>>>> that the final data type is known for the entire computed column.
> As I
> >>>>>> mentioned before, I would avoid the suggested option b) that would
> be
> >>>>>> similar to your suggestion. The CAST should be enough and allows for
> >>>>>> complex expressions in the computed column. Option b) would need
> >> parser
> >>>>>> changes.
> >>>>>>
> >>>>>> Regards,
> >>>>>> Timo
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>> On 08.09.20 06:21, Leonard Xu wrote:
> >>>>>>> Hi, Timo
> >>>>>>>
> >>>>>>> Thanks for you explanation and update,  I have only one question
> for
> >>>>>> the latest FLIP.
> >>>>>>>
> >>>>>>> About the MAP<STRING, STRING> DataType of key
> >> 'debezium-json.source', if
> >>>>>> user want to use the table name metadata, they need to write:
> >>>>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
> >>>>>> MAP<STRING, STRING>)['table']
> >>>>>>>
> >>>>>>> the expression is a little complex for user, Could we only support
> >>>>>> necessary metas with simple DataType as following?
> >>>>>>> tableName STRING AS
> >> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
> >>>>>> STRING),
> >>>>>>> transactionTime LONG AS
> >>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
> >>>>>>>
> >>>>>>> In this way, we can simplify the expression, the mainly used
> >> metadata in
> >>>>>> changelog format may include
> >> 'database','table','source.ts_ms','ts_ms' from
> >>>>>> my side,
> >>>>>>> maybe we could only support them at first version.
> >>>>>>>
> >>>>>>> Both Debezium and Canal have above four metadata, and I‘m willing
> to
> >>>>>> take some subtasks in next development if necessary.
> >>>>>>>
> >>>>>>> Debezium:
> >>>>>>> {
> >>>>>>>      "before": null,
> >>>>>>>      "after": {  "id": 101,"name": "scooter"},
> >>>>>>>      "source": {
> >>>>>>>        "db": "inventory",                  # 1. database name the
> >>>>>> changelog belongs to.
> >>>>>>>        "table": "products",                # 2. table name the
> >> changelog
> >>>>>> belongs to.
> >>>>>>>        "ts_ms": 1589355504100,             # 3. timestamp of the
> >> change
> >>>>>> happened in database system, i.e.: transaction time in database.
> >>>>>>>        "connector": "mysql",
> >>>>>>>        ….
> >>>>>>>      },
> >>>>>>>      "ts_ms": 1589355606100,              # 4. timestamp when the
> >> debezium
> >>>>>> processed the changelog.
> >>>>>>>      "op": "c",
> >>>>>>>      "transaction": null
> >>>>>>> }
> >>>>>>>
> >>>>>>> Canal:
> >>>>>>> {
> >>>>>>>      "data": [{  "id": "102", "name": "car battery" }],
> >>>>>>>      "database": "inventory",      # 1. database name the changelog
> >>>>>> belongs to.
> >>>>>>>      "table": "products",          # 2. table name the changelog
> >> belongs
> >>>>>> to.
> >>>>>>>      "es": 1589374013000,          # 3. execution time of the
> change
> >> in
> >>>>>> database system, i.e.: transaction time in database.
> >>>>>>>      "ts": 1589374013680,          # 4. timestamp when the cannal
> >>>>>> processed the changelog.
> >>>>>>>      "isDdl": false,
> >>>>>>>      "mysqlType": {},
> >>>>>>>      ....
> >>>>>>> }
> >>>>>>>
> >>>>>>>
> >>>>>>> Best
> >>>>>>> Leonard
> >>>>>>>
> >>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
> >>>>>>>>
> >>>>>>>> Thanks Timo ~
> >>>>>>>>
> >>>>>>>> The FLIP was already in pretty good shape, I have only 2 questions
> >> here:
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid
> >> read-only
> >>>>>> computed column for Kafka and can be extracted by the planner.”
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually an
> >>>>>> expression return type can be inferred automatically. But I guess
> >>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
> actually
> >> does
> >>>>>> not have a specific return type.
> >>>>>>>>
> >>>>>>>> And why not use the Oracle or MySQL syntax there ?
> >>>>>>>>
> >>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
> [VIRTUAL]
> >>>>>>>> Which is more straight-forward.
> >>>>>>>>
> >>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>>>>>>>
> >>>>>>>> The default type should not be NULL because only NULL literal does
> >>>>>> that. Usually we use ANY as the type if we do not know the specific
> >> type in
> >>>>>> the SQL context. ANY means the physical value can be any java
> object.
> >>>>>>>>
> >>>>>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
> >>>>>>>> [2]
> >>>>>>
> >>
> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
> >>>>>>>>
> >>>>>>>> Best,
> >>>>>>>> Danny Chan
> >>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
> >>>>>>>>> Hi everyone,
> >>>>>>>>>
> >>>>>>>>> I completely reworked FLIP-107. It now covers the full story how
> to
> >>>>>> read
> >>>>>>>>> and write metadata from/to connectors and formats. It considers
> >> all of
> >>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
> >> introduces
> >>>>>>>>> the concept of PERSISTED computed columns and leaves out
> >> partitioning
> >>>>>>>>> for now.
> >>>>>>>>>
> >>>>>>>>> Looking forward to your feedback.
> >>>>>>>>>
> >>>>>>>>> Regards,
> >>>>>>>>> Timo
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
> >>>>>>>>>> Sorry, forgot one question.
> >>>>>>>>>>
> >>>>>>>>>> 4. Can we make the value.fields-include more orthogonal? Like
> one
> >> can
> >>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> >>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
> >>>>>> config to
> >>>>>>>>>> just ignore timestamp but keep key.
> >>>>>>>>>>
> >>>>>>>>>> Best,
> >>>>>>>>>> Kurt
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]>
> >> wrote:
> >>>>>>>>>>
> >>>>>>>>>>> Hi Dawid,
> >>>>>>>>>>>
> >>>>>>>>>>> I have a couple of questions around key fields, actually I also
> >> have
> >>>>>> some
> >>>>>>>>>>> other questions but want to be focused on key fields first.
> >>>>>>>>>>>
> >>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is this
> >>>>>> option only
> >>>>>>>>>>> valid during write operation? Because for
> >>>>>>>>>>> reading, I can't imagine how such options can be applied. I
> would
> >>>>>> expect
> >>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
> >>>>>>>>>>> to read and assign the key to a normal field?
> >>>>>>>>>>>
> >>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I want to
> >>>>>> propose we
> >>>>>>>>>>> can simplify the options to not introducing key.format.type and
> >>>>>>>>>>> other related options. I think a single "key.field" (not
> fields)
> >>>>>> would be
> >>>>>>>>>>> enough, users can use UDF to calculate whatever key they
> >>>>>>>>>>> want before sink.
> >>>>>>>>>>>
> >>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
> >>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every connector
> >> has a
> >>>>>>>>>>> concept
> >>>>>>>>>>> of key and values. The old parameter "format.type" already good
> >>>>>> enough to
> >>>>>>>>>>> use.
> >>>>>>>>>>>
> >>>>>>>>>>> Best,
> >>>>>>>>>>> Kurt
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]>
> >> wrote:
> >>>>>>>>>>>
> >>>>>>>>>>>> Thanks Dawid,
> >>>>>>>>>>>>
> >>>>>>>>>>>> I have two more questions.
> >>>>>>>>>>>>
> >>>>>>>>>>>>> SupportsMetadata
> >>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have
> some
> >>>>>> questions
> >>>>>>>>>>>> regarding to this interface.
> >>>>>>>>>>>> 1) How do the source know what the expected return type of
> each
> >>>>>> metadata?
> >>>>>>>>>>>> 2) Where to put the metadata fields? Append to the existing
> >> physical
> >>>>>>>>>>>> fields?
> >>>>>>>>>>>> If yes, I would suggest to change the signature to
> `TableSource
> >>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
> >>>>>> metadataTypes)`
> >>>>>>>>>>>>
> >>>>>>>>>>>>> SYSTEM_METADATA("partition")
> >>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed
> >> column
> >>>>>>>>>>>> expression? If yes, how to specify the return type of
> >>>>>> SYSTEM_METADATA?
> >>>>>>>>>>>>
> >>>>>>>>>>>> Best,
> >>>>>>>>>>>> Jark
> >>>>>>>>>>>>
> >>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
> >>>>>> [hidden email]>
> >>>>>>>>>>>> wrote:
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 1. I thought a bit more on how the source would emit the
> >> columns
> >>>>>> and I
> >>>>>>>>>>>>> now see its not exactly the same as regular columns. I see a
> >> need
> >>>>>> to
> >>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> I do agree mostly with Danny on how we should do that. One
> >>>>>> additional
> >>>>>>>>>>>>> things I would introduce is an
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> interface SupportsMetadata {
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
> metadataFields);
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> }
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> This way the source would have to declare/emit only the
> >> requested
> >>>>>>>>>>>>> metadata fields. In order not to clash with user defined
> >> fields.
> >>>>>> When
> >>>>>>>>>>>>> emitting the metadata field I would prepend the column name
> >> with
> >>>>>>>>>>>>> __system_{property_name}. Therefore when requested
> >>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a field
> >>>>>>>>>>>>> __system_partition to the schema. This would be never visible
> >> to
> >>>>>> the
> >>>>>>>>>>>>> user as it would be used only for the subsequent computed
> >> columns.
> >>>>>> If
> >>>>>>>>>>>>> that makes sense to you, I will update the FLIP with this
> >>>>>> description.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Here I agree with Danny. It is also the current state of the
> >>>>>> proposal.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 3. Partitioning on computed column vs function
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Here I also agree with Danny. I also think those are
> >> orthogonal. I
> >>>>>> would
> >>>>>>>>>>>>> leave out the STORED computed columns out of the discussion.
> I
> >>>>>> don't see
> >>>>>>>>>>>>> how do they relate to the partitioning. I already put both of
> >> those
> >>>>>>>>>>>>> cases in the document. We can either partition on a computed
> >>>>>> column or
> >>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving
> out
> >> the
> >>>>>>>>>>>>> partitioning by udf in the first version if you still have
> some
> >>>>>>>>>>>> concerns.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> As for your question Danny. It depends which partitioning
> >> strategy
> >>>>>> you
> >>>>>>>>>>>> use.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> For the HASH partitioning strategy I thought it would work as
> >> you
> >>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure
> >> though if
> >>>>>> we
> >>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does
> not
> >> own
> >>>>>> the
> >>>>>>>>>>>>> data and the partitions are already an intrinsic property of
> >> the
> >>>>>>>>>>>>> underlying source e.g. for kafka we do not create topics, but
> >> we
> >>>>>> just
> >>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
> consistent
> >> with
> >>>>>>>>>>>>> other value.fields and key.fields. Actually that was also my
> >>>>>> initial
> >>>>>>>>>>>>> proposal in a first draft I prepared. I changed it afterwards
> >> to
> >>>>>> shorten
> >>>>>>>>>>>>> the key.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
> >>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is
> a
> >>>>>> useful
> >>>>>>>>>>>>> feature ~
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> About how the metadata outputs from source
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> I think it is completely orthogonal, computed column push
> >> down is
> >>>>>>>>>>>>> another topic, this should not be a blocker but a promotion,
> >> if we
> >>>>>> do
> >>>>>>>>>>>> not
> >>>>>>>>>>>>> have any filters on the computed column, there is no need to
> >> do any
> >>>>>>>>>>>>> pushings; the source node just emit the complete record with
> >> full
> >>>>>>>>>>>> metadata
> >>>>>>>>>>>>> with the declared physical schema, then when generating the
> >> virtual
> >>>>>>>>>>>>> columns, we would extract the metadata info and output as
> full
> >>>>>>>>>>>> columns(with
> >>>>>>>>>>>>> full schema).
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> About the type of metadata column
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they are
> >>>>>> symantic
> >>>>>>>>>>>>> equivalent though, explict type is more straight-forward and
> >> we can
> >>>>>>>>>>>> declare
> >>>>>>>>>>>>> the nullable attribute there.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> About option A: partitioning based on acomputed column VS
> >> option
> >>>>>> B:
> >>>>>>>>>>>>> partitioning with just a function
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>    From the FLIP, it seems that B's partitioning is just a
> >> strategy
> >>>>>> when
> >>>>>>>>>>>>> writing data, the partiton column is not included in the
> table
> >>>>>> schema,
> >>>>>>>>>>>> so
> >>>>>>>>>>>>> it's just useless when reading from that.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> - Compared to A, we do not need to generate the partition
> >> column
> >>>>>> when
> >>>>>>>>>>>>> selecting from the table(but insert into)
> >>>>>>>>>>>>>> - For A we can also mark the column as STORED when we want
> to
> >>>>>> persist
> >>>>>>>>>>>>> that
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> So in my opition they are orthogonal, we can support both, i
> >> saw
> >>>>>> that
> >>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
> PARTITIONS
> >>>>>> num, and
> >>>>>>>>>>>> the
> >>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
> partition
> >> in
> >>>>>> which
> >>>>>>>>>>>> the
> >>>>>>>>>>>>> record is stored is partition number N, where N = MOD(expr,
> >> num),
> >>>>>> for
> >>>>>>>>>>>> your
> >>>>>>>>>>>>> design, which partiton the record would persist ?
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> [1]
> >>>>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> >>>>>>>>>>>>>> [2]
> >>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>
> >>
> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>> Danny Chan
> >>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
> >> [hidden email]
> >>>>>>>>>>>>> ，写道：
> >>>>>>>>>>>>>>> Hi Jark,
> >>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
> >>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
> >> properties.
> >>>>>>>>>>>>> Therefore you have the key.format.type.
> >>>>>>>>>>>>>>> I also considered exactly what you are suggesting
> (prefixing
> >> with
> >>>>>>>>>>>>> connector or kafka). I should've put that into an
> >> Option/Rejected
> >>>>>>>>>>>>> alternatives.
> >>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector properties.
> >> Why I
> >>>>>>>>>>>>> wanted to suggest not adding that prefix in the first version
> >> is
> >>>>>> that
> >>>>>>>>>>>>> actually all the properties in the WITH section are connector
> >>>>>>>>>>>> properties.
> >>>>>>>>>>>>> Even format is in the end a connector property as some of the
> >>>>>> sources
> >>>>>>>>>>>> might
> >>>>>>>>>>>>> not have a format, imo. The benefit of not adding the prefix
> is
> >>>>>> that it
> >>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
> >> properties
> >>>>>> with
> >>>>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
> >>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
> >>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
> >>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
> >>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
> >>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
> >> approach
> >>>>>> in the
> >>>>>>>>>>>>> community.
> >>>>>>>>>>>>>>> Ad in-line comments:
> >>>>>>>>>>>>>>> I forgot to update the `value.fields.include` property. It
> >>>>>> should be
> >>>>>>>>>>>>> value.fields-include. Which I think you also suggested in the
> >>>>>> comment,
> >>>>>>>>>>>>> right?
> >>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
> column.
> >> I
> >>>>>> think
> >>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
> >> expression
> >>>>>> and
> >>>>>>>>>>>> later
> >>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I
> >> think
> >>>>>> this
> >>>>>>>>>>>> way
> >>>>>>>>>>>>> it will be easier to implement e.g. filter push downs when
> >> working
> >>>>>> with
> >>>>>>>>>>>> the
> >>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's offset, i
> >>>>>> think it's
> >>>>>>>>>>>>> better to pushdown long rather than string. This could let us
> >> push
> >>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
> >> Otherwise we
> >>>>>> would
> >>>>>>>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset,
> >> long)
> >>>>>> <
> >>>>>>>>>>>> 59382.
> >>>>>>>>>>>>> Moreover I think we need to introduce the type for computed
> >> columns
> >>>>>>>>>>>> anyway
> >>>>>>>>>>>>> to support functions that infer output type based on expected
> >>>>>> return
> >>>>>>>>>>>> type.
> >>>>>>>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA
> >> would
> >>>>>> have
> >>>>>>>>>>>>> to be pushed down to the source. If it is not possible the
> >> planner
> >>>>>>>>>>>> should
> >>>>>>>>>>>>> fail. As far as I know computed columns push down will be
> part
> >> of
> >>>>>> source
> >>>>>>>>>>>>> rework, won't it? ;)
> >>>>>>>>>>>>>>> As for the persisted computed column. I think it is
> >> completely
> >>>>>>>>>>>>> orthogonal. In my current proposal you can also partition by
> a
> >>>>>> computed
> >>>>>>>>>>>>> column. The difference between using a udf in partitioned by
> vs
> >>>>>>>>>>>> partitioned
> >>>>>>>>>>>>> by a computed column is that when you partition by a computed
> >>>>>> column
> >>>>>>>>>>>> this
> >>>>>>>>>>>>> column must be also computed when reading the table. If you
> >> use a
> >>>>>> udf in
> >>>>>>>>>>>>> the partitioned by, the expression is computed only when
> >> inserting
> >>>>>> into
> >>>>>>>>>>>> the
> >>>>>>>>>>>>> table.
> >>>>>>>>>>>>>>> Hope this answers some of your questions. Looking forward
> for
> >>>>>> further
> >>>>>>>>>>>>> suggestions.
> >>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
> >>>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
> >>>>>> metadata
> >>>>>>>>>>>> and
> >>>>>>>>>>>>>>>> key-part information from source is an important feature
> for
> >>>>>>>>>>>> streaming
> >>>>>>>>>>>>>>>> users.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
> >>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing
> >> HEADER
> >>>>>>>>>>>>> keyword as
> >>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
> >>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe
> we
> >>>>>> should
> >>>>>>>>>>>>> add a
> >>>>>>>>>>>>>>>> section to explain what's the relationship between them.
> >>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used
> >> on
> >>>>>> the
> >>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
> >>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL.
> >> Shall we
> >>>>>>>>>>>> make
> >>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>> new introduced properties more hierarchical?
> >>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
> >> (actually, I
> >>>>>>>>>>>>> prefer
> >>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
> >> properties
> >>>>>>>>>>>>> FLINK-12557)
> >>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users that
> >> the
> >>>>>>>>>>>> field
> >>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>> a rowtime attribute.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Thanks,
> >>>>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> >>>>>>>>>>>> [hidden email]>
> >>>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> I would like to propose an improvement that would enable
> >>>>>> reading
> >>>>>>>>>>>> table
> >>>>>>>>>>>>>>>>> columns from different parts of source records. Besides
> the
> >>>>>> main
> >>>>>>>>>>>>> payload
> >>>>>>>>>>>>>>>>> majority (if not all of the sources) expose additional
> >>>>>>>>>>>> information. It
> >>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
> >> ingestion
> >>>>>> time
> >>>>>>>>>>>> or a
> >>>>>>>>>>>>>>>>> read and write parts of the record that contain data but
> >>>>>>>>>>>> additionally
> >>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction etc.),
> >> e.g.
> >>>>>> key
> >>>>>>>>>>>> or
> >>>>>>>>>>>>>>>>> timestamp in Kafka.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> We should make it possible to read and write data from
> all
> >> of
> >>>>>> those
> >>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
> partitioning
> >>>>>> data,
> >>>>>>>>>>>> for
> >>>>>>>>>>>>>>>>> completeness this proposal discusses also the
> partitioning
> >> when
> >>>>>>>>>>>>> writing
> >>>>>>>>>>>>>>>>> data out.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> I am looking forward to your comments.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> You can access the FLIP here:
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>
> >>>>>>
> >>>>
> >>>
> >>>
> >>
> >>
> >
>
>

[Timo Walther-2]

Hi Jark,

now we are back at the original design proposed by Dawid :D Yes, we
should be cautious about adding new syntax. But the length of this
discussion shows that we are looking for a good long-term solution. In
this case I would rather vote for a deep integration into the syntax.

Computed columns are also not SQL standard compliant. And our DDL is
neither, so we have some degree of freedom here.

Trying to solve everything via properties sounds rather like a hack to
me. You are right that one could argue that "timestamp", "headers" are
something like "key" and "value". However, mixing

`offset AS SYSTEM_METADATA("offset")`

and

`'timestamp.field' = 'ts'`

looks more confusing to users that an explicit

`offset AS CAST(SYSTEM_METADATA("offset") AS INT)`

or

`offset INT SYSTEM_METADATA("offset")`

that is symetric for both source and sink.

What do others think?

Regards,
Timo


On 09.09.20 10:09, Jark Wu wrote:

> Hi everyone,
>
> I think we have a conclusion that the writable metadata shouldn't be
> defined as a computed column, but a normal column.
>
> "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the approaches.
> However, it is not SQL standard compliant, we need to be cautious enough
> when adding new syntax.
> Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
> resolve the query-sink schema problem if it is read-only metadata. That
> adds more stuff to learn for users.
>
>>From my point of view, the "timestamp", "headers" are something like "key"
> and "value" that stores with the real data. So why not define the
> "timestamp" in the same way with "key" by using a "timestamp.field"
> connector option?
> On the other side, the read-only metadata, such as "offset", shouldn't be
> defined as a normal column. So why not use the existing computed column
> syntax for such metadata? Then we don't have the query-sink schema problem.
> So here is my proposal:
>
> CREATE TABLE kafka_table (
>    id BIGINT,
>    name STRING,
>    col1 STRING,
>    col2 STRING,
>    ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field, so can
> be read and written.
>    offset AS SYSTEM_METADATA("offset")
> ) WITH (
>    'connector' = 'kafka',
>    'topic' = 'test-topic',
>    'key.fields' = 'id, name',
>    'key.format' = 'csv',
>    'value.format' = 'avro',
>    'timestamp.field' = 'ts'    -- define the mapping of Kafka timestamp
> );
>
> INSERT INTO kafka_table
> SELECT id, name, col1, col2, rowtime FROM another_table;
>
> I think this can solve all the problems without introducing any new syntax.
> The only minor disadvantage is that we separate the definition way/syntax
> of read-only metadata and read-write fields.
> However, I don't think this is a big problem.
>
> Best,
> Jark
>
>
> On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]> wrote:
>
>> Hi Kurt,
>>
>> thanks for sharing your opinion. I'm totally up for not reusing computed
>> columns. I think Jark was a big supporter of this syntax, @Jark are you
>> fine with this as well? The non-computed column approach was only a
>> "slightly rejected alternative".
>>
>> Furthermore, we would need to think about how such a new design
>> influences the LIKE clause though.
>>
>> However, we should still keep the `PERSISTED` keyword as it influences
>> the query->sink schema. If you look at the list of metadata for existing
>> connectors and formats, we currently offer only two writable metadata
>> fields. Otherwise, one would need to declare two tables whenever a
>> metadata columns is read (one for the source, one for the sink). This
>> can be quite inconvientient e.g. for just reading the topic.
>>
>> Regards,
>> Timo
>>
>>
>> On 09.09.20 08:52, Kurt Young wrote:
>>> I also share the concern that reusing the computed column syntax but have
>>> different semantics
>>> would confuse users a lot.
>>>
>>> Besides, I think metadata fields are conceptually not the same with
>>> computed columns. The metadata
>>> field is a connector specific thing and it only contains the information
>>> that where does the field come
>>> from (during source) or where does the field need to write to (during
>>> sink). It's more similar with normal
>>> fields, with assumption that all these fields need going to the data
>> part.
>>>
>>> Thus I'm more lean to the rejected alternative that Timo mentioned. And I
>>> think we don't need the
>>> PERSISTED keyword, SYSTEM_METADATA should be enough.
>>>
>>> During implementation, the framework only needs to pass such <field,
>>> metadata field> information to the
>>> connector, and the logic of handling such fields inside the connector
>>> should be straightforward.
>>>
>>> Regarding the downside Timo mentioned:
>>>
>>>> The disadvantage is that users cannot call UDFs or parse timestamps.
>>>
>>> I think this is fairly simple to solve. Since the metadata field isn't a
>>> computed column anymore, we can support
>>> referencing such fields in the computed column. For example:
>>>
>>> CREATE TABLE kafka_table (
>>>        id BIGINT,
>>>        name STRING,
>>>        timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
>> timestamp
>>> field from metadata
>>>        ts AS to_timestamp(timestamp) // normal computed column, parse the
>>> string to TIMESTAMP type by using the metadata field
>>> ) WITH (
>>>       ...
>>> )
>>>
>>> Best,
>>> Kurt
>>>
>>>
>>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]> wrote:
>>>
>>>> Hi Leonard,
>>>>
>>>> the only alternative I see is that we introduce a concept that is
>>>> completely different to computed columns. This is also mentioned in the
>>>> rejected alternative section of the FLIP. Something like:
>>>>
>>>> CREATE TABLE kafka_table (
>>>>        id BIGINT,
>>>>        name STRING,
>>>>        timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
>>>>        headers MAP<STRING, BYTES> SYSTEM_METADATA("headers") PERSISTED
>>>> ) WITH (
>>>>       ...
>>>> )
>>>>
>>>> This way we would avoid confusion at all and can easily map columns to
>>>> metadata columns. The disadvantage is that users cannot call UDFs or
>>>> parse timestamps. This would need to be done in a real computed column.
>>>>
>>>> I'm happy about better alternatives.
>>>>
>>>> Regards,
>>>> Timo
>>>>
>>>>
>>>> On 08.09.20 15:37, Leonard Xu wrote:
>>>>> HI, Timo
>>>>>
>>>>> Thanks for driving this FLIP.
>>>>>
>>>>> Sorry but I have a concern about Writing metadata via DynamicTableSink
>>>> section:
>>>>>
>>>>> CREATE TABLE kafka_table (
>>>>>      id BIGINT,
>>>>>      name STRING,
>>>>>      timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
>> PERSISTED,
>>>>>      headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING, BYTES>)
>>>> PERSISTED
>>>>> ) WITH (
>>>>>      ...
>>>>> )
>>>>> An insert statement could look like:
>>>>>
>>>>> INSERT INTO kafka_table VALUES (
>>>>>      (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
>>>>> )
>>>>>
>>>>> The proposed INERT syntax does not make sense to me, because it
>> contains
>>>> computed(generated) column.
>>>>> Both SQL server and Postgresql do not allow to insert value to computed
>>>> columns even they are persisted, this boke the generated column
>> semantics
>>>> and may confuse user much.
>>>>>
>>>>> For SQL server computed column[1]:
>>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ]
>> ]...
>>>>>> NOTE: A computed column cannot be the target of an INSERT or UPDATE
>>>> statement.
>>>>>
>>>>> For Postgresql generated column[2]:
>>>>>>     height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
>>>>>> NOTE: A generated column cannot be written to directly. In INSERT or
>>>> UPDATE commands, a value cannot be specified for a generated column, but
>>>> the keyword DEFAULT may be specified.
>>>>>
>>>>> It shouldn't be allowed to set/update value for generated column after
>>>> lookup the SQL 2016:
>>>>>> <insert statement> ::=
>>>>>> INSERT INTO <insertion target> <insert columns and source>
>>>>>>
>>>>>> If <contextually typed table value constructor> CTTVC is specified,
>>>> then every <contextually typed row
>>>>>> value constructor element> simply contained in CTTVC whose
>> positionally
>>>> corresponding <column name>
>>>>>> in <insert column list> references a column of which some underlying
>>>> column is a generated column shall
>>>>>> be a <default specification>.
>>>>>> A <default specification> specifies the default value of some
>>>> associated item.
>>>>>
>>>>>
>>>>> [1]
>>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>> <
>>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>>>
>>>>> [2] https://www.postgresql.org/docs/12/ddl-generated-columns.html <
>>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
>>>>>
>>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>>>>>>
>>>>>> Hi Jark,
>>>>>>
>>>>>> according to Flink's and Calcite's casting definition in [1][2]
>>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not,
>> we
>>>> will make it possible ;-)
>>>>>>
>>>>>> I'm aware of DeserializationSchema.getProducedType but I think that
>>>> this method is actually misplaced. The type should rather be passed to
>> the
>>>> source itself.
>>>>>>
>>>>>> For our Kafka SQL source, we will also not use this method because the
>>>> Kafka source will add own metadata in addition to the
>>>> DeserializationSchema. So DeserializationSchema.getProducedType will
>> never
>>>> be read.
>>>>>>
>>>>>> For now I suggest to leave out the `DataType` from
>>>> DecodingFormat.applyReadableMetadata. Also because the format's physical
>>>> type is passed later in `createRuntimeDecoder`. If necessary, it can be
>>>> computed manually by consumedType + metadata types. We will provide a
>>>> metadata utility class for that.
>>>>>>
>>>>>> Regards,
>>>>>> Timo
>>>>>>
>>>>>>
>>>>>> [1]
>>>>
>> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
>>>>>> [2]
>>>>
>> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>>>>>>
>>>>>>
>>>>>> On 08.09.20 10:52, Jark Wu wrote:
>>>>>>> Hi Timo,
>>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just
>>>> noticed
>>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME
>>>> ZONE".
>>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL
>> TIME
>>>>>>> ZONE" as the defined type of Kafka timestamp? I think this makes
>> sense,
>>>>>>> because it represents the milli-seconds since epoch.
>>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't
>> think
>>>> so.
>>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus the
>>>>>>> implementation needs to return an output TypeInfo.
>>>>>>> Besides, FlinkKafkaConsumer also
>>>>>>> calls DeserializationSchema.getProducedType as the produced type of
>> the
>>>>>>> source function [1].
>>>>>>> Best,
>>>>>>> Jark
>>>>>>> [1]:
>>>>>>>
>>>>
>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
>> wrote:
>>>>>>>> Hi everyone,
>>>>>>>>
>>>>>>>> I updated the FLIP again and hope that I could address the mentioned
>>>>>>>> concerns.
>>>>>>>>
>>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms and
>>>>>>>> source.ts_ms have different semantics. I updated the FLIP and expose
>>>> the
>>>>>>>> most commonly used properties separately. So frequently used
>>>> properties
>>>>>>>> are not hidden in the MAP anymore:
>>>>>>>>
>>>>>>>> debezium-json.ingestion-timestamp
>>>>>>>> debezium-json.source.timestamp
>>>>>>>> debezium-json.source.database
>>>>>>>> debezium-json.source.schema
>>>>>>>> debezium-json.source.table
>>>>>>>>
>>>>>>>> However, since other properties depend on the used connector/vendor,
>>>> the
>>>>>>>> remaining options are stored in:
>>>>>>>>
>>>>>>>> debezium-json.source.properties
>>>>>>>>
>>>>>>>> And accessed with:
>>>>>>>>
>>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
>> MAP<STRING,
>>>>>>>> STRING>)['table']
>>>>>>>>
>>>>>>>> Otherwise it is not possible to figure out the value and column type
>>>>>>>> during validation.
>>>>>>>>
>>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I added a
>>>>>>>> dedicacated sub-section to the FLIP:
>>>>>>>>
>>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
>> casting
>>>> we
>>>>>>>> allow explicit casting to a target data type:
>>>>>>>>
>>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH
>>>> LOCAL
>>>>>>>> TIME ZONE)
>>>>>>>>
>>>>>>>> A connector still produces and consumes the data type returned by
>>>>>>>> `listMetadata()`. The planner will insert necessary explicit casts.
>>>>>>>>
>>>>>>>> In any case, the user must provide a CAST such that the computed
>>>> column
>>>>>>>> receives a valid data type when constructing the table schema.
>>>>>>>>
>>>>>>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata`
>>>> needs a
>>>>>>>> DataType argument."
>>>>>>>>
>>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is always
>>>>>>>> executed locally. It is the source that needs TypeInfo for
>> serializing
>>>>>>>> the record to the next operator. And that's this is what we provide.
>>>>>>>>
>>>>>>>> @Danny:
>>>>>>>>
>>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>>>
>>>>>>>> We can also use some other means to represent an UNKNOWN data type.
>> In
>>>>>>>> the Flink type system, we use the NullType for it. The important
>> part
>>>> is
>>>>>>>> that the final data type is known for the entire computed column.
>> As I
>>>>>>>> mentioned before, I would avoid the suggested option b) that would
>> be
>>>>>>>> similar to your suggestion. The CAST should be enough and allows for
>>>>>>>> complex expressions in the computed column. Option b) would need
>>>> parser
>>>>>>>> changes.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>> Timo
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>>>>>>> Hi, Timo
>>>>>>>>>
>>>>>>>>> Thanks for you explanation and update,  I have only one question
>> for
>>>>>>>> the latest FLIP.
>>>>>>>>>
>>>>>>>>> About the MAP<STRING, STRING> DataType of key
>>>> 'debezium-json.source', if
>>>>>>>> user want to use the table name metadata, they need to write:
>>>>>>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source') AS
>>>>>>>> MAP<STRING, STRING>)['table']
>>>>>>>>>
>>>>>>>>> the expression is a little complex for user, Could we only support
>>>>>>>> necessary metas with simple DataType as following?
>>>>>>>>> tableName STRING AS
>>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>>>>>>> STRING),
>>>>>>>>> transactionTime LONG AS
>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>>>>>>
>>>>>>>>> In this way, we can simplify the expression, the mainly used
>>>> metadata in
>>>>>>>> changelog format may include
>>>> 'database','table','source.ts_ms','ts_ms' from
>>>>>>>> my side,
>>>>>>>>> maybe we could only support them at first version.
>>>>>>>>>
>>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m willing
>> to
>>>>>>>> take some subtasks in next development if necessary.
>>>>>>>>>
>>>>>>>>> Debezium:
>>>>>>>>> {
>>>>>>>>>       "before": null,
>>>>>>>>>       "after": {  "id": 101,"name": "scooter"},
>>>>>>>>>       "source": {
>>>>>>>>>         "db": "inventory",                  # 1. database name the
>>>>>>>> changelog belongs to.
>>>>>>>>>         "table": "products",                # 2. table name the
>>>> changelog
>>>>>>>> belongs to.
>>>>>>>>>         "ts_ms": 1589355504100,             # 3. timestamp of the
>>>> change
>>>>>>>> happened in database system, i.e.: transaction time in database.
>>>>>>>>>         "connector": "mysql",
>>>>>>>>>         ….
>>>>>>>>>       },
>>>>>>>>>       "ts_ms": 1589355606100,              # 4. timestamp when the
>>>> debezium
>>>>>>>> processed the changelog.
>>>>>>>>>       "op": "c",
>>>>>>>>>       "transaction": null
>>>>>>>>> }
>>>>>>>>>
>>>>>>>>> Canal:
>>>>>>>>> {
>>>>>>>>>       "data": [{  "id": "102", "name": "car battery" }],
>>>>>>>>>       "database": "inventory",      # 1. database name the changelog
>>>>>>>> belongs to.
>>>>>>>>>       "table": "products",          # 2. table name the changelog
>>>> belongs
>>>>>>>> to.
>>>>>>>>>       "es": 1589374013000,          # 3. execution time of the
>> change
>>>> in
>>>>>>>> database system, i.e.: transaction time in database.
>>>>>>>>>       "ts": 1589374013680,          # 4. timestamp when the cannal
>>>>>>>> processed the changelog.
>>>>>>>>>       "isDdl": false,
>>>>>>>>>       "mysqlType": {},
>>>>>>>>>       ....
>>>>>>>>> }
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Best
>>>>>>>>> Leonard
>>>>>>>>>
>>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>>>>>>>
>>>>>>>>>> Thanks Timo ~
>>>>>>>>>>
>>>>>>>>>> The FLIP was already in pretty good shape, I have only 2 questions
>>>> here:
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid
>>>> read-only
>>>>>>>> computed column for Kafka and can be extracted by the planner.”
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually an
>>>>>>>> expression return type can be inferred automatically. But I guess
>>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
>> actually
>>>> does
>>>>>>>> not have a specific return type.
>>>>>>>>>>
>>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>>>>>>
>>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
>> [VIRTUAL]
>>>>>>>>>> Which is more straight-forward.
>>>>>>>>>>
>>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>>>>>
>>>>>>>>>> The default type should not be NULL because only NULL literal does
>>>>>>>> that. Usually we use ANY as the type if we do not know the specific
>>>> type in
>>>>>>>> the SQL context. ANY means the physical value can be any java
>> object.
>>>>>>>>>>
>>>>>>>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>>>>>>> [2]
>>>>>>>>
>>>>
>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>> Danny Chan
>>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>
>>>>>>>>>>> I completely reworked FLIP-107. It now covers the full story how
>> to
>>>>>>>> read
>>>>>>>>>>> and write metadata from/to connectors and formats. It considers
>>>> all of
>>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
>>>> introduces
>>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
>>>> partitioning
>>>>>>>>>>> for now.
>>>>>>>>>>>
>>>>>>>>>>> Looking forward to your feedback.
>>>>>>>>>>>
>>>>>>>>>>> Regards,
>>>>>>>>>>> Timo
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>>>>>>> Sorry, forgot one question.
>>>>>>>>>>>>
>>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal? Like
>> one
>>>> can
>>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can not
>>>>>>>> config to
>>>>>>>>>>>> just ignore timestamp but keep key.
>>>>>>>>>>>>
>>>>>>>>>>>> Best,
>>>>>>>>>>>> Kurt
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]>
>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi Dawid,
>>>>>>>>>>>>>
>>>>>>>>>>>>> I have a couple of questions around key fields, actually I also
>>>> have
>>>>>>>> some
>>>>>>>>>>>>> other questions but want to be focused on key fields first.
>>>>>>>>>>>>>
>>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is this
>>>>>>>> option only
>>>>>>>>>>>>> valid during write operation? Because for
>>>>>>>>>>>>> reading, I can't imagine how such options can be applied. I
>> would
>>>>>>>> expect
>>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>>>>>>
>>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I want to
>>>>>>>> propose we
>>>>>>>>>>>>> can simplify the options to not introducing key.format.type and
>>>>>>>>>>>>> other related options. I think a single "key.field" (not
>> fields)
>>>>>>>> would be
>>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>>>>>>> want before sink.
>>>>>>>>>>>>>
>>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every connector
>>>> has a
>>>>>>>>>>>>> concept
>>>>>>>>>>>>> of key and values. The old parameter "format.type" already good
>>>>>>>> enough to
>>>>>>>>>>>>> use.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]>
>>>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks Dawid,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I have two more questions.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> SupportsMetadata
>>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have
>> some
>>>>>>>> questions
>>>>>>>>>>>>>> regarding to this interface.
>>>>>>>>>>>>>> 1) How do the source know what the expected return type of
>> each
>>>>>>>> metadata?
>>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the existing
>>>> physical
>>>>>>>>>>>>>> fields?
>>>>>>>>>>>>>> If yes, I would suggest to change the signature to
>> `TableSource
>>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>>>>>>> metadataTypes)`
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed
>>>> column
>>>>>>>>>>>>>> expression? If yes, how to specify the return type of
>>>>>>>> SYSTEM_METADATA?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>>>>>>> [hidden email]>
>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit the
>>>> columns
>>>>>>>> and I
>>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I see a
>>>> need
>>>>>>>> to
>>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked, Jark.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that. One
>>>>>>>> additional
>>>>>>>>>>>>>>> things I would introduce is an
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
>> metadataFields);
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> This way the source would have to declare/emit only the
>>>> requested
>>>>>>>>>>>>>>> metadata fields. In order not to clash with user defined
>>>> fields.
>>>>>>>> When
>>>>>>>>>>>>>>> emitting the metadata field I would prepend the column name
>>>> with
>>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a field
>>>>>>>>>>>>>>> __system_partition to the schema. This would be never visible
>>>> to
>>>>>>>> the
>>>>>>>>>>>>>>> user as it would be used only for the subsequent computed
>>>> columns.
>>>>>>>> If
>>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with this
>>>>>>>> description.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state of the
>>>>>>>> proposal.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
>>>> orthogonal. I
>>>>>>>> would
>>>>>>>>>>>>>>> leave out the STORED computed columns out of the discussion.
>> I
>>>>>>>> don't see
>>>>>>>>>>>>>>> how do they relate to the partitioning. I already put both of
>>>> those
>>>>>>>>>>>>>>> cases in the document. We can either partition on a computed
>>>>>>>> column or
>>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving
>> out
>>>> the
>>>>>>>>>>>>>>> partitioning by udf in the first version if you still have
>> some
>>>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> As for your question Danny. It depends which partitioning
>>>> strategy
>>>>>>>> you
>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would work as
>>>> you
>>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure
>>>> though if
>>>>>>>> we
>>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does
>> not
>>>> own
>>>>>>>> the
>>>>>>>>>>>>>>> data and the partitions are already an intrinsic property of
>>>> the
>>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create topics, but
>>>> we
>>>>>>>> just
>>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
>> consistent
>>>> with
>>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was also my
>>>>>>>> initial
>>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it afterwards
>>>> to
>>>>>>>> shorten
>>>>>>>>>>>>>>> the key.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it is
>> a
>>>>>>>> useful
>>>>>>>>>>>>>>> feature ~
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column push
>>>> down is
>>>>>>>>>>>>>>> another topic, this should not be a blocker but a promotion,
>>>> if we
>>>>>>>> do
>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>> have any filters on the computed column, there is no need to
>>>> do any
>>>>>>>>>>>>>>> pushings; the source node just emit the complete record with
>>>> full
>>>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>> with the declared physical schema, then when generating the
>>>> virtual
>>>>>>>>>>>>>>> columns, we would extract the metadata info and output as
>> full
>>>>>>>>>>>>>> columns(with
>>>>>>>>>>>>>>> full schema).
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they are
>>>>>>>> symantic
>>>>>>>>>>>>>>> equivalent though, explict type is more straight-forward and
>>>> we can
>>>>>>>>>>>>>> declare
>>>>>>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column VS
>>>> option
>>>>>>>> B:
>>>>>>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>     From the FLIP, it seems that B's partitioning is just a
>>>> strategy
>>>>>>>> when
>>>>>>>>>>>>>>> writing data, the partiton column is not included in the
>> table
>>>>>>>> schema,
>>>>>>>>>>>>>> so
>>>>>>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the partition
>>>> column
>>>>>>>> when
>>>>>>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we want
>> to
>>>>>>>> persist
>>>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support both, i
>>>> saw
>>>>>>>> that
>>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
>> PARTITIONS
>>>>>>>> num, and
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
>> partition
>>>> in
>>>>>>>> which
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>> record is stored is partition number N, where N = MOD(expr,
>>>> num),
>>>>>>>> for
>>>>>>>>>>>>>> your
>>>>>>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> [1]
>>>>>>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>
>>>>
>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
>>>> [hidden email]
>>>>>>>>>>>>>>> ，写道：
>>>>>>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
>>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
>>>> properties.
>>>>>>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
>> (prefixing
>>>> with
>>>>>>>>>>>>>>> connector or kafka). I should've put that into an
>>>> Option/Rejected
>>>>>>>>>>>>>>> alternatives.
>>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector properties.
>>>> Why I
>>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first version
>>>> is
>>>>>>>> that
>>>>>>>>>>>>>>> actually all the properties in the WITH section are connector
>>>>>>>>>>>>>> properties.
>>>>>>>>>>>>>>> Even format is in the end a connector property as some of the
>>>>>>>> sources
>>>>>>>>>>>>>> might
>>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the prefix
>> is
>>>>>>>> that it
>>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
>>>> properties
>>>>>>>> with
>>>>>>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
>>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
>>>> approach
>>>>>>>> in the
>>>>>>>>>>>>>>> community.
>>>>>>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include` property. It
>>>>>>>> should be
>>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested in the
>>>>>>>> comment,
>>>>>>>>>>>>>>> right?
>>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
>> column.
>>>> I
>>>>>>>> think
>>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
>>>> expression
>>>>>>>> and
>>>>>>>>>>>>>> later
>>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is I
>>>> think
>>>>>>>> this
>>>>>>>>>>>>>> way
>>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs when
>>>> working
>>>>>>>> with
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's offset, i
>>>>>>>> think it's
>>>>>>>>>>>>>>> better to pushdown long rather than string. This could let us
>>>> push
>>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
>>>> Otherwise we
>>>>>>>> would
>>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 && cast(offset,
>>>> long)
>>>>>>>> <
>>>>>>>>>>>>>> 59382.
>>>>>>>>>>>>>>> Moreover I think we need to introduce the type for computed
>>>> columns
>>>>>>>>>>>>>> anyway
>>>>>>>>>>>>>>> to support functions that infer output type based on expected
>>>>>>>> return
>>>>>>>>>>>>>> type.
>>>>>>>>>>>>>>>>> As for the computed column push down. Yes, SYSTEM_METADATA
>>>> would
>>>>>>>> have
>>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible the
>>>> planner
>>>>>>>>>>>>>> should
>>>>>>>>>>>>>>> fail. As far as I know computed columns push down will be
>> part
>>>> of
>>>>>>>> source
>>>>>>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
>>>> completely
>>>>>>>>>>>>>>> orthogonal. In my current proposal you can also partition by
>> a
>>>>>>>> computed
>>>>>>>>>>>>>>> column. The difference between using a udf in partitioned by
>> vs
>>>>>>>>>>>>>> partitioned
>>>>>>>>>>>>>>> by a computed column is that when you partition by a computed
>>>>>>>> column
>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>> column must be also computed when reading the table. If you
>>>> use a
>>>>>>>> udf in
>>>>>>>>>>>>>>> the partitioned by, the expression is computed only when
>>>> inserting
>>>>>>>> into
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>> table.
>>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking forward
>> for
>>>>>>>> further
>>>>>>>>>>>>>>> suggestions.
>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion. Reaing
>>>>>>>> metadata
>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>> key-part information from source is an important feature
>> for
>>>>>>>>>>>>>> streaming
>>>>>>>>>>>>>>>>>> users.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing
>>>> HEADER
>>>>>>>>>>>>>>> keyword as
>>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63. Maybe
>> we
>>>>>>>> should
>>>>>>>>>>>>>>> add a
>>>>>>>>>>>>>>>>>> section to explain what's the relationship between them.
>>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be used
>>>> on
>>>>>>>> the
>>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL.
>>>> Shall we
>>>>>>>>>>>>>> make
>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
>>>> (actually, I
>>>>>>>>>>>>>>> prefer
>>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
>>>> properties
>>>>>>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users that
>>>> the
>>>>>>>>>>>>>> field
>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would enable
>>>>>>>> reading
>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>>>> columns from different parts of source records. Besides
>> the
>>>>>>>> main
>>>>>>>>>>>>>>> payload
>>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose additional
>>>>>>>>>>>>>> information. It
>>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
>>>> ingestion
>>>>>>>> time
>>>>>>>>>>>>>> or a
>>>>>>>>>>>>>>>>>>> read and write parts of the record that contain data but
>>>>>>>>>>>>>> additionally
>>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction etc.),
>>>> e.g.
>>>>>>>> key
>>>>>>>>>>>>>> or
>>>>>>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> We should make it possible to read and write data from
>> all
>>>> of
>>>>>>>> those
>>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
>> partitioning
>>>>>>>> data,
>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
>> partitioning
>>>> when
>>>>>>>>>>>>>>> writing
>>>>>>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>
>>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>>
>

[Kurt Young]

 I would vote for `offset INT SYSTEM_METADATA("offset")`.

I don't think we can stick with the SQL standard in DDL part forever,
especially as there are more and more
requirements coming from different connectors and external systems.

Best,
Kurt


On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]> wrote:

> Hi Jark,
>
> now we are back at the original design proposed by Dawid :D Yes, we
> should be cautious about adding new syntax. But the length of this
> discussion shows that we are looking for a good long-term solution. In
> this case I would rather vote for a deep integration into the syntax.
>
> Computed columns are also not SQL standard compliant. And our DDL is
> neither, so we have some degree of freedom here.
>
> Trying to solve everything via properties sounds rather like a hack to
> me. You are right that one could argue that "timestamp", "headers" are
> something like "key" and "value". However, mixing
>
> `offset AS SYSTEM_METADATA("offset")`
>
> and
>
> `'timestamp.field' = 'ts'`
>
> looks more confusing to users that an explicit
>
> `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
>
> or
>
> `offset INT SYSTEM_METADATA("offset")`
>
> that is symetric for both source and sink.
>
> What do others think?
>
> Regards,
> Timo
>
>
> On 09.09.20 10:09, Jark Wu wrote:
> > Hi everyone,
> >
> > I think we have a conclusion that the writable metadata shouldn't be
> > defined as a computed column, but a normal column.
> >
> > "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the approaches.
> > However, it is not SQL standard compliant, we need to be cautious enough
> > when adding new syntax.
> > Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
> > resolve the query-sink schema problem if it is read-only metadata. That
> > adds more stuff to learn for users.
> >
> >>From my point of view, the "timestamp", "headers" are something like
> "key"
> > and "value" that stores with the real data. So why not define the
> > "timestamp" in the same way with "key" by using a "timestamp.field"
> > connector option?
> > On the other side, the read-only metadata, such as "offset", shouldn't be
> > defined as a normal column. So why not use the existing computed column
> > syntax for such metadata? Then we don't have the query-sink schema
> problem.
> > So here is my proposal:
> >
> > CREATE TABLE kafka_table (
> >    id BIGINT,
> >    name STRING,
> >    col1 STRING,
> >    col2 STRING,
> >    ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field, so
> can
> > be read and written.
> >    offset AS SYSTEM_METADATA("offset")
> > ) WITH (
> >    'connector' = 'kafka',
> >    'topic' = 'test-topic',
> >    'key.fields' = 'id, name',
> >    'key.format' = 'csv',
> >    'value.format' = 'avro',
> >    'timestamp.field' = 'ts'    -- define the mapping of Kafka timestamp
> > );
> >
> > INSERT INTO kafka_table
> > SELECT id, name, col1, col2, rowtime FROM another_table;
> >
> > I think this can solve all the problems without introducing any new
> syntax.
> > The only minor disadvantage is that we separate the definition way/syntax
> > of read-only metadata and read-write fields.
> > However, I don't think this is a big problem.
> >
> > Best,
> > Jark
> >
> >
> > On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]> wrote:
> >
> >> Hi Kurt,
> >>
> >> thanks for sharing your opinion. I'm totally up for not reusing computed
> >> columns. I think Jark was a big supporter of this syntax, @Jark are you
> >> fine with this as well? The non-computed column approach was only a
> >> "slightly rejected alternative".
> >>
> >> Furthermore, we would need to think about how such a new design
> >> influences the LIKE clause though.
> >>
> >> However, we should still keep the `PERSISTED` keyword as it influences
> >> the query->sink schema. If you look at the list of metadata for existing
> >> connectors and formats, we currently offer only two writable metadata
> >> fields. Otherwise, one would need to declare two tables whenever a
> >> metadata columns is read (one for the source, one for the sink). This
> >> can be quite inconvientient e.g. for just reading the topic.
> >>
> >> Regards,
> >> Timo
> >>
> >>
> >> On 09.09.20 08:52, Kurt Young wrote:
> >>> I also share the concern that reusing the computed column syntax but
> have
> >>> different semantics
> >>> would confuse users a lot.
> >>>
> >>> Besides, I think metadata fields are conceptually not the same with
> >>> computed columns. The metadata
> >>> field is a connector specific thing and it only contains the
> information
> >>> that where does the field come
> >>> from (during source) or where does the field need to write to (during
> >>> sink). It's more similar with normal
> >>> fields, with assumption that all these fields need going to the data
> >> part.
> >>>
> >>> Thus I'm more lean to the rejected alternative that Timo mentioned.
> And I
> >>> think we don't need the
> >>> PERSISTED keyword, SYSTEM_METADATA should be enough.
> >>>
> >>> During implementation, the framework only needs to pass such <field,
> >>> metadata field> information to the
> >>> connector, and the logic of handling such fields inside the connector
> >>> should be straightforward.
> >>>
> >>> Regarding the downside Timo mentioned:
> >>>
> >>>> The disadvantage is that users cannot call UDFs or parse timestamps.
> >>>
> >>> I think this is fairly simple to solve. Since the metadata field isn't
> a
> >>> computed column anymore, we can support
> >>> referencing such fields in the computed column. For example:
> >>>
> >>> CREATE TABLE kafka_table (
> >>>        id BIGINT,
> >>>        name STRING,
> >>>        timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
> >> timestamp
> >>> field from metadata
> >>>        ts AS to_timestamp(timestamp) // normal computed column, parse
> the
> >>> string to TIMESTAMP type by using the metadata field
> >>> ) WITH (
> >>>       ...
> >>> )
> >>>
> >>> Best,
> >>> Kurt
> >>>
> >>>
> >>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
> wrote:
> >>>
> >>>> Hi Leonard,
> >>>>
> >>>> the only alternative I see is that we introduce a concept that is
> >>>> completely different to computed columns. This is also mentioned in
> the
> >>>> rejected alternative section of the FLIP. Something like:
> >>>>
> >>>> CREATE TABLE kafka_table (
> >>>>        id BIGINT,
> >>>>        name STRING,
> >>>>        timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
> >>>>        headers MAP<STRING, BYTES> SYSTEM_METADATA("headers") PERSISTED
> >>>> ) WITH (
> >>>>       ...
> >>>> )
> >>>>
> >>>> This way we would avoid confusion at all and can easily map columns to
> >>>> metadata columns. The disadvantage is that users cannot call UDFs or
> >>>> parse timestamps. This would need to be done in a real computed
> column.
> >>>>
> >>>> I'm happy about better alternatives.
> >>>>
> >>>> Regards,
> >>>> Timo
> >>>>
> >>>>
> >>>> On 08.09.20 15:37, Leonard Xu wrote:
> >>>>> HI, Timo
> >>>>>
> >>>>> Thanks for driving this FLIP.
> >>>>>
> >>>>> Sorry but I have a concern about Writing metadata via
> DynamicTableSink
> >>>> section:
> >>>>>
> >>>>> CREATE TABLE kafka_table (
> >>>>>      id BIGINT,
> >>>>>      name STRING,
> >>>>>      timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
> >> PERSISTED,
> >>>>>      headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
> BYTES>)
> >>>> PERSISTED
> >>>>> ) WITH (
> >>>>>      ...
> >>>>> )
> >>>>> An insert statement could look like:
> >>>>>
> >>>>> INSERT INTO kafka_table VALUES (
> >>>>>      (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
> >>>>> )
> >>>>>
> >>>>> The proposed INERT syntax does not make sense to me, because it
> >> contains
> >>>> computed(generated) column.
> >>>>> Both SQL server and Postgresql do not allow to insert value to
> computed
> >>>> columns even they are persisted, this boke the generated column
> >> semantics
> >>>> and may confuse user much.
> >>>>>
> >>>>> For SQL server computed column[1]:
> >>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ]
> >> ]...
> >>>>>> NOTE: A computed column cannot be the target of an INSERT or UPDATE
> >>>> statement.
> >>>>>
> >>>>> For Postgresql generated column[2]:
> >>>>>>     height_in numeric GENERATED ALWAYS AS (height_cm / 2.54) STORED
> >>>>>> NOTE: A generated column cannot be written to directly. In INSERT or
> >>>> UPDATE commands, a value cannot be specified for a generated column,
> but
> >>>> the keyword DEFAULT may be specified.
> >>>>>
> >>>>> It shouldn't be allowed to set/update value for generated column
> after
> >>>> lookup the SQL 2016:
> >>>>>> <insert statement> ::=
> >>>>>> INSERT INTO <insertion target> <insert columns and source>
> >>>>>>
> >>>>>> If <contextually typed table value constructor> CTTVC is specified,
> >>>> then every <contextually typed row
> >>>>>> value constructor element> simply contained in CTTVC whose
> >> positionally
> >>>> corresponding <column name>
> >>>>>> in <insert column list> references a column of which some underlying
> >>>> column is a generated column shall
> >>>>>> be a <default specification>.
> >>>>>> A <default specification> specifies the default value of some
> >>>> associated item.
> >>>>>
> >>>>>
> >>>>> [1]
> >>>>
> >>
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> >>>> <
> >>>>
> >>
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> >>>>>
> >>>>> [2] https://www.postgresql.org/docs/12/ddl-generated-columns.html <
> >>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
> >>>>>
> >>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
> >>>>>>
> >>>>>> Hi Jark,
> >>>>>>
> >>>>>> according to Flink's and Calcite's casting definition in [1][2]
> >>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If not,
> >> we
> >>>> will make it possible ;-)
> >>>>>>
> >>>>>> I'm aware of DeserializationSchema.getProducedType but I think that
> >>>> this method is actually misplaced. The type should rather be passed to
> >> the
> >>>> source itself.
> >>>>>>
> >>>>>> For our Kafka SQL source, we will also not use this method because
> the
> >>>> Kafka source will add own metadata in addition to the
> >>>> DeserializationSchema. So DeserializationSchema.getProducedType will
> >> never
> >>>> be read.
> >>>>>>
> >>>>>> For now I suggest to leave out the `DataType` from
> >>>> DecodingFormat.applyReadableMetadata. Also because the format's
> physical
> >>>> type is passed later in `createRuntimeDecoder`. If necessary, it can
> be
> >>>> computed manually by consumedType + metadata types. We will provide a
> >>>> metadata utility class for that.
> >>>>>>
> >>>>>> Regards,
> >>>>>> Timo
> >>>>>>
> >>>>>>
> >>>>>> [1]
> >>>>
> >>
> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
> >>>>>> [2]
> >>>>
> >>
> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
> >>>>>>
> >>>>>>
> >>>>>> On 08.09.20 10:52, Jark Wu wrote:
> >>>>>>> Hi Timo,
> >>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I just
> >>>> noticed
> >>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME
> >>>> ZONE".
> >>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL
> >> TIME
> >>>>>>> ZONE" as the defined type of Kafka timestamp? I think this makes
> >> sense,
> >>>>>>> because it represents the milli-seconds since epoch.
> >>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't
> >> think
> >>>> so.
> >>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus the
> >>>>>>> implementation needs to return an output TypeInfo.
> >>>>>>> Besides, FlinkKafkaConsumer also
> >>>>>>> calls DeserializationSchema.getProducedType as the produced type of
> >> the
> >>>>>>> source function [1].
> >>>>>>> Best,
> >>>>>>> Jark
> >>>>>>> [1]:
> >>>>>>>
> >>>>
> >>
> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
> >>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
> >> wrote:
> >>>>>>>> Hi everyone,
> >>>>>>>>
> >>>>>>>> I updated the FLIP again and hope that I could address the
> mentioned
> >>>>>>>> concerns.
> >>>>>>>>
> >>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms
> and
> >>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
> expose
> >>>> the
> >>>>>>>> most commonly used properties separately. So frequently used
> >>>> properties
> >>>>>>>> are not hidden in the MAP anymore:
> >>>>>>>>
> >>>>>>>> debezium-json.ingestion-timestamp
> >>>>>>>> debezium-json.source.timestamp
> >>>>>>>> debezium-json.source.database
> >>>>>>>> debezium-json.source.schema
> >>>>>>>> debezium-json.source.table
> >>>>>>>>
> >>>>>>>> However, since other properties depend on the used
> connector/vendor,
> >>>> the
> >>>>>>>> remaining options are stored in:
> >>>>>>>>
> >>>>>>>> debezium-json.source.properties
> >>>>>>>>
> >>>>>>>> And accessed with:
> >>>>>>>>
> >>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
> >> MAP<STRING,
> >>>>>>>> STRING>)['table']
> >>>>>>>>
> >>>>>>>> Otherwise it is not possible to figure out the value and column
> type
> >>>>>>>> during validation.
> >>>>>>>>
> >>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I added
> a
> >>>>>>>> dedicacated sub-section to the FLIP:
> >>>>>>>>
> >>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
> >> casting
> >>>> we
> >>>>>>>> allow explicit casting to a target data type:
> >>>>>>>>
> >>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3) WITH
> >>>> LOCAL
> >>>>>>>> TIME ZONE)
> >>>>>>>>
> >>>>>>>> A connector still produces and consumes the data type returned by
> >>>>>>>> `listMetadata()`. The planner will insert necessary explicit
> casts.
> >>>>>>>>
> >>>>>>>> In any case, the user must provide a CAST such that the computed
> >>>> column
> >>>>>>>> receives a valid data type when constructing the table schema.
> >>>>>>>>
> >>>>>>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata`
> >>>> needs a
> >>>>>>>> DataType argument."
> >>>>>>>>
> >>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
> always
> >>>>>>>> executed locally. It is the source that needs TypeInfo for
> >> serializing
> >>>>>>>> the record to the next operator. And that's this is what we
> provide.
> >>>>>>>>
> >>>>>>>> @Danny:
> >>>>>>>>
> >>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>>>>>>>
> >>>>>>>> We can also use some other means to represent an UNKNOWN data
> type.
> >> In
> >>>>>>>> the Flink type system, we use the NullType for it. The important
> >> part
> >>>> is
> >>>>>>>> that the final data type is known for the entire computed column.
> >> As I
> >>>>>>>> mentioned before, I would avoid the suggested option b) that would
> >> be
> >>>>>>>> similar to your suggestion. The CAST should be enough and allows
> for
> >>>>>>>> complex expressions in the computed column. Option b) would need
> >>>> parser
> >>>>>>>> changes.
> >>>>>>>>
> >>>>>>>> Regards,
> >>>>>>>> Timo
> >>>>>>>>
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
> >>>>>>>>> Hi, Timo
> >>>>>>>>>
> >>>>>>>>> Thanks for you explanation and update,  I have only one question
> >> for
> >>>>>>>> the latest FLIP.
> >>>>>>>>>
> >>>>>>>>> About the MAP<STRING, STRING> DataType of key
> >>>> 'debezium-json.source', if
> >>>>>>>> user want to use the table name metadata, they need to write:
> >>>>>>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source')
> AS
> >>>>>>>> MAP<STRING, STRING>)['table']
> >>>>>>>>>
> >>>>>>>>> the expression is a little complex for user, Could we only
> support
> >>>>>>>> necessary metas with simple DataType as following?
> >>>>>>>>> tableName STRING AS
> >>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
> >>>>>>>> STRING),
> >>>>>>>>> transactionTime LONG AS
> >>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
> >>>>>>>>>
> >>>>>>>>> In this way, we can simplify the expression, the mainly used
> >>>> metadata in
> >>>>>>>> changelog format may include
> >>>> 'database','table','source.ts_ms','ts_ms' from
> >>>>>>>> my side,
> >>>>>>>>> maybe we could only support them at first version.
> >>>>>>>>>
> >>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m willing
> >> to
> >>>>>>>> take some subtasks in next development if necessary.
> >>>>>>>>>
> >>>>>>>>> Debezium:
> >>>>>>>>> {
> >>>>>>>>>       "before": null,
> >>>>>>>>>       "after": {  "id": 101,"name": "scooter"},
> >>>>>>>>>       "source": {
> >>>>>>>>>         "db": "inventory",                  # 1. database name
> the
> >>>>>>>> changelog belongs to.
> >>>>>>>>>         "table": "products",                # 2. table name the
> >>>> changelog
> >>>>>>>> belongs to.
> >>>>>>>>>         "ts_ms": 1589355504100,             # 3. timestamp of the
> >>>> change
> >>>>>>>> happened in database system, i.e.: transaction time in database.
> >>>>>>>>>         "connector": "mysql",
> >>>>>>>>>         ….
> >>>>>>>>>       },
> >>>>>>>>>       "ts_ms": 1589355606100,              # 4. timestamp when
> the
> >>>> debezium
> >>>>>>>> processed the changelog.
> >>>>>>>>>       "op": "c",
> >>>>>>>>>       "transaction": null
> >>>>>>>>> }
> >>>>>>>>>
> >>>>>>>>> Canal:
> >>>>>>>>> {
> >>>>>>>>>       "data": [{  "id": "102", "name": "car battery" }],
> >>>>>>>>>       "database": "inventory",      # 1. database name the
> changelog
> >>>>>>>> belongs to.
> >>>>>>>>>       "table": "products",          # 2. table name the changelog
> >>>> belongs
> >>>>>>>> to.
> >>>>>>>>>       "es": 1589374013000,          # 3. execution time of the
> >> change
> >>>> in
> >>>>>>>> database system, i.e.: transaction time in database.
> >>>>>>>>>       "ts": 1589374013680,          # 4. timestamp when the
> cannal
> >>>>>>>> processed the changelog.
> >>>>>>>>>       "isDdl": false,
> >>>>>>>>>       "mysqlType": {},
> >>>>>>>>>       ....
> >>>>>>>>> }
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> Best
> >>>>>>>>> Leonard
> >>>>>>>>>
> >>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
> >>>>>>>>>>
> >>>>>>>>>> Thanks Timo ~
> >>>>>>>>>>
> >>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
> questions
> >>>> here:
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid
> >>>> read-only
> >>>>>>>> computed column for Kafka and can be extracted by the planner.”
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ? Usually
> an
> >>>>>>>> expression return type can be inferred automatically. But I guess
> >>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
> >> actually
> >>>> does
> >>>>>>>> not have a specific return type.
> >>>>>>>>>>
> >>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
> >>>>>>>>>>
> >>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
> >> [VIRTUAL]
> >>>>>>>>>> Which is more straight-forward.
> >>>>>>>>>>
> >>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>>>>>>>>>
> >>>>>>>>>> The default type should not be NULL because only NULL literal
> does
> >>>>>>>> that. Usually we use ANY as the type if we do not know the
> specific
> >>>> type in
> >>>>>>>> the SQL context. ANY means the physical value can be any java
> >> object.
> >>>>>>>>>>
> >>>>>>>>>> [1] https://oracle-base.com/articles/11g/virtual-columns-11gr1
> >>>>>>>>>> [2]
> >>>>>>>>
> >>>>
> >>
> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
> >>>>>>>>>>
> >>>>>>>>>> Best,
> >>>>>>>>>> Danny Chan
> >>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]>，写道：
> >>>>>>>>>>> Hi everyone,
> >>>>>>>>>>>
> >>>>>>>>>>> I completely reworked FLIP-107. It now covers the full story
> how
> >> to
> >>>>>>>> read
> >>>>>>>>>>> and write metadata from/to connectors and formats. It considers
> >>>> all of
> >>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
> >>>> introduces
> >>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
> >>>> partitioning
> >>>>>>>>>>> for now.
> >>>>>>>>>>>
> >>>>>>>>>>> Looking forward to your feedback.
> >>>>>>>>>>>
> >>>>>>>>>>> Regards,
> >>>>>>>>>>> Timo
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
> >>>>>>>>>>>> Sorry, forgot one question.
> >>>>>>>>>>>>
> >>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal? Like
> >> one
> >>>> can
> >>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> >>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can
> not
> >>>>>>>> config to
> >>>>>>>>>>>> just ignore timestamp but keep key.
> >>>>>>>>>>>>
> >>>>>>>>>>>> Best,
> >>>>>>>>>>>> Kurt
> >>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]>
> >>>> wrote:
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Hi Dawid,
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> I have a couple of questions around key fields, actually I
> also
> >>>> have
> >>>>>>>> some
> >>>>>>>>>>>>> other questions but want to be focused on key fields first.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is
> this
> >>>>>>>> option only
> >>>>>>>>>>>>> valid during write operation? Because for
> >>>>>>>>>>>>> reading, I can't imagine how such options can be applied. I
> >> would
> >>>>>>>> expect
> >>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
> >>>>>>>>>>>>> to read and assign the key to a normal field?
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I want
> to
> >>>>>>>> propose we
> >>>>>>>>>>>>> can simplify the options to not introducing key.format.type
> and
> >>>>>>>>>>>>> other related options. I think a single "key.field" (not
> >> fields)
> >>>>>>>> would be
> >>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
> >>>>>>>>>>>>> want before sink.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
> >>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
> connector
> >>>> has a
> >>>>>>>>>>>>> concept
> >>>>>>>>>>>>> of key and values. The old parameter "format.type" already
> good
> >>>>>>>> enough to
> >>>>>>>>>>>>> use.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Best,
> >>>>>>>>>>>>> Kurt
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]>
> >>>> wrote:
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>> Thanks Dawid,
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> I have two more questions.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> SupportsMetadata
> >>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have
> >> some
> >>>>>>>> questions
> >>>>>>>>>>>>>> regarding to this interface.
> >>>>>>>>>>>>>> 1) How do the source know what the expected return type of
> >> each
> >>>>>>>> metadata?
> >>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the existing
> >>>> physical
> >>>>>>>>>>>>>> fields?
> >>>>>>>>>>>>>> If yes, I would suggest to change the signature to
> >> `TableSource
> >>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
> >>>>>>>> metadataTypes)`
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
> >>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a computed
> >>>> column
> >>>>>>>>>>>>>> expression? If yes, how to specify the return type of
> >>>>>>>> SYSTEM_METADATA?
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
> >>>>>>>> [hidden email]>
> >>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit the
> >>>> columns
> >>>>>>>> and I
> >>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I see
> a
> >>>> need
> >>>>>>>> to
> >>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
> Jark.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that. One
> >>>>>>>> additional
> >>>>>>>>>>>>>>> things I would introduce is an
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> interface SupportsMetadata {
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
> >> metadataFields);
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> This way the source would have to declare/emit only the
> >>>> requested
> >>>>>>>>>>>>>>> metadata fields. In order not to clash with user defined
> >>>> fields.
> >>>>>>>> When
> >>>>>>>>>>>>>>> emitting the metadata field I would prepend the column name
> >>>> with
> >>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
> >>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
> field
> >>>>>>>>>>>>>>> __system_partition to the schema. This would be never
> visible
> >>>> to
> >>>>>>>> the
> >>>>>>>>>>>>>>> user as it would be used only for the subsequent computed
> >>>> columns.
> >>>>>>>> If
> >>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with this
> >>>>>>>> description.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state of
> the
> >>>>>>>> proposal.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
> >>>> orthogonal. I
> >>>>>>>> would
> >>>>>>>>>>>>>>> leave out the STORED computed columns out of the
> discussion.
> >> I
> >>>>>>>> don't see
> >>>>>>>>>>>>>>> how do they relate to the partitioning. I already put both
> of
> >>>> those
> >>>>>>>>>>>>>>> cases in the document. We can either partition on a
> computed
> >>>>>>>> column or
> >>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with leaving
> >> out
> >>>> the
> >>>>>>>>>>>>>>> partitioning by udf in the first version if you still have
> >> some
> >>>>>>>>>>>>>> concerns.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> As for your question Danny. It depends which partitioning
> >>>> strategy
> >>>>>>>> you
> >>>>>>>>>>>>>> use.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would work
> as
> >>>> you
> >>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure
> >>>> though if
> >>>>>>>> we
> >>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink does
> >> not
> >>>> own
> >>>>>>>> the
> >>>>>>>>>>>>>>> data and the partitions are already an intrinsic property
> of
> >>>> the
> >>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create topics,
> but
> >>>> we
> >>>>>>>> just
> >>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
> >> consistent
> >>>> with
> >>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was also
> my
> >>>>>>>> initial
> >>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
> afterwards
> >>>> to
> >>>>>>>> shorten
> >>>>>>>>>>>>>>> the key.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
> >>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it
> is
> >> a
> >>>>>>>> useful
> >>>>>>>>>>>>>>> feature ~
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> About how the metadata outputs from source
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column push
> >>>> down is
> >>>>>>>>>>>>>>> another topic, this should not be a blocker but a
> promotion,
> >>>> if we
> >>>>>>>> do
> >>>>>>>>>>>>>> not
> >>>>>>>>>>>>>>> have any filters on the computed column, there is no need
> to
> >>>> do any
> >>>>>>>>>>>>>>> pushings; the source node just emit the complete record
> with
> >>>> full
> >>>>>>>>>>>>>> metadata
> >>>>>>>>>>>>>>> with the declared physical schema, then when generating the
> >>>> virtual
> >>>>>>>>>>>>>>> columns, we would extract the metadata info and output as
> >> full
> >>>>>>>>>>>>>> columns(with
> >>>>>>>>>>>>>>> full schema).
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> About the type of metadata column
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they
> are
> >>>>>>>> symantic
> >>>>>>>>>>>>>>> equivalent though, explict type is more straight-forward
> and
> >>>> we can
> >>>>>>>>>>>>>> declare
> >>>>>>>>>>>>>>> the nullable attribute there.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column VS
> >>>> option
> >>>>>>>> B:
> >>>>>>>>>>>>>>> partitioning with just a function
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>     From the FLIP, it seems that B's partitioning is just
> a
> >>>> strategy
> >>>>>>>> when
> >>>>>>>>>>>>>>> writing data, the partiton column is not included in the
> >> table
> >>>>>>>> schema,
> >>>>>>>>>>>>>> so
> >>>>>>>>>>>>>>> it's just useless when reading from that.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the partition
> >>>> column
> >>>>>>>> when
> >>>>>>>>>>>>>>> selecting from the table(but insert into)
> >>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we want
> >> to
> >>>>>>>> persist
> >>>>>>>>>>>>>>> that
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
> both, i
> >>>> saw
> >>>>>>>> that
> >>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
> >> PARTITIONS
> >>>>>>>> num, and
> >>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
> >> partition
> >>>> in
> >>>>>>>> which
> >>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>> record is stored is partition number N, where N = MOD(expr,
> >>>> num),
> >>>>>>>> for
> >>>>>>>>>>>>>> your
> >>>>>>>>>>>>>>> design, which partiton the record would persist ?
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> [1]
> >>>>>>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> >>>>>>>>>>>>>>>> [2]
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>
> >>>>
> >>
> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>> Danny Chan
> >>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
> >>>> [hidden email]
> >>>>>>>>>>>>>>> ，写道：
> >>>>>>>>>>>>>>>>> Hi Jark,
> >>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
> >>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
> >>>> properties.
> >>>>>>>>>>>>>>> Therefore you have the key.format.type.
> >>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
> >> (prefixing
> >>>> with
> >>>>>>>>>>>>>>> connector or kafka). I should've put that into an
> >>>> Option/Rejected
> >>>>>>>>>>>>>>> alternatives.
> >>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
> properties.
> >>>> Why I
> >>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
> version
> >>>> is
> >>>>>>>> that
> >>>>>>>>>>>>>>> actually all the properties in the WITH section are
> connector
> >>>>>>>>>>>>>> properties.
> >>>>>>>>>>>>>>> Even format is in the end a connector property as some of
> the
> >>>>>>>> sources
> >>>>>>>>>>>>>> might
> >>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
> prefix
> >> is
> >>>>>>>> that it
> >>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
> >>>> properties
> >>>>>>>> with
> >>>>>>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
> >>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
> >>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
> >>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
> >>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
> >>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
> >>>> approach
> >>>>>>>> in the
> >>>>>>>>>>>>>>> community.
> >>>>>>>>>>>>>>>>> Ad in-line comments:
> >>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include` property.
> It
> >>>>>>>> should be
> >>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested in
> the
> >>>>>>>> comment,
> >>>>>>>>>>>>>>> right?
> >>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
> >> column.
> >>>> I
> >>>>>>>> think
> >>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
> >>>> expression
> >>>>>>>> and
> >>>>>>>>>>>>>> later
> >>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason is
> I
> >>>> think
> >>>>>>>> this
> >>>>>>>>>>>>>> way
> >>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs when
> >>>> working
> >>>>>>>> with
> >>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
> offset, i
> >>>>>>>> think it's
> >>>>>>>>>>>>>>> better to pushdown long rather than string. This could let
> us
> >>>> push
> >>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
> >>>> Otherwise we
> >>>>>>>> would
> >>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
> cast(offset,
> >>>> long)
> >>>>>>>> <
> >>>>>>>>>>>>>> 59382.
> >>>>>>>>>>>>>>> Moreover I think we need to introduce the type for computed
> >>>> columns
> >>>>>>>>>>>>>> anyway
> >>>>>>>>>>>>>>> to support functions that infer output type based on
> expected
> >>>>>>>> return
> >>>>>>>>>>>>>> type.
> >>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
> SYSTEM_METADATA
> >>>> would
> >>>>>>>> have
> >>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible the
> >>>> planner
> >>>>>>>>>>>>>> should
> >>>>>>>>>>>>>>> fail. As far as I know computed columns push down will be
> >> part
> >>>> of
> >>>>>>>> source
> >>>>>>>>>>>>>>> rework, won't it? ;)
> >>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
> >>>> completely
> >>>>>>>>>>>>>>> orthogonal. In my current proposal you can also partition
> by
> >> a
> >>>>>>>> computed
> >>>>>>>>>>>>>>> column. The difference between using a udf in partitioned
> by
> >> vs
> >>>>>>>>>>>>>> partitioned
> >>>>>>>>>>>>>>> by a computed column is that when you partition by a
> computed
> >>>>>>>> column
> >>>>>>>>>>>>>> this
> >>>>>>>>>>>>>>> column must be also computed when reading the table. If you
> >>>> use a
> >>>>>>>> udf in
> >>>>>>>>>>>>>>> the partitioned by, the expression is computed only when
> >>>> inserting
> >>>>>>>> into
> >>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>> table.
> >>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking forward
> >> for
> >>>>>>>> further
> >>>>>>>>>>>>>>> suggestions.
> >>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
> >>>>>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
> Reaing
> >>>>>>>> metadata
> >>>>>>>>>>>>>> and
> >>>>>>>>>>>>>>>>>> key-part information from source is an important feature
> >> for
> >>>>>>>>>>>>>> streaming
> >>>>>>>>>>>>>>>>>> users.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
> >>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of introducing
> >>>> HEADER
> >>>>>>>>>>>>>>> keyword as
> >>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
> >>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
> Maybe
> >> we
> >>>>>>>> should
> >>>>>>>>>>>>>>> add a
> >>>>>>>>>>>>>>>>>> section to explain what's the relationship between them.
> >>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be
> used
> >>>> on
> >>>>>>>> the
> >>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
> >>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink SQL.
> >>>> Shall we
> >>>>>>>>>>>>>> make
> >>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
> >>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
> >>>> (actually, I
> >>>>>>>>>>>>>>> prefer
> >>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
> >>>> properties
> >>>>>>>>>>>>>>> FLINK-12557)
> >>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users
> that
> >>>> the
> >>>>>>>>>>>>>> field
> >>>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>>>> a rowtime attribute.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Thanks,
> >>>>>>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> >>>>>>>>>>>>>> [hidden email]>
> >>>>>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
> enable
> >>>>>>>> reading
> >>>>>>>>>>>>>> table
> >>>>>>>>>>>>>>>>>>> columns from different parts of source records. Besides
> >> the
> >>>>>>>> main
> >>>>>>>>>>>>>>> payload
> >>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose additional
> >>>>>>>>>>>>>> information. It
> >>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
> >>>> ingestion
> >>>>>>>> time
> >>>>>>>>>>>>>> or a
> >>>>>>>>>>>>>>>>>>> read and write parts of the record that contain data
> but
> >>>>>>>>>>>>>> additionally
> >>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
> etc.),
> >>>> e.g.
> >>>>>>>> key
> >>>>>>>>>>>>>> or
> >>>>>>>>>>>>>>>>>>> timestamp in Kafka.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> We should make it possible to read and write data from
> >> all
> >>>> of
> >>>>>>>> those
> >>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
> >> partitioning
> >>>>>>>> data,
> >>>>>>>>>>>>>> for
> >>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
> >> partitioning
> >>>> when
> >>>>>>>>>>>>>>> writing
> >>>>>>>>>>>>>>>>>>> data out.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> You can access the FLIP here:
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>
> >>>>
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>>
> >>>>>
> >>>>
> >>>>
> >>>
> >>
> >>
> >
>
>

[Danny Chan-2]

"offset INT SYSTEM_METADATA("offset")"

This is actually Oracle or MySQL style computed column syntax.

"You are right that one could argue that "timestamp", "headers" are
something like "key" and "value""

I have the same feeling, both key value and headers timestamp are *real* data
stored in the consumed record, they are not computed or generated.

"Trying to solve everything via properties sounds rather like a hack to
me"

Things are not that hack if we can unify the routines or the definitions
(all from the computed column way or all from the table options), i also
think that it is a hacky that we mix in 2 kinds of syntax for different
kinds of metadata (read-only and read-write). In this FLIP, we declare the
Kafka key fields with table options but SYSTEM_METADATA for other metadata,
that is a hacky thing or something in-consistent.

Kurt Young <[hidden email]> 于2020年9月9日周三 下午4:48写道：

>  I would vote for `offset INT SYSTEM_METADATA("offset")`.
>
> I don't think we can stick with the SQL standard in DDL part forever,
> especially as there are more and more
> requirements coming from different connectors and external systems.
>
> Best,
> Kurt
>
>
> On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]> wrote:
>
> > Hi Jark,
> >
> > now we are back at the original design proposed by Dawid :D Yes, we
> > should be cautious about adding new syntax. But the length of this
> > discussion shows that we are looking for a good long-term solution. In
> > this case I would rather vote for a deep integration into the syntax.
> >
> > Computed columns are also not SQL standard compliant. And our DDL is
> > neither, so we have some degree of freedom here.
> >
> > Trying to solve everything via properties sounds rather like a hack to
> > me. You are right that one could argue that "timestamp", "headers" are
> > something like "key" and "value". However, mixing
> >
> > `offset AS SYSTEM_METADATA("offset")`
> >
> > and
> >
> > `'timestamp.field' = 'ts'`
> >
> > looks more confusing to users that an explicit
> >
> > `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
> >
> > or
> >
> > `offset INT SYSTEM_METADATA("offset")`
> >
> > that is symetric for both source and sink.
> >
> > What do others think?
> >
> > Regards,
> > Timo
> >
> >
> > On 09.09.20 10:09, Jark Wu wrote:
> > > Hi everyone,
> > >
> > > I think we have a conclusion that the writable metadata shouldn't be
> > > defined as a computed column, but a normal column.
> > >
> > > "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the
> approaches.
> > > However, it is not SQL standard compliant, we need to be cautious
> enough
> > > when adding new syntax.
> > > Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
> > > resolve the query-sink schema problem if it is read-only metadata. That
> > > adds more stuff to learn for users.
> > >
> > >>From my point of view, the "timestamp", "headers" are something like
> > "key"
> > > and "value" that stores with the real data. So why not define the
> > > "timestamp" in the same way with "key" by using a "timestamp.field"
> > > connector option?
> > > On the other side, the read-only metadata, such as "offset", shouldn't
> be
> > > defined as a normal column. So why not use the existing computed column
> > > syntax for such metadata? Then we don't have the query-sink schema
> > problem.
> > > So here is my proposal:
> > >
> > > CREATE TABLE kafka_table (
> > >    id BIGINT,
> > >    name STRING,
> > >    col1 STRING,
> > >    col2 STRING,
> > >    ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field, so
> > can
> > > be read and written.
> > >    offset AS SYSTEM_METADATA("offset")
> > > ) WITH (
> > >    'connector' = 'kafka',
> > >    'topic' = 'test-topic',
> > >    'key.fields' = 'id, name',
> > >    'key.format' = 'csv',
> > >    'value.format' = 'avro',
> > >    'timestamp.field' = 'ts'    -- define the mapping of Kafka timestamp
> > > );
> > >
> > > INSERT INTO kafka_table
> > > SELECT id, name, col1, col2, rowtime FROM another_table;
> > >
> > > I think this can solve all the problems without introducing any new
> > syntax.
> > > The only minor disadvantage is that we separate the definition
> way/syntax
> > > of read-only metadata and read-write fields.
> > > However, I don't think this is a big problem.
> > >
> > > Best,
> > > Jark
> > >
> > >
> > > On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]> wrote:
> > >
> > >> Hi Kurt,
> > >>
> > >> thanks for sharing your opinion. I'm totally up for not reusing
> computed
> > >> columns. I think Jark was a big supporter of this syntax, @Jark are
> you
> > >> fine with this as well? The non-computed column approach was only a
> > >> "slightly rejected alternative".
> > >>
> > >> Furthermore, we would need to think about how such a new design
> > >> influences the LIKE clause though.
> > >>
> > >> However, we should still keep the `PERSISTED` keyword as it influences
> > >> the query->sink schema. If you look at the list of metadata for
> existing
> > >> connectors and formats, we currently offer only two writable metadata
> > >> fields. Otherwise, one would need to declare two tables whenever a
> > >> metadata columns is read (one for the source, one for the sink). This
> > >> can be quite inconvientient e.g. for just reading the topic.
> > >>
> > >> Regards,
> > >> Timo
> > >>
> > >>
> > >> On 09.09.20 08:52, Kurt Young wrote:
> > >>> I also share the concern that reusing the computed column syntax but
> > have
> > >>> different semantics
> > >>> would confuse users a lot.
> > >>>
> > >>> Besides, I think metadata fields are conceptually not the same with
> > >>> computed columns. The metadata
> > >>> field is a connector specific thing and it only contains the
> > information
> > >>> that where does the field come
> > >>> from (during source) or where does the field need to write to (during
> > >>> sink). It's more similar with normal
> > >>> fields, with assumption that all these fields need going to the data
> > >> part.
> > >>>
> > >>> Thus I'm more lean to the rejected alternative that Timo mentioned.
> > And I
> > >>> think we don't need the
> > >>> PERSISTED keyword, SYSTEM_METADATA should be enough.
> > >>>
> > >>> During implementation, the framework only needs to pass such <field,
> > >>> metadata field> information to the
> > >>> connector, and the logic of handling such fields inside the connector
> > >>> should be straightforward.
> > >>>
> > >>> Regarding the downside Timo mentioned:
> > >>>
> > >>>> The disadvantage is that users cannot call UDFs or parse timestamps.
> > >>>
> > >>> I think this is fairly simple to solve. Since the metadata field
> isn't
> > a
> > >>> computed column anymore, we can support
> > >>> referencing such fields in the computed column. For example:
> > >>>
> > >>> CREATE TABLE kafka_table (
> > >>>        id BIGINT,
> > >>>        name STRING,
> > >>>        timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
> > >> timestamp
> > >>> field from metadata
> > >>>        ts AS to_timestamp(timestamp) // normal computed column, parse
> > the
> > >>> string to TIMESTAMP type by using the metadata field
> > >>> ) WITH (
> > >>>       ...
> > >>> )
> > >>>
> > >>> Best,
> > >>> Kurt
> > >>>
> > >>>
> > >>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
> > wrote:
> > >>>
> > >>>> Hi Leonard,
> > >>>>
> > >>>> the only alternative I see is that we introduce a concept that is
> > >>>> completely different to computed columns. This is also mentioned in
> > the
> > >>>> rejected alternative section of the FLIP. Something like:
> > >>>>
> > >>>> CREATE TABLE kafka_table (
> > >>>>        id BIGINT,
> > >>>>        name STRING,
> > >>>>        timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
> > >>>>        headers MAP<STRING, BYTES> SYSTEM_METADATA("headers")
> PERSISTED
> > >>>> ) WITH (
> > >>>>       ...
> > >>>> )
> > >>>>
> > >>>> This way we would avoid confusion at all and can easily map columns
> to
> > >>>> metadata columns. The disadvantage is that users cannot call UDFs or
> > >>>> parse timestamps. This would need to be done in a real computed
> > column.
> > >>>>
> > >>>> I'm happy about better alternatives.
> > >>>>
> > >>>> Regards,
> > >>>> Timo
> > >>>>
> > >>>>
> > >>>> On 08.09.20 15:37, Leonard Xu wrote:
> > >>>>> HI, Timo
> > >>>>>
> > >>>>> Thanks for driving this FLIP.
> > >>>>>
> > >>>>> Sorry but I have a concern about Writing metadata via
> > DynamicTableSink
> > >>>> section:
> > >>>>>
> > >>>>> CREATE TABLE kafka_table (
> > >>>>>      id BIGINT,
> > >>>>>      name STRING,
> > >>>>>      timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
> > >> PERSISTED,
> > >>>>>      headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
> > BYTES>)
> > >>>> PERSISTED
> > >>>>> ) WITH (
> > >>>>>      ...
> > >>>>> )
> > >>>>> An insert statement could look like:
> > >>>>>
> > >>>>> INSERT INTO kafka_table VALUES (
> > >>>>>      (1, "ABC", 1599133672, MAP('checksum', computeChecksum(...)))
> > >>>>> )
> > >>>>>
> > >>>>> The proposed INERT syntax does not make sense to me, because it
> > >> contains
> > >>>> computed(generated) column.
> > >>>>> Both SQL server and Postgresql do not allow to insert value to
> > computed
> > >>>> columns even they are persisted, this boke the generated column
> > >> semantics
> > >>>> and may confuse user much.
> > >>>>>
> > >>>>> For SQL server computed column[1]:
> > >>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT NULL ]
> > >> ]...
> > >>>>>> NOTE: A computed column cannot be the target of an INSERT or
> UPDATE
> > >>>> statement.
> > >>>>>
> > >>>>> For Postgresql generated column[2]:
> > >>>>>>     height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
> STORED
> > >>>>>> NOTE: A generated column cannot be written to directly. In INSERT
> or
> > >>>> UPDATE commands, a value cannot be specified for a generated column,
> > but
> > >>>> the keyword DEFAULT may be specified.
> > >>>>>
> > >>>>> It shouldn't be allowed to set/update value for generated column
> > after
> > >>>> lookup the SQL 2016:
> > >>>>>> <insert statement> ::=
> > >>>>>> INSERT INTO <insertion target> <insert columns and source>
> > >>>>>>
> > >>>>>> If <contextually typed table value constructor> CTTVC is
> specified,
> > >>>> then every <contextually typed row
> > >>>>>> value constructor element> simply contained in CTTVC whose
> > >> positionally
> > >>>> corresponding <column name>
> > >>>>>> in <insert column list> references a column of which some
> underlying
> > >>>> column is a generated column shall
> > >>>>>> be a <default specification>.
> > >>>>>> A <default specification> specifies the default value of some
> > >>>> associated item.
> > >>>>>
> > >>>>>
> > >>>>> [1]
> > >>>>
> > >>
> >
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> > >>>> <
> > >>>>
> > >>
> >
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> > >>>>>
> > >>>>> [2] https://www.postgresql.org/docs/12/ddl-generated-columns.html
> <
> > >>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
> > >>>>>
> > >>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
> > >>>>>>
> > >>>>>> Hi Jark,
> > >>>>>>
> > >>>>>> according to Flink's and Calcite's casting definition in [1][2]
> > >>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If
> not,
> > >> we
> > >>>> will make it possible ;-)
> > >>>>>>
> > >>>>>> I'm aware of DeserializationSchema.getProducedType but I think
> that
> > >>>> this method is actually misplaced. The type should rather be passed
> to
> > >> the
> > >>>> source itself.
> > >>>>>>
> > >>>>>> For our Kafka SQL source, we will also not use this method because
> > the
> > >>>> Kafka source will add own metadata in addition to the
> > >>>> DeserializationSchema. So DeserializationSchema.getProducedType will
> > >> never
> > >>>> be read.
> > >>>>>>
> > >>>>>> For now I suggest to leave out the `DataType` from
> > >>>> DecodingFormat.applyReadableMetadata. Also because the format's
> > physical
> > >>>> type is passed later in `createRuntimeDecoder`. If necessary, it can
> > be
> > >>>> computed manually by consumedType + metadata types. We will provide
> a
> > >>>> metadata utility class for that.
> > >>>>>>
> > >>>>>> Regards,
> > >>>>>> Timo
> > >>>>>>
> > >>>>>>
> > >>>>>> [1]
> > >>>>
> > >>
> >
> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
> > >>>>>> [2]
> > >>>>
> > >>
> >
> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
> > >>>>>>
> > >>>>>>
> > >>>>>> On 08.09.20 10:52, Jark Wu wrote:
> > >>>>>>> Hi Timo,
> > >>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I
> just
> > >>>> noticed
> > >>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL TIME
> > >>>> ZONE".
> > >>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH LOCAL
> > >> TIME
> > >>>>>>> ZONE" as the defined type of Kafka timestamp? I think this makes
> > >> sense,
> > >>>>>>> because it represents the milli-seconds since epoch.
> > >>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I don't
> > >> think
> > >>>> so.
> > >>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus
> the
> > >>>>>>> implementation needs to return an output TypeInfo.
> > >>>>>>> Besides, FlinkKafkaConsumer also
> > >>>>>>> calls DeserializationSchema.getProducedType as the produced type
> of
> > >> the
> > >>>>>>> source function [1].
> > >>>>>>> Best,
> > >>>>>>> Jark
> > >>>>>>> [1]:
> > >>>>>>>
> > >>>>
> > >>
> >
> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
> > >>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
> > >> wrote:
> > >>>>>>>> Hi everyone,
> > >>>>>>>>
> > >>>>>>>> I updated the FLIP again and hope that I could address the
> > mentioned
> > >>>>>>>> concerns.
> > >>>>>>>>
> > >>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that ts_ms
> > and
> > >>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
> > expose
> > >>>> the
> > >>>>>>>> most commonly used properties separately. So frequently used
> > >>>> properties
> > >>>>>>>> are not hidden in the MAP anymore:
> > >>>>>>>>
> > >>>>>>>> debezium-json.ingestion-timestamp
> > >>>>>>>> debezium-json.source.timestamp
> > >>>>>>>> debezium-json.source.database
> > >>>>>>>> debezium-json.source.schema
> > >>>>>>>> debezium-json.source.table
> > >>>>>>>>
> > >>>>>>>> However, since other properties depend on the used
> > connector/vendor,
> > >>>> the
> > >>>>>>>> remaining options are stored in:
> > >>>>>>>>
> > >>>>>>>> debezium-json.source.properties
> > >>>>>>>>
> > >>>>>>>> And accessed with:
> > >>>>>>>>
> > >>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
> > >> MAP<STRING,
> > >>>>>>>> STRING>)['table']
> > >>>>>>>>
> > >>>>>>>> Otherwise it is not possible to figure out the value and column
> > type
> > >>>>>>>> during validation.
> > >>>>>>>>
> > >>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I
> added
> > a
> > >>>>>>>> dedicacated sub-section to the FLIP:
> > >>>>>>>>
> > >>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
> > >> casting
> > >>>> we
> > >>>>>>>> allow explicit casting to a target data type:
> > >>>>>>>>
> > >>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3)
> WITH
> > >>>> LOCAL
> > >>>>>>>> TIME ZONE)
> > >>>>>>>>
> > >>>>>>>> A connector still produces and consumes the data type returned
> by
> > >>>>>>>> `listMetadata()`. The planner will insert necessary explicit
> > casts.
> > >>>>>>>>
> > >>>>>>>> In any case, the user must provide a CAST such that the computed
> > >>>> column
> > >>>>>>>> receives a valid data type when constructing the table schema.
> > >>>>>>>>
> > >>>>>>>> "I don't see a reason why `DecodingFormat#applyReadableMetadata`
> > >>>> needs a
> > >>>>>>>> DataType argument."
> > >>>>>>>>
> > >>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
> > always
> > >>>>>>>> executed locally. It is the source that needs TypeInfo for
> > >> serializing
> > >>>>>>>> the record to the next operator. And that's this is what we
> > provide.
> > >>>>>>>>
> > >>>>>>>> @Danny:
> > >>>>>>>>
> > >>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
> > >>>>>>>>
> > >>>>>>>> We can also use some other means to represent an UNKNOWN data
> > type.
> > >> In
> > >>>>>>>> the Flink type system, we use the NullType for it. The important
> > >> part
> > >>>> is
> > >>>>>>>> that the final data type is known for the entire computed
> column.
> > >> As I
> > >>>>>>>> mentioned before, I would avoid the suggested option b) that
> would
> > >> be
> > >>>>>>>> similar to your suggestion. The CAST should be enough and allows
> > for
> > >>>>>>>> complex expressions in the computed column. Option b) would need
> > >>>> parser
> > >>>>>>>> changes.
> > >>>>>>>>
> > >>>>>>>> Regards,
> > >>>>>>>> Timo
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
> > >>>>>>>>> Hi, Timo
> > >>>>>>>>>
> > >>>>>>>>> Thanks for you explanation and update,  I have only one
> question
> > >> for
> > >>>>>>>> the latest FLIP.
> > >>>>>>>>>
> > >>>>>>>>> About the MAP<STRING, STRING> DataType of key
> > >>>> 'debezium-json.source', if
> > >>>>>>>> user want to use the table name metadata, they need to write:
> > >>>>>>>>> tableName STRING AS CAST(SYSTEM_METADATA('debeuim-json.source')
> > AS
> > >>>>>>>> MAP<STRING, STRING>)['table']
> > >>>>>>>>>
> > >>>>>>>>> the expression is a little complex for user, Could we only
> > support
> > >>>>>>>> necessary metas with simple DataType as following?
> > >>>>>>>>> tableName STRING AS
> > >>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
> > >>>>>>>> STRING),
> > >>>>>>>>> transactionTime LONG AS
> > >>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
> > >>>>>>>>>
> > >>>>>>>>> In this way, we can simplify the expression, the mainly used
> > >>>> metadata in
> > >>>>>>>> changelog format may include
> > >>>> 'database','table','source.ts_ms','ts_ms' from
> > >>>>>>>> my side,
> > >>>>>>>>> maybe we could only support them at first version.
> > >>>>>>>>>
> > >>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m
> willing
> > >> to
> > >>>>>>>> take some subtasks in next development if necessary.
> > >>>>>>>>>
> > >>>>>>>>> Debezium:
> > >>>>>>>>> {
> > >>>>>>>>>       "before": null,
> > >>>>>>>>>       "after": {  "id": 101,"name": "scooter"},
> > >>>>>>>>>       "source": {
> > >>>>>>>>>         "db": "inventory",                  # 1. database name
> > the
> > >>>>>>>> changelog belongs to.
> > >>>>>>>>>         "table": "products",                # 2. table name the
> > >>>> changelog
> > >>>>>>>> belongs to.
> > >>>>>>>>>         "ts_ms": 1589355504100,             # 3. timestamp of
> the
> > >>>> change
> > >>>>>>>> happened in database system, i.e.: transaction time in database.
> > >>>>>>>>>         "connector": "mysql",
> > >>>>>>>>>         ….
> > >>>>>>>>>       },
> > >>>>>>>>>       "ts_ms": 1589355606100,              # 4. timestamp when
> > the
> > >>>> debezium
> > >>>>>>>> processed the changelog.
> > >>>>>>>>>       "op": "c",
> > >>>>>>>>>       "transaction": null
> > >>>>>>>>> }
> > >>>>>>>>>
> > >>>>>>>>> Canal:
> > >>>>>>>>> {
> > >>>>>>>>>       "data": [{  "id": "102", "name": "car battery" }],
> > >>>>>>>>>       "database": "inventory",      # 1. database name the
> > changelog
> > >>>>>>>> belongs to.
> > >>>>>>>>>       "table": "products",          # 2. table name the
> changelog
> > >>>> belongs
> > >>>>>>>> to.
> > >>>>>>>>>       "es": 1589374013000,          # 3. execution time of the
> > >> change
> > >>>> in
> > >>>>>>>> database system, i.e.: transaction time in database.
> > >>>>>>>>>       "ts": 1589374013680,          # 4. timestamp when the
> > cannal
> > >>>>>>>> processed the changelog.
> > >>>>>>>>>       "isDdl": false,
> > >>>>>>>>>       "mysqlType": {},
> > >>>>>>>>>       ....
> > >>>>>>>>> }
> > >>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>> Best
> > >>>>>>>>> Leonard
> > >>>>>>>>>
> > >>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
> > >>>>>>>>>>
> > >>>>>>>>>> Thanks Timo ~
> > >>>>>>>>>>
> > >>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
> > questions
> > >>>> here:
> > >>>>>>>>>>
> > >>>>>>>>>>
> > >>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a valid
> > >>>> read-only
> > >>>>>>>> computed column for Kafka and can be extracted by the planner.”
> > >>>>>>>>>>
> > >>>>>>>>>>
> > >>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ?
> Usually
> > an
> > >>>>>>>> expression return type can be inferred automatically. But I
> guess
> > >>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
> > >> actually
> > >>>> does
> > >>>>>>>> not have a specific return type.
> > >>>>>>>>>>
> > >>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
> > >>>>>>>>>>
> > >>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
> > >> [VIRTUAL]
> > >>>>>>>>>> Which is more straight-forward.
> > >>>>>>>>>>
> > >>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by
> default”
> > >>>>>>>>>>
> > >>>>>>>>>> The default type should not be NULL because only NULL literal
> > does
> > >>>>>>>> that. Usually we use ANY as the type if we do not know the
> > specific
> > >>>> type in
> > >>>>>>>> the SQL context. ANY means the physical value can be any java
> > >> object.
> > >>>>>>>>>>
> > >>>>>>>>>> [1]
> https://oracle-base.com/articles/11g/virtual-columns-11gr1
> > >>>>>>>>>> [2]
> > >>>>>>>>
> > >>>>
> > >>
> >
> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
> > >>>>>>>>>>
> > >>>>>>>>>> Best,
> > >>>>>>>>>> Danny Chan
> > >>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]
> >，写道：
> > >>>>>>>>>>> Hi everyone,
> > >>>>>>>>>>>
> > >>>>>>>>>>> I completely reworked FLIP-107. It now covers the full story
> > how
> > >> to
> > >>>>>>>> read
> > >>>>>>>>>>> and write metadata from/to connectors and formats. It
> considers
> > >>>> all of
> > >>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
> > >>>> introduces
> > >>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
> > >>>> partitioning
> > >>>>>>>>>>> for now.
> > >>>>>>>>>>>
> > >>>>>>>>>>> Looking forward to your feedback.
> > >>>>>>>>>>>
> > >>>>>>>>>>> Regards,
> > >>>>>>>>>>> Timo
> > >>>>>>>>>>>
> > >>>>>>>>>>>
> > >>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
> > >>>>>>>>>>>> Sorry, forgot one question.
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal?
> Like
> > >> one
> > >>>> can
> > >>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> > >>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users can
> > not
> > >>>>>>>> config to
> > >>>>>>>>>>>> just ignore timestamp but keep key.
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> Best,
> > >>>>>>>>>>>> Kurt
> > >>>>>>>>>>>>
> > >>>>>>>>>>>>
> > >>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <[hidden email]
> >
> > >>>> wrote:
> > >>>>>>>>>>>>
> > >>>>>>>>>>>>> Hi Dawid,
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> I have a couple of questions around key fields, actually I
> > also
> > >>>> have
> > >>>>>>>> some
> > >>>>>>>>>>>>> other questions but want to be focused on key fields first.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is
> > this
> > >>>>>>>> option only
> > >>>>>>>>>>>>> valid during write operation? Because for
> > >>>>>>>>>>>>> reading, I can't imagine how such options can be applied. I
> > >> would
> > >>>>>>>> expect
> > >>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
> > >>>>>>>>>>>>> to read and assign the key to a normal field?
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I want
> > to
> > >>>>>>>> propose we
> > >>>>>>>>>>>>> can simplify the options to not introducing key.format.type
> > and
> > >>>>>>>>>>>>> other related options. I think a single "key.field" (not
> > >> fields)
> > >>>>>>>> would be
> > >>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
> > >>>>>>>>>>>>> want before sink.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
> > >>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
> > connector
> > >>>> has a
> > >>>>>>>>>>>>> concept
> > >>>>>>>>>>>>> of key and values. The old parameter "format.type" already
> > good
> > >>>>>>>> enough to
> > >>>>>>>>>>>>> use.
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>> Kurt
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <[hidden email]>
> > >>>> wrote:
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Thanks Dawid,
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> I have two more questions.
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> SupportsMetadata
> > >>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I have
> > >> some
> > >>>>>>>> questions
> > >>>>>>>>>>>>>> regarding to this interface.
> > >>>>>>>>>>>>>> 1) How do the source know what the expected return type of
> > >> each
> > >>>>>>>> metadata?
> > >>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the
> existing
> > >>>> physical
> > >>>>>>>>>>>>>> fields?
> > >>>>>>>>>>>>>> If yes, I would suggest to change the signature to
> > >> `TableSource
> > >>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
> > >>>>>>>> metadataTypes)`
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
> > >>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a
> computed
> > >>>> column
> > >>>>>>>>>>>>>> expression? If yes, how to specify the return type of
> > >>>>>>>> SYSTEM_METADATA?
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>>> Jark
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
> > >>>>>>>> [hidden email]>
> > >>>>>>>>>>>>>> wrote:
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> Hi,
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit the
> > >>>> columns
> > >>>>>>>> and I
> > >>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I
> see
> > a
> > >>>> need
> > >>>>>>>> to
> > >>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
> > Jark.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that.
> One
> > >>>>>>>> additional
> > >>>>>>>>>>>>>>> things I would introduce is an
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> interface SupportsMetadata {
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
> > >> metadataFields);
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> }
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> This way the source would have to declare/emit only the
> > >>>> requested
> > >>>>>>>>>>>>>>> metadata fields. In order not to clash with user defined
> > >>>> fields.
> > >>>>>>>> When
> > >>>>>>>>>>>>>>> emitting the metadata field I would prepend the column
> name
> > >>>> with
> > >>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
> > >>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
> > field
> > >>>>>>>>>>>>>>> __system_partition to the schema. This would be never
> > visible
> > >>>> to
> > >>>>>>>> the
> > >>>>>>>>>>>>>>> user as it would be used only for the subsequent computed
> > >>>> columns.
> > >>>>>>>> If
> > >>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with this
> > >>>>>>>> description.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state of
> > the
> > >>>>>>>> proposal.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
> > >>>> orthogonal. I
> > >>>>>>>> would
> > >>>>>>>>>>>>>>> leave out the STORED computed columns out of the
> > discussion.
> > >> I
> > >>>>>>>> don't see
> > >>>>>>>>>>>>>>> how do they relate to the partitioning. I already put
> both
> > of
> > >>>> those
> > >>>>>>>>>>>>>>> cases in the document. We can either partition on a
> > computed
> > >>>>>>>> column or
> > >>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with
> leaving
> > >> out
> > >>>> the
> > >>>>>>>>>>>>>>> partitioning by udf in the first version if you still
> have
> > >> some
> > >>>>>>>>>>>>>> concerns.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> As for your question Danny. It depends which partitioning
> > >>>> strategy
> > >>>>>>>> you
> > >>>>>>>>>>>>>> use.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would
> work
> > as
> > >>>> you
> > >>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not sure
> > >>>> though if
> > >>>>>>>> we
> > >>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink
> does
> > >> not
> > >>>> own
> > >>>>>>>> the
> > >>>>>>>>>>>>>>> data and the partitions are already an intrinsic property
> > of
> > >>>> the
> > >>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create topics,
> > but
> > >>>> we
> > >>>>>>>> just
> > >>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs ...
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
> > >> consistent
> > >>>> with
> > >>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was also
> > my
> > >>>>>>>> initial
> > >>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
> > afterwards
> > >>>> to
> > >>>>>>>> shorten
> > >>>>>>>>>>>>>>> the key.
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> Dawid
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
> > >>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think it
> > is
> > >> a
> > >>>>>>>> useful
> > >>>>>>>>>>>>>>> feature ~
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> About how the metadata outputs from source
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column
> push
> > >>>> down is
> > >>>>>>>>>>>>>>> another topic, this should not be a blocker but a
> > promotion,
> > >>>> if we
> > >>>>>>>> do
> > >>>>>>>>>>>>>> not
> > >>>>>>>>>>>>>>> have any filters on the computed column, there is no need
> > to
> > >>>> do any
> > >>>>>>>>>>>>>>> pushings; the source node just emit the complete record
> > with
> > >>>> full
> > >>>>>>>>>>>>>> metadata
> > >>>>>>>>>>>>>>> with the declared physical schema, then when generating
> the
> > >>>> virtual
> > >>>>>>>>>>>>>>> columns, we would extract the metadata info and output as
> > >> full
> > >>>>>>>>>>>>>> columns(with
> > >>>>>>>>>>>>>>> full schema).
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> About the type of metadata column
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST, they
> > are
> > >>>>>>>> symantic
> > >>>>>>>>>>>>>>> equivalent though, explict type is more straight-forward
> > and
> > >>>> we can
> > >>>>>>>>>>>>>> declare
> > >>>>>>>>>>>>>>> the nullable attribute there.
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column
> VS
> > >>>> option
> > >>>>>>>> B:
> > >>>>>>>>>>>>>>> partitioning with just a function
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>     From the FLIP, it seems that B's partitioning is
> just
> > a
> > >>>> strategy
> > >>>>>>>> when
> > >>>>>>>>>>>>>>> writing data, the partiton column is not included in the
> > >> table
> > >>>>>>>> schema,
> > >>>>>>>>>>>>>> so
> > >>>>>>>>>>>>>>> it's just useless when reading from that.
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the
> partition
> > >>>> column
> > >>>>>>>> when
> > >>>>>>>>>>>>>>> selecting from the table(but insert into)
> > >>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we
> want
> > >> to
> > >>>>>>>> persist
> > >>>>>>>>>>>>>>> that
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
> > both, i
> > >>>> saw
> > >>>>>>>> that
> > >>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
> > >> PARTITIONS
> > >>>>>>>> num, and
> > >>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
> > >> partition
> > >>>> in
> > >>>>>>>> which
> > >>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>> record is stored is partition number N, where N =
> MOD(expr,
> > >>>> num),
> > >>>>>>>> for
> > >>>>>>>>>>>>>> your
> > >>>>>>>>>>>>>>> design, which partiton the record would persist ?
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> [1]
> > >>>>>>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> > >>>>>>>>>>>>>>>> [2]
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>
> > >>>>
> > >>
> >
> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> > >>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>>>>> Danny Chan
> > >>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
> > >>>> [hidden email]
> > >>>>>>>>>>>>>>> ，写道：
> > >>>>>>>>>>>>>>>>> Hi Jark,
> > >>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to FLIP-63
> > >>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
> > >>>> properties.
> > >>>>>>>>>>>>>>> Therefore you have the key.format.type.
> > >>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
> > >> (prefixing
> > >>>> with
> > >>>>>>>>>>>>>>> connector or kafka). I should've put that into an
> > >>>> Option/Rejected
> > >>>>>>>>>>>>>>> alternatives.
> > >>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
> > properties.
> > >>>> Why I
> > >>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
> > version
> > >>>> is
> > >>>>>>>> that
> > >>>>>>>>>>>>>>> actually all the properties in the WITH section are
> > connector
> > >>>>>>>>>>>>>> properties.
> > >>>>>>>>>>>>>>> Even format is in the end a connector property as some of
> > the
> > >>>>>>>> sources
> > >>>>>>>>>>>>>> might
> > >>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
> > prefix
> > >> is
> > >>>>>>>> that it
> > >>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
> > >>>> properties
> > >>>>>>>> with
> > >>>>>>>>>>>>>>> connector (or if we go with FLINK-12557: elasticsearch):
> > >>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
> > >>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
> > >>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
> > >>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
> > >>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
> > >>>> approach
> > >>>>>>>> in the
> > >>>>>>>>>>>>>>> community.
> > >>>>>>>>>>>>>>>>> Ad in-line comments:
> > >>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include` property.
> > It
> > >>>>>>>> should be
> > >>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested in
> > the
> > >>>>>>>> comment,
> > >>>>>>>>>>>>>>> right?
> > >>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
> > >> column.
> > >>>> I
> > >>>>>>>> think
> > >>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
> > >>>> expression
> > >>>>>>>> and
> > >>>>>>>>>>>>>> later
> > >>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason
> is
> > I
> > >>>> think
> > >>>>>>>> this
> > >>>>>>>>>>>>>> way
> > >>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs
> when
> > >>>> working
> > >>>>>>>> with
> > >>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
> > offset, i
> > >>>>>>>> think it's
> > >>>>>>>>>>>>>>> better to pushdown long rather than string. This could
> let
> > us
> > >>>> push
> > >>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
> > >>>> Otherwise we
> > >>>>>>>> would
> > >>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
> > cast(offset,
> > >>>> long)
> > >>>>>>>> <
> > >>>>>>>>>>>>>> 59382.
> > >>>>>>>>>>>>>>> Moreover I think we need to introduce the type for
> computed
> > >>>> columns
> > >>>>>>>>>>>>>> anyway
> > >>>>>>>>>>>>>>> to support functions that infer output type based on
> > expected
> > >>>>>>>> return
> > >>>>>>>>>>>>>> type.
> > >>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
> > SYSTEM_METADATA
> > >>>> would
> > >>>>>>>> have
> > >>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible
> the
> > >>>> planner
> > >>>>>>>>>>>>>> should
> > >>>>>>>>>>>>>>> fail. As far as I know computed columns push down will be
> > >> part
> > >>>> of
> > >>>>>>>> source
> > >>>>>>>>>>>>>>> rework, won't it? ;)
> > >>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
> > >>>> completely
> > >>>>>>>>>>>>>>> orthogonal. In my current proposal you can also partition
> > by
> > >> a
> > >>>>>>>> computed
> > >>>>>>>>>>>>>>> column. The difference between using a udf in partitioned
> > by
> > >> vs
> > >>>>>>>>>>>>>> partitioned
> > >>>>>>>>>>>>>>> by a computed column is that when you partition by a
> > computed
> > >>>>>>>> column
> > >>>>>>>>>>>>>> this
> > >>>>>>>>>>>>>>> column must be also computed when reading the table. If
> you
> > >>>> use a
> > >>>>>>>> udf in
> > >>>>>>>>>>>>>>> the partitioned by, the expression is computed only when
> > >>>> inserting
> > >>>>>>>> into
> > >>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>> table.
> > >>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking
> forward
> > >> for
> > >>>>>>>> further
> > >>>>>>>>>>>>>>> suggestions.
> > >>>>>>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>>>>>> Dawid
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
> > >>>>>>>>>>>>>>>>>> Hi,
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
> > Reaing
> > >>>>>>>> metadata
> > >>>>>>>>>>>>>> and
> > >>>>>>>>>>>>>>>>>> key-part information from source is an important
> feature
> > >> for
> > >>>>>>>>>>>>>> streaming
> > >>>>>>>>>>>>>>>>>> users.
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
> > >>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of
> introducing
> > >>>> HEADER
> > >>>>>>>>>>>>>>> keyword as
> > >>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
> > >>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
> > Maybe
> > >> we
> > >>>>>>>> should
> > >>>>>>>>>>>>>>> add a
> > >>>>>>>>>>>>>>>>>> section to explain what's the relationship between
> them.
> > >>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION be
> > used
> > >>>> on
> > >>>>>>>> the
> > >>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
> > >>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink
> SQL.
> > >>>> Shall we
> > >>>>>>>>>>>>>> make
> > >>>>>>>>>>>>>>> the
> > >>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
> > >>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
> > >>>> (actually, I
> > >>>>>>>>>>>>>>> prefer
> > >>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
> > >>>> properties
> > >>>>>>>>>>>>>>> FLINK-12557)
> > >>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users
> > that
> > >>>> the
> > >>>>>>>>>>>>>> field
> > >>>>>>>>>>>>>>> is
> > >>>>>>>>>>>>>>>>>> a rowtime attribute.
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> Thanks,
> > >>>>>>>>>>>>>>>>>> Jark
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> > >>>>>>>>>>>>>> [hidden email]>
> > >>>>>>>>>>>>>>>>>> wrote:
> > >>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> Hi,
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
> > enable
> > >>>>>>>> reading
> > >>>>>>>>>>>>>> table
> > >>>>>>>>>>>>>>>>>>> columns from different parts of source records.
> Besides
> > >> the
> > >>>>>>>> main
> > >>>>>>>>>>>>>>> payload
> > >>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose
> additional
> > >>>>>>>>>>>>>> information. It
> > >>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
> > >>>> ingestion
> > >>>>>>>> time
> > >>>>>>>>>>>>>> or a
> > >>>>>>>>>>>>>>>>>>> read and write parts of the record that contain data
> > but
> > >>>>>>>>>>>>>> additionally
> > >>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
> > etc.),
> > >>>> e.g.
> > >>>>>>>> key
> > >>>>>>>>>>>>>> or
> > >>>>>>>>>>>>>>>>>>> timestamp in Kafka.
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> We should make it possible to read and write data
> from
> > >> all
> > >>>> of
> > >>>>>>>> those
> > >>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
> > >> partitioning
> > >>>>>>>> data,
> > >>>>>>>>>>>>>> for
> > >>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
> > >> partitioning
> > >>>> when
> > >>>>>>>>>>>>>>> writing
> > >>>>>>>>>>>>>>>>>>> data out.
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> You can access the FLIP here:
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>
> > >>>>
> > >>
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> Best,
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>> Dawid
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>>
> > >>>>>>>>>>>>>>
> > >>>>>>>>>>>>>
> > >>>>>>>>>>>>
> > >>>>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>>
> > >>>>>>>>
> > >>>>>>>>
> > >>>>>>
> > >>>>>
> > >>>>>
> > >>>>
> > >>>>
> > >>>
> > >>
> > >>
> > >
> >
> >
>

[Jark Wu-2]

Hi Danny,

This is not Oracle and MySQL computed column syntax, because there is no
"AS" after the type.

Hi everyone,

If we want to use "offset INT SYSTEM_METADATA("offset")", then I think we
must further discuss about "PERSISED" or "VIRTUAL" keyword for query-sink
schema problem.
Personally, I think we can use a shorter keyword "METADATA" for
"SYSTEM_METADATA". Because "SYSTEM_METADATA" sounds like a system function
and confuse users this looks like a computed column.


Best,
Jark



On Wed, 9 Sep 2020 at 17:23, Danny Chan <[hidden email]> wrote:

> "offset INT SYSTEM_METADATA("offset")"
>
> This is actually Oracle or MySQL style computed column syntax.
>
> "You are right that one could argue that "timestamp", "headers" are
> something like "key" and "value""
>
> I have the same feeling, both key value and headers timestamp are *real*
> data
> stored in the consumed record, they are not computed or generated.
>
> "Trying to solve everything via properties sounds rather like a hack to
> me"
>
> Things are not that hack if we can unify the routines or the definitions
> (all from the computed column way or all from the table options), i also
> think that it is a hacky that we mix in 2 kinds of syntax for different
> kinds of metadata (read-only and read-write). In this FLIP, we declare the
> Kafka key fields with table options but SYSTEM_METADATA for other metadata,
> that is a hacky thing or something in-consistent.
>
> Kurt Young <[hidden email]> 于2020年9月9日周三 下午4:48写道：
>
> >  I would vote for `offset INT SYSTEM_METADATA("offset")`.
> >
> > I don't think we can stick with the SQL standard in DDL part forever,
> > especially as there are more and more
> > requirements coming from different connectors and external systems.
> >
> > Best,
> > Kurt
> >
> >
> > On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]> wrote:
> >
> > > Hi Jark,
> > >
> > > now we are back at the original design proposed by Dawid :D Yes, we
> > > should be cautious about adding new syntax. But the length of this
> > > discussion shows that we are looking for a good long-term solution. In
> > > this case I would rather vote for a deep integration into the syntax.
> > >
> > > Computed columns are also not SQL standard compliant. And our DDL is
> > > neither, so we have some degree of freedom here.
> > >
> > > Trying to solve everything via properties sounds rather like a hack to
> > > me. You are right that one could argue that "timestamp", "headers" are
> > > something like "key" and "value". However, mixing
> > >
> > > `offset AS SYSTEM_METADATA("offset")`
> > >
> > > and
> > >
> > > `'timestamp.field' = 'ts'`
> > >
> > > looks more confusing to users that an explicit
> > >
> > > `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
> > >
> > > or
> > >
> > > `offset INT SYSTEM_METADATA("offset")`
> > >
> > > that is symetric for both source and sink.
> > >
> > > What do others think?
> > >
> > > Regards,
> > > Timo
> > >
> > >
> > > On 09.09.20 10:09, Jark Wu wrote:
> > > > Hi everyone,
> > > >
> > > > I think we have a conclusion that the writable metadata shouldn't be
> > > > defined as a computed column, but a normal column.
> > > >
> > > > "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the
> > approaches.
> > > > However, it is not SQL standard compliant, we need to be cautious
> > enough
> > > > when adding new syntax.
> > > > Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
> > > > resolve the query-sink schema problem if it is read-only metadata.
> That
> > > > adds more stuff to learn for users.
> > > >
> > > >>From my point of view, the "timestamp", "headers" are something like
> > > "key"
> > > > and "value" that stores with the real data. So why not define the
> > > > "timestamp" in the same way with "key" by using a "timestamp.field"
> > > > connector option?
> > > > On the other side, the read-only metadata, such as "offset",
> shouldn't
> > be
> > > > defined as a normal column. So why not use the existing computed
> column
> > > > syntax for such metadata? Then we don't have the query-sink schema
> > > problem.
> > > > So here is my proposal:
> > > >
> > > > CREATE TABLE kafka_table (
> > > >    id BIGINT,
> > > >    name STRING,
> > > >    col1 STRING,
> > > >    col2 STRING,
> > > >    ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field,
> so
> > > can
> > > > be read and written.
> > > >    offset AS SYSTEM_METADATA("offset")
> > > > ) WITH (
> > > >    'connector' = 'kafka',
> > > >    'topic' = 'test-topic',
> > > >    'key.fields' = 'id, name',
> > > >    'key.format' = 'csv',
> > > >    'value.format' = 'avro',
> > > >    'timestamp.field' = 'ts'    -- define the mapping of Kafka
> timestamp
> > > > );
> > > >
> > > > INSERT INTO kafka_table
> > > > SELECT id, name, col1, col2, rowtime FROM another_table;
> > > >
> > > > I think this can solve all the problems without introducing any new
> > > syntax.
> > > > The only minor disadvantage is that we separate the definition
> > way/syntax
> > > > of read-only metadata and read-write fields.
> > > > However, I don't think this is a big problem.
> > > >
> > > > Best,
> > > > Jark
> > > >
> > > >
> > > > On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]>
> wrote:
> > > >
> > > >> Hi Kurt,
> > > >>
> > > >> thanks for sharing your opinion. I'm totally up for not reusing
> > computed
> > > >> columns. I think Jark was a big supporter of this syntax, @Jark are
> > you
> > > >> fine with this as well? The non-computed column approach was only a
> > > >> "slightly rejected alternative".
> > > >>
> > > >> Furthermore, we would need to think about how such a new design
> > > >> influences the LIKE clause though.
> > > >>
> > > >> However, we should still keep the `PERSISTED` keyword as it
> influences
> > > >> the query->sink schema. If you look at the list of metadata for
> > existing
> > > >> connectors and formats, we currently offer only two writable
> metadata
> > > >> fields. Otherwise, one would need to declare two tables whenever a
> > > >> metadata columns is read (one for the source, one for the sink).
> This
> > > >> can be quite inconvientient e.g. for just reading the topic.
> > > >>
> > > >> Regards,
> > > >> Timo
> > > >>
> > > >>
> > > >> On 09.09.20 08:52, Kurt Young wrote:
> > > >>> I also share the concern that reusing the computed column syntax
> but
> > > have
> > > >>> different semantics
> > > >>> would confuse users a lot.
> > > >>>
> > > >>> Besides, I think metadata fields are conceptually not the same with
> > > >>> computed columns. The metadata
> > > >>> field is a connector specific thing and it only contains the
> > > information
> > > >>> that where does the field come
> > > >>> from (during source) or where does the field need to write to
> (during
> > > >>> sink). It's more similar with normal
> > > >>> fields, with assumption that all these fields need going to the
> data
> > > >> part.
> > > >>>
> > > >>> Thus I'm more lean to the rejected alternative that Timo mentioned.
> > > And I
> > > >>> think we don't need the
> > > >>> PERSISTED keyword, SYSTEM_METADATA should be enough.
> > > >>>
> > > >>> During implementation, the framework only needs to pass such
> <field,
> > > >>> metadata field> information to the
> > > >>> connector, and the logic of handling such fields inside the
> connector
> > > >>> should be straightforward.
> > > >>>
> > > >>> Regarding the downside Timo mentioned:
> > > >>>
> > > >>>> The disadvantage is that users cannot call UDFs or parse
> timestamps.
> > > >>>
> > > >>> I think this is fairly simple to solve. Since the metadata field
> > isn't
> > > a
> > > >>> computed column anymore, we can support
> > > >>> referencing such fields in the computed column. For example:
> > > >>>
> > > >>> CREATE TABLE kafka_table (
> > > >>>        id BIGINT,
> > > >>>        name STRING,
> > > >>>        timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
> > > >> timestamp
> > > >>> field from metadata
> > > >>>        ts AS to_timestamp(timestamp) // normal computed column,
> parse
> > > the
> > > >>> string to TIMESTAMP type by using the metadata field
> > > >>> ) WITH (
> > > >>>       ...
> > > >>> )
> > > >>>
> > > >>> Best,
> > > >>> Kurt
> > > >>>
> > > >>>
> > > >>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
> > > wrote:
> > > >>>
> > > >>>> Hi Leonard,
> > > >>>>
> > > >>>> the only alternative I see is that we introduce a concept that is
> > > >>>> completely different to computed columns. This is also mentioned
> in
> > > the
> > > >>>> rejected alternative section of the FLIP. Something like:
> > > >>>>
> > > >>>> CREATE TABLE kafka_table (
> > > >>>>        id BIGINT,
> > > >>>>        name STRING,
> > > >>>>        timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
> > > >>>>        headers MAP<STRING, BYTES> SYSTEM_METADATA("headers")
> > PERSISTED
> > > >>>> ) WITH (
> > > >>>>       ...
> > > >>>> )
> > > >>>>
> > > >>>> This way we would avoid confusion at all and can easily map
> columns
> > to
> > > >>>> metadata columns. The disadvantage is that users cannot call UDFs
> or
> > > >>>> parse timestamps. This would need to be done in a real computed
> > > column.
> > > >>>>
> > > >>>> I'm happy about better alternatives.
> > > >>>>
> > > >>>> Regards,
> > > >>>> Timo
> > > >>>>
> > > >>>>
> > > >>>> On 08.09.20 15:37, Leonard Xu wrote:
> > > >>>>> HI, Timo
> > > >>>>>
> > > >>>>> Thanks for driving this FLIP.
> > > >>>>>
> > > >>>>> Sorry but I have a concern about Writing metadata via
> > > DynamicTableSink
> > > >>>> section:
> > > >>>>>
> > > >>>>> CREATE TABLE kafka_table (
> > > >>>>>      id BIGINT,
> > > >>>>>      name STRING,
> > > >>>>>      timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
> > > >> PERSISTED,
> > > >>>>>      headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
> > > BYTES>)
> > > >>>> PERSISTED
> > > >>>>> ) WITH (
> > > >>>>>      ...
> > > >>>>> )
> > > >>>>> An insert statement could look like:
> > > >>>>>
> > > >>>>> INSERT INTO kafka_table VALUES (
> > > >>>>>      (1, "ABC", 1599133672, MAP('checksum',
> computeChecksum(...)))
> > > >>>>> )
> > > >>>>>
> > > >>>>> The proposed INERT syntax does not make sense to me, because it
> > > >> contains
> > > >>>> computed(generated) column.
> > > >>>>> Both SQL server and Postgresql do not allow to insert value to
> > > computed
> > > >>>> columns even they are persisted, this boke the generated column
> > > >> semantics
> > > >>>> and may confuse user much.
> > > >>>>>
> > > >>>>> For SQL server computed column[1]:
> > > >>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT
> NULL ]
> > > >> ]...
> > > >>>>>> NOTE: A computed column cannot be the target of an INSERT or
> > UPDATE
> > > >>>> statement.
> > > >>>>>
> > > >>>>> For Postgresql generated column[2]:
> > > >>>>>>     height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
> > STORED
> > > >>>>>> NOTE: A generated column cannot be written to directly. In
> INSERT
> > or
> > > >>>> UPDATE commands, a value cannot be specified for a generated
> column,
> > > but
> > > >>>> the keyword DEFAULT may be specified.
> > > >>>>>
> > > >>>>> It shouldn't be allowed to set/update value for generated column
> > > after
> > > >>>> lookup the SQL 2016:
> > > >>>>>> <insert statement> ::=
> > > >>>>>> INSERT INTO <insertion target> <insert columns and source>
> > > >>>>>>
> > > >>>>>> If <contextually typed table value constructor> CTTVC is
> > specified,
> > > >>>> then every <contextually typed row
> > > >>>>>> value constructor element> simply contained in CTTVC whose
> > > >> positionally
> > > >>>> corresponding <column name>
> > > >>>>>> in <insert column list> references a column of which some
> > underlying
> > > >>>> column is a generated column shall
> > > >>>>>> be a <default specification>.
> > > >>>>>> A <default specification> specifies the default value of some
> > > >>>> associated item.
> > > >>>>>
> > > >>>>>
> > > >>>>> [1]
> > > >>>>
> > > >>
> > >
> >
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> > > >>>> <
> > > >>>>
> > > >>
> > >
> >
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> > > >>>>>
> > > >>>>> [2]
> https://www.postgresql.org/docs/12/ddl-generated-columns.html
> > <
> > > >>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
> > > >>>>>
> > > >>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
> > > >>>>>>
> > > >>>>>> Hi Jark,
> > > >>>>>>
> > > >>>>>> according to Flink's and Calcite's casting definition in [1][2]
> > > >>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If
> > not,
> > > >> we
> > > >>>> will make it possible ;-)
> > > >>>>>>
> > > >>>>>> I'm aware of DeserializationSchema.getProducedType but I think
> > that
> > > >>>> this method is actually misplaced. The type should rather be
> passed
> > to
> > > >> the
> > > >>>> source itself.
> > > >>>>>>
> > > >>>>>> For our Kafka SQL source, we will also not use this method
> because
> > > the
> > > >>>> Kafka source will add own metadata in addition to the
> > > >>>> DeserializationSchema. So DeserializationSchema.getProducedType
> will
> > > >> never
> > > >>>> be read.
> > > >>>>>>
> > > >>>>>> For now I suggest to leave out the `DataType` from
> > > >>>> DecodingFormat.applyReadableMetadata. Also because the format's
> > > physical
> > > >>>> type is passed later in `createRuntimeDecoder`. If necessary, it
> can
> > > be
> > > >>>> computed manually by consumedType + metadata types. We will
> provide
> > a
> > > >>>> metadata utility class for that.
> > > >>>>>>
> > > >>>>>> Regards,
> > > >>>>>> Timo
> > > >>>>>>
> > > >>>>>>
> > > >>>>>> [1]
> > > >>>>
> > > >>
> > >
> >
> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
> > > >>>>>> [2]
> > > >>>>
> > > >>
> > >
> >
> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
> > > >>>>>>
> > > >>>>>>
> > > >>>>>> On 08.09.20 10:52, Jark Wu wrote:
> > > >>>>>>> Hi Timo,
> > > >>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I
> > just
> > > >>>> noticed
> > > >>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL
> TIME
> > > >>>> ZONE".
> > > >>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH
> LOCAL
> > > >> TIME
> > > >>>>>>> ZONE" as the defined type of Kafka timestamp? I think this
> makes
> > > >> sense,
> > > >>>>>>> because it represents the milli-seconds since epoch.
> > > >>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I
> don't
> > > >> think
> > > >>>> so.
> > > >>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus
> > the
> > > >>>>>>> implementation needs to return an output TypeInfo.
> > > >>>>>>> Besides, FlinkKafkaConsumer also
> > > >>>>>>> calls DeserializationSchema.getProducedType as the produced
> type
> > of
> > > >> the
> > > >>>>>>> source function [1].
> > > >>>>>>> Best,
> > > >>>>>>> Jark
> > > >>>>>>> [1]:
> > > >>>>>>>
> > > >>>>
> > > >>
> > >
> >
> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
> > > >>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
> > > >> wrote:
> > > >>>>>>>> Hi everyone,
> > > >>>>>>>>
> > > >>>>>>>> I updated the FLIP again and hope that I could address the
> > > mentioned
> > > >>>>>>>> concerns.
> > > >>>>>>>>
> > > >>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that
> ts_ms
> > > and
> > > >>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
> > > expose
> > > >>>> the
> > > >>>>>>>> most commonly used properties separately. So frequently used
> > > >>>> properties
> > > >>>>>>>> are not hidden in the MAP anymore:
> > > >>>>>>>>
> > > >>>>>>>> debezium-json.ingestion-timestamp
> > > >>>>>>>> debezium-json.source.timestamp
> > > >>>>>>>> debezium-json.source.database
> > > >>>>>>>> debezium-json.source.schema
> > > >>>>>>>> debezium-json.source.table
> > > >>>>>>>>
> > > >>>>>>>> However, since other properties depend on the used
> > > connector/vendor,
> > > >>>> the
> > > >>>>>>>> remaining options are stored in:
> > > >>>>>>>>
> > > >>>>>>>> debezium-json.source.properties
> > > >>>>>>>>
> > > >>>>>>>> And accessed with:
> > > >>>>>>>>
> > > >>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
> > > >> MAP<STRING,
> > > >>>>>>>> STRING>)['table']
> > > >>>>>>>>
> > > >>>>>>>> Otherwise it is not possible to figure out the value and
> column
> > > type
> > > >>>>>>>> during validation.
> > > >>>>>>>>
> > > >>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I
> > added
> > > a
> > > >>>>>>>> dedicacated sub-section to the FLIP:
> > > >>>>>>>>
> > > >>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
> > > >> casting
> > > >>>> we
> > > >>>>>>>> allow explicit casting to a target data type:
> > > >>>>>>>>
> > > >>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3)
> > WITH
> > > >>>> LOCAL
> > > >>>>>>>> TIME ZONE)
> > > >>>>>>>>
> > > >>>>>>>> A connector still produces and consumes the data type returned
> > by
> > > >>>>>>>> `listMetadata()`. The planner will insert necessary explicit
> > > casts.
> > > >>>>>>>>
> > > >>>>>>>> In any case, the user must provide a CAST such that the
> computed
> > > >>>> column
> > > >>>>>>>> receives a valid data type when constructing the table schema.
> > > >>>>>>>>
> > > >>>>>>>> "I don't see a reason why
> `DecodingFormat#applyReadableMetadata`
> > > >>>> needs a
> > > >>>>>>>> DataType argument."
> > > >>>>>>>>
> > > >>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
> > > always
> > > >>>>>>>> executed locally. It is the source that needs TypeInfo for
> > > >> serializing
> > > >>>>>>>> the record to the next operator. And that's this is what we
> > > provide.
> > > >>>>>>>>
> > > >>>>>>>> @Danny:
> > > >>>>>>>>
> > > >>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
> > > >>>>>>>>
> > > >>>>>>>> We can also use some other means to represent an UNKNOWN data
> > > type.
> > > >> In
> > > >>>>>>>> the Flink type system, we use the NullType for it. The
> important
> > > >> part
> > > >>>> is
> > > >>>>>>>> that the final data type is known for the entire computed
> > column.
> > > >> As I
> > > >>>>>>>> mentioned before, I would avoid the suggested option b) that
> > would
> > > >> be
> > > >>>>>>>> similar to your suggestion. The CAST should be enough and
> allows
> > > for
> > > >>>>>>>> complex expressions in the computed column. Option b) would
> need
> > > >>>> parser
> > > >>>>>>>> changes.
> > > >>>>>>>>
> > > >>>>>>>> Regards,
> > > >>>>>>>> Timo
> > > >>>>>>>>
> > > >>>>>>>>
> > > >>>>>>>>
> > > >>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
> > > >>>>>>>>> Hi, Timo
> > > >>>>>>>>>
> > > >>>>>>>>> Thanks for you explanation and update,  I have only one
> > question
> > > >> for
> > > >>>>>>>> the latest FLIP.
> > > >>>>>>>>>
> > > >>>>>>>>> About the MAP<STRING, STRING> DataType of key
> > > >>>> 'debezium-json.source', if
> > > >>>>>>>> user want to use the table name metadata, they need to write:
> > > >>>>>>>>> tableName STRING AS
> CAST(SYSTEM_METADATA('debeuim-json.source')
> > > AS
> > > >>>>>>>> MAP<STRING, STRING>)['table']
> > > >>>>>>>>>
> > > >>>>>>>>> the expression is a little complex for user, Could we only
> > > support
> > > >>>>>>>> necessary metas with simple DataType as following?
> > > >>>>>>>>> tableName STRING AS
> > > >>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
> > > >>>>>>>> STRING),
> > > >>>>>>>>> transactionTime LONG AS
> > > >>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
> > > >>>>>>>>>
> > > >>>>>>>>> In this way, we can simplify the expression, the mainly used
> > > >>>> metadata in
> > > >>>>>>>> changelog format may include
> > > >>>> 'database','table','source.ts_ms','ts_ms' from
> > > >>>>>>>> my side,
> > > >>>>>>>>> maybe we could only support them at first version.
> > > >>>>>>>>>
> > > >>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m
> > willing
> > > >> to
> > > >>>>>>>> take some subtasks in next development if necessary.
> > > >>>>>>>>>
> > > >>>>>>>>> Debezium:
> > > >>>>>>>>> {
> > > >>>>>>>>>       "before": null,
> > > >>>>>>>>>       "after": {  "id": 101,"name": "scooter"},
> > > >>>>>>>>>       "source": {
> > > >>>>>>>>>         "db": "inventory",                  # 1. database
> name
> > > the
> > > >>>>>>>> changelog belongs to.
> > > >>>>>>>>>         "table": "products",                # 2. table name
> the
> > > >>>> changelog
> > > >>>>>>>> belongs to.
> > > >>>>>>>>>         "ts_ms": 1589355504100,             # 3. timestamp of
> > the
> > > >>>> change
> > > >>>>>>>> happened in database system, i.e.: transaction time in
> database.
> > > >>>>>>>>>         "connector": "mysql",
> > > >>>>>>>>>         ….
> > > >>>>>>>>>       },
> > > >>>>>>>>>       "ts_ms": 1589355606100,              # 4. timestamp
> when
> > > the
> > > >>>> debezium
> > > >>>>>>>> processed the changelog.
> > > >>>>>>>>>       "op": "c",
> > > >>>>>>>>>       "transaction": null
> > > >>>>>>>>> }
> > > >>>>>>>>>
> > > >>>>>>>>> Canal:
> > > >>>>>>>>> {
> > > >>>>>>>>>       "data": [{  "id": "102", "name": "car battery" }],
> > > >>>>>>>>>       "database": "inventory",      # 1. database name the
> > > changelog
> > > >>>>>>>> belongs to.
> > > >>>>>>>>>       "table": "products",          # 2. table name the
> > changelog
> > > >>>> belongs
> > > >>>>>>>> to.
> > > >>>>>>>>>       "es": 1589374013000,          # 3. execution time of
> the
> > > >> change
> > > >>>> in
> > > >>>>>>>> database system, i.e.: transaction time in database.
> > > >>>>>>>>>       "ts": 1589374013680,          # 4. timestamp when the
> > > cannal
> > > >>>>>>>> processed the changelog.
> > > >>>>>>>>>       "isDdl": false,
> > > >>>>>>>>>       "mysqlType": {},
> > > >>>>>>>>>       ....
> > > >>>>>>>>> }
> > > >>>>>>>>>
> > > >>>>>>>>>
> > > >>>>>>>>> Best
> > > >>>>>>>>> Leonard
> > > >>>>>>>>>
> > > >>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
> > > >>>>>>>>>>
> > > >>>>>>>>>> Thanks Timo ~
> > > >>>>>>>>>>
> > > >>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
> > > questions
> > > >>>> here:
> > > >>>>>>>>>>
> > > >>>>>>>>>>
> > > >>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a
> valid
> > > >>>> read-only
> > > >>>>>>>> computed column for Kafka and can be extracted by the
> planner.”
> > > >>>>>>>>>>
> > > >>>>>>>>>>
> > > >>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ?
> > Usually
> > > an
> > > >>>>>>>> expression return type can be inferred automatically. But I
> > guess
> > > >>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
> > > >> actually
> > > >>>> does
> > > >>>>>>>> not have a specific return type.
> > > >>>>>>>>>>
> > > >>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
> > > >>>>>>>>>>
> > > >>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
> > > >> [VIRTUAL]
> > > >>>>>>>>>> Which is more straight-forward.
> > > >>>>>>>>>>
> > > >>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by
> > default”
> > > >>>>>>>>>>
> > > >>>>>>>>>> The default type should not be NULL because only NULL
> literal
> > > does
> > > >>>>>>>> that. Usually we use ANY as the type if we do not know the
> > > specific
> > > >>>> type in
> > > >>>>>>>> the SQL context. ANY means the physical value can be any java
> > > >> object.
> > > >>>>>>>>>>
> > > >>>>>>>>>> [1]
> > https://oracle-base.com/articles/11g/virtual-columns-11gr1
> > > >>>>>>>>>> [2]
> > > >>>>>>>>
> > > >>>>
> > > >>
> > >
> >
> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
> > > >>>>>>>>>>
> > > >>>>>>>>>> Best,
> > > >>>>>>>>>> Danny Chan
> > > >>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]
> > >，写道：
> > > >>>>>>>>>>> Hi everyone,
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> I completely reworked FLIP-107. It now covers the full
> story
> > > how
> > > >> to
> > > >>>>>>>> read
> > > >>>>>>>>>>> and write metadata from/to connectors and formats. It
> > considers
> > > >>>> all of
> > > >>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
> > > >>>> introduces
> > > >>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
> > > >>>> partitioning
> > > >>>>>>>>>>> for now.
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> Looking forward to your feedback.
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> Regards,
> > > >>>>>>>>>>> Timo
> > > >>>>>>>>>>>
> > > >>>>>>>>>>>
> > > >>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
> > > >>>>>>>>>>>> Sorry, forgot one question.
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal?
> > Like
> > > >> one
> > > >>>> can
> > > >>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> > > >>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users
> can
> > > not
> > > >>>>>>>> config to
> > > >>>>>>>>>>>> just ignore timestamp but keep key.
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> Best,
> > > >>>>>>>>>>>> Kurt
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <
> [hidden email]
> > >
> > > >>>> wrote:
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>>> Hi Dawid,
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> I have a couple of questions around key fields, actually
> I
> > > also
> > > >>>> have
> > > >>>>>>>> some
> > > >>>>>>>>>>>>> other questions but want to be focused on key fields
> first.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is
> > > this
> > > >>>>>>>> option only
> > > >>>>>>>>>>>>> valid during write operation? Because for
> > > >>>>>>>>>>>>> reading, I can't imagine how such options can be
> applied. I
> > > >> would
> > > >>>>>>>> expect
> > > >>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
> > > >>>>>>>>>>>>> to read and assign the key to a normal field?
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I
> want
> > > to
> > > >>>>>>>> propose we
> > > >>>>>>>>>>>>> can simplify the options to not introducing
> key.format.type
> > > and
> > > >>>>>>>>>>>>> other related options. I think a single "key.field" (not
> > > >> fields)
> > > >>>>>>>> would be
> > > >>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
> > > >>>>>>>>>>>>> want before sink.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
> > > >>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
> > > connector
> > > >>>> has a
> > > >>>>>>>>>>>>> concept
> > > >>>>>>>>>>>>> of key and values. The old parameter "format.type"
> already
> > > good
> > > >>>>>>>> enough to
> > > >>>>>>>>>>>>> use.
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> Best,
> > > >>>>>>>>>>>>> Kurt
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <
> [hidden email]>
> > > >>>> wrote:
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>>> Thanks Dawid,
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>> I have two more questions.
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> SupportsMetadata
> > > >>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I
> have
> > > >> some
> > > >>>>>>>> questions
> > > >>>>>>>>>>>>>> regarding to this interface.
> > > >>>>>>>>>>>>>> 1) How do the source know what the expected return type
> of
> > > >> each
> > > >>>>>>>> metadata?
> > > >>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the
> > existing
> > > >>>> physical
> > > >>>>>>>>>>>>>> fields?
> > > >>>>>>>>>>>>>> If yes, I would suggest to change the signature to
> > > >> `TableSource
> > > >>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
> > > >>>>>>>> metadataTypes)`
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
> > > >>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a
> > computed
> > > >>>> column
> > > >>>>>>>>>>>>>> expression? If yes, how to specify the return type of
> > > >>>>>>>> SYSTEM_METADATA?
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>> Best,
> > > >>>>>>>>>>>>>> Jark
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
> > > >>>>>>>> [hidden email]>
> > > >>>>>>>>>>>>>> wrote:
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> Hi,
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit
> the
> > > >>>> columns
> > > >>>>>>>> and I
> > > >>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I
> > see
> > > a
> > > >>>> need
> > > >>>>>>>> to
> > > >>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
> > > Jark.
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that.
> > One
> > > >>>>>>>> additional
> > > >>>>>>>>>>>>>>> things I would introduce is an
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> interface SupportsMetadata {
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
> > > >> metadataFields);
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> }
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> This way the source would have to declare/emit only the
> > > >>>> requested
> > > >>>>>>>>>>>>>>> metadata fields. In order not to clash with user
> defined
> > > >>>> fields.
> > > >>>>>>>> When
> > > >>>>>>>>>>>>>>> emitting the metadata field I would prepend the column
> > name
> > > >>>> with
> > > >>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
> > > >>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
> > > field
> > > >>>>>>>>>>>>>>> __system_partition to the schema. This would be never
> > > visible
> > > >>>> to
> > > >>>>>>>> the
> > > >>>>>>>>>>>>>>> user as it would be used only for the subsequent
> computed
> > > >>>> columns.
> > > >>>>>>>> If
> > > >>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with
> this
> > > >>>>>>>> description.
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state
> of
> > > the
> > > >>>>>>>> proposal.
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
> > > >>>> orthogonal. I
> > > >>>>>>>> would
> > > >>>>>>>>>>>>>>> leave out the STORED computed columns out of the
> > > discussion.
> > > >> I
> > > >>>>>>>> don't see
> > > >>>>>>>>>>>>>>> how do they relate to the partitioning. I already put
> > both
> > > of
> > > >>>> those
> > > >>>>>>>>>>>>>>> cases in the document. We can either partition on a
> > > computed
> > > >>>>>>>> column or
> > > >>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with
> > leaving
> > > >> out
> > > >>>> the
> > > >>>>>>>>>>>>>>> partitioning by udf in the first version if you still
> > have
> > > >> some
> > > >>>>>>>>>>>>>> concerns.
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> As for your question Danny. It depends which
> partitioning
> > > >>>> strategy
> > > >>>>>>>> you
> > > >>>>>>>>>>>>>> use.
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would
> > work
> > > as
> > > >>>> you
> > > >>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not
> sure
> > > >>>> though if
> > > >>>>>>>> we
> > > >>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink
> > does
> > > >> not
> > > >>>> own
> > > >>>>>>>> the
> > > >>>>>>>>>>>>>>> data and the partitions are already an intrinsic
> property
> > > of
> > > >>>> the
> > > >>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create
> topics,
> > > but
> > > >>>> we
> > > >>>>>>>> just
> > > >>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs
> ...
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
> > > >> consistent
> > > >>>> with
> > > >>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was
> also
> > > my
> > > >>>>>>>> initial
> > > >>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
> > > afterwards
> > > >>>> to
> > > >>>>>>>> shorten
> > > >>>>>>>>>>>>>>> the key.
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> Best,
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> Dawid
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
> > > >>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think
> it
> > > is
> > > >> a
> > > >>>>>>>> useful
> > > >>>>>>>>>>>>>>> feature ~
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> About how the metadata outputs from source
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column
> > push
> > > >>>> down is
> > > >>>>>>>>>>>>>>> another topic, this should not be a blocker but a
> > > promotion,
> > > >>>> if we
> > > >>>>>>>> do
> > > >>>>>>>>>>>>>> not
> > > >>>>>>>>>>>>>>> have any filters on the computed column, there is no
> need
> > > to
> > > >>>> do any
> > > >>>>>>>>>>>>>>> pushings; the source node just emit the complete record
> > > with
> > > >>>> full
> > > >>>>>>>>>>>>>> metadata
> > > >>>>>>>>>>>>>>> with the declared physical schema, then when generating
> > the
> > > >>>> virtual
> > > >>>>>>>>>>>>>>> columns, we would extract the metadata info and output
> as
> > > >> full
> > > >>>>>>>>>>>>>> columns(with
> > > >>>>>>>>>>>>>>> full schema).
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> About the type of metadata column
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST,
> they
> > > are
> > > >>>>>>>> symantic
> > > >>>>>>>>>>>>>>> equivalent though, explict type is more
> straight-forward
> > > and
> > > >>>> we can
> > > >>>>>>>>>>>>>> declare
> > > >>>>>>>>>>>>>>> the nullable attribute there.
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column
> > VS
> > > >>>> option
> > > >>>>>>>> B:
> > > >>>>>>>>>>>>>>> partitioning with just a function
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>     From the FLIP, it seems that B's partitioning is
> > just
> > > a
> > > >>>> strategy
> > > >>>>>>>> when
> > > >>>>>>>>>>>>>>> writing data, the partiton column is not included in
> the
> > > >> table
> > > >>>>>>>> schema,
> > > >>>>>>>>>>>>>> so
> > > >>>>>>>>>>>>>>> it's just useless when reading from that.
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the
> > partition
> > > >>>> column
> > > >>>>>>>> when
> > > >>>>>>>>>>>>>>> selecting from the table(but insert into)
> > > >>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we
> > want
> > > >> to
> > > >>>>>>>> persist
> > > >>>>>>>>>>>>>>> that
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
> > > both, i
> > > >>>> saw
> > > >>>>>>>> that
> > > >>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
> > > >> PARTITIONS
> > > >>>>>>>> num, and
> > > >>>>>>>>>>>>>> the
> > > >>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
> > > >> partition
> > > >>>> in
> > > >>>>>>>> which
> > > >>>>>>>>>>>>>> the
> > > >>>>>>>>>>>>>>> record is stored is partition number N, where N =
> > MOD(expr,
> > > >>>> num),
> > > >>>>>>>> for
> > > >>>>>>>>>>>>>> your
> > > >>>>>>>>>>>>>>> design, which partiton the record would persist ?
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> [1]
> > > >>>>>>>>
> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> > > >>>>>>>>>>>>>>>> [2]
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>
> > > >>>>
> > > >>
> > >
> >
> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> > > >>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>> Best,
> > > >>>>>>>>>>>>>>>> Danny Chan
> > > >>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
> > > >>>> [hidden email]
> > > >>>>>>>>>>>>>>> ，写道：
> > > >>>>>>>>>>>>>>>>> Hi Jark,
> > > >>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to
> FLIP-63
> > > >>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
> > > >>>> properties.
> > > >>>>>>>>>>>>>>> Therefore you have the key.format.type.
> > > >>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
> > > >> (prefixing
> > > >>>> with
> > > >>>>>>>>>>>>>>> connector or kafka). I should've put that into an
> > > >>>> Option/Rejected
> > > >>>>>>>>>>>>>>> alternatives.
> > > >>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
> > > properties.
> > > >>>> Why I
> > > >>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
> > > version
> > > >>>> is
> > > >>>>>>>> that
> > > >>>>>>>>>>>>>>> actually all the properties in the WITH section are
> > > connector
> > > >>>>>>>>>>>>>> properties.
> > > >>>>>>>>>>>>>>> Even format is in the end a connector property as some
> of
> > > the
> > > >>>>>>>> sources
> > > >>>>>>>>>>>>>> might
> > > >>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
> > > prefix
> > > >> is
> > > >>>>>>>> that it
> > > >>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
> > > >>>> properties
> > > >>>>>>>> with
> > > >>>>>>>>>>>>>>> connector (or if we go with FLINK-12557:
> elasticsearch):
> > > >>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
> > > >>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
> > > >>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
> > > >>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
> > > >>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
> > > >>>> approach
> > > >>>>>>>> in the
> > > >>>>>>>>>>>>>>> community.
> > > >>>>>>>>>>>>>>>>> Ad in-line comments:
> > > >>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include`
> property.
> > > It
> > > >>>>>>>> should be
> > > >>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested
> in
> > > the
> > > >>>>>>>> comment,
> > > >>>>>>>>>>>>>>> right?
> > > >>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
> > > >> column.
> > > >>>> I
> > > >>>>>>>> think
> > > >>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
> > > >>>> expression
> > > >>>>>>>> and
> > > >>>>>>>>>>>>>> later
> > > >>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason
> > is
> > > I
> > > >>>> think
> > > >>>>>>>> this
> > > >>>>>>>>>>>>>> way
> > > >>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs
> > when
> > > >>>> working
> > > >>>>>>>> with
> > > >>>>>>>>>>>>>> the
> > > >>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
> > > offset, i
> > > >>>>>>>> think it's
> > > >>>>>>>>>>>>>>> better to pushdown long rather than string. This could
> > let
> > > us
> > > >>>> push
> > > >>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
> > > >>>> Otherwise we
> > > >>>>>>>> would
> > > >>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
> > > cast(offset,
> > > >>>> long)
> > > >>>>>>>> <
> > > >>>>>>>>>>>>>> 59382.
> > > >>>>>>>>>>>>>>> Moreover I think we need to introduce the type for
> > computed
> > > >>>> columns
> > > >>>>>>>>>>>>>> anyway
> > > >>>>>>>>>>>>>>> to support functions that infer output type based on
> > > expected
> > > >>>>>>>> return
> > > >>>>>>>>>>>>>> type.
> > > >>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
> > > SYSTEM_METADATA
> > > >>>> would
> > > >>>>>>>> have
> > > >>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible
> > the
> > > >>>> planner
> > > >>>>>>>>>>>>>> should
> > > >>>>>>>>>>>>>>> fail. As far as I know computed columns push down will
> be
> > > >> part
> > > >>>> of
> > > >>>>>>>> source
> > > >>>>>>>>>>>>>>> rework, won't it? ;)
> > > >>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
> > > >>>> completely
> > > >>>>>>>>>>>>>>> orthogonal. In my current proposal you can also
> partition
> > > by
> > > >> a
> > > >>>>>>>> computed
> > > >>>>>>>>>>>>>>> column. The difference between using a udf in
> partitioned
> > > by
> > > >> vs
> > > >>>>>>>>>>>>>> partitioned
> > > >>>>>>>>>>>>>>> by a computed column is that when you partition by a
> > > computed
> > > >>>>>>>> column
> > > >>>>>>>>>>>>>> this
> > > >>>>>>>>>>>>>>> column must be also computed when reading the table. If
> > you
> > > >>>> use a
> > > >>>>>>>> udf in
> > > >>>>>>>>>>>>>>> the partitioned by, the expression is computed only
> when
> > > >>>> inserting
> > > >>>>>>>> into
> > > >>>>>>>>>>>>>> the
> > > >>>>>>>>>>>>>>> table.
> > > >>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking
> > forward
> > > >> for
> > > >>>>>>>> further
> > > >>>>>>>>>>>>>>> suggestions.
> > > >>>>>>>>>>>>>>>>> Best,
> > > >>>>>>>>>>>>>>>>> Dawid
> > > >>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
> > > >>>>>>>>>>>>>>>>>> Hi,
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
> > > Reaing
> > > >>>>>>>> metadata
> > > >>>>>>>>>>>>>> and
> > > >>>>>>>>>>>>>>>>>> key-part information from source is an important
> > feature
> > > >> for
> > > >>>>>>>>>>>>>> streaming
> > > >>>>>>>>>>>>>>>>>> users.
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
> > > >>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of
> > introducing
> > > >>>> HEADER
> > > >>>>>>>>>>>>>>> keyword as
> > > >>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
> > > >>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
> > > Maybe
> > > >> we
> > > >>>>>>>> should
> > > >>>>>>>>>>>>>>> add a
> > > >>>>>>>>>>>>>>>>>> section to explain what's the relationship between
> > them.
> > > >>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION
> be
> > > used
> > > >>>> on
> > > >>>>>>>> the
> > > >>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
> > > >>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink
> > SQL.
> > > >>>> Shall we
> > > >>>>>>>>>>>>>> make
> > > >>>>>>>>>>>>>>> the
> > > >>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
> > > >>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
> > > >>>> (actually, I
> > > >>>>>>>>>>>>>>> prefer
> > > >>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
> > > >>>> properties
> > > >>>>>>>>>>>>>>> FLINK-12557)
> > > >>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users
> > > that
> > > >>>> the
> > > >>>>>>>>>>>>>> field
> > > >>>>>>>>>>>>>>> is
> > > >>>>>>>>>>>>>>>>>> a rowtime attribute.
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>> Thanks,
> > > >>>>>>>>>>>>>>>>>> Jark
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> > > >>>>>>>>>>>>>> [hidden email]>
> > > >>>>>>>>>>>>>>>>>> wrote:
> > > >>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>> Hi,
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
> > > enable
> > > >>>>>>>> reading
> > > >>>>>>>>>>>>>> table
> > > >>>>>>>>>>>>>>>>>>> columns from different parts of source records.
> > Besides
> > > >> the
> > > >>>>>>>> main
> > > >>>>>>>>>>>>>>> payload
> > > >>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose
> > additional
> > > >>>>>>>>>>>>>> information. It
> > > >>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
> > > >>>> ingestion
> > > >>>>>>>> time
> > > >>>>>>>>>>>>>> or a
> > > >>>>>>>>>>>>>>>>>>> read and write parts of the record that contain
> data
> > > but
> > > >>>>>>>>>>>>>> additionally
> > > >>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
> > > etc.),
> > > >>>> e.g.
> > > >>>>>>>> key
> > > >>>>>>>>>>>>>> or
> > > >>>>>>>>>>>>>>>>>>> timestamp in Kafka.
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>> We should make it possible to read and write data
> > from
> > > >> all
> > > >>>> of
> > > >>>>>>>> those
> > > >>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
> > > >> partitioning
> > > >>>>>>>> data,
> > > >>>>>>>>>>>>>> for
> > > >>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
> > > >> partitioning
> > > >>>> when
> > > >>>>>>>>>>>>>>> writing
> > > >>>>>>>>>>>>>>>>>>> data out.
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>> You can access the FLIP here:
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>
> > > >>>>
> > > >>
> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>> Best,
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>> Dawid
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>>
> > > >>>>>>>>>>>>>
> > > >>>>>>>>>>>>
> > > >>>>>>>>>>>
> > > >>>>>>>>>
> > > >>>>>>>>>
> > > >>>>>>>>
> > > >>>>>>>>
> > > >>>>>>
> > > >>>>>
> > > >>>>>
> > > >>>>
> > > >>>>
> > > >>>
> > > >>
> > > >>
> > > >
> > >
> > >
> >
>

[Leonard Xu]

Hi everyone,

I’m +1 for "offset INT SYSTEM_METADATA("offset”)” if we have to make a choice.

It’s not a generated column syntax and thus we can get rid of the limitation of generated column.

About distinguishing the read-only metadata and writeable metadata, I prefer to add keyword after  SYSTEM_METADATA rather than declaring in with option fields.
And for the keyword, I tend to do not use "PERSISTED” or “STORED” which have been used in SQL server and Postgresql.  All metadata are readable and only two are writeable,
how about simply use “WRITABLE” for “timestamp” and “headers” ?

+1 for Jark’s proposal to make the SYSTEM_METADATA keyword shorter.



Best,
Leonard
 

> 在 2020年9月9日，17:41，Jark Wu <[hidden email]> 写道：
>
> Hi Danny,
>
> This is not Oracle and MySQL computed column syntax, because there is no
> "AS" after the type.
>
> Hi everyone,
>
> If we want to use "offset INT SYSTEM_METADATA("offset")", then I think we
> must further discuss about "PERSISED" or "VIRTUAL" keyword for query-sink
> schema problem.
> Personally, I think we can use a shorter keyword "METADATA" for
> "SYSTEM_METADATA". Because "SYSTEM_METADATA" sounds like a system function
> and confuse users this looks like a computed column.
>
>
> Best,
> Jark
>
>
>
> On Wed, 9 Sep 2020 at 17:23, Danny Chan <[hidden email]> wrote:
>
>> "offset INT SYSTEM_METADATA("offset")"
>>
>> This is actually Oracle or MySQL style computed column syntax.
>>
>> "You are right that one could argue that "timestamp", "headers" are
>> something like "key" and "value""
>>
>> I have the same feeling, both key value and headers timestamp are *real*
>> data
>> stored in the consumed record, they are not computed or generated.
>>
>> "Trying to solve everything via properties sounds rather like a hack to
>> me"
>>
>> Things are not that hack if we can unify the routines or the definitions
>> (all from the computed column way or all from the table options), i also
>> think that it is a hacky that we mix in 2 kinds of syntax for different
>> kinds of metadata (read-only and read-write). In this FLIP, we declare the
>> Kafka key fields with table options but SYSTEM_METADATA for other metadata,
>> that is a hacky thing or something in-consistent.
>>
>> Kurt Young <[hidden email]> 于2020年9月9日周三 下午4:48写道：
>>
>>> I would vote for `offset INT SYSTEM_METADATA("offset")`.
>>>
>>> I don't think we can stick with the SQL standard in DDL part forever,
>>> especially as there are more and more
>>> requirements coming from different connectors and external systems.
>>>
>>> Best,
>>> Kurt
>>>
>>>
>>> On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]> wrote:
>>>
>>>> Hi Jark,
>>>>
>>>> now we are back at the original design proposed by Dawid :D Yes, we
>>>> should be cautious about adding new syntax. But the length of this
>>>> discussion shows that we are looking for a good long-term solution. In
>>>> this case I would rather vote for a deep integration into the syntax.
>>>>
>>>> Computed columns are also not SQL standard compliant. And our DDL is
>>>> neither, so we have some degree of freedom here.
>>>>
>>>> Trying to solve everything via properties sounds rather like a hack to
>>>> me. You are right that one could argue that "timestamp", "headers" are
>>>> something like "key" and "value". However, mixing
>>>>
>>>> `offset AS SYSTEM_METADATA("offset")`
>>>>
>>>> and
>>>>
>>>> `'timestamp.field' = 'ts'`
>>>>
>>>> looks more confusing to users that an explicit
>>>>
>>>> `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
>>>>
>>>> or
>>>>
>>>> `offset INT SYSTEM_METADATA("offset")`
>>>>
>>>> that is symetric for both source and sink.
>>>>
>>>> What do others think?
>>>>
>>>> Regards,
>>>> Timo
>>>>
>>>>
>>>> On 09.09.20 10:09, Jark Wu wrote:
>>>>> Hi everyone,
>>>>>
>>>>> I think we have a conclusion that the writable metadata shouldn't be
>>>>> defined as a computed column, but a normal column.
>>>>>
>>>>> "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the
>>> approaches.
>>>>> However, it is not SQL standard compliant, we need to be cautious
>>> enough
>>>>> when adding new syntax.
>>>>> Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
>>>>> resolve the query-sink schema problem if it is read-only metadata.
>> That
>>>>> adds more stuff to learn for users.
>>>>>
>>>>>> From my point of view, the "timestamp", "headers" are something like
>>>> "key"
>>>>> and "value" that stores with the real data. So why not define the
>>>>> "timestamp" in the same way with "key" by using a "timestamp.field"
>>>>> connector option?
>>>>> On the other side, the read-only metadata, such as "offset",
>> shouldn't
>>> be
>>>>> defined as a normal column. So why not use the existing computed
>> column
>>>>> syntax for such metadata? Then we don't have the query-sink schema
>>>> problem.
>>>>> So here is my proposal:
>>>>>
>>>>> CREATE TABLE kafka_table (
>>>>>   id BIGINT,
>>>>>   name STRING,
>>>>>   col1 STRING,
>>>>>   col2 STRING,
>>>>>   ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field,
>> so
>>>> can
>>>>> be read and written.
>>>>>   offset AS SYSTEM_METADATA("offset")
>>>>> ) WITH (
>>>>>   'connector' = 'kafka',
>>>>>   'topic' = 'test-topic',
>>>>>   'key.fields' = 'id, name',
>>>>>   'key.format' = 'csv',
>>>>>   'value.format' = 'avro',
>>>>>   'timestamp.field' = 'ts'    -- define the mapping of Kafka
>> timestamp
>>>>> );
>>>>>
>>>>> INSERT INTO kafka_table
>>>>> SELECT id, name, col1, col2, rowtime FROM another_table;
>>>>>
>>>>> I think this can solve all the problems without introducing any new
>>>> syntax.
>>>>> The only minor disadvantage is that we separate the definition
>>> way/syntax
>>>>> of read-only metadata and read-write fields.
>>>>> However, I don't think this is a big problem.
>>>>>
>>>>> Best,
>>>>> Jark
>>>>>
>>>>>
>>>>> On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]>
>> wrote:
>>>>>
>>>>>> Hi Kurt,
>>>>>>
>>>>>> thanks for sharing your opinion. I'm totally up for not reusing
>>> computed
>>>>>> columns. I think Jark was a big supporter of this syntax, @Jark are
>>> you
>>>>>> fine with this as well? The non-computed column approach was only a
>>>>>> "slightly rejected alternative".
>>>>>>
>>>>>> Furthermore, we would need to think about how such a new design
>>>>>> influences the LIKE clause though.
>>>>>>
>>>>>> However, we should still keep the `PERSISTED` keyword as it
>> influences
>>>>>> the query->sink schema. If you look at the list of metadata for
>>> existing
>>>>>> connectors and formats, we currently offer only two writable
>> metadata
>>>>>> fields. Otherwise, one would need to declare two tables whenever a
>>>>>> metadata columns is read (one for the source, one for the sink).
>> This
>>>>>> can be quite inconvientient e.g. for just reading the topic.
>>>>>>
>>>>>> Regards,
>>>>>> Timo
>>>>>>
>>>>>>
>>>>>> On 09.09.20 08:52, Kurt Young wrote:
>>>>>>> I also share the concern that reusing the computed column syntax
>> but
>>>> have
>>>>>>> different semantics
>>>>>>> would confuse users a lot.
>>>>>>>
>>>>>>> Besides, I think metadata fields are conceptually not the same with
>>>>>>> computed columns. The metadata
>>>>>>> field is a connector specific thing and it only contains the
>>>> information
>>>>>>> that where does the field come
>>>>>>> from (during source) or where does the field need to write to
>> (during
>>>>>>> sink). It's more similar with normal
>>>>>>> fields, with assumption that all these fields need going to the
>> data
>>>>>> part.
>>>>>>>
>>>>>>> Thus I'm more lean to the rejected alternative that Timo mentioned.
>>>> And I
>>>>>>> think we don't need the
>>>>>>> PERSISTED keyword, SYSTEM_METADATA should be enough.
>>>>>>>
>>>>>>> During implementation, the framework only needs to pass such
>> <field,
>>>>>>> metadata field> information to the
>>>>>>> connector, and the logic of handling such fields inside the
>> connector
>>>>>>> should be straightforward.
>>>>>>>
>>>>>>> Regarding the downside Timo mentioned:
>>>>>>>
>>>>>>>> The disadvantage is that users cannot call UDFs or parse
>> timestamps.
>>>>>>>
>>>>>>> I think this is fairly simple to solve. Since the metadata field
>>> isn't
>>>> a
>>>>>>> computed column anymore, we can support
>>>>>>> referencing such fields in the computed column. For example:
>>>>>>>
>>>>>>> CREATE TABLE kafka_table (
>>>>>>>       id BIGINT,
>>>>>>>       name STRING,
>>>>>>>       timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
>>>>>> timestamp
>>>>>>> field from metadata
>>>>>>>       ts AS to_timestamp(timestamp) // normal computed column,
>> parse
>>>> the
>>>>>>> string to TIMESTAMP type by using the metadata field
>>>>>>> ) WITH (
>>>>>>>      ...
>>>>>>> )
>>>>>>>
>>>>>>> Best,
>>>>>>> Kurt
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
>>>> wrote:
>>>>>>>
>>>>>>>> Hi Leonard,
>>>>>>>>
>>>>>>>> the only alternative I see is that we introduce a concept that is
>>>>>>>> completely different to computed columns. This is also mentioned
>> in
>>>> the
>>>>>>>> rejected alternative section of the FLIP. Something like:
>>>>>>>>
>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>       id BIGINT,
>>>>>>>>       name STRING,
>>>>>>>>       timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
>>>>>>>>       headers MAP<STRING, BYTES> SYSTEM_METADATA("headers")
>>> PERSISTED
>>>>>>>> ) WITH (
>>>>>>>>      ...
>>>>>>>> )
>>>>>>>>
>>>>>>>> This way we would avoid confusion at all and can easily map
>> columns
>>> to
>>>>>>>> metadata columns. The disadvantage is that users cannot call UDFs
>> or
>>>>>>>> parse timestamps. This would need to be done in a real computed
>>>> column.
>>>>>>>>
>>>>>>>> I'm happy about better alternatives.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>> Timo
>>>>>>>>
>>>>>>>>
>>>>>>>> On 08.09.20 15:37, Leonard Xu wrote:
>>>>>>>>> HI, Timo
>>>>>>>>>
>>>>>>>>> Thanks for driving this FLIP.
>>>>>>>>>
>>>>>>>>> Sorry but I have a concern about Writing metadata via
>>>> DynamicTableSink
>>>>>>>> section:
>>>>>>>>>
>>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>>     id BIGINT,
>>>>>>>>>     name STRING,
>>>>>>>>>     timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
>>>>>> PERSISTED,
>>>>>>>>>     headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
>>>> BYTES>)
>>>>>>>> PERSISTED
>>>>>>>>> ) WITH (
>>>>>>>>>     ...
>>>>>>>>> )
>>>>>>>>> An insert statement could look like:
>>>>>>>>>
>>>>>>>>> INSERT INTO kafka_table VALUES (
>>>>>>>>>     (1, "ABC", 1599133672, MAP('checksum',
>> computeChecksum(...)))
>>>>>>>>> )
>>>>>>>>>
>>>>>>>>> The proposed INERT syntax does not make sense to me, because it
>>>>>> contains
>>>>>>>> computed(generated) column.
>>>>>>>>> Both SQL server and Postgresql do not allow to insert value to
>>>> computed
>>>>>>>> columns even they are persisted, this boke the generated column
>>>>>> semantics
>>>>>>>> and may confuse user much.
>>>>>>>>>
>>>>>>>>> For SQL server computed column[1]:
>>>>>>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT
>> NULL ]
>>>>>> ]...
>>>>>>>>>> NOTE: A computed column cannot be the target of an INSERT or
>>> UPDATE
>>>>>>>> statement.
>>>>>>>>>
>>>>>>>>> For Postgresql generated column[2]:
>>>>>>>>>>    height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
>>> STORED
>>>>>>>>>> NOTE: A generated column cannot be written to directly. In
>> INSERT
>>> or
>>>>>>>> UPDATE commands, a value cannot be specified for a generated
>> column,
>>>> but
>>>>>>>> the keyword DEFAULT may be specified.
>>>>>>>>>
>>>>>>>>> It shouldn't be allowed to set/update value for generated column
>>>> after
>>>>>>>> lookup the SQL 2016:
>>>>>>>>>> <insert statement> ::=
>>>>>>>>>> INSERT INTO <insertion target> <insert columns and source>
>>>>>>>>>>
>>>>>>>>>> If <contextually typed table value constructor> CTTVC is
>>> specified,
>>>>>>>> then every <contextually typed row
>>>>>>>>>> value constructor element> simply contained in CTTVC whose
>>>>>> positionally
>>>>>>>> corresponding <column name>
>>>>>>>>>> in <insert column list> references a column of which some
>>> underlying
>>>>>>>> column is a generated column shall
>>>>>>>>>> be a <default specification>.
>>>>>>>>>> A <default specification> specifies the default value of some
>>>>>>>> associated item.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> [1]
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>>>>>> <
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>>>>>>>
>>>>>>>>> [2]
>> https://www.postgresql.org/docs/12/ddl-generated-columns.html
>>> <
>>>>>>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
>>>>>>>>>
>>>>>>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>>>>>>>>>>
>>>>>>>>>> Hi Jark,
>>>>>>>>>>
>>>>>>>>>> according to Flink's and Calcite's casting definition in [1][2]
>>>>>>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If
>>> not,
>>>>>> we
>>>>>>>> will make it possible ;-)
>>>>>>>>>>
>>>>>>>>>> I'm aware of DeserializationSchema.getProducedType but I think
>>> that
>>>>>>>> this method is actually misplaced. The type should rather be
>> passed
>>> to
>>>>>> the
>>>>>>>> source itself.
>>>>>>>>>>
>>>>>>>>>> For our Kafka SQL source, we will also not use this method
>> because
>>>> the
>>>>>>>> Kafka source will add own metadata in addition to the
>>>>>>>> DeserializationSchema. So DeserializationSchema.getProducedType
>> will
>>>>>> never
>>>>>>>> be read.
>>>>>>>>>>
>>>>>>>>>> For now I suggest to leave out the `DataType` from
>>>>>>>> DecodingFormat.applyReadableMetadata. Also because the format's
>>>> physical
>>>>>>>> type is passed later in `createRuntimeDecoder`. If necessary, it
>> can
>>>> be
>>>>>>>> computed manually by consumedType + metadata types. We will
>> provide
>>> a
>>>>>>>> metadata utility class for that.
>>>>>>>>>>
>>>>>>>>>> Regards,
>>>>>>>>>> Timo
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> [1]
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
>>>>>>>>>> [2]
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 08.09.20 10:52, Jark Wu wrote:
>>>>>>>>>>> Hi Timo,
>>>>>>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I
>>> just
>>>>>>>> noticed
>>>>>>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL
>> TIME
>>>>>>>> ZONE".
>>>>>>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH
>> LOCAL
>>>>>> TIME
>>>>>>>>>>> ZONE" as the defined type of Kafka timestamp? I think this
>> makes
>>>>>> sense,
>>>>>>>>>>> because it represents the milli-seconds since epoch.
>>>>>>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I
>> don't
>>>>>> think
>>>>>>>> so.
>>>>>>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus
>>> the
>>>>>>>>>>> implementation needs to return an output TypeInfo.
>>>>>>>>>>> Besides, FlinkKafkaConsumer also
>>>>>>>>>>> calls DeserializationSchema.getProducedType as the produced
>> type
>>> of
>>>>>> the
>>>>>>>>>>> source function [1].
>>>>>>>>>>> Best,
>>>>>>>>>>> Jark
>>>>>>>>>>> [1]:
>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>>>>>>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
>>>>>> wrote:
>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>
>>>>>>>>>>>> I updated the FLIP again and hope that I could address the
>>>> mentioned
>>>>>>>>>>>> concerns.
>>>>>>>>>>>>
>>>>>>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that
>> ts_ms
>>>> and
>>>>>>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
>>>> expose
>>>>>>>> the
>>>>>>>>>>>> most commonly used properties separately. So frequently used
>>>>>>>> properties
>>>>>>>>>>>> are not hidden in the MAP anymore:
>>>>>>>>>>>>
>>>>>>>>>>>> debezium-json.ingestion-timestamp
>>>>>>>>>>>> debezium-json.source.timestamp
>>>>>>>>>>>> debezium-json.source.database
>>>>>>>>>>>> debezium-json.source.schema
>>>>>>>>>>>> debezium-json.source.table
>>>>>>>>>>>>
>>>>>>>>>>>> However, since other properties depend on the used
>>>> connector/vendor,
>>>>>>>> the
>>>>>>>>>>>> remaining options are stored in:
>>>>>>>>>>>>
>>>>>>>>>>>> debezium-json.source.properties
>>>>>>>>>>>>
>>>>>>>>>>>> And accessed with:
>>>>>>>>>>>>
>>>>>>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
>>>>>> MAP<STRING,
>>>>>>>>>>>> STRING>)['table']
>>>>>>>>>>>>
>>>>>>>>>>>> Otherwise it is not possible to figure out the value and
>> column
>>>> type
>>>>>>>>>>>> during validation.
>>>>>>>>>>>>
>>>>>>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I
>>> added
>>>> a
>>>>>>>>>>>> dedicacated sub-section to the FLIP:
>>>>>>>>>>>>
>>>>>>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
>>>>>> casting
>>>>>>>> we
>>>>>>>>>>>> allow explicit casting to a target data type:
>>>>>>>>>>>>
>>>>>>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3)
>>> WITH
>>>>>>>> LOCAL
>>>>>>>>>>>> TIME ZONE)
>>>>>>>>>>>>
>>>>>>>>>>>> A connector still produces and consumes the data type returned
>>> by
>>>>>>>>>>>> `listMetadata()`. The planner will insert necessary explicit
>>>> casts.
>>>>>>>>>>>>
>>>>>>>>>>>> In any case, the user must provide a CAST such that the
>> computed
>>>>>>>> column
>>>>>>>>>>>> receives a valid data type when constructing the table schema.
>>>>>>>>>>>>
>>>>>>>>>>>> "I don't see a reason why
>> `DecodingFormat#applyReadableMetadata`
>>>>>>>> needs a
>>>>>>>>>>>> DataType argument."
>>>>>>>>>>>>
>>>>>>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
>>>> always
>>>>>>>>>>>> executed locally. It is the source that needs TypeInfo for
>>>>>> serializing
>>>>>>>>>>>> the record to the next operator. And that's this is what we
>>>> provide.
>>>>>>>>>>>>
>>>>>>>>>>>> @Danny:
>>>>>>>>>>>>
>>>>>>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>>>>>>>
>>>>>>>>>>>> We can also use some other means to represent an UNKNOWN data
>>>> type.
>>>>>> In
>>>>>>>>>>>> the Flink type system, we use the NullType for it. The
>> important
>>>>>> part
>>>>>>>> is
>>>>>>>>>>>> that the final data type is known for the entire computed
>>> column.
>>>>>> As I
>>>>>>>>>>>> mentioned before, I would avoid the suggested option b) that
>>> would
>>>>>> be
>>>>>>>>>>>> similar to your suggestion. The CAST should be enough and
>> allows
>>>> for
>>>>>>>>>>>> complex expressions in the computed column. Option b) would
>> need
>>>>>>>> parser
>>>>>>>>>>>> changes.
>>>>>>>>>>>>
>>>>>>>>>>>> Regards,
>>>>>>>>>>>> Timo
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>>>>>>>>>>> Hi, Timo
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks for you explanation and update,  I have only one
>>> question
>>>>>> for
>>>>>>>>>>>> the latest FLIP.
>>>>>>>>>>>>>
>>>>>>>>>>>>> About the MAP<STRING, STRING> DataType of key
>>>>>>>> 'debezium-json.source', if
>>>>>>>>>>>> user want to use the table name metadata, they need to write:
>>>>>>>>>>>>> tableName STRING AS
>> CAST(SYSTEM_METADATA('debeuim-json.source')
>>>> AS
>>>>>>>>>>>> MAP<STRING, STRING>)['table']
>>>>>>>>>>>>>
>>>>>>>>>>>>> the expression is a little complex for user, Could we only
>>>> support
>>>>>>>>>>>> necessary metas with simple DataType as following?
>>>>>>>>>>>>> tableName STRING AS
>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>>>>>>>>>>> STRING),
>>>>>>>>>>>>> transactionTime LONG AS
>>>>>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>>>>>>>>>>
>>>>>>>>>>>>> In this way, we can simplify the expression, the mainly used
>>>>>>>> metadata in
>>>>>>>>>>>> changelog format may include
>>>>>>>> 'database','table','source.ts_ms','ts_ms' from
>>>>>>>>>>>> my side,
>>>>>>>>>>>>> maybe we could only support them at first version.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m
>>> willing
>>>>>> to
>>>>>>>>>>>> take some subtasks in next development if necessary.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Debezium:
>>>>>>>>>>>>> {
>>>>>>>>>>>>>      "before": null,
>>>>>>>>>>>>>      "after": {  "id": 101,"name": "scooter"},
>>>>>>>>>>>>>      "source": {
>>>>>>>>>>>>>        "db": "inventory",                  # 1. database
>> name
>>>> the
>>>>>>>>>>>> changelog belongs to.
>>>>>>>>>>>>>        "table": "products",                # 2. table name
>> the
>>>>>>>> changelog
>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>        "ts_ms": 1589355504100,             # 3. timestamp of
>>> the
>>>>>>>> change
>>>>>>>>>>>> happened in database system, i.e.: transaction time in
>> database.
>>>>>>>>>>>>>        "connector": "mysql",
>>>>>>>>>>>>>        ….
>>>>>>>>>>>>>      },
>>>>>>>>>>>>>      "ts_ms": 1589355606100,              # 4. timestamp
>> when
>>>> the
>>>>>>>> debezium
>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>      "op": "c",
>>>>>>>>>>>>>      "transaction": null
>>>>>>>>>>>>> }
>>>>>>>>>>>>>
>>>>>>>>>>>>> Canal:
>>>>>>>>>>>>> {
>>>>>>>>>>>>>      "data": [{  "id": "102", "name": "car battery" }],
>>>>>>>>>>>>>      "database": "inventory",      # 1. database name the
>>>> changelog
>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>      "table": "products",          # 2. table name the
>>> changelog
>>>>>>>> belongs
>>>>>>>>>>>> to.
>>>>>>>>>>>>>      "es": 1589374013000,          # 3. execution time of
>> the
>>>>>> change
>>>>>>>> in
>>>>>>>>>>>> database system, i.e.: transaction time in database.
>>>>>>>>>>>>>      "ts": 1589374013680,          # 4. timestamp when the
>>>> cannal
>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>      "isDdl": false,
>>>>>>>>>>>>>      "mysqlType": {},
>>>>>>>>>>>>>      ....
>>>>>>>>>>>>> }
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best
>>>>>>>>>>>>> Leonard
>>>>>>>>>>>>>
>>>>>>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks Timo ~
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
>>>> questions
>>>>>>>> here:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a
>> valid
>>>>>>>> read-only
>>>>>>>>>>>> computed column for Kafka and can be extracted by the
>> planner.”
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ?
>>> Usually
>>>> an
>>>>>>>>>>>> expression return type can be inferred automatically. But I
>>> guess
>>>>>>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
>>>>>> actually
>>>>>>>> does
>>>>>>>>>>>> not have a specific return type.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
>>>>>> [VIRTUAL]
>>>>>>>>>>>>>> Which is more straight-forward.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by
>>> default”
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The default type should not be NULL because only NULL
>> literal
>>>> does
>>>>>>>>>>>> that. Usually we use ANY as the type if we do not know the
>>>> specific
>>>>>>>> type in
>>>>>>>>>>>> the SQL context. ANY means the physical value can be any java
>>>>>> object.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [1]
>>> https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]
>>>> ，写道：
>>>>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I completely reworked FLIP-107. It now covers the full
>> story
>>>> how
>>>>>> to
>>>>>>>>>>>> read
>>>>>>>>>>>>>>> and write metadata from/to connectors and formats. It
>>> considers
>>>>>>>> all of
>>>>>>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
>>>>>>>> introduces
>>>>>>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
>>>>>>>> partitioning
>>>>>>>>>>>>>>> for now.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Looking forward to your feedback.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>> Timo
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>>>>>>>>>>> Sorry, forgot one question.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal?
>>> Like
>>>>>> one
>>>>>>>> can
>>>>>>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users
>> can
>>>> not
>>>>>>>>>>>> config to
>>>>>>>>>>>>>>>> just ignore timestamp but keep key.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <
>> [hidden email]
>>>>
>>>>>>>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi Dawid,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I have a couple of questions around key fields, actually
>> I
>>>> also
>>>>>>>> have
>>>>>>>>>>>> some
>>>>>>>>>>>>>>>>> other questions but want to be focused on key fields
>> first.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is
>>>> this
>>>>>>>>>>>> option only
>>>>>>>>>>>>>>>>> valid during write operation? Because for
>>>>>>>>>>>>>>>>> reading, I can't imagine how such options can be
>> applied. I
>>>>>> would
>>>>>>>>>>>> expect
>>>>>>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>>>>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I
>> want
>>>> to
>>>>>>>>>>>> propose we
>>>>>>>>>>>>>>>>> can simplify the options to not introducing
>> key.format.type
>>>> and
>>>>>>>>>>>>>>>>> other related options. I think a single "key.field" (not
>>>>>> fields)
>>>>>>>>>>>> would be
>>>>>>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>>>>>>>>>>> want before sink.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
>>>> connector
>>>>>>>> has a
>>>>>>>>>>>>>>>>> concept
>>>>>>>>>>>>>>>>> of key and values. The old parameter "format.type"
>> already
>>>> good
>>>>>>>>>>>> enough to
>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <
>> [hidden email]>
>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks Dawid,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I have two more questions.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> SupportsMetadata
>>>>>>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I
>> have
>>>>>> some
>>>>>>>>>>>> questions
>>>>>>>>>>>>>>>>>> regarding to this interface.
>>>>>>>>>>>>>>>>>> 1) How do the source know what the expected return type
>> of
>>>>>> each
>>>>>>>>>>>> metadata?
>>>>>>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the
>>> existing
>>>>>>>> physical
>>>>>>>>>>>>>>>>>> fields?
>>>>>>>>>>>>>>>>>> If yes, I would suggest to change the signature to
>>>>>> `TableSource
>>>>>>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>>>>>>>>>>> metadataTypes)`
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a
>>> computed
>>>>>>>> column
>>>>>>>>>>>>>>>>>> expression? If yes, how to specify the return type of
>>>>>>>>>>>> SYSTEM_METADATA?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit
>> the
>>>>>>>> columns
>>>>>>>>>>>> and I
>>>>>>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I
>>> see
>>>> a
>>>>>>>> need
>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
>>>> Jark.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that.
>>> One
>>>>>>>>>>>> additional
>>>>>>>>>>>>>>>>>>> things I would introduce is an
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
>>>>>> metadataFields);
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> This way the source would have to declare/emit only the
>>>>>>>> requested
>>>>>>>>>>>>>>>>>>> metadata fields. In order not to clash with user
>> defined
>>>>>>>> fields.
>>>>>>>>>>>> When
>>>>>>>>>>>>>>>>>>> emitting the metadata field I would prepend the column
>>> name
>>>>>>>> with
>>>>>>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
>>>> field
>>>>>>>>>>>>>>>>>>> __system_partition to the schema. This would be never
>>>> visible
>>>>>>>> to
>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> user as it would be used only for the subsequent
>> computed
>>>>>>>> columns.
>>>>>>>>>>>> If
>>>>>>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with
>> this
>>>>>>>>>>>> description.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state
>> of
>>>> the
>>>>>>>>>>>> proposal.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
>>>>>>>> orthogonal. I
>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>> leave out the STORED computed columns out of the
>>>> discussion.
>>>>>> I
>>>>>>>>>>>> don't see
>>>>>>>>>>>>>>>>>>> how do they relate to the partitioning. I already put
>>> both
>>>> of
>>>>>>>> those
>>>>>>>>>>>>>>>>>>> cases in the document. We can either partition on a
>>>> computed
>>>>>>>>>>>> column or
>>>>>>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with
>>> leaving
>>>>>> out
>>>>>>>> the
>>>>>>>>>>>>>>>>>>> partitioning by udf in the first version if you still
>>> have
>>>>>> some
>>>>>>>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> As for your question Danny. It depends which
>> partitioning
>>>>>>>> strategy
>>>>>>>>>>>> you
>>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would
>>> work
>>>> as
>>>>>>>> you
>>>>>>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not
>> sure
>>>>>>>> though if
>>>>>>>>>>>> we
>>>>>>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink
>>> does
>>>>>> not
>>>>>>>> own
>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> data and the partitions are already an intrinsic
>> property
>>>> of
>>>>>>>> the
>>>>>>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create
>> topics,
>>>> but
>>>>>>>> we
>>>>>>>>>>>> just
>>>>>>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs
>> ...
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
>>>>>> consistent
>>>>>>>> with
>>>>>>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was
>> also
>>>> my
>>>>>>>>>>>> initial
>>>>>>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
>>>> afterwards
>>>>>>>> to
>>>>>>>>>>>> shorten
>>>>>>>>>>>>>>>>>>> the key.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think
>> it
>>>> is
>>>>>> a
>>>>>>>>>>>> useful
>>>>>>>>>>>>>>>>>>> feature ~
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column
>>> push
>>>>>>>> down is
>>>>>>>>>>>>>>>>>>> another topic, this should not be a blocker but a
>>>> promotion,
>>>>>>>> if we
>>>>>>>>>>>> do
>>>>>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>>>>>> have any filters on the computed column, there is no
>> need
>>>> to
>>>>>>>> do any
>>>>>>>>>>>>>>>>>>> pushings; the source node just emit the complete record
>>>> with
>>>>>>>> full
>>>>>>>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>>> with the declared physical schema, then when generating
>>> the
>>>>>>>> virtual
>>>>>>>>>>>>>>>>>>> columns, we would extract the metadata info and output
>> as
>>>>>> full
>>>>>>>>>>>>>>>>>> columns(with
>>>>>>>>>>>>>>>>>>> full schema).
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST,
>> they
>>>> are
>>>>>>>>>>>> symantic
>>>>>>>>>>>>>>>>>>> equivalent though, explict type is more
>> straight-forward
>>>> and
>>>>>>>> we can
>>>>>>>>>>>>>>>>>> declare
>>>>>>>>>>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column
>>> VS
>>>>>>>> option
>>>>>>>>>>>> B:
>>>>>>>>>>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>    From the FLIP, it seems that B's partitioning is
>>> just
>>>> a
>>>>>>>> strategy
>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>> writing data, the partiton column is not included in
>> the
>>>>>> table
>>>>>>>>>>>> schema,
>>>>>>>>>>>>>>>>>> so
>>>>>>>>>>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the
>>> partition
>>>>>>>> column
>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we
>>> want
>>>>>> to
>>>>>>>>>>>> persist
>>>>>>>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
>>>> both, i
>>>>>>>> saw
>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
>>>>>> PARTITIONS
>>>>>>>>>>>> num, and
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
>>>>>> partition
>>>>>>>> in
>>>>>>>>>>>> which
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> record is stored is partition number N, where N =
>>> MOD(expr,
>>>>>>>> num),
>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>> your
>>>>>>>>>>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> [1]
>>>>>>>>>>>>
>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
>>>>>>>> [hidden email]
>>>>>>>>>>>>>>>>>>> ，写道：
>>>>>>>>>>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to
>> FLIP-63
>>>>>>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
>>>>>> (prefixing
>>>>>>>> with
>>>>>>>>>>>>>>>>>>> connector or kafka). I should've put that into an
>>>>>>>> Option/Rejected
>>>>>>>>>>>>>>>>>>> alternatives.
>>>>>>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
>>>> properties.
>>>>>>>> Why I
>>>>>>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
>>>> version
>>>>>>>> is
>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>> actually all the properties in the WITH section are
>>>> connector
>>>>>>>>>>>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>> Even format is in the end a connector property as some
>> of
>>>> the
>>>>>>>>>>>> sources
>>>>>>>>>>>>>>>>>> might
>>>>>>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
>>>> prefix
>>>>>> is
>>>>>>>>>>>> that it
>>>>>>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
>>>>>>>> properties
>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>> connector (or if we go with FLINK-12557:
>> elasticsearch):
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
>>>>>>>> approach
>>>>>>>>>>>> in the
>>>>>>>>>>>>>>>>>>> community.
>>>>>>>>>>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include`
>> property.
>>>> It
>>>>>>>>>>>> should be
>>>>>>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested
>> in
>>>> the
>>>>>>>>>>>> comment,
>>>>>>>>>>>>>>>>>>> right?
>>>>>>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
>>>>>> column.
>>>>>>>> I
>>>>>>>>>>>> think
>>>>>>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
>>>>>>>> expression
>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>> later
>>>>>>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason
>>> is
>>>> I
>>>>>>>> think
>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>> way
>>>>>>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs
>>> when
>>>>>>>> working
>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
>>>> offset, i
>>>>>>>>>>>> think it's
>>>>>>>>>>>>>>>>>>> better to pushdown long rather than string. This could
>>> let
>>>> us
>>>>>>>> push
>>>>>>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
>>>>>>>> Otherwise we
>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
>>>> cast(offset,
>>>>>>>> long)
>>>>>>>>>>>> <
>>>>>>>>>>>>>>>>>> 59382.
>>>>>>>>>>>>>>>>>>> Moreover I think we need to introduce the type for
>>> computed
>>>>>>>> columns
>>>>>>>>>>>>>>>>>> anyway
>>>>>>>>>>>>>>>>>>> to support functions that infer output type based on
>>>> expected
>>>>>>>>>>>> return
>>>>>>>>>>>>>>>>>> type.
>>>>>>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
>>>> SYSTEM_METADATA
>>>>>>>> would
>>>>>>>>>>>> have
>>>>>>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible
>>> the
>>>>>>>> planner
>>>>>>>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>> fail. As far as I know computed columns push down will
>> be
>>>>>> part
>>>>>>>> of
>>>>>>>>>>>> source
>>>>>>>>>>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
>>>>>>>> completely
>>>>>>>>>>>>>>>>>>> orthogonal. In my current proposal you can also
>> partition
>>>> by
>>>>>> a
>>>>>>>>>>>> computed
>>>>>>>>>>>>>>>>>>> column. The difference between using a udf in
>> partitioned
>>>> by
>>>>>> vs
>>>>>>>>>>>>>>>>>> partitioned
>>>>>>>>>>>>>>>>>>> by a computed column is that when you partition by a
>>>> computed
>>>>>>>>>>>> column
>>>>>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>> column must be also computed when reading the table. If
>>> you
>>>>>>>> use a
>>>>>>>>>>>> udf in
>>>>>>>>>>>>>>>>>>> the partitioned by, the expression is computed only
>> when
>>>>>>>> inserting
>>>>>>>>>>>> into
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> table.
>>>>>>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking
>>> forward
>>>>>> for
>>>>>>>>>>>> further
>>>>>>>>>>>>>>>>>>> suggestions.
>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
>>>> Reaing
>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>>>>> key-part information from source is an important
>>> feature
>>>>>> for
>>>>>>>>>>>>>>>>>> streaming
>>>>>>>>>>>>>>>>>>>>>> users.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of
>>> introducing
>>>>>>>> HEADER
>>>>>>>>>>>>>>>>>>> keyword as
>>>>>>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
>>>> Maybe
>>>>>> we
>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>> add a
>>>>>>>>>>>>>>>>>>>>>> section to explain what's the relationship between
>>> them.
>>>>>>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION
>> be
>>>> used
>>>>>>>> on
>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink
>>> SQL.
>>>>>>>> Shall we
>>>>>>>>>>>>>>>>>> make
>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
>>>>>>>> (actually, I
>>>>>>>>>>>>>>>>>>> prefer
>>>>>>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
>>>>>>>> properties
>>>>>>>>>>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users
>>>> that
>>>>>>>> the
>>>>>>>>>>>>>>>>>> field
>>>>>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
>>>> enable
>>>>>>>>>>>> reading
>>>>>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>>>>>>>> columns from different parts of source records.
>>> Besides
>>>>>> the
>>>>>>>>>>>> main
>>>>>>>>>>>>>>>>>>> payload
>>>>>>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose
>>> additional
>>>>>>>>>>>>>>>>>> information. It
>>>>>>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
>>>>>>>> ingestion
>>>>>>>>>>>> time
>>>>>>>>>>>>>>>>>> or a
>>>>>>>>>>>>>>>>>>>>>>> read and write parts of the record that contain
>> data
>>>> but
>>>>>>>>>>>>>>>>>> additionally
>>>>>>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
>>>> etc.),
>>>>>>>> e.g.
>>>>>>>>>>>> key
>>>>>>>>>>>>>>>>>> or
>>>>>>>>>>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> We should make it possible to read and write data
>>> from
>>>>>> all
>>>>>>>> of
>>>>>>>>>>>> those
>>>>>>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
>>>>>> partitioning
>>>>>>>>>>>> data,
>>>>>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
>>>>>> partitioning
>>>>>>>> when
>>>>>>>>>>>>>>>>>>> writing
>>>>>>>>>>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>
>>

[Timo Walther-2]

Hi everyone,

"key" and "value" in the properties are a special case because they need
to configure a format. So key and value are more than just metadata.
Jark's example for setting a timestamp would work but as the FLIP
discusses, we have way more metadata fields like headers, epoch-leader,
etc. Having a property for all of this metadata would mess up the WITH
section entirely. Furthermore, we also want to deal with metadata from
the formats. Solving this through properties as well would further
complicate the property design.

Personally, I still like the computed column design more because it
allows to have full flexibility to compute the final column:

timestamp AS adjustTimestamp(CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3)))

Instead of having a helper column and a real column in the table:

helperTimestamp AS CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3))
realTimestamp AS adjustTimestamp(helperTimestamp)

But I see that the discussion leans towards:

timestamp INT SYSTEM_METADATA("ts")

Which is fine with me. It is the shortest solution, because we don't
need additional CAST. We can discuss the syntax, so that confusion with
computed columns can be avoided.

timestamp INT USING SYSTEM_METADATA("ts")
timestamp INT FROM SYSTEM_METADATA("ts")
timestamp INT FROM SYSTEM_METADATA("ts") PERSISTED

We use `SYSTEM_TIME` for temporal tables. I think prefixing with SYSTEM
makes it clearer that it comes magically from the system.

What do you think?

Regards,
Timo



On 09.09.20 11:41, Jark Wu wrote:

> Hi Danny,
>
> This is not Oracle and MySQL computed column syntax, because there is no
> "AS" after the type.
>
> Hi everyone,
>
> If we want to use "offset INT SYSTEM_METADATA("offset")", then I think we
> must further discuss about "PERSISED" or "VIRTUAL" keyword for query-sink
> schema problem.
> Personally, I think we can use a shorter keyword "METADATA" for
> "SYSTEM_METADATA". Because "SYSTEM_METADATA" sounds like a system function
> and confuse users this looks like a computed column.
>
>
> Best,
> Jark
>
>
>
> On Wed, 9 Sep 2020 at 17:23, Danny Chan <[hidden email]> wrote:
>
>> "offset INT SYSTEM_METADATA("offset")"
>>
>> This is actually Oracle or MySQL style computed column syntax.
>>
>> "You are right that one could argue that "timestamp", "headers" are
>> something like "key" and "value""
>>
>> I have the same feeling, both key value and headers timestamp are *real*
>> data
>> stored in the consumed record, they are not computed or generated.
>>
>> "Trying to solve everything via properties sounds rather like a hack to
>> me"
>>
>> Things are not that hack if we can unify the routines or the definitions
>> (all from the computed column way or all from the table options), i also
>> think that it is a hacky that we mix in 2 kinds of syntax for different
>> kinds of metadata (read-only and read-write). In this FLIP, we declare the
>> Kafka key fields with table options but SYSTEM_METADATA for other metadata,
>> that is a hacky thing or something in-consistent.
>>
>> Kurt Young <[hidden email]> 于2020年9月9日周三 下午4:48写道：
>>
>>>   I would vote for `offset INT SYSTEM_METADATA("offset")`.
>>>
>>> I don't think we can stick with the SQL standard in DDL part forever,
>>> especially as there are more and more
>>> requirements coming from different connectors and external systems.
>>>
>>> Best,
>>> Kurt
>>>
>>>
>>> On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]> wrote:
>>>
>>>> Hi Jark,
>>>>
>>>> now we are back at the original design proposed by Dawid :D Yes, we
>>>> should be cautious about adding new syntax. But the length of this
>>>> discussion shows that we are looking for a good long-term solution. In
>>>> this case I would rather vote for a deep integration into the syntax.
>>>>
>>>> Computed columns are also not SQL standard compliant. And our DDL is
>>>> neither, so we have some degree of freedom here.
>>>>
>>>> Trying to solve everything via properties sounds rather like a hack to
>>>> me. You are right that one could argue that "timestamp", "headers" are
>>>> something like "key" and "value". However, mixing
>>>>
>>>> `offset AS SYSTEM_METADATA("offset")`
>>>>
>>>> and
>>>>
>>>> `'timestamp.field' = 'ts'`
>>>>
>>>> looks more confusing to users that an explicit
>>>>
>>>> `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
>>>>
>>>> or
>>>>
>>>> `offset INT SYSTEM_METADATA("offset")`
>>>>
>>>> that is symetric for both source and sink.
>>>>
>>>> What do others think?
>>>>
>>>> Regards,
>>>> Timo
>>>>
>>>>
>>>> On 09.09.20 10:09, Jark Wu wrote:
>>>>> Hi everyone,
>>>>>
>>>>> I think we have a conclusion that the writable metadata shouldn't be
>>>>> defined as a computed column, but a normal column.
>>>>>
>>>>> "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the
>>> approaches.
>>>>> However, it is not SQL standard compliant, we need to be cautious
>>> enough
>>>>> when adding new syntax.
>>>>> Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
>>>>> resolve the query-sink schema problem if it is read-only metadata.
>> That
>>>>> adds more stuff to learn for users.
>>>>>
>>>>> >From my point of view, the "timestamp", "headers" are something like
>>>> "key"
>>>>> and "value" that stores with the real data. So why not define the
>>>>> "timestamp" in the same way with "key" by using a "timestamp.field"
>>>>> connector option?
>>>>> On the other side, the read-only metadata, such as "offset",
>> shouldn't
>>> be
>>>>> defined as a normal column. So why not use the existing computed
>> column
>>>>> syntax for such metadata? Then we don't have the query-sink schema
>>>> problem.
>>>>> So here is my proposal:
>>>>>
>>>>> CREATE TABLE kafka_table (
>>>>>     id BIGINT,
>>>>>     name STRING,
>>>>>     col1 STRING,
>>>>>     col2 STRING,
>>>>>     ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field,
>> so
>>>> can
>>>>> be read and written.
>>>>>     offset AS SYSTEM_METADATA("offset")
>>>>> ) WITH (
>>>>>     'connector' = 'kafka',
>>>>>     'topic' = 'test-topic',
>>>>>     'key.fields' = 'id, name',
>>>>>     'key.format' = 'csv',
>>>>>     'value.format' = 'avro',
>>>>>     'timestamp.field' = 'ts'    -- define the mapping of Kafka
>> timestamp
>>>>> );
>>>>>
>>>>> INSERT INTO kafka_table
>>>>> SELECT id, name, col1, col2, rowtime FROM another_table;
>>>>>
>>>>> I think this can solve all the problems without introducing any new
>>>> syntax.
>>>>> The only minor disadvantage is that we separate the definition
>>> way/syntax
>>>>> of read-only metadata and read-write fields.
>>>>> However, I don't think this is a big problem.
>>>>>
>>>>> Best,
>>>>> Jark
>>>>>
>>>>>
>>>>> On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]>
>> wrote:
>>>>>
>>>>>> Hi Kurt,
>>>>>>
>>>>>> thanks for sharing your opinion. I'm totally up for not reusing
>>> computed
>>>>>> columns. I think Jark was a big supporter of this syntax, @Jark are
>>> you
>>>>>> fine with this as well? The non-computed column approach was only a
>>>>>> "slightly rejected alternative".
>>>>>>
>>>>>> Furthermore, we would need to think about how such a new design
>>>>>> influences the LIKE clause though.
>>>>>>
>>>>>> However, we should still keep the `PERSISTED` keyword as it
>> influences
>>>>>> the query->sink schema. If you look at the list of metadata for
>>> existing
>>>>>> connectors and formats, we currently offer only two writable
>> metadata
>>>>>> fields. Otherwise, one would need to declare two tables whenever a
>>>>>> metadata columns is read (one for the source, one for the sink).
>> This
>>>>>> can be quite inconvientient e.g. for just reading the topic.
>>>>>>
>>>>>> Regards,
>>>>>> Timo
>>>>>>
>>>>>>
>>>>>> On 09.09.20 08:52, Kurt Young wrote:
>>>>>>> I also share the concern that reusing the computed column syntax
>> but
>>>> have
>>>>>>> different semantics
>>>>>>> would confuse users a lot.
>>>>>>>
>>>>>>> Besides, I think metadata fields are conceptually not the same with
>>>>>>> computed columns. The metadata
>>>>>>> field is a connector specific thing and it only contains the
>>>> information
>>>>>>> that where does the field come
>>>>>>> from (during source) or where does the field need to write to
>> (during
>>>>>>> sink). It's more similar with normal
>>>>>>> fields, with assumption that all these fields need going to the
>> data
>>>>>> part.
>>>>>>>
>>>>>>> Thus I'm more lean to the rejected alternative that Timo mentioned.
>>>> And I
>>>>>>> think we don't need the
>>>>>>> PERSISTED keyword, SYSTEM_METADATA should be enough.
>>>>>>>
>>>>>>> During implementation, the framework only needs to pass such
>> <field,
>>>>>>> metadata field> information to the
>>>>>>> connector, and the logic of handling such fields inside the
>> connector
>>>>>>> should be straightforward.
>>>>>>>
>>>>>>> Regarding the downside Timo mentioned:
>>>>>>>
>>>>>>>> The disadvantage is that users cannot call UDFs or parse
>> timestamps.
>>>>>>>
>>>>>>> I think this is fairly simple to solve. Since the metadata field
>>> isn't
>>>> a
>>>>>>> computed column anymore, we can support
>>>>>>> referencing such fields in the computed column. For example:
>>>>>>>
>>>>>>> CREATE TABLE kafka_table (
>>>>>>>         id BIGINT,
>>>>>>>         name STRING,
>>>>>>>         timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
>>>>>> timestamp
>>>>>>> field from metadata
>>>>>>>         ts AS to_timestamp(timestamp) // normal computed column,
>> parse
>>>> the
>>>>>>> string to TIMESTAMP type by using the metadata field
>>>>>>> ) WITH (
>>>>>>>        ...
>>>>>>> )
>>>>>>>
>>>>>>> Best,
>>>>>>> Kurt
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
>>>> wrote:
>>>>>>>
>>>>>>>> Hi Leonard,
>>>>>>>>
>>>>>>>> the only alternative I see is that we introduce a concept that is
>>>>>>>> completely different to computed columns. This is also mentioned
>> in
>>>> the
>>>>>>>> rejected alternative section of the FLIP. Something like:
>>>>>>>>
>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>         id BIGINT,
>>>>>>>>         name STRING,
>>>>>>>>         timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
>>>>>>>>         headers MAP<STRING, BYTES> SYSTEM_METADATA("headers")
>>> PERSISTED
>>>>>>>> ) WITH (
>>>>>>>>        ...
>>>>>>>> )
>>>>>>>>
>>>>>>>> This way we would avoid confusion at all and can easily map
>> columns
>>> to
>>>>>>>> metadata columns. The disadvantage is that users cannot call UDFs
>> or
>>>>>>>> parse timestamps. This would need to be done in a real computed
>>>> column.
>>>>>>>>
>>>>>>>> I'm happy about better alternatives.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>> Timo
>>>>>>>>
>>>>>>>>
>>>>>>>> On 08.09.20 15:37, Leonard Xu wrote:
>>>>>>>>> HI, Timo
>>>>>>>>>
>>>>>>>>> Thanks for driving this FLIP.
>>>>>>>>>
>>>>>>>>> Sorry but I have a concern about Writing metadata via
>>>> DynamicTableSink
>>>>>>>> section:
>>>>>>>>>
>>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>>       id BIGINT,
>>>>>>>>>       name STRING,
>>>>>>>>>       timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
>>>>>> PERSISTED,
>>>>>>>>>       headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
>>>> BYTES>)
>>>>>>>> PERSISTED
>>>>>>>>> ) WITH (
>>>>>>>>>       ...
>>>>>>>>> )
>>>>>>>>> An insert statement could look like:
>>>>>>>>>
>>>>>>>>> INSERT INTO kafka_table VALUES (
>>>>>>>>>       (1, "ABC", 1599133672, MAP('checksum',
>> computeChecksum(...)))
>>>>>>>>> )
>>>>>>>>>
>>>>>>>>> The proposed INERT syntax does not make sense to me, because it
>>>>>> contains
>>>>>>>> computed(generated) column.
>>>>>>>>> Both SQL server and Postgresql do not allow to insert value to
>>>> computed
>>>>>>>> columns even they are persisted, this boke the generated column
>>>>>> semantics
>>>>>>>> and may confuse user much.
>>>>>>>>>
>>>>>>>>> For SQL server computed column[1]:
>>>>>>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT
>> NULL ]
>>>>>> ]...
>>>>>>>>>> NOTE: A computed column cannot be the target of an INSERT or
>>> UPDATE
>>>>>>>> statement.
>>>>>>>>>
>>>>>>>>> For Postgresql generated column[2]:
>>>>>>>>>>      height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
>>> STORED
>>>>>>>>>> NOTE: A generated column cannot be written to directly. In
>> INSERT
>>> or
>>>>>>>> UPDATE commands, a value cannot be specified for a generated
>> column,
>>>> but
>>>>>>>> the keyword DEFAULT may be specified.
>>>>>>>>>
>>>>>>>>> It shouldn't be allowed to set/update value for generated column
>>>> after
>>>>>>>> lookup the SQL 2016:
>>>>>>>>>> <insert statement> ::=
>>>>>>>>>> INSERT INTO <insertion target> <insert columns and source>
>>>>>>>>>>
>>>>>>>>>> If <contextually typed table value constructor> CTTVC is
>>> specified,
>>>>>>>> then every <contextually typed row
>>>>>>>>>> value constructor element> simply contained in CTTVC whose
>>>>>> positionally
>>>>>>>> corresponding <column name>
>>>>>>>>>> in <insert column list> references a column of which some
>>> underlying
>>>>>>>> column is a generated column shall
>>>>>>>>>> be a <default specification>.
>>>>>>>>>> A <default specification> specifies the default value of some
>>>>>>>> associated item.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> [1]
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>>>>>> <
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>>>>>>>
>>>>>>>>> [2]
>> https://www.postgresql.org/docs/12/ddl-generated-columns.html
>>> <
>>>>>>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
>>>>>>>>>
>>>>>>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>>>>>>>>>>
>>>>>>>>>> Hi Jark,
>>>>>>>>>>
>>>>>>>>>> according to Flink's and Calcite's casting definition in [1][2]
>>>>>>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If
>>> not,
>>>>>> we
>>>>>>>> will make it possible ;-)
>>>>>>>>>>
>>>>>>>>>> I'm aware of DeserializationSchema.getProducedType but I think
>>> that
>>>>>>>> this method is actually misplaced. The type should rather be
>> passed
>>> to
>>>>>> the
>>>>>>>> source itself.
>>>>>>>>>>
>>>>>>>>>> For our Kafka SQL source, we will also not use this method
>> because
>>>> the
>>>>>>>> Kafka source will add own metadata in addition to the
>>>>>>>> DeserializationSchema. So DeserializationSchema.getProducedType
>> will
>>>>>> never
>>>>>>>> be read.
>>>>>>>>>>
>>>>>>>>>> For now I suggest to leave out the `DataType` from
>>>>>>>> DecodingFormat.applyReadableMetadata. Also because the format's
>>>> physical
>>>>>>>> type is passed later in `createRuntimeDecoder`. If necessary, it
>> can
>>>> be
>>>>>>>> computed manually by consumedType + metadata types. We will
>> provide
>>> a
>>>>>>>> metadata utility class for that.
>>>>>>>>>>
>>>>>>>>>> Regards,
>>>>>>>>>> Timo
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> [1]
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
>>>>>>>>>> [2]
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 08.09.20 10:52, Jark Wu wrote:
>>>>>>>>>>> Hi Timo,
>>>>>>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I
>>> just
>>>>>>>> noticed
>>>>>>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL
>> TIME
>>>>>>>> ZONE".
>>>>>>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH
>> LOCAL
>>>>>> TIME
>>>>>>>>>>> ZONE" as the defined type of Kafka timestamp? I think this
>> makes
>>>>>> sense,
>>>>>>>>>>> because it represents the milli-seconds since epoch.
>>>>>>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I
>> don't
>>>>>> think
>>>>>>>> so.
>>>>>>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus
>>> the
>>>>>>>>>>> implementation needs to return an output TypeInfo.
>>>>>>>>>>> Besides, FlinkKafkaConsumer also
>>>>>>>>>>> calls DeserializationSchema.getProducedType as the produced
>> type
>>> of
>>>>>> the
>>>>>>>>>>> source function [1].
>>>>>>>>>>> Best,
>>>>>>>>>>> Jark
>>>>>>>>>>> [1]:
>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>>>>>>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
>>>>>> wrote:
>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>
>>>>>>>>>>>> I updated the FLIP again and hope that I could address the
>>>> mentioned
>>>>>>>>>>>> concerns.
>>>>>>>>>>>>
>>>>>>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that
>> ts_ms
>>>> and
>>>>>>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
>>>> expose
>>>>>>>> the
>>>>>>>>>>>> most commonly used properties separately. So frequently used
>>>>>>>> properties
>>>>>>>>>>>> are not hidden in the MAP anymore:
>>>>>>>>>>>>
>>>>>>>>>>>> debezium-json.ingestion-timestamp
>>>>>>>>>>>> debezium-json.source.timestamp
>>>>>>>>>>>> debezium-json.source.database
>>>>>>>>>>>> debezium-json.source.schema
>>>>>>>>>>>> debezium-json.source.table
>>>>>>>>>>>>
>>>>>>>>>>>> However, since other properties depend on the used
>>>> connector/vendor,
>>>>>>>> the
>>>>>>>>>>>> remaining options are stored in:
>>>>>>>>>>>>
>>>>>>>>>>>> debezium-json.source.properties
>>>>>>>>>>>>
>>>>>>>>>>>> And accessed with:
>>>>>>>>>>>>
>>>>>>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
>>>>>> MAP<STRING,
>>>>>>>>>>>> STRING>)['table']
>>>>>>>>>>>>
>>>>>>>>>>>> Otherwise it is not possible to figure out the value and
>> column
>>>> type
>>>>>>>>>>>> during validation.
>>>>>>>>>>>>
>>>>>>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I
>>> added
>>>> a
>>>>>>>>>>>> dedicacated sub-section to the FLIP:
>>>>>>>>>>>>
>>>>>>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
>>>>>> casting
>>>>>>>> we
>>>>>>>>>>>> allow explicit casting to a target data type:
>>>>>>>>>>>>
>>>>>>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3)
>>> WITH
>>>>>>>> LOCAL
>>>>>>>>>>>> TIME ZONE)
>>>>>>>>>>>>
>>>>>>>>>>>> A connector still produces and consumes the data type returned
>>> by
>>>>>>>>>>>> `listMetadata()`. The planner will insert necessary explicit
>>>> casts.
>>>>>>>>>>>>
>>>>>>>>>>>> In any case, the user must provide a CAST such that the
>> computed
>>>>>>>> column
>>>>>>>>>>>> receives a valid data type when constructing the table schema.
>>>>>>>>>>>>
>>>>>>>>>>>> "I don't see a reason why
>> `DecodingFormat#applyReadableMetadata`
>>>>>>>> needs a
>>>>>>>>>>>> DataType argument."
>>>>>>>>>>>>
>>>>>>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
>>>> always
>>>>>>>>>>>> executed locally. It is the source that needs TypeInfo for
>>>>>> serializing
>>>>>>>>>>>> the record to the next operator. And that's this is what we
>>>> provide.
>>>>>>>>>>>>
>>>>>>>>>>>> @Danny:
>>>>>>>>>>>>
>>>>>>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>>>>>>>
>>>>>>>>>>>> We can also use some other means to represent an UNKNOWN data
>>>> type.
>>>>>> In
>>>>>>>>>>>> the Flink type system, we use the NullType for it. The
>> important
>>>>>> part
>>>>>>>> is
>>>>>>>>>>>> that the final data type is known for the entire computed
>>> column.
>>>>>> As I
>>>>>>>>>>>> mentioned before, I would avoid the suggested option b) that
>>> would
>>>>>> be
>>>>>>>>>>>> similar to your suggestion. The CAST should be enough and
>> allows
>>>> for
>>>>>>>>>>>> complex expressions in the computed column. Option b) would
>> need
>>>>>>>> parser
>>>>>>>>>>>> changes.
>>>>>>>>>>>>
>>>>>>>>>>>> Regards,
>>>>>>>>>>>> Timo
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>>>>>>>>>>> Hi, Timo
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks for you explanation and update,  I have only one
>>> question
>>>>>> for
>>>>>>>>>>>> the latest FLIP.
>>>>>>>>>>>>>
>>>>>>>>>>>>> About the MAP<STRING, STRING> DataType of key
>>>>>>>> 'debezium-json.source', if
>>>>>>>>>>>> user want to use the table name metadata, they need to write:
>>>>>>>>>>>>> tableName STRING AS
>> CAST(SYSTEM_METADATA('debeuim-json.source')
>>>> AS
>>>>>>>>>>>> MAP<STRING, STRING>)['table']
>>>>>>>>>>>>>
>>>>>>>>>>>>> the expression is a little complex for user, Could we only
>>>> support
>>>>>>>>>>>> necessary metas with simple DataType as following?
>>>>>>>>>>>>> tableName STRING AS
>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>>>>>>>>>>> STRING),
>>>>>>>>>>>>> transactionTime LONG AS
>>>>>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>>>>>>>>>>
>>>>>>>>>>>>> In this way, we can simplify the expression, the mainly used
>>>>>>>> metadata in
>>>>>>>>>>>> changelog format may include
>>>>>>>> 'database','table','source.ts_ms','ts_ms' from
>>>>>>>>>>>> my side,
>>>>>>>>>>>>> maybe we could only support them at first version.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m
>>> willing
>>>>>> to
>>>>>>>>>>>> take some subtasks in next development if necessary.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Debezium:
>>>>>>>>>>>>> {
>>>>>>>>>>>>>        "before": null,
>>>>>>>>>>>>>        "after": {  "id": 101,"name": "scooter"},
>>>>>>>>>>>>>        "source": {
>>>>>>>>>>>>>          "db": "inventory",                  # 1. database
>> name
>>>> the
>>>>>>>>>>>> changelog belongs to.
>>>>>>>>>>>>>          "table": "products",                # 2. table name
>> the
>>>>>>>> changelog
>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>          "ts_ms": 1589355504100,             # 3. timestamp of
>>> the
>>>>>>>> change
>>>>>>>>>>>> happened in database system, i.e.: transaction time in
>> database.
>>>>>>>>>>>>>          "connector": "mysql",
>>>>>>>>>>>>>          ….
>>>>>>>>>>>>>        },
>>>>>>>>>>>>>        "ts_ms": 1589355606100,              # 4. timestamp
>> when
>>>> the
>>>>>>>> debezium
>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>        "op": "c",
>>>>>>>>>>>>>        "transaction": null
>>>>>>>>>>>>> }
>>>>>>>>>>>>>
>>>>>>>>>>>>> Canal:
>>>>>>>>>>>>> {
>>>>>>>>>>>>>        "data": [{  "id": "102", "name": "car battery" }],
>>>>>>>>>>>>>        "database": "inventory",      # 1. database name the
>>>> changelog
>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>        "table": "products",          # 2. table name the
>>> changelog
>>>>>>>> belongs
>>>>>>>>>>>> to.
>>>>>>>>>>>>>        "es": 1589374013000,          # 3. execution time of
>> the
>>>>>> change
>>>>>>>> in
>>>>>>>>>>>> database system, i.e.: transaction time in database.
>>>>>>>>>>>>>        "ts": 1589374013680,          # 4. timestamp when the
>>>> cannal
>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>        "isDdl": false,
>>>>>>>>>>>>>        "mysqlType": {},
>>>>>>>>>>>>>        ....
>>>>>>>>>>>>> }
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best
>>>>>>>>>>>>> Leonard
>>>>>>>>>>>>>
>>>>>>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks Timo ~
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
>>>> questions
>>>>>>>> here:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a
>> valid
>>>>>>>> read-only
>>>>>>>>>>>> computed column for Kafka and can be extracted by the
>> planner.”
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ?
>>> Usually
>>>> an
>>>>>>>>>>>> expression return type can be inferred automatically. But I
>>> guess
>>>>>>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
>>>>>> actually
>>>>>>>> does
>>>>>>>>>>>> not have a specific return type.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
>>>>>> [VIRTUAL]
>>>>>>>>>>>>>> Which is more straight-forward.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by
>>> default”
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The default type should not be NULL because only NULL
>> literal
>>>> does
>>>>>>>>>>>> that. Usually we use ANY as the type if we do not know the
>>>> specific
>>>>>>>> type in
>>>>>>>>>>>> the SQL context. ANY means the physical value can be any java
>>>>>> object.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [1]
>>> https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]
>>>> ，写道：
>>>>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I completely reworked FLIP-107. It now covers the full
>> story
>>>> how
>>>>>> to
>>>>>>>>>>>> read
>>>>>>>>>>>>>>> and write metadata from/to connectors and formats. It
>>> considers
>>>>>>>> all of
>>>>>>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
>>>>>>>> introduces
>>>>>>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
>>>>>>>> partitioning
>>>>>>>>>>>>>>> for now.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Looking forward to your feedback.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>> Timo
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>>>>>>>>>>> Sorry, forgot one question.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal?
>>> Like
>>>>>> one
>>>>>>>> can
>>>>>>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users
>> can
>>>> not
>>>>>>>>>>>> config to
>>>>>>>>>>>>>>>> just ignore timestamp but keep key.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <
>> [hidden email]
>>>>
>>>>>>>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi Dawid,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I have a couple of questions around key fields, actually
>> I
>>>> also
>>>>>>>> have
>>>>>>>>>>>> some
>>>>>>>>>>>>>>>>> other questions but want to be focused on key fields
>> first.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is
>>>> this
>>>>>>>>>>>> option only
>>>>>>>>>>>>>>>>> valid during write operation? Because for
>>>>>>>>>>>>>>>>> reading, I can't imagine how such options can be
>> applied. I
>>>>>> would
>>>>>>>>>>>> expect
>>>>>>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>>>>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I
>> want
>>>> to
>>>>>>>>>>>> propose we
>>>>>>>>>>>>>>>>> can simplify the options to not introducing
>> key.format.type
>>>> and
>>>>>>>>>>>>>>>>> other related options. I think a single "key.field" (not
>>>>>> fields)
>>>>>>>>>>>> would be
>>>>>>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>>>>>>>>>>> want before sink.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
>>>> connector
>>>>>>>> has a
>>>>>>>>>>>>>>>>> concept
>>>>>>>>>>>>>>>>> of key and values. The old parameter "format.type"
>> already
>>>> good
>>>>>>>>>>>> enough to
>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <
>> [hidden email]>
>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks Dawid,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I have two more questions.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> SupportsMetadata
>>>>>>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I
>> have
>>>>>> some
>>>>>>>>>>>> questions
>>>>>>>>>>>>>>>>>> regarding to this interface.
>>>>>>>>>>>>>>>>>> 1) How do the source know what the expected return type
>> of
>>>>>> each
>>>>>>>>>>>> metadata?
>>>>>>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the
>>> existing
>>>>>>>> physical
>>>>>>>>>>>>>>>>>> fields?
>>>>>>>>>>>>>>>>>> If yes, I would suggest to change the signature to
>>>>>> `TableSource
>>>>>>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>>>>>>>>>>> metadataTypes)`
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a
>>> computed
>>>>>>>> column
>>>>>>>>>>>>>>>>>> expression? If yes, how to specify the return type of
>>>>>>>>>>>> SYSTEM_METADATA?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit
>> the
>>>>>>>> columns
>>>>>>>>>>>> and I
>>>>>>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I
>>> see
>>>> a
>>>>>>>> need
>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
>>>> Jark.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that.
>>> One
>>>>>>>>>>>> additional
>>>>>>>>>>>>>>>>>>> things I would introduce is an
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
>>>>>> metadataFields);
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> This way the source would have to declare/emit only the
>>>>>>>> requested
>>>>>>>>>>>>>>>>>>> metadata fields. In order not to clash with user
>> defined
>>>>>>>> fields.
>>>>>>>>>>>> When
>>>>>>>>>>>>>>>>>>> emitting the metadata field I would prepend the column
>>> name
>>>>>>>> with
>>>>>>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
>>>> field
>>>>>>>>>>>>>>>>>>> __system_partition to the schema. This would be never
>>>> visible
>>>>>>>> to
>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> user as it would be used only for the subsequent
>> computed
>>>>>>>> columns.
>>>>>>>>>>>> If
>>>>>>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with
>> this
>>>>>>>>>>>> description.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state
>> of
>>>> the
>>>>>>>>>>>> proposal.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
>>>>>>>> orthogonal. I
>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>> leave out the STORED computed columns out of the
>>>> discussion.
>>>>>> I
>>>>>>>>>>>> don't see
>>>>>>>>>>>>>>>>>>> how do they relate to the partitioning. I already put
>>> both
>>>> of
>>>>>>>> those
>>>>>>>>>>>>>>>>>>> cases in the document. We can either partition on a
>>>> computed
>>>>>>>>>>>> column or
>>>>>>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with
>>> leaving
>>>>>> out
>>>>>>>> the
>>>>>>>>>>>>>>>>>>> partitioning by udf in the first version if you still
>>> have
>>>>>> some
>>>>>>>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> As for your question Danny. It depends which
>> partitioning
>>>>>>>> strategy
>>>>>>>>>>>> you
>>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would
>>> work
>>>> as
>>>>>>>> you
>>>>>>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not
>> sure
>>>>>>>> though if
>>>>>>>>>>>> we
>>>>>>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink
>>> does
>>>>>> not
>>>>>>>> own
>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> data and the partitions are already an intrinsic
>> property
>>>> of
>>>>>>>> the
>>>>>>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create
>> topics,
>>>> but
>>>>>>>> we
>>>>>>>>>>>> just
>>>>>>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs
>> ...
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
>>>>>> consistent
>>>>>>>> with
>>>>>>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was
>> also
>>>> my
>>>>>>>>>>>> initial
>>>>>>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
>>>> afterwards
>>>>>>>> to
>>>>>>>>>>>> shorten
>>>>>>>>>>>>>>>>>>> the key.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think
>> it
>>>> is
>>>>>> a
>>>>>>>>>>>> useful
>>>>>>>>>>>>>>>>>>> feature ~
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column
>>> push
>>>>>>>> down is
>>>>>>>>>>>>>>>>>>> another topic, this should not be a blocker but a
>>>> promotion,
>>>>>>>> if we
>>>>>>>>>>>> do
>>>>>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>>>>>> have any filters on the computed column, there is no
>> need
>>>> to
>>>>>>>> do any
>>>>>>>>>>>>>>>>>>> pushings; the source node just emit the complete record
>>>> with
>>>>>>>> full
>>>>>>>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>>> with the declared physical schema, then when generating
>>> the
>>>>>>>> virtual
>>>>>>>>>>>>>>>>>>> columns, we would extract the metadata info and output
>> as
>>>>>> full
>>>>>>>>>>>>>>>>>> columns(with
>>>>>>>>>>>>>>>>>>> full schema).
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST,
>> they
>>>> are
>>>>>>>>>>>> symantic
>>>>>>>>>>>>>>>>>>> equivalent though, explict type is more
>> straight-forward
>>>> and
>>>>>>>> we can
>>>>>>>>>>>>>>>>>> declare
>>>>>>>>>>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column
>>> VS
>>>>>>>> option
>>>>>>>>>>>> B:
>>>>>>>>>>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>      From the FLIP, it seems that B's partitioning is
>>> just
>>>> a
>>>>>>>> strategy
>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>> writing data, the partiton column is not included in
>> the
>>>>>> table
>>>>>>>>>>>> schema,
>>>>>>>>>>>>>>>>>> so
>>>>>>>>>>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the
>>> partition
>>>>>>>> column
>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we
>>> want
>>>>>> to
>>>>>>>>>>>> persist
>>>>>>>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
>>>> both, i
>>>>>>>> saw
>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
>>>>>> PARTITIONS
>>>>>>>>>>>> num, and
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
>>>>>> partition
>>>>>>>> in
>>>>>>>>>>>> which
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> record is stored is partition number N, where N =
>>> MOD(expr,
>>>>>>>> num),
>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>> your
>>>>>>>>>>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> [1]
>>>>>>>>>>>>
>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
>>>>>>>> [hidden email]
>>>>>>>>>>>>>>>>>>> ，写道：
>>>>>>>>>>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to
>> FLIP-63
>>>>>>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
>>>>>> (prefixing
>>>>>>>> with
>>>>>>>>>>>>>>>>>>> connector or kafka). I should've put that into an
>>>>>>>> Option/Rejected
>>>>>>>>>>>>>>>>>>> alternatives.
>>>>>>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
>>>> properties.
>>>>>>>> Why I
>>>>>>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
>>>> version
>>>>>>>> is
>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>> actually all the properties in the WITH section are
>>>> connector
>>>>>>>>>>>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>> Even format is in the end a connector property as some
>> of
>>>> the
>>>>>>>>>>>> sources
>>>>>>>>>>>>>>>>>> might
>>>>>>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
>>>> prefix
>>>>>> is
>>>>>>>>>>>> that it
>>>>>>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
>>>>>>>> properties
>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>> connector (or if we go with FLINK-12557:
>> elasticsearch):
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
>>>>>>>> approach
>>>>>>>>>>>> in the
>>>>>>>>>>>>>>>>>>> community.
>>>>>>>>>>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include`
>> property.
>>>> It
>>>>>>>>>>>> should be
>>>>>>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested
>> in
>>>> the
>>>>>>>>>>>> comment,
>>>>>>>>>>>>>>>>>>> right?
>>>>>>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
>>>>>> column.
>>>>>>>> I
>>>>>>>>>>>> think
>>>>>>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
>>>>>>>> expression
>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>> later
>>>>>>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason
>>> is
>>>> I
>>>>>>>> think
>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>> way
>>>>>>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs
>>> when
>>>>>>>> working
>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
>>>> offset, i
>>>>>>>>>>>> think it's
>>>>>>>>>>>>>>>>>>> better to pushdown long rather than string. This could
>>> let
>>>> us
>>>>>>>> push
>>>>>>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
>>>>>>>> Otherwise we
>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
>>>> cast(offset,
>>>>>>>> long)
>>>>>>>>>>>> <
>>>>>>>>>>>>>>>>>> 59382.
>>>>>>>>>>>>>>>>>>> Moreover I think we need to introduce the type for
>>> computed
>>>>>>>> columns
>>>>>>>>>>>>>>>>>> anyway
>>>>>>>>>>>>>>>>>>> to support functions that infer output type based on
>>>> expected
>>>>>>>>>>>> return
>>>>>>>>>>>>>>>>>> type.
>>>>>>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
>>>> SYSTEM_METADATA
>>>>>>>> would
>>>>>>>>>>>> have
>>>>>>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible
>>> the
>>>>>>>> planner
>>>>>>>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>> fail. As far as I know computed columns push down will
>> be
>>>>>> part
>>>>>>>> of
>>>>>>>>>>>> source
>>>>>>>>>>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
>>>>>>>> completely
>>>>>>>>>>>>>>>>>>> orthogonal. In my current proposal you can also
>> partition
>>>> by
>>>>>> a
>>>>>>>>>>>> computed
>>>>>>>>>>>>>>>>>>> column. The difference between using a udf in
>> partitioned
>>>> by
>>>>>> vs
>>>>>>>>>>>>>>>>>> partitioned
>>>>>>>>>>>>>>>>>>> by a computed column is that when you partition by a
>>>> computed
>>>>>>>>>>>> column
>>>>>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>> column must be also computed when reading the table. If
>>> you
>>>>>>>> use a
>>>>>>>>>>>> udf in
>>>>>>>>>>>>>>>>>>> the partitioned by, the expression is computed only
>> when
>>>>>>>> inserting
>>>>>>>>>>>> into
>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> table.
>>>>>>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking
>>> forward
>>>>>> for
>>>>>>>>>>>> further
>>>>>>>>>>>>>>>>>>> suggestions.
>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
>>>> Reaing
>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>>>>> key-part information from source is an important
>>> feature
>>>>>> for
>>>>>>>>>>>>>>>>>> streaming
>>>>>>>>>>>>>>>>>>>>>> users.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of
>>> introducing
>>>>>>>> HEADER
>>>>>>>>>>>>>>>>>>> keyword as
>>>>>>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
>>>> Maybe
>>>>>> we
>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>> add a
>>>>>>>>>>>>>>>>>>>>>> section to explain what's the relationship between
>>> them.
>>>>>>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION
>> be
>>>> used
>>>>>>>> on
>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink
>>> SQL.
>>>>>>>> Shall we
>>>>>>>>>>>>>>>>>> make
>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
>>>>>>>> (actually, I
>>>>>>>>>>>>>>>>>>> prefer
>>>>>>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
>>>>>>>> properties
>>>>>>>>>>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users
>>>> that
>>>>>>>> the
>>>>>>>>>>>>>>>>>> field
>>>>>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
>>>> enable
>>>>>>>>>>>> reading
>>>>>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>>>>>>>> columns from different parts of source records.
>>> Besides
>>>>>> the
>>>>>>>>>>>> main
>>>>>>>>>>>>>>>>>>> payload
>>>>>>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose
>>> additional
>>>>>>>>>>>>>>>>>> information. It
>>>>>>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
>>>>>>>> ingestion
>>>>>>>>>>>> time
>>>>>>>>>>>>>>>>>> or a
>>>>>>>>>>>>>>>>>>>>>>> read and write parts of the record that contain
>> data
>>>> but
>>>>>>>>>>>>>>>>>> additionally
>>>>>>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
>>>> etc.),
>>>>>>>> e.g.
>>>>>>>>>>>> key
>>>>>>>>>>>>>>>>>> or
>>>>>>>>>>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> We should make it possible to read and write data
>>> from
>>>>>> all
>>>>>>>> of
>>>>>>>>>>>> those
>>>>>>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
>>>>>> partitioning
>>>>>>>>>>>> data,
>>>>>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
>>>>>> partitioning
>>>>>>>> when
>>>>>>>>>>>>>>>>>>> writing
>>>>>>>>>>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>

[dwysakowicz]

Hi,

Sorry for joining so late. First of all, I don't want to distract the
discussion, but I thought maybe my opinion could help a bit, but maybe
it won't ;)

The first observation I got is that I think everyone agrees we need a
way distinguish the read-only from r/w columns. Is that correct?

Secondly if I understand the discussion correctly there are three
competing approaches:

Option 1)

If a metadata column is r/w use the WITH section for declaring such field

If a metadata column is r use computed column e.g.: offset AS
CAST(SYSTEM_METADATA("offset") AS long)

Option 2)

Use the computed column syntax, but add a keyword for marking a column
writable e.g.:

r-only: offset AS CAST(SYSTEM_METADATA("offset") AS long)

r/w: timestamp AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3))
PERSISTED/WRITABLE/STORED

Option 3)

Use a new syntax, not to confuse it with computed columns.

r-only: offset USING/FROM/(blank) SYSTEM_METADATA("offset")

r/w-only: timestamp USING/FROM/(blank) SYSTEM_METADATA("timestamp")
PERSISTED/WRITABLE/STORED

My personal preference is in that order 1>2>3. Let me explain why I
think that.

Ad. 1

I sort of agree with @Jark and @Danny that if a field is readable and
writable than it is actually a *real* data. Moreover I think in Kafka it
is quite common to include a field in all different parts of the record.
Take this code snippet from ksqlDB for example[1].

I understand @Timo's argument that it would not be too generic if we had
more writable columns. But at least the way I see it, so far we have
only a single r/w metadata field: timestamp. I am not sure if we should
make the Kafka's headers writable. As per the motivation in the FLIP it
introduced them, they are mostly for system meta-information, which does
not necessarily contain business logic[2]. There are no more metadata
columns marked as writable in the FLIP, as far as I can tell.

The additional benefit is that the concept of computed columns is
intact. They are only ever computed and you can not store into the columns.

Ad. 2

The option two is more flexible than option 3, because it allows for
computed expressions. In some sense this is also its disadvantage
because computed expressions can not be used for r/w columns. Therefore
we are loosing the flexibility for STORED/PERSISTED/WRITABLE columns.

Ad. 3

The argument that reusing computed columns can be misleading does not
really appeal to me. I think any new syntax that a user needs to learn
is equally misleading. The only benefit I see is that it makes the
situation more symmetric, as you cannot have computed expressions for
both r-only and r/w columns, which at the same time is a disadvantage of
that proposal.

As for the issue of shortening the SYSTEM_METADATA to METADATA. Here I
very much prefer the SYSTEM_ prefix. In my opinion in this case the
clarity is more important than brevity. Moreover personally I never
found a couple of letters that are usually copy-pasted, or
auto-completed a real problem. This might be though my personal preference.

Hope I will not distract the discussion too much.

Best,

Dawid

[1]
https://docs.ksqldb.io/en/latest/developer-guide/create-a-stream/#create-a-stream-with-timestamps

[2]
https://cwiki.apache.org/confluence/display/KAFKA/KIP-82+-+Add+Record+Headers#KIP82AddRecordHeaders-Motivation

On 09/09/2020 12:40, Timo Walther wrote:

> Hi everyone,
>
> "key" and "value" in the properties are a special case because they
> need to configure a format. So key and value are more than just
> metadata. Jark's example for setting a timestamp would work but as the
> FLIP discusses, we have way more metadata fields like headers,
> epoch-leader, etc. Having a property for all of this metadata would
> mess up the WITH section entirely. Furthermore, we also want to deal
> with metadata from the formats. Solving this through properties as
> well would further complicate the property design.
>
> Personally, I still like the computed column design more because it
> allows to have full flexibility to compute the final column:
>
> timestamp AS adjustTimestamp(CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3)))
>
> Instead of having a helper column and a real column in the table:
>
> helperTimestamp AS CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3))
> realTimestamp AS adjustTimestamp(helperTimestamp)
>
> But I see that the discussion leans towards:
>
> timestamp INT SYSTEM_METADATA("ts")
>
> Which is fine with me. It is the shortest solution, because we don't
> need additional CAST. We can discuss the syntax, so that confusion
> with computed columns can be avoided.
>
> timestamp INT USING SYSTEM_METADATA("ts")
> timestamp INT FROM SYSTEM_METADATA("ts")
> timestamp INT FROM SYSTEM_METADATA("ts") PERSISTED
>
> We use `SYSTEM_TIME` for temporal tables. I think prefixing with
> SYSTEM makes it clearer that it comes magically from the system.
>
> What do you think?
>
> Regards,
> Timo
>
>
>
> On 09.09.20 11:41, Jark Wu wrote:
>> Hi Danny,
>>
>> This is not Oracle and MySQL computed column syntax, because there is no
>> "AS" after the type.
>>
>> Hi everyone,
>>
>> If we want to use "offset INT SYSTEM_METADATA("offset")", then I
>> think we
>> must further discuss about "PERSISED" or "VIRTUAL" keyword for
>> query-sink
>> schema problem.
>> Personally, I think we can use a shorter keyword "METADATA" for
>> "SYSTEM_METADATA". Because "SYSTEM_METADATA" sounds like a system
>> function
>> and confuse users this looks like a computed column.
>>
>>
>> Best,
>> Jark
>>
>>
>>
>> On Wed, 9 Sep 2020 at 17:23, Danny Chan <[hidden email]> wrote:
>>
>>> "offset INT SYSTEM_METADATA("offset")"
>>>
>>> This is actually Oracle or MySQL style computed column syntax.
>>>
>>> "You are right that one could argue that "timestamp", "headers" are
>>> something like "key" and "value""
>>>
>>> I have the same feeling, both key value and headers timestamp are
>>> *real*
>>> data
>>> stored in the consumed record, they are not computed or generated.
>>>
>>> "Trying to solve everything via properties sounds rather like a hack to
>>> me"
>>>
>>> Things are not that hack if we can unify the routines or the
>>> definitions
>>> (all from the computed column way or all from the table options), i
>>> also
>>> think that it is a hacky that we mix in 2 kinds of syntax for different
>>> kinds of metadata (read-only and read-write). In this FLIP, we
>>> declare the
>>> Kafka key fields with table options but SYSTEM_METADATA for other
>>> metadata,
>>> that is a hacky thing or something in-consistent.
>>>
>>> Kurt Young <[hidden email]> 于2020年9月9日周三 下午4:48写道：
>>>
>>>>   I would vote for `offset INT SYSTEM_METADATA("offset")`.
>>>>
>>>> I don't think we can stick with the SQL standard in DDL part forever,
>>>> especially as there are more and more
>>>> requirements coming from different connectors and external systems.
>>>>
>>>> Best,
>>>> Kurt
>>>>
>>>>
>>>> On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]>
>>>> wrote:
>>>>
>>>>> Hi Jark,
>>>>>
>>>>> now we are back at the original design proposed by Dawid :D Yes, we
>>>>> should be cautious about adding new syntax. But the length of this
>>>>> discussion shows that we are looking for a good long-term
>>>>> solution. In
>>>>> this case I would rather vote for a deep integration into the syntax.
>>>>>
>>>>> Computed columns are also not SQL standard compliant. And our DDL is
>>>>> neither, so we have some degree of freedom here.
>>>>>
>>>>> Trying to solve everything via properties sounds rather like a
>>>>> hack to
>>>>> me. You are right that one could argue that "timestamp", "headers"
>>>>> are
>>>>> something like "key" and "value". However, mixing
>>>>>
>>>>> `offset AS SYSTEM_METADATA("offset")`
>>>>>
>>>>> and
>>>>>
>>>>> `'timestamp.field' = 'ts'`
>>>>>
>>>>> looks more confusing to users that an explicit
>>>>>
>>>>> `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
>>>>>
>>>>> or
>>>>>
>>>>> `offset INT SYSTEM_METADATA("offset")`
>>>>>
>>>>> that is symetric for both source and sink.
>>>>>
>>>>> What do others think?
>>>>>
>>>>> Regards,
>>>>> Timo
>>>>>
>>>>>
>>>>> On 09.09.20 10:09, Jark Wu wrote:
>>>>>> Hi everyone,
>>>>>>
>>>>>> I think we have a conclusion that the writable metadata shouldn't be
>>>>>> defined as a computed column, but a normal column.
>>>>>>
>>>>>> "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the
>>>> approaches.
>>>>>> However, it is not SQL standard compliant, we need to be cautious
>>>> enough
>>>>>> when adding new syntax.
>>>>>> Besides, we have to introduce the `PERSISTED` or `VIRTUAL`
>>>>>> keyword to
>>>>>> resolve the query-sink schema problem if it is read-only metadata.
>>> That
>>>>>> adds more stuff to learn for users.
>>>>>>
>>>>>> >From my point of view, the "timestamp", "headers" are something
>>>>>> like
>>>>> "key"
>>>>>> and "value" that stores with the real data. So why not define the
>>>>>> "timestamp" in the same way with "key" by using a "timestamp.field"
>>>>>> connector option?
>>>>>> On the other side, the read-only metadata, such as "offset",
>>> shouldn't
>>>> be
>>>>>> defined as a normal column. So why not use the existing computed
>>> column
>>>>>> syntax for such metadata? Then we don't have the query-sink schema
>>>>> problem.
>>>>>> So here is my proposal:
>>>>>>
>>>>>> CREATE TABLE kafka_table (
>>>>>>     id BIGINT,
>>>>>>     name STRING,
>>>>>>     col1 STRING,
>>>>>>     col2 STRING,
>>>>>>     ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal
>>>>>> field,
>>> so
>>>>> can
>>>>>> be read and written.
>>>>>>     offset AS SYSTEM_METADATA("offset")
>>>>>> ) WITH (
>>>>>>     'connector' = 'kafka',
>>>>>>     'topic' = 'test-topic',
>>>>>>     'key.fields' = 'id, name',
>>>>>>     'key.format' = 'csv',
>>>>>>     'value.format' = 'avro',
>>>>>>     'timestamp.field' = 'ts'    -- define the mapping of Kafka
>>> timestamp
>>>>>> );
>>>>>>
>>>>>> INSERT INTO kafka_table
>>>>>> SELECT id, name, col1, col2, rowtime FROM another_table;
>>>>>>
>>>>>> I think this can solve all the problems without introducing any new
>>>>> syntax.
>>>>>> The only minor disadvantage is that we separate the definition
>>>> way/syntax
>>>>>> of read-only metadata and read-write fields.
>>>>>> However, I don't think this is a big problem.
>>>>>>
>>>>>> Best,
>>>>>> Jark
>>>>>>
>>>>>>
>>>>>> On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]>
>>> wrote:
>>>>>>
>>>>>>> Hi Kurt,
>>>>>>>
>>>>>>> thanks for sharing your opinion. I'm totally up for not reusing
>>>> computed
>>>>>>> columns. I think Jark was a big supporter of this syntax, @Jark are
>>>> you
>>>>>>> fine with this as well? The non-computed column approach was only a
>>>>>>> "slightly rejected alternative".
>>>>>>>
>>>>>>> Furthermore, we would need to think about how such a new design
>>>>>>> influences the LIKE clause though.
>>>>>>>
>>>>>>> However, we should still keep the `PERSISTED` keyword as it
>>> influences
>>>>>>> the query->sink schema. If you look at the list of metadata for
>>>> existing
>>>>>>> connectors and formats, we currently offer only two writable
>>> metadata
>>>>>>> fields. Otherwise, one would need to declare two tables whenever a
>>>>>>> metadata columns is read (one for the source, one for the sink).
>>> This
>>>>>>> can be quite inconvientient e.g. for just reading the topic.
>>>>>>>
>>>>>>> Regards,
>>>>>>> Timo
>>>>>>>
>>>>>>>
>>>>>>> On 09.09.20 08:52, Kurt Young wrote:
>>>>>>>> I also share the concern that reusing the computed column syntax
>>> but
>>>>> have
>>>>>>>> different semantics
>>>>>>>> would confuse users a lot.
>>>>>>>>
>>>>>>>> Besides, I think metadata fields are conceptually not the same
>>>>>>>> with
>>>>>>>> computed columns. The metadata
>>>>>>>> field is a connector specific thing and it only contains the
>>>>> information
>>>>>>>> that where does the field come
>>>>>>>> from (during source) or where does the field need to write to
>>> (during
>>>>>>>> sink). It's more similar with normal
>>>>>>>> fields, with assumption that all these fields need going to the
>>> data
>>>>>>> part.
>>>>>>>>
>>>>>>>> Thus I'm more lean to the rejected alternative that Timo
>>>>>>>> mentioned.
>>>>> And I
>>>>>>>> think we don't need the
>>>>>>>> PERSISTED keyword, SYSTEM_METADATA should be enough.
>>>>>>>>
>>>>>>>> During implementation, the framework only needs to pass such
>>> <field,
>>>>>>>> metadata field> information to the
>>>>>>>> connector, and the logic of handling such fields inside the
>>> connector
>>>>>>>> should be straightforward.
>>>>>>>>
>>>>>>>> Regarding the downside Timo mentioned:
>>>>>>>>
>>>>>>>>> The disadvantage is that users cannot call UDFs or parse
>>> timestamps.
>>>>>>>>
>>>>>>>> I think this is fairly simple to solve. Since the metadata field
>>>> isn't
>>>>> a
>>>>>>>> computed column anymore, we can support
>>>>>>>> referencing such fields in the computed column. For example:
>>>>>>>>
>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>         id BIGINT,
>>>>>>>>         name STRING,
>>>>>>>>         timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
>>>>>>> timestamp
>>>>>>>> field from metadata
>>>>>>>>         ts AS to_timestamp(timestamp) // normal computed column,
>>> parse
>>>>> the
>>>>>>>> string to TIMESTAMP type by using the metadata field
>>>>>>>> ) WITH (
>>>>>>>>        ...
>>>>>>>> )
>>>>>>>>
>>>>>>>> Best,
>>>>>>>> Kurt
>>>>>>>>
>>>>>>>>
>>>>>>>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi Leonard,
>>>>>>>>>
>>>>>>>>> the only alternative I see is that we introduce a concept that is
>>>>>>>>> completely different to computed columns. This is also mentioned
>>> in
>>>>> the
>>>>>>>>> rejected alternative section of the FLIP. Something like:
>>>>>>>>>
>>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>>         id BIGINT,
>>>>>>>>>         name STRING,
>>>>>>>>>         timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
>>>>>>>>>         headers MAP<STRING, BYTES> SYSTEM_METADATA("headers")
>>>> PERSISTED
>>>>>>>>> ) WITH (
>>>>>>>>>        ...
>>>>>>>>> )
>>>>>>>>>
>>>>>>>>> This way we would avoid confusion at all and can easily map
>>> columns
>>>> to
>>>>>>>>> metadata columns. The disadvantage is that users cannot call UDFs
>>> or
>>>>>>>>> parse timestamps. This would need to be done in a real computed
>>>>> column.
>>>>>>>>>
>>>>>>>>> I'm happy about better alternatives.
>>>>>>>>>
>>>>>>>>> Regards,
>>>>>>>>> Timo
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 08.09.20 15:37, Leonard Xu wrote:
>>>>>>>>>> HI, Timo
>>>>>>>>>>
>>>>>>>>>> Thanks for driving this FLIP.
>>>>>>>>>>
>>>>>>>>>> Sorry but I have a concern about Writing metadata via
>>>>> DynamicTableSink
>>>>>>>>> section:
>>>>>>>>>>
>>>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>>>       id BIGINT,
>>>>>>>>>>       name STRING,
>>>>>>>>>>       timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
>>>>>>> PERSISTED,
>>>>>>>>>>       headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
>>>>> BYTES>)
>>>>>>>>> PERSISTED
>>>>>>>>>> ) WITH (
>>>>>>>>>>       ...
>>>>>>>>>> )
>>>>>>>>>> An insert statement could look like:
>>>>>>>>>>
>>>>>>>>>> INSERT INTO kafka_table VALUES (
>>>>>>>>>>       (1, "ABC", 1599133672, MAP('checksum',
>>> computeChecksum(...)))
>>>>>>>>>> )
>>>>>>>>>>
>>>>>>>>>> The proposed INERT syntax does not make sense to me, because it
>>>>>>> contains
>>>>>>>>> computed(generated) column.
>>>>>>>>>> Both SQL server and Postgresql do not allow to insert value to
>>>>> computed
>>>>>>>>> columns even they are persisted, this boke the generated column
>>>>>>> semantics
>>>>>>>>> and may confuse user much.
>>>>>>>>>>
>>>>>>>>>> For SQL server computed column[1]:
>>>>>>>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT
>>> NULL ]
>>>>>>> ]...
>>>>>>>>>>> NOTE: A computed column cannot be the target of an INSERT or
>>>> UPDATE
>>>>>>>>> statement.
>>>>>>>>>>
>>>>>>>>>> For Postgresql generated column[2]:
>>>>>>>>>>>      height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
>>>> STORED
>>>>>>>>>>> NOTE: A generated column cannot be written to directly. In
>>> INSERT
>>>> or
>>>>>>>>> UPDATE commands, a value cannot be specified for a generated
>>> column,
>>>>> but
>>>>>>>>> the keyword DEFAULT may be specified.
>>>>>>>>>>
>>>>>>>>>> It shouldn't be allowed to set/update value for generated column
>>>>> after
>>>>>>>>> lookup the SQL 2016:
>>>>>>>>>>> <insert statement> ::=
>>>>>>>>>>> INSERT INTO <insertion target> <insert columns and source>
>>>>>>>>>>>
>>>>>>>>>>> If <contextually typed table value constructor> CTTVC is
>>>> specified,
>>>>>>>>> then every <contextually typed row
>>>>>>>>>>> value constructor element> simply contained in CTTVC whose
>>>>>>> positionally
>>>>>>>>> corresponding <column name>
>>>>>>>>>>> in <insert column list> references a column of which some
>>>> underlying
>>>>>>>>> column is a generated column shall
>>>>>>>>>>> be a <default specification>.
>>>>>>>>>>> A <default specification> specifies the default value of some
>>>>>>>>> associated item.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> [1]
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>
>>>>>>>>> <
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>
>>>>>>>>>>
>>>>>>>>>> [2]
>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html
>>>> <
>>>>>>>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
>>>>>>>>>>
>>>>>>>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>
>>>>>>>>>>> according to Flink's and Calcite's casting definition in [1][2]
>>>>>>>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If
>>>> not,
>>>>>>> we
>>>>>>>>> will make it possible ;-)
>>>>>>>>>>>
>>>>>>>>>>> I'm aware of DeserializationSchema.getProducedType but I think
>>>> that
>>>>>>>>> this method is actually misplaced. The type should rather be
>>> passed
>>>> to
>>>>>>> the
>>>>>>>>> source itself.
>>>>>>>>>>>
>>>>>>>>>>> For our Kafka SQL source, we will also not use this method
>>> because
>>>>> the
>>>>>>>>> Kafka source will add own metadata in addition to the
>>>>>>>>> DeserializationSchema. So DeserializationSchema.getProducedType
>>> will
>>>>>>> never
>>>>>>>>> be read.
>>>>>>>>>>>
>>>>>>>>>>> For now I suggest to leave out the `DataType` from
>>>>>>>>> DecodingFormat.applyReadableMetadata. Also because the format's
>>>>> physical
>>>>>>>>> type is passed later in `createRuntimeDecoder`. If necessary, it
>>> can
>>>>> be
>>>>>>>>> computed manually by consumedType + metadata types. We will
>>> provide
>>>> a
>>>>>>>>> metadata utility class for that.
>>>>>>>>>>>
>>>>>>>>>>> Regards,
>>>>>>>>>>> Timo
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> [1]
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
>>>
>>>>>>>>>>> [2]
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 08.09.20 10:52, Jark Wu wrote:
>>>>>>>>>>>> Hi Timo,
>>>>>>>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I
>>>> just
>>>>>>>>> noticed
>>>>>>>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL
>>> TIME
>>>>>>>>> ZONE".
>>>>>>>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH
>>> LOCAL
>>>>>>> TIME
>>>>>>>>>>>> ZONE" as the defined type of Kafka timestamp? I think this
>>> makes
>>>>>>> sense,
>>>>>>>>>>>> because it represents the milli-seconds since epoch.
>>>>>>>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I
>>> don't
>>>>>>> think
>>>>>>>>> so.
>>>>>>>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus
>>>> the
>>>>>>>>>>>> implementation needs to return an output TypeInfo.
>>>>>>>>>>>> Besides, FlinkKafkaConsumer also
>>>>>>>>>>>> calls DeserializationSchema.getProducedType as the produced
>>> type
>>>> of
>>>>>>> the
>>>>>>>>>>>> source function [1].
>>>>>>>>>>>> Best,
>>>>>>>>>>>> Jark
>>>>>>>>>>>> [1]:
>>>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>>>
>>>>>>>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
>>>>>>> wrote:
>>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>>
>>>>>>>>>>>>> I updated the FLIP again and hope that I could address the
>>>>> mentioned
>>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>
>>>>>>>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that
>>> ts_ms
>>>>> and
>>>>>>>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
>>>>> expose
>>>>>>>>> the
>>>>>>>>>>>>> most commonly used properties separately. So frequently used
>>>>>>>>> properties
>>>>>>>>>>>>> are not hidden in the MAP anymore:
>>>>>>>>>>>>>
>>>>>>>>>>>>> debezium-json.ingestion-timestamp
>>>>>>>>>>>>> debezium-json.source.timestamp
>>>>>>>>>>>>> debezium-json.source.database
>>>>>>>>>>>>> debezium-json.source.schema
>>>>>>>>>>>>> debezium-json.source.table
>>>>>>>>>>>>>
>>>>>>>>>>>>> However, since other properties depend on the used
>>>>> connector/vendor,
>>>>>>>>> the
>>>>>>>>>>>>> remaining options are stored in:
>>>>>>>>>>>>>
>>>>>>>>>>>>> debezium-json.source.properties
>>>>>>>>>>>>>
>>>>>>>>>>>>> And accessed with:
>>>>>>>>>>>>>
>>>>>>>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
>>>>>>> MAP<STRING,
>>>>>>>>>>>>> STRING>)['table']
>>>>>>>>>>>>>
>>>>>>>>>>>>> Otherwise it is not possible to figure out the value and
>>> column
>>>>> type
>>>>>>>>>>>>> during validation.
>>>>>>>>>>>>>
>>>>>>>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I
>>>> added
>>>>> a
>>>>>>>>>>>>> dedicacated sub-section to the FLIP:
>>>>>>>>>>>>>
>>>>>>>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
>>>>>>> casting
>>>>>>>>> we
>>>>>>>>>>>>> allow explicit casting to a target data type:
>>>>>>>>>>>>>
>>>>>>>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3)
>>>> WITH
>>>>>>>>> LOCAL
>>>>>>>>>>>>> TIME ZONE)
>>>>>>>>>>>>>
>>>>>>>>>>>>> A connector still produces and consumes the data type
>>>>>>>>>>>>> returned
>>>> by
>>>>>>>>>>>>> `listMetadata()`. The planner will insert necessary explicit
>>>>> casts.
>>>>>>>>>>>>>
>>>>>>>>>>>>> In any case, the user must provide a CAST such that the
>>> computed
>>>>>>>>> column
>>>>>>>>>>>>> receives a valid data type when constructing the table
>>>>>>>>>>>>> schema.
>>>>>>>>>>>>>
>>>>>>>>>>>>> "I don't see a reason why
>>> `DecodingFormat#applyReadableMetadata`
>>>>>>>>> needs a
>>>>>>>>>>>>> DataType argument."
>>>>>>>>>>>>>
>>>>>>>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
>>>>> always
>>>>>>>>>>>>> executed locally. It is the source that needs TypeInfo for
>>>>>>> serializing
>>>>>>>>>>>>> the record to the next operator. And that's this is what we
>>>>> provide.
>>>>>>>>>>>>>
>>>>>>>>>>>>> @Danny:
>>>>>>>>>>>>>
>>>>>>>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>>>>>>>>
>>>>>>>>>>>>> We can also use some other means to represent an UNKNOWN data
>>>>> type.
>>>>>>> In
>>>>>>>>>>>>> the Flink type system, we use the NullType for it. The
>>> important
>>>>>>> part
>>>>>>>>> is
>>>>>>>>>>>>> that the final data type is known for the entire computed
>>>> column.
>>>>>>> As I
>>>>>>>>>>>>> mentioned before, I would avoid the suggested option b) that
>>>> would
>>>>>>> be
>>>>>>>>>>>>> similar to your suggestion. The CAST should be enough and
>>> allows
>>>>> for
>>>>>>>>>>>>> complex expressions in the computed column. Option b) would
>>> need
>>>>>>>>> parser
>>>>>>>>>>>>> changes.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>> Timo
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>>>>>>>>>>>> Hi, Timo
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks for you explanation and update,  I have only one
>>>> question
>>>>>>> for
>>>>>>>>>>>>> the latest FLIP.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> About the MAP<STRING, STRING> DataType of key
>>>>>>>>> 'debezium-json.source', if
>>>>>>>>>>>>> user want to use the table name metadata, they need to write:
>>>>>>>>>>>>>> tableName STRING AS
>>> CAST(SYSTEM_METADATA('debeuim-json.source')
>>>>> AS
>>>>>>>>>>>>> MAP<STRING, STRING>)['table']
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> the expression is a little complex for user, Could we only
>>>>> support
>>>>>>>>>>>>> necessary metas with simple DataType as following?
>>>>>>>>>>>>>> tableName STRING AS
>>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>>>>>>>>>>>> STRING),
>>>>>>>>>>>>>> transactionTime LONG AS
>>>>>>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> In this way, we can simplify the expression, the mainly used
>>>>>>>>> metadata in
>>>>>>>>>>>>> changelog format may include
>>>>>>>>> 'database','table','source.ts_ms','ts_ms' from
>>>>>>>>>>>>> my side,
>>>>>>>>>>>>>> maybe we could only support them at first version.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m
>>>> willing
>>>>>>> to
>>>>>>>>>>>>> take some subtasks in next development if necessary.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Debezium:
>>>>>>>>>>>>>> {
>>>>>>>>>>>>>>        "before": null,
>>>>>>>>>>>>>>        "after": {  "id": 101,"name": "scooter"},
>>>>>>>>>>>>>>        "source": {
>>>>>>>>>>>>>>          "db": "inventory",                  # 1. database
>>> name
>>>>> the
>>>>>>>>>>>>> changelog belongs to.
>>>>>>>>>>>>>>          "table": "products",                # 2. table name
>>> the
>>>>>>>>> changelog
>>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>>          "ts_ms": 1589355504100,             # 3.
>>>>>>>>>>>>>> timestamp of
>>>> the
>>>>>>>>> change
>>>>>>>>>>>>> happened in database system, i.e.: transaction time in
>>> database.
>>>>>>>>>>>>>>          "connector": "mysql",
>>>>>>>>>>>>>>          ….
>>>>>>>>>>>>>>        },
>>>>>>>>>>>>>>        "ts_ms": 1589355606100,              # 4. timestamp
>>> when
>>>>> the
>>>>>>>>> debezium
>>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>>        "op": "c",
>>>>>>>>>>>>>>        "transaction": null
>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Canal:
>>>>>>>>>>>>>> {
>>>>>>>>>>>>>>        "data": [{  "id": "102", "name": "car battery" }],
>>>>>>>>>>>>>>        "database": "inventory",      # 1. database name the
>>>>> changelog
>>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>>        "table": "products",          # 2. table name the
>>>> changelog
>>>>>>>>> belongs
>>>>>>>>>>>>> to.
>>>>>>>>>>>>>>        "es": 1589374013000,          # 3. execution time of
>>> the
>>>>>>> change
>>>>>>>>> in
>>>>>>>>>>>>> database system, i.e.: transaction time in database.
>>>>>>>>>>>>>>        "ts": 1589374013680,          # 4. timestamp when the
>>>>> cannal
>>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>>        "isDdl": false,
>>>>>>>>>>>>>>        "mysqlType": {},
>>>>>>>>>>>>>>        ....
>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best
>>>>>>>>>>>>>> Leonard
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]>
>>>>>>>>>>>>>>> 写道：
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks Timo ~
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
>>>>> questions
>>>>>>>>> here:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a
>>> valid
>>>>>>>>> read-only
>>>>>>>>>>>>> computed column for Kafka and can be extracted by the
>>> planner.”
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ?
>>>> Usually
>>>>> an
>>>>>>>>>>>>> expression return type can be inferred automatically. But I
>>>> guess
>>>>>>>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
>>>>>>> actually
>>>>>>>>> does
>>>>>>>>>>>>> not have a specific return type.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
>>>>>>> [VIRTUAL]
>>>>>>>>>>>>>>> Which is more straight-forward.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by
>>>> default”
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The default type should not be NULL because only NULL
>>> literal
>>>>> does
>>>>>>>>>>>>> that. Usually we use ANY as the type if we do not know the
>>>>> specific
>>>>>>>>> type in
>>>>>>>>>>>>> the SQL context. ANY means the physical value can be any java
>>>>>>> object.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> [1]
>>>> https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther
>>>>>>>>>>>>>>> <[hidden email]
>>>>> ，写道：
>>>>>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I completely reworked FLIP-107. It now covers the full
>>> story
>>>>> how
>>>>>>> to
>>>>>>>>>>>>> read
>>>>>>>>>>>>>>>> and write metadata from/to connectors and formats. It
>>>> considers
>>>>>>>>> all of
>>>>>>>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and
>>>>>>>>>>>>>>>> FLIP-122. It
>>>>>>>>> introduces
>>>>>>>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
>>>>>>>>> partitioning
>>>>>>>>>>>>>>>> for now.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Looking forward to your feedback.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>> Timo
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>>>>>>>>>>>> Sorry, forgot one question.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal?
>>>> Like
>>>>>>> one
>>>>>>>>> can
>>>>>>>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users
>>> can
>>>>> not
>>>>>>>>>>>>> config to
>>>>>>>>>>>>>>>>> just ignore timestamp but keep key.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <
>>> [hidden email]
>>>>>
>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hi Dawid,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I have a couple of questions around key fields, actually
>>> I
>>>>> also
>>>>>>>>> have
>>>>>>>>>>>>> some
>>>>>>>>>>>>>>>>>> other questions but want to be focused on key fields
>>> first.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> 1. I don't fully understand the usage of
>>>>>>>>>>>>>>>>>> "key.fields". Is
>>>>> this
>>>>>>>>>>>>> option only
>>>>>>>>>>>>>>>>>> valid during write operation? Because for
>>>>>>>>>>>>>>>>>> reading, I can't imagine how such options can be
>>> applied. I
>>>>>>> would
>>>>>>>>>>>>> expect
>>>>>>>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>>>>>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I
>>> want
>>>>> to
>>>>>>>>>>>>> propose we
>>>>>>>>>>>>>>>>>> can simplify the options to not introducing
>>> key.format.type
>>>>> and
>>>>>>>>>>>>>>>>>> other related options. I think a single "key.field" (not
>>>>>>> fields)
>>>>>>>>>>>>> would be
>>>>>>>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>>>>>>>>>>>> want before sink.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type"
>>>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
>>>>> connector
>>>>>>>>> has a
>>>>>>>>>>>>>>>>>> concept
>>>>>>>>>>>>>>>>>> of key and values. The old parameter "format.type"
>>> already
>>>>> good
>>>>>>>>>>>>> enough to
>>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <
>>> [hidden email]>
>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Thanks Dawid,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I have two more questions.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> SupportsMetadata
>>>>>>>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I
>>> have
>>>>>>> some
>>>>>>>>>>>>> questions
>>>>>>>>>>>>>>>>>>> regarding to this interface.
>>>>>>>>>>>>>>>>>>> 1) How do the source know what the expected return type
>>> of
>>>>>>> each
>>>>>>>>>>>>> metadata?
>>>>>>>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the
>>>> existing
>>>>>>>>> physical
>>>>>>>>>>>>>>>>>>> fields?
>>>>>>>>>>>>>>>>>>> If yes, I would suggest to change the signature to
>>>>>>> `TableSource
>>>>>>>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>>>>>>>>>>>> metadataTypes)`
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a
>>>> computed
>>>>>>>>> column
>>>>>>>>>>>>>>>>>>> expression? If yes, how to specify the return type of
>>>>>>>>>>>>> SYSTEM_METADATA?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit
>>> the
>>>>>>>>> columns
>>>>>>>>>>>>> and I
>>>>>>>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I
>>>> see
>>>>> a
>>>>>>>>> need
>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
>>>>> Jark.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that.
>>>> One
>>>>>>>>>>>>> additional
>>>>>>>>>>>>>>>>>>>> things I would introduce is an
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
>>>>>>> metadataFields);
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> This way the source would have to declare/emit only
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>> requested
>>>>>>>>>>>>>>>>>>>> metadata fields. In order not to clash with user
>>> defined
>>>>>>>>> fields.
>>>>>>>>>>>>> When
>>>>>>>>>>>>>>>>>>>> emitting the metadata field I would prepend the column
>>>> name
>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
>>>>> field
>>>>>>>>>>>>>>>>>>>> __system_partition to the schema. This would be never
>>>>> visible
>>>>>>>>> to
>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> user as it would be used only for the subsequent
>>> computed
>>>>>>>>> columns.
>>>>>>>>>>>>> If
>>>>>>>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with
>>> this
>>>>>>>>>>>>> description.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state
>>> of
>>>>> the
>>>>>>>>>>>>> proposal.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
>>>>>>>>> orthogonal. I
>>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>>> leave out the STORED computed columns out of the
>>>>> discussion.
>>>>>>> I
>>>>>>>>>>>>> don't see
>>>>>>>>>>>>>>>>>>>> how do they relate to the partitioning. I already put
>>>> both
>>>>> of
>>>>>>>>> those
>>>>>>>>>>>>>>>>>>>> cases in the document. We can either partition on a
>>>>> computed
>>>>>>>>>>>>> column or
>>>>>>>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with
>>>> leaving
>>>>>>> out
>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> partitioning by udf in the first version if you still
>>>> have
>>>>>>> some
>>>>>>>>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> As for your question Danny. It depends which
>>> partitioning
>>>>>>>>> strategy
>>>>>>>>>>>>> you
>>>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would
>>>> work
>>>>> as
>>>>>>>>> you
>>>>>>>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not
>>> sure
>>>>>>>>> though if
>>>>>>>>>>>>> we
>>>>>>>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink
>>>> does
>>>>>>> not
>>>>>>>>> own
>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> data and the partitions are already an intrinsic
>>> property
>>>>> of
>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create
>>> topics,
>>>>> but
>>>>>>>>> we
>>>>>>>>>>>>> just
>>>>>>>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs
>>> ...
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
>>>>>>> consistent
>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was
>>> also
>>>>> my
>>>>>>>>>>>>> initial
>>>>>>>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
>>>>> afterwards
>>>>>>>>> to
>>>>>>>>>>>>> shorten
>>>>>>>>>>>>>>>>>>>> the key.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think
>>> it
>>>>> is
>>>>>>> a
>>>>>>>>>>>>> useful
>>>>>>>>>>>>>>>>>>>> feature ~
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column
>>>> push
>>>>>>>>> down is
>>>>>>>>>>>>>>>>>>>> another topic, this should not be a blocker but a
>>>>> promotion,
>>>>>>>>> if we
>>>>>>>>>>>>> do
>>>>>>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>>>>>>> have any filters on the computed column, there is no
>>> need
>>>>> to
>>>>>>>>> do any
>>>>>>>>>>>>>>>>>>>> pushings; the source node just emit the complete
>>>>>>>>>>>>>>>>>>>> record
>>>>> with
>>>>>>>>> full
>>>>>>>>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>>>> with the declared physical schema, then when
>>>>>>>>>>>>>>>>>>>> generating
>>>> the
>>>>>>>>> virtual
>>>>>>>>>>>>>>>>>>>> columns, we would extract the metadata info and output
>>> as
>>>>>>> full
>>>>>>>>>>>>>>>>>>> columns(with
>>>>>>>>>>>>>>>>>>>> full schema).
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST,
>>> they
>>>>> are
>>>>>>>>>>>>> symantic
>>>>>>>>>>>>>>>>>>>> equivalent though, explict type is more
>>> straight-forward
>>>>> and
>>>>>>>>> we can
>>>>>>>>>>>>>>>>>>> declare
>>>>>>>>>>>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> About option A: partitioning based on acomputed
>>>>>>>>>>>>>>>>>>>>> column
>>>> VS
>>>>>>>>> option
>>>>>>>>>>>>> B:
>>>>>>>>>>>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>      From the FLIP, it seems that B's partitioning is
>>>> just
>>>>> a
>>>>>>>>> strategy
>>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>>> writing data, the partiton column is not included in
>>> the
>>>>>>> table
>>>>>>>>>>>>> schema,
>>>>>>>>>>>>>>>>>>> so
>>>>>>>>>>>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the
>>>> partition
>>>>>>>>> column
>>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we
>>>> want
>>>>>>> to
>>>>>>>>>>>>> persist
>>>>>>>>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
>>>>> both, i
>>>>>>>>> saw
>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
>>>>>>> PARTITIONS
>>>>>>>>>>>>> num, and
>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
>>>>>>> partition
>>>>>>>>> in
>>>>>>>>>>>>> which
>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> record is stored is partition number N, where N =
>>>> MOD(expr,
>>>>>>>>> num),
>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>> your
>>>>>>>>>>>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> [1]
>>>>>>>>>>>>>
>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
>>>>>>>>> [hidden email]
>>>>>>>>>>>>>>>>>>>> ，写道：
>>>>>>>>>>>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to
>>> FLIP-63
>>>>>>>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep
>>>>>>>>>>>>>>>>>>>>>> hierarchy of
>>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
>>>>>>> (prefixing
>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>> connector or kafka). I should've put that into an
>>>>>>>>> Option/Rejected
>>>>>>>>>>>>>>>>>>>> alternatives.
>>>>>>>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
>>>>> properties.
>>>>>>>>> Why I
>>>>>>>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
>>>>> version
>>>>>>>>> is
>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>> actually all the properties in the WITH section are
>>>>> connector
>>>>>>>>>>>>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>>> Even format is in the end a connector property as some
>>> of
>>>>> the
>>>>>>>>>>>>> sources
>>>>>>>>>>>>>>>>>>> might
>>>>>>>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
>>>>> prefix
>>>>>>> is
>>>>>>>>>>>>> that it
>>>>>>>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>> properties
>>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>> connector (or if we go with FLINK-12557:
>>> elasticsearch):
>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>>>>>>>>>>>> I am fine with doing it though if this is a
>>>>>>>>>>>>>>>>>>>>>> preferred
>>>>>>>>> approach
>>>>>>>>>>>>> in the
>>>>>>>>>>>>>>>>>>>> community.
>>>>>>>>>>>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include`
>>> property.
>>>>> It
>>>>>>>>>>>>> should be
>>>>>>>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested
>>> in
>>>>> the
>>>>>>>>>>>>> comment,
>>>>>>>>>>>>>>>>>>>> right?
>>>>>>>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
>>>>>>> column.
>>>>>>>>> I
>>>>>>>>>>>>> think
>>>>>>>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
>>>>>>>>> expression
>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>> later
>>>>>>>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The
>>>>>>>>>>>>>>>>>>>> reason
>>>> is
>>>>> I
>>>>>>>>> think
>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>> way
>>>>>>>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs
>>>> when
>>>>>>>>> working
>>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
>>>>> offset, i
>>>>>>>>>>>>> think it's
>>>>>>>>>>>>>>>>>>>> better to pushdown long rather than string. This could
>>>> let
>>>>> us
>>>>>>>>> push
>>>>>>>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
>>>>>>>>> Otherwise we
>>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
>>>>> cast(offset,
>>>>>>>>> long)
>>>>>>>>>>>>> <
>>>>>>>>>>>>>>>>>>> 59382.
>>>>>>>>>>>>>>>>>>>> Moreover I think we need to introduce the type for
>>>> computed
>>>>>>>>> columns
>>>>>>>>>>>>>>>>>>> anyway
>>>>>>>>>>>>>>>>>>>> to support functions that infer output type based on
>>>>> expected
>>>>>>>>>>>>> return
>>>>>>>>>>>>>>>>>>> type.
>>>>>>>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
>>>>> SYSTEM_METADATA
>>>>>>>>> would
>>>>>>>>>>>>> have
>>>>>>>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible
>>>> the
>>>>>>>>> planner
>>>>>>>>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>>> fail. As far as I know computed columns push down will
>>> be
>>>>>>> part
>>>>>>>>> of
>>>>>>>>>>>>> source
>>>>>>>>>>>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
>>>>>>>>> completely
>>>>>>>>>>>>>>>>>>>> orthogonal. In my current proposal you can also
>>> partition
>>>>> by
>>>>>>> a
>>>>>>>>>>>>> computed
>>>>>>>>>>>>>>>>>>>> column. The difference between using a udf in
>>> partitioned
>>>>> by
>>>>>>> vs
>>>>>>>>>>>>>>>>>>> partitioned
>>>>>>>>>>>>>>>>>>>> by a computed column is that when you partition by a
>>>>> computed
>>>>>>>>>>>>> column
>>>>>>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>>> column must be also computed when reading the
>>>>>>>>>>>>>>>>>>>> table. If
>>>> you
>>>>>>>>> use a
>>>>>>>>>>>>> udf in
>>>>>>>>>>>>>>>>>>>> the partitioned by, the expression is computed only
>>> when
>>>>>>>>> inserting
>>>>>>>>>>>>> into
>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> table.
>>>>>>>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking
>>>> forward
>>>>>>> for
>>>>>>>>>>>>> further
>>>>>>>>>>>>>>>>>>>> suggestions.
>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
>>>>> Reaing
>>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>>>>>> key-part information from source is an important
>>>> feature
>>>>>>> for
>>>>>>>>>>>>>>>>>>> streaming
>>>>>>>>>>>>>>>>>>>>>>> users.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of
>>>> introducing
>>>>>>>>> HEADER
>>>>>>>>>>>>>>>>>>>> keyword as
>>>>>>>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
>>>>> Maybe
>>>>>>> we
>>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>>> add a
>>>>>>>>>>>>>>>>>>>>>>> section to explain what's the relationship between
>>>> them.
>>>>>>>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION
>>> be
>>>>> used
>>>>>>>>> on
>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink
>>>> SQL.
>>>>>>>>> Shall we
>>>>>>>>>>>>>>>>>>> make
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
>>>>>>>>> (actually, I
>>>>>>>>>>>>>>>>>>>> prefer
>>>>>>>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
>>>>>>>>> properties
>>>>>>>>>>>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead
>>>>>>>>>>>>>>>>>>>>>>> users
>>>>> that
>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> field
>>>>>>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
>>>>> enable
>>>>>>>>>>>>> reading
>>>>>>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>>>>>>>>> columns from different parts of source records.
>>>> Besides
>>>>>>> the
>>>>>>>>>>>>> main
>>>>>>>>>>>>>>>>>>>> payload
>>>>>>>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose
>>>> additional
>>>>>>>>>>>>>>>>>>> information. It
>>>>>>>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
>>>>>>>>> ingestion
>>>>>>>>>>>>> time
>>>>>>>>>>>>>>>>>>> or a
>>>>>>>>>>>>>>>>>>>>>>>> read and write parts of the record that contain
>>> data
>>>>> but
>>>>>>>>>>>>>>>>>>> additionally
>>>>>>>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
>>>>> etc.),
>>>>>>>>> e.g.
>>>>>>>>>>>>> key
>>>>>>>>>>>>>>>>>>> or
>>>>>>>>>>>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> We should make it possible to read and write data
>>>> from
>>>>>>> all
>>>>>>>>> of
>>>>>>>>>>>>> those
>>>>>>>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
>>>>>>> partitioning
>>>>>>>>>>>>> data,
>>>>>>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
>>>>>>> partitioning
>>>>>>>>> when
>>>>>>>>>>>>>>>>>>>> writing
>>>>>>>>>>>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>
>

[Danny Chan-2]

“Personally, I still like the computed column design more because it
allows to have full flexibility to compute the final column”

I have the same feeling, the non-standard syntax "timestamp INT
SYSTEM_METADATA("ts")" is neither a computed column nor normal column. It
looks very likely a computed column but it's not (no AS keyword there), we
should be cautious for such syntax because we use a function as a column
constraint. No SQL vendor has such a syntax.

Can we just use the SQL keyword as a constraint to mark the column as
metadata ?

timestamp INT METADATA [FROM 'my-timestamp-field'] [VIRTUAL]

Note that the "FROM 'field name'" is only needed when the name conflicts
with the declared table column name, when there are no conflicts, we can
simplify it to:

timestamp INT METADATA

By default, the field is non-virtual and can be read and written, users
need to mark the column as virtual when it is only readable.

Timo Walther <[hidden email]> 于2020年9月9日周三 下午6:41写道：

> Hi everyone,
>
> "key" and "value" in the properties are a special case because they need
> to configure a format. So key and value are more than just metadata.
> Jark's example for setting a timestamp would work but as the FLIP
> discusses, we have way more metadata fields like headers, epoch-leader,
> etc. Having a property for all of this metadata would mess up the WITH
> section entirely. Furthermore, we also want to deal with metadata from
> the formats. Solving this through properties as well would further
> complicate the property design.
>
> Personally, I still like the computed column design more because it
> allows to have full flexibility to compute the final column:
>
> timestamp AS adjustTimestamp(CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3)))
>
> Instead of having a helper column and a real column in the table:
>
> helperTimestamp AS CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3))
> realTimestamp AS adjustTimestamp(helperTimestamp)
>
> But I see that the discussion leans towards:
>
> timestamp INT SYSTEM_METADATA("ts")
>
> Which is fine with me. It is the shortest solution, because we don't
> need additional CAST. We can discuss the syntax, so that confusion with
> computed columns can be avoided.
>
> timestamp INT USING SYSTEM_METADATA("ts")
> timestamp INT FROM SYSTEM_METADATA("ts")
> timestamp INT FROM SYSTEM_METADATA("ts") PERSISTED
>
> We use `SYSTEM_TIME` for temporal tables. I think prefixing with SYSTEM
> makes it clearer that it comes magically from the system.
>
> What do you think?
>
> Regards,
> Timo
>
>
>
> On 09.09.20 11:41, Jark Wu wrote:
> > Hi Danny,
> >
> > This is not Oracle and MySQL computed column syntax, because there is no
> > "AS" after the type.
> >
> > Hi everyone,
> >
> > If we want to use "offset INT SYSTEM_METADATA("offset")", then I think we
> > must further discuss about "PERSISED" or "VIRTUAL" keyword for query-sink
> > schema problem.
> > Personally, I think we can use a shorter keyword "METADATA" for
> > "SYSTEM_METADATA". Because "SYSTEM_METADATA" sounds like a system
> function
> > and confuse users this looks like a computed column.
> >
> >
> > Best,
> > Jark
> >
> >
> >
> > On Wed, 9 Sep 2020 at 17:23, Danny Chan <[hidden email]> wrote:
> >
> >> "offset INT SYSTEM_METADATA("offset")"
> >>
> >> This is actually Oracle or MySQL style computed column syntax.
> >>
> >> "You are right that one could argue that "timestamp", "headers" are
> >> something like "key" and "value""
> >>
> >> I have the same feeling, both key value and headers timestamp are *real*
> >> data
> >> stored in the consumed record, they are not computed or generated.
> >>
> >> "Trying to solve everything via properties sounds rather like a hack to
> >> me"
> >>
> >> Things are not that hack if we can unify the routines or the definitions
> >> (all from the computed column way or all from the table options), i also
> >> think that it is a hacky that we mix in 2 kinds of syntax for different
> >> kinds of metadata (read-only and read-write). In this FLIP, we declare
> the
> >> Kafka key fields with table options but SYSTEM_METADATA for other
> metadata,
> >> that is a hacky thing or something in-consistent.
> >>
> >> Kurt Young <[hidden email]> 于2020年9月9日周三 下午4:48写道：
> >>
> >>>   I would vote for `offset INT SYSTEM_METADATA("offset")`.
> >>>
> >>> I don't think we can stick with the SQL standard in DDL part forever,
> >>> especially as there are more and more
> >>> requirements coming from different connectors and external systems.
> >>>
> >>> Best,
> >>> Kurt
> >>>
> >>>
> >>> On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]>
> wrote:
> >>>
> >>>> Hi Jark,
> >>>>
> >>>> now we are back at the original design proposed by Dawid :D Yes, we
> >>>> should be cautious about adding new syntax. But the length of this
> >>>> discussion shows that we are looking for a good long-term solution. In
> >>>> this case I would rather vote for a deep integration into the syntax.
> >>>>
> >>>> Computed columns are also not SQL standard compliant. And our DDL is
> >>>> neither, so we have some degree of freedom here.
> >>>>
> >>>> Trying to solve everything via properties sounds rather like a hack to
> >>>> me. You are right that one could argue that "timestamp", "headers" are
> >>>> something like "key" and "value". However, mixing
> >>>>
> >>>> `offset AS SYSTEM_METADATA("offset")`
> >>>>
> >>>> and
> >>>>
> >>>> `'timestamp.field' = 'ts'`
> >>>>
> >>>> looks more confusing to users that an explicit
> >>>>
> >>>> `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
> >>>>
> >>>> or
> >>>>
> >>>> `offset INT SYSTEM_METADATA("offset")`
> >>>>
> >>>> that is symetric for both source and sink.
> >>>>
> >>>> What do others think?
> >>>>
> >>>> Regards,
> >>>> Timo
> >>>>
> >>>>
> >>>> On 09.09.20 10:09, Jark Wu wrote:
> >>>>> Hi everyone,
> >>>>>
> >>>>> I think we have a conclusion that the writable metadata shouldn't be
> >>>>> defined as a computed column, but a normal column.
> >>>>>
> >>>>> "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the
> >>> approaches.
> >>>>> However, it is not SQL standard compliant, we need to be cautious
> >>> enough
> >>>>> when adding new syntax.
> >>>>> Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
> >>>>> resolve the query-sink schema problem if it is read-only metadata.
> >> That
> >>>>> adds more stuff to learn for users.
> >>>>>
> >>>>> >From my point of view, the "timestamp", "headers" are something like
> >>>> "key"
> >>>>> and "value" that stores with the real data. So why not define the
> >>>>> "timestamp" in the same way with "key" by using a "timestamp.field"
> >>>>> connector option?
> >>>>> On the other side, the read-only metadata, such as "offset",
> >> shouldn't
> >>> be
> >>>>> defined as a normal column. So why not use the existing computed
> >> column
> >>>>> syntax for such metadata? Then we don't have the query-sink schema
> >>>> problem.
> >>>>> So here is my proposal:
> >>>>>
> >>>>> CREATE TABLE kafka_table (
> >>>>>     id BIGINT,
> >>>>>     name STRING,
> >>>>>     col1 STRING,
> >>>>>     col2 STRING,
> >>>>>     ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field,
> >> so
> >>>> can
> >>>>> be read and written.
> >>>>>     offset AS SYSTEM_METADATA("offset")
> >>>>> ) WITH (
> >>>>>     'connector' = 'kafka',
> >>>>>     'topic' = 'test-topic',
> >>>>>     'key.fields' = 'id, name',
> >>>>>     'key.format' = 'csv',
> >>>>>     'value.format' = 'avro',
> >>>>>     'timestamp.field' = 'ts'    -- define the mapping of Kafka
> >> timestamp
> >>>>> );
> >>>>>
> >>>>> INSERT INTO kafka_table
> >>>>> SELECT id, name, col1, col2, rowtime FROM another_table;
> >>>>>
> >>>>> I think this can solve all the problems without introducing any new
> >>>> syntax.
> >>>>> The only minor disadvantage is that we separate the definition
> >>> way/syntax
> >>>>> of read-only metadata and read-write fields.
> >>>>> However, I don't think this is a big problem.
> >>>>>
> >>>>> Best,
> >>>>> Jark
> >>>>>
> >>>>>
> >>>>> On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]>
> >> wrote:
> >>>>>
> >>>>>> Hi Kurt,
> >>>>>>
> >>>>>> thanks for sharing your opinion. I'm totally up for not reusing
> >>> computed
> >>>>>> columns. I think Jark was a big supporter of this syntax, @Jark are
> >>> you
> >>>>>> fine with this as well? The non-computed column approach was only a
> >>>>>> "slightly rejected alternative".
> >>>>>>
> >>>>>> Furthermore, we would need to think about how such a new design
> >>>>>> influences the LIKE clause though.
> >>>>>>
> >>>>>> However, we should still keep the `PERSISTED` keyword as it
> >> influences
> >>>>>> the query->sink schema. If you look at the list of metadata for
> >>> existing
> >>>>>> connectors and formats, we currently offer only two writable
> >> metadata
> >>>>>> fields. Otherwise, one would need to declare two tables whenever a
> >>>>>> metadata columns is read (one for the source, one for the sink).
> >> This
> >>>>>> can be quite inconvientient e.g. for just reading the topic.
> >>>>>>
> >>>>>> Regards,
> >>>>>> Timo
> >>>>>>
> >>>>>>
> >>>>>> On 09.09.20 08:52, Kurt Young wrote:
> >>>>>>> I also share the concern that reusing the computed column syntax
> >> but
> >>>> have
> >>>>>>> different semantics
> >>>>>>> would confuse users a lot.
> >>>>>>>
> >>>>>>> Besides, I think metadata fields are conceptually not the same with
> >>>>>>> computed columns. The metadata
> >>>>>>> field is a connector specific thing and it only contains the
> >>>> information
> >>>>>>> that where does the field come
> >>>>>>> from (during source) or where does the field need to write to
> >> (during
> >>>>>>> sink). It's more similar with normal
> >>>>>>> fields, with assumption that all these fields need going to the
> >> data
> >>>>>> part.
> >>>>>>>
> >>>>>>> Thus I'm more lean to the rejected alternative that Timo mentioned.
> >>>> And I
> >>>>>>> think we don't need the
> >>>>>>> PERSISTED keyword, SYSTEM_METADATA should be enough.
> >>>>>>>
> >>>>>>> During implementation, the framework only needs to pass such
> >> <field,
> >>>>>>> metadata field> information to the
> >>>>>>> connector, and the logic of handling such fields inside the
> >> connector
> >>>>>>> should be straightforward.
> >>>>>>>
> >>>>>>> Regarding the downside Timo mentioned:
> >>>>>>>
> >>>>>>>> The disadvantage is that users cannot call UDFs or parse
> >> timestamps.
> >>>>>>>
> >>>>>>> I think this is fairly simple to solve. Since the metadata field
> >>> isn't
> >>>> a
> >>>>>>> computed column anymore, we can support
> >>>>>>> referencing such fields in the computed column. For example:
> >>>>>>>
> >>>>>>> CREATE TABLE kafka_table (
> >>>>>>>         id BIGINT,
> >>>>>>>         name STRING,
> >>>>>>>         timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
> >>>>>> timestamp
> >>>>>>> field from metadata
> >>>>>>>         ts AS to_timestamp(timestamp) // normal computed column,
> >> parse
> >>>> the
> >>>>>>> string to TIMESTAMP type by using the metadata field
> >>>>>>> ) WITH (
> >>>>>>>        ...
> >>>>>>> )
> >>>>>>>
> >>>>>>> Best,
> >>>>>>> Kurt
> >>>>>>>
> >>>>>>>
> >>>>>>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
> >>>> wrote:
> >>>>>>>
> >>>>>>>> Hi Leonard,
> >>>>>>>>
> >>>>>>>> the only alternative I see is that we introduce a concept that is
> >>>>>>>> completely different to computed columns. This is also mentioned
> >> in
> >>>> the
> >>>>>>>> rejected alternative section of the FLIP. Something like:
> >>>>>>>>
> >>>>>>>> CREATE TABLE kafka_table (
> >>>>>>>>         id BIGINT,
> >>>>>>>>         name STRING,
> >>>>>>>>         timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
> >>>>>>>>         headers MAP<STRING, BYTES> SYSTEM_METADATA("headers")
> >>> PERSISTED
> >>>>>>>> ) WITH (
> >>>>>>>>        ...
> >>>>>>>> )
> >>>>>>>>
> >>>>>>>> This way we would avoid confusion at all and can easily map
> >> columns
> >>> to
> >>>>>>>> metadata columns. The disadvantage is that users cannot call UDFs
> >> or
> >>>>>>>> parse timestamps. This would need to be done in a real computed
> >>>> column.
> >>>>>>>>
> >>>>>>>> I'm happy about better alternatives.
> >>>>>>>>
> >>>>>>>> Regards,
> >>>>>>>> Timo
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On 08.09.20 15:37, Leonard Xu wrote:
> >>>>>>>>> HI, Timo
> >>>>>>>>>
> >>>>>>>>> Thanks for driving this FLIP.
> >>>>>>>>>
> >>>>>>>>> Sorry but I have a concern about Writing metadata via
> >>>> DynamicTableSink
> >>>>>>>> section:
> >>>>>>>>>
> >>>>>>>>> CREATE TABLE kafka_table (
> >>>>>>>>>       id BIGINT,
> >>>>>>>>>       name STRING,
> >>>>>>>>>       timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
> >>>>>> PERSISTED,
> >>>>>>>>>       headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
> >>>> BYTES>)
> >>>>>>>> PERSISTED
> >>>>>>>>> ) WITH (
> >>>>>>>>>       ...
> >>>>>>>>> )
> >>>>>>>>> An insert statement could look like:
> >>>>>>>>>
> >>>>>>>>> INSERT INTO kafka_table VALUES (
> >>>>>>>>>       (1, "ABC", 1599133672, MAP('checksum',
> >> computeChecksum(...)))
> >>>>>>>>> )
> >>>>>>>>>
> >>>>>>>>> The proposed INERT syntax does not make sense to me, because it
> >>>>>> contains
> >>>>>>>> computed(generated) column.
> >>>>>>>>> Both SQL server and Postgresql do not allow to insert value to
> >>>> computed
> >>>>>>>> columns even they are persisted, this boke the generated column
> >>>>>> semantics
> >>>>>>>> and may confuse user much.
> >>>>>>>>>
> >>>>>>>>> For SQL server computed column[1]:
> >>>>>>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT
> >> NULL ]
> >>>>>> ]...
> >>>>>>>>>> NOTE: A computed column cannot be the target of an INSERT or
> >>> UPDATE
> >>>>>>>> statement.
> >>>>>>>>>
> >>>>>>>>> For Postgresql generated column[2]:
> >>>>>>>>>>      height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
> >>> STORED
> >>>>>>>>>> NOTE: A generated column cannot be written to directly. In
> >> INSERT
> >>> or
> >>>>>>>> UPDATE commands, a value cannot be specified for a generated
> >> column,
> >>>> but
> >>>>>>>> the keyword DEFAULT may be specified.
> >>>>>>>>>
> >>>>>>>>> It shouldn't be allowed to set/update value for generated column
> >>>> after
> >>>>>>>> lookup the SQL 2016:
> >>>>>>>>>> <insert statement> ::=
> >>>>>>>>>> INSERT INTO <insertion target> <insert columns and source>
> >>>>>>>>>>
> >>>>>>>>>> If <contextually typed table value constructor> CTTVC is
> >>> specified,
> >>>>>>>> then every <contextually typed row
> >>>>>>>>>> value constructor element> simply contained in CTTVC whose
> >>>>>> positionally
> >>>>>>>> corresponding <column name>
> >>>>>>>>>> in <insert column list> references a column of which some
> >>> underlying
> >>>>>>>> column is a generated column shall
> >>>>>>>>>> be a <default specification>.
> >>>>>>>>>> A <default specification> specifies the default value of some
> >>>>>>>> associated item.
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> [1]
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> >>>>>>>> <
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
> >>>>>>>>>
> >>>>>>>>> [2]
> >> https://www.postgresql.org/docs/12/ddl-generated-columns.html
> >>> <
> >>>>>>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
> >>>>>>>>>
> >>>>>>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
> >>>>>>>>>>
> >>>>>>>>>> Hi Jark,
> >>>>>>>>>>
> >>>>>>>>>> according to Flink's and Calcite's casting definition in [1][2]
> >>>>>>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If
> >>> not,
> >>>>>> we
> >>>>>>>> will make it possible ;-)
> >>>>>>>>>>
> >>>>>>>>>> I'm aware of DeserializationSchema.getProducedType but I think
> >>> that
> >>>>>>>> this method is actually misplaced. The type should rather be
> >> passed
> >>> to
> >>>>>> the
> >>>>>>>> source itself.
> >>>>>>>>>>
> >>>>>>>>>> For our Kafka SQL source, we will also not use this method
> >> because
> >>>> the
> >>>>>>>> Kafka source will add own metadata in addition to the
> >>>>>>>> DeserializationSchema. So DeserializationSchema.getProducedType
> >> will
> >>>>>> never
> >>>>>>>> be read.
> >>>>>>>>>>
> >>>>>>>>>> For now I suggest to leave out the `DataType` from
> >>>>>>>> DecodingFormat.applyReadableMetadata. Also because the format's
> >>>> physical
> >>>>>>>> type is passed later in `createRuntimeDecoder`. If necessary, it
> >> can
> >>>> be
> >>>>>>>> computed manually by consumedType + metadata types. We will
> >> provide
> >>> a
> >>>>>>>> metadata utility class for that.
> >>>>>>>>>>
> >>>>>>>>>> Regards,
> >>>>>>>>>> Timo
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> [1]
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
> >>>>>>>>>> [2]
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
> >>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>> On 08.09.20 10:52, Jark Wu wrote:
> >>>>>>>>>>> Hi Timo,
> >>>>>>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I
> >>> just
> >>>>>>>> noticed
> >>>>>>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL
> >> TIME
> >>>>>>>> ZONE".
> >>>>>>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH
> >> LOCAL
> >>>>>> TIME
> >>>>>>>>>>> ZONE" as the defined type of Kafka timestamp? I think this
> >> makes
> >>>>>> sense,
> >>>>>>>>>>> because it represents the milli-seconds since epoch.
> >>>>>>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I
> >> don't
> >>>>>> think
> >>>>>>>> so.
> >>>>>>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus
> >>> the
> >>>>>>>>>>> implementation needs to return an output TypeInfo.
> >>>>>>>>>>> Besides, FlinkKafkaConsumer also
> >>>>>>>>>>> calls DeserializationSchema.getProducedType as the produced
> >> type
> >>> of
> >>>>>> the
> >>>>>>>>>>> source function [1].
> >>>>>>>>>>> Best,
> >>>>>>>>>>> Jark
> >>>>>>>>>>> [1]:
> >>>>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
> >>>>>>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
> >>>>>> wrote:
> >>>>>>>>>>>> Hi everyone,
> >>>>>>>>>>>>
> >>>>>>>>>>>> I updated the FLIP again and hope that I could address the
> >>>> mentioned
> >>>>>>>>>>>> concerns.
> >>>>>>>>>>>>
> >>>>>>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that
> >> ts_ms
> >>>> and
> >>>>>>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
> >>>> expose
> >>>>>>>> the
> >>>>>>>>>>>> most commonly used properties separately. So frequently used
> >>>>>>>> properties
> >>>>>>>>>>>> are not hidden in the MAP anymore:
> >>>>>>>>>>>>
> >>>>>>>>>>>> debezium-json.ingestion-timestamp
> >>>>>>>>>>>> debezium-json.source.timestamp
> >>>>>>>>>>>> debezium-json.source.database
> >>>>>>>>>>>> debezium-json.source.schema
> >>>>>>>>>>>> debezium-json.source.table
> >>>>>>>>>>>>
> >>>>>>>>>>>> However, since other properties depend on the used
> >>>> connector/vendor,
> >>>>>>>> the
> >>>>>>>>>>>> remaining options are stored in:
> >>>>>>>>>>>>
> >>>>>>>>>>>> debezium-json.source.properties
> >>>>>>>>>>>>
> >>>>>>>>>>>> And accessed with:
> >>>>>>>>>>>>
> >>>>>>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
> >>>>>> MAP<STRING,
> >>>>>>>>>>>> STRING>)['table']
> >>>>>>>>>>>>
> >>>>>>>>>>>> Otherwise it is not possible to figure out the value and
> >> column
> >>>> type
> >>>>>>>>>>>> during validation.
> >>>>>>>>>>>>
> >>>>>>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I
> >>> added
> >>>> a
> >>>>>>>>>>>> dedicacated sub-section to the FLIP:
> >>>>>>>>>>>>
> >>>>>>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
> >>>>>> casting
> >>>>>>>> we
> >>>>>>>>>>>> allow explicit casting to a target data type:
> >>>>>>>>>>>>
> >>>>>>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3)
> >>> WITH
> >>>>>>>> LOCAL
> >>>>>>>>>>>> TIME ZONE)
> >>>>>>>>>>>>
> >>>>>>>>>>>> A connector still produces and consumes the data type returned
> >>> by
> >>>>>>>>>>>> `listMetadata()`. The planner will insert necessary explicit
> >>>> casts.
> >>>>>>>>>>>>
> >>>>>>>>>>>> In any case, the user must provide a CAST such that the
> >> computed
> >>>>>>>> column
> >>>>>>>>>>>> receives a valid data type when constructing the table schema.
> >>>>>>>>>>>>
> >>>>>>>>>>>> "I don't see a reason why
> >> `DecodingFormat#applyReadableMetadata`
> >>>>>>>> needs a
> >>>>>>>>>>>> DataType argument."
> >>>>>>>>>>>>
> >>>>>>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
> >>>> always
> >>>>>>>>>>>> executed locally. It is the source that needs TypeInfo for
> >>>>>> serializing
> >>>>>>>>>>>> the record to the next operator. And that's this is what we
> >>>> provide.
> >>>>>>>>>>>>
> >>>>>>>>>>>> @Danny:
> >>>>>>>>>>>>
> >>>>>>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
> >>>>>>>>>>>>
> >>>>>>>>>>>> We can also use some other means to represent an UNKNOWN data
> >>>> type.
> >>>>>> In
> >>>>>>>>>>>> the Flink type system, we use the NullType for it. The
> >> important
> >>>>>> part
> >>>>>>>> is
> >>>>>>>>>>>> that the final data type is known for the entire computed
> >>> column.
> >>>>>> As I
> >>>>>>>>>>>> mentioned before, I would avoid the suggested option b) that
> >>> would
> >>>>>> be
> >>>>>>>>>>>> similar to your suggestion. The CAST should be enough and
> >> allows
> >>>> for
> >>>>>>>>>>>> complex expressions in the computed column. Option b) would
> >> need
> >>>>>>>> parser
> >>>>>>>>>>>> changes.
> >>>>>>>>>>>>
> >>>>>>>>>>>> Regards,
> >>>>>>>>>>>> Timo
> >>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
> >>>>>>>>>>>>> Hi, Timo
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Thanks for you explanation and update,  I have only one
> >>> question
> >>>>>> for
> >>>>>>>>>>>> the latest FLIP.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> About the MAP<STRING, STRING> DataType of key
> >>>>>>>> 'debezium-json.source', if
> >>>>>>>>>>>> user want to use the table name metadata, they need to write:
> >>>>>>>>>>>>> tableName STRING AS
> >> CAST(SYSTEM_METADATA('debeuim-json.source')
> >>>> AS
> >>>>>>>>>>>> MAP<STRING, STRING>)['table']
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> the expression is a little complex for user, Could we only
> >>>> support
> >>>>>>>>>>>> necessary metas with simple DataType as following?
> >>>>>>>>>>>>> tableName STRING AS
> >>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
> >>>>>>>>>>>> STRING),
> >>>>>>>>>>>>> transactionTime LONG AS
> >>>>>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> In this way, we can simplify the expression, the mainly used
> >>>>>>>> metadata in
> >>>>>>>>>>>> changelog format may include
> >>>>>>>> 'database','table','source.ts_ms','ts_ms' from
> >>>>>>>>>>>> my side,
> >>>>>>>>>>>>> maybe we could only support them at first version.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m
> >>> willing
> >>>>>> to
> >>>>>>>>>>>> take some subtasks in next development if necessary.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Debezium:
> >>>>>>>>>>>>> {
> >>>>>>>>>>>>>        "before": null,
> >>>>>>>>>>>>>        "after": {  "id": 101,"name": "scooter"},
> >>>>>>>>>>>>>        "source": {
> >>>>>>>>>>>>>          "db": "inventory",                  # 1. database
> >> name
> >>>> the
> >>>>>>>>>>>> changelog belongs to.
> >>>>>>>>>>>>>          "table": "products",                # 2. table name
> >> the
> >>>>>>>> changelog
> >>>>>>>>>>>> belongs to.
> >>>>>>>>>>>>>          "ts_ms": 1589355504100,             # 3. timestamp
> of
> >>> the
> >>>>>>>> change
> >>>>>>>>>>>> happened in database system, i.e.: transaction time in
> >> database.
> >>>>>>>>>>>>>          "connector": "mysql",
> >>>>>>>>>>>>>          ….
> >>>>>>>>>>>>>        },
> >>>>>>>>>>>>>        "ts_ms": 1589355606100,              # 4. timestamp
> >> when
> >>>> the
> >>>>>>>> debezium
> >>>>>>>>>>>> processed the changelog.
> >>>>>>>>>>>>>        "op": "c",
> >>>>>>>>>>>>>        "transaction": null
> >>>>>>>>>>>>> }
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Canal:
> >>>>>>>>>>>>> {
> >>>>>>>>>>>>>        "data": [{  "id": "102", "name": "car battery" }],
> >>>>>>>>>>>>>        "database": "inventory",      # 1. database name the
> >>>> changelog
> >>>>>>>>>>>> belongs to.
> >>>>>>>>>>>>>        "table": "products",          # 2. table name the
> >>> changelog
> >>>>>>>> belongs
> >>>>>>>>>>>> to.
> >>>>>>>>>>>>>        "es": 1589374013000,          # 3. execution time of
> >> the
> >>>>>> change
> >>>>>>>> in
> >>>>>>>>>>>> database system, i.e.: transaction time in database.
> >>>>>>>>>>>>>        "ts": 1589374013680,          # 4. timestamp when the
> >>>> cannal
> >>>>>>>>>>>> processed the changelog.
> >>>>>>>>>>>>>        "isDdl": false,
> >>>>>>>>>>>>>        "mysqlType": {},
> >>>>>>>>>>>>>        ....
> >>>>>>>>>>>>> }
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Best
> >>>>>>>>>>>>> Leonard
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Thanks Timo ~
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
> >>>> questions
> >>>>>>>> here:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a
> >> valid
> >>>>>>>> read-only
> >>>>>>>>>>>> computed column for Kafka and can be extracted by the
> >> planner.”
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ?
> >>> Usually
> >>>> an
> >>>>>>>>>>>> expression return type can be inferred automatically. But I
> >>> guess
> >>>>>>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
> >>>>>> actually
> >>>>>>>> does
> >>>>>>>>>>>> not have a specific return type.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
> >>>>>> [VIRTUAL]
> >>>>>>>>>>>>>> Which is more straight-forward.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by
> >>> default”
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> The default type should not be NULL because only NULL
> >> literal
> >>>> does
> >>>>>>>>>>>> that. Usually we use ANY as the type if we do not know the
> >>>> specific
> >>>>>>>> type in
> >>>>>>>>>>>> the SQL context. ANY means the physical value can be any java
> >>>>>> object.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> [1]
> >>> https://oracle-base.com/articles/11g/virtual-columns-11gr1
> >>>>>>>>>>>>>> [2]
> >>>>>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>> Danny Chan
> >>>>>>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]
> >>>> ，写道：
> >>>>>>>>>>>>>>> Hi everyone,
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> I completely reworked FLIP-107. It now covers the full
> >> story
> >>>> how
> >>>>>> to
> >>>>>>>>>>>> read
> >>>>>>>>>>>>>>> and write metadata from/to connectors and formats. It
> >>> considers
> >>>>>>>> all of
> >>>>>>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
> >>>>>>>> introduces
> >>>>>>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
> >>>>>>>> partitioning
> >>>>>>>>>>>>>>> for now.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Looking forward to your feedback.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Regards,
> >>>>>>>>>>>>>>> Timo
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
> >>>>>>>>>>>>>>>> Sorry, forgot one question.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal?
> >>> Like
> >>>>>> one
> >>>>>>>> can
> >>>>>>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
> >>>>>>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users
> >> can
> >>>> not
> >>>>>>>>>>>> config to
> >>>>>>>>>>>>>>>> just ignore timestamp but keep key.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>> Kurt
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <
> >> [hidden email]
> >>>>
> >>>>>>>> wrote:
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Hi Dawid,
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> I have a couple of questions around key fields, actually
> >> I
> >>>> also
> >>>>>>>> have
> >>>>>>>>>>>> some
> >>>>>>>>>>>>>>>>> other questions but want to be focused on key fields
> >> first.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is
> >>>> this
> >>>>>>>>>>>> option only
> >>>>>>>>>>>>>>>>> valid during write operation? Because for
> >>>>>>>>>>>>>>>>> reading, I can't imagine how such options can be
> >> applied. I
> >>>>>> would
> >>>>>>>>>>>> expect
> >>>>>>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
> >>>>>>>>>>>>>>>>> to read and assign the key to a normal field?
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I
> >> want
> >>>> to
> >>>>>>>>>>>> propose we
> >>>>>>>>>>>>>>>>> can simplify the options to not introducing
> >> key.format.type
> >>>> and
> >>>>>>>>>>>>>>>>> other related options. I think a single "key.field" (not
> >>>>>> fields)
> >>>>>>>>>>>> would be
> >>>>>>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
> >>>>>>>>>>>>>>>>> want before sink.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
> >>>>>>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
> >>>> connector
> >>>>>>>> has a
> >>>>>>>>>>>>>>>>> concept
> >>>>>>>>>>>>>>>>> of key and values. The old parameter "format.type"
> >> already
> >>>> good
> >>>>>>>>>>>> enough to
> >>>>>>>>>>>>>>>>> use.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>> Kurt
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <
> >> [hidden email]>
> >>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Thanks Dawid,
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> I have two more questions.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> SupportsMetadata
> >>>>>>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I
> >> have
> >>>>>> some
> >>>>>>>>>>>> questions
> >>>>>>>>>>>>>>>>>> regarding to this interface.
> >>>>>>>>>>>>>>>>>> 1) How do the source know what the expected return type
> >> of
> >>>>>> each
> >>>>>>>>>>>> metadata?
> >>>>>>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the
> >>> existing
> >>>>>>>> physical
> >>>>>>>>>>>>>>>>>> fields?
> >>>>>>>>>>>>>>>>>> If yes, I would suggest to change the signature to
> >>>>>> `TableSource
> >>>>>>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
> >>>>>>>>>>>> metadataTypes)`
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
> >>>>>>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a
> >>> computed
> >>>>>>>> column
> >>>>>>>>>>>>>>>>>> expression? If yes, how to specify the return type of
> >>>>>>>>>>>> SYSTEM_METADATA?
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
> >>>>>>>>>>>> [hidden email]>
> >>>>>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit
> >> the
> >>>>>>>> columns
> >>>>>>>>>>>> and I
> >>>>>>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I
> >>> see
> >>>> a
> >>>>>>>> need
> >>>>>>>>>>>> to
> >>>>>>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
> >>>> Jark.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that.
> >>> One
> >>>>>>>>>>>> additional
> >>>>>>>>>>>>>>>>>>> things I would introduce is an
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> interface SupportsMetadata {
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
> >>>>>> metadataFields);
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> This way the source would have to declare/emit only the
> >>>>>>>> requested
> >>>>>>>>>>>>>>>>>>> metadata fields. In order not to clash with user
> >> defined
> >>>>>>>> fields.
> >>>>>>>>>>>> When
> >>>>>>>>>>>>>>>>>>> emitting the metadata field I would prepend the column
> >>> name
> >>>>>>>> with
> >>>>>>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
> >>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
> >>>> field
> >>>>>>>>>>>>>>>>>>> __system_partition to the schema. This would be never
> >>>> visible
> >>>>>>>> to
> >>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> user as it would be used only for the subsequent
> >> computed
> >>>>>>>> columns.
> >>>>>>>>>>>> If
> >>>>>>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with
> >> this
> >>>>>>>>>>>> description.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state
> >> of
> >>>> the
> >>>>>>>>>>>> proposal.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
> >>>>>>>> orthogonal. I
> >>>>>>>>>>>> would
> >>>>>>>>>>>>>>>>>>> leave out the STORED computed columns out of the
> >>>> discussion.
> >>>>>> I
> >>>>>>>>>>>> don't see
> >>>>>>>>>>>>>>>>>>> how do they relate to the partitioning. I already put
> >>> both
> >>>> of
> >>>>>>>> those
> >>>>>>>>>>>>>>>>>>> cases in the document. We can either partition on a
> >>>> computed
> >>>>>>>>>>>> column or
> >>>>>>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with
> >>> leaving
> >>>>>> out
> >>>>>>>> the
> >>>>>>>>>>>>>>>>>>> partitioning by udf in the first version if you still
> >>> have
> >>>>>> some
> >>>>>>>>>>>>>>>>>> concerns.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> As for your question Danny. It depends which
> >> partitioning
> >>>>>>>> strategy
> >>>>>>>>>>>> you
> >>>>>>>>>>>>>>>>>> use.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would
> >>> work
> >>>> as
> >>>>>>>> you
> >>>>>>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not
> >> sure
> >>>>>>>> though if
> >>>>>>>>>>>> we
> >>>>>>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink
> >>> does
> >>>>>> not
> >>>>>>>> own
> >>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> data and the partitions are already an intrinsic
> >> property
> >>>> of
> >>>>>>>> the
> >>>>>>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create
> >> topics,
> >>>> but
> >>>>>>>> we
> >>>>>>>>>>>> just
> >>>>>>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs
> >> ...
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
> >>>>>> consistent
> >>>>>>>> with
> >>>>>>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was
> >> also
> >>>> my
> >>>>>>>>>>>> initial
> >>>>>>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
> >>>> afterwards
> >>>>>>>> to
> >>>>>>>>>>>> shorten
> >>>>>>>>>>>>>>>>>>> the key.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
> >>>>>>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think
> >> it
> >>>> is
> >>>>>> a
> >>>>>>>>>>>> useful
> >>>>>>>>>>>>>>>>>>> feature ~
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> About how the metadata outputs from source
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column
> >>> push
> >>>>>>>> down is
> >>>>>>>>>>>>>>>>>>> another topic, this should not be a blocker but a
> >>>> promotion,
> >>>>>>>> if we
> >>>>>>>>>>>> do
> >>>>>>>>>>>>>>>>>> not
> >>>>>>>>>>>>>>>>>>> have any filters on the computed column, there is no
> >> need
> >>>> to
> >>>>>>>> do any
> >>>>>>>>>>>>>>>>>>> pushings; the source node just emit the complete record
> >>>> with
> >>>>>>>> full
> >>>>>>>>>>>>>>>>>> metadata
> >>>>>>>>>>>>>>>>>>> with the declared physical schema, then when generating
> >>> the
> >>>>>>>> virtual
> >>>>>>>>>>>>>>>>>>> columns, we would extract the metadata info and output
> >> as
> >>>>>> full
> >>>>>>>>>>>>>>>>>> columns(with
> >>>>>>>>>>>>>>>>>>> full schema).
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> About the type of metadata column
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST,
> >> they
> >>>> are
> >>>>>>>>>>>> symantic
> >>>>>>>>>>>>>>>>>>> equivalent though, explict type is more
> >> straight-forward
> >>>> and
> >>>>>>>> we can
> >>>>>>>>>>>>>>>>>> declare
> >>>>>>>>>>>>>>>>>>> the nullable attribute there.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column
> >>> VS
> >>>>>>>> option
> >>>>>>>>>>>> B:
> >>>>>>>>>>>>>>>>>>> partitioning with just a function
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>      From the FLIP, it seems that B's partitioning is
> >>> just
> >>>> a
> >>>>>>>> strategy
> >>>>>>>>>>>> when
> >>>>>>>>>>>>>>>>>>> writing data, the partiton column is not included in
> >> the
> >>>>>> table
> >>>>>>>>>>>> schema,
> >>>>>>>>>>>>>>>>>> so
> >>>>>>>>>>>>>>>>>>> it's just useless when reading from that.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the
> >>> partition
> >>>>>>>> column
> >>>>>>>>>>>> when
> >>>>>>>>>>>>>>>>>>> selecting from the table(but insert into)
> >>>>>>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we
> >>> want
> >>>>>> to
> >>>>>>>>>>>> persist
> >>>>>>>>>>>>>>>>>>> that
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
> >>>> both, i
> >>>>>>>> saw
> >>>>>>>>>>>> that
> >>>>>>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
> >>>>>> PARTITIONS
> >>>>>>>>>>>> num, and
> >>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
> >>>>>> partition
> >>>>>>>> in
> >>>>>>>>>>>> which
> >>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> record is stored is partition number N, where N =
> >>> MOD(expr,
> >>>>>>>> num),
> >>>>>>>>>>>> for
> >>>>>>>>>>>>>>>>>> your
> >>>>>>>>>>>>>>>>>>> design, which partiton the record would persist ?
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> [1]
> >>>>>>>>>>>>
> >> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
> >>>>>>>>>>>>>>>>>>>> [2]
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>>>> Danny Chan
> >>>>>>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
> >>>>>>>> [hidden email]
> >>>>>>>>>>>>>>>>>>> ，写道：
> >>>>>>>>>>>>>>>>>>>>> Hi Jark,
> >>>>>>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to
> >> FLIP-63
> >>>>>>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
> >>>>>>>> properties.
> >>>>>>>>>>>>>>>>>>> Therefore you have the key.format.type.
> >>>>>>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
> >>>>>> (prefixing
> >>>>>>>> with
> >>>>>>>>>>>>>>>>>>> connector or kafka). I should've put that into an
> >>>>>>>> Option/Rejected
> >>>>>>>>>>>>>>>>>>> alternatives.
> >>>>>>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
> >>>> properties.
> >>>>>>>> Why I
> >>>>>>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
> >>>> version
> >>>>>>>> is
> >>>>>>>>>>>> that
> >>>>>>>>>>>>>>>>>>> actually all the properties in the WITH section are
> >>>> connector
> >>>>>>>>>>>>>>>>>> properties.
> >>>>>>>>>>>>>>>>>>> Even format is in the end a connector property as some
> >> of
> >>>> the
> >>>>>>>>>>>> sources
> >>>>>>>>>>>>>>>>>> might
> >>>>>>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
> >>>> prefix
> >>>>>> is
> >>>>>>>>>>>> that it
> >>>>>>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
> >>>>>>>> properties
> >>>>>>>>>>>> with
> >>>>>>>>>>>>>>>>>>> connector (or if we go with FLINK-12557:
> >> elasticsearch):
> >>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
> >>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
> >>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
> >>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
> >>>>>>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
> >>>>>>>> approach
> >>>>>>>>>>>> in the
> >>>>>>>>>>>>>>>>>>> community.
> >>>>>>>>>>>>>>>>>>>>> Ad in-line comments:
> >>>>>>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include`
> >> property.
> >>>> It
> >>>>>>>>>>>> should be
> >>>>>>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested
> >> in
> >>>> the
> >>>>>>>>>>>> comment,
> >>>>>>>>>>>>>>>>>>> right?
> >>>>>>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
> >>>>>> column.
> >>>>>>>> I
> >>>>>>>>>>>> think
> >>>>>>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
> >>>>>>>> expression
> >>>>>>>>>>>> and
> >>>>>>>>>>>>>>>>>> later
> >>>>>>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason
> >>> is
> >>>> I
> >>>>>>>> think
> >>>>>>>>>>>> this
> >>>>>>>>>>>>>>>>>> way
> >>>>>>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs
> >>> when
> >>>>>>>> working
> >>>>>>>>>>>> with
> >>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
> >>>> offset, i
> >>>>>>>>>>>> think it's
> >>>>>>>>>>>>>>>>>>> better to pushdown long rather than string. This could
> >>> let
> >>>> us
> >>>>>>>> push
> >>>>>>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
> >>>>>>>> Otherwise we
> >>>>>>>>>>>> would
> >>>>>>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
> >>>> cast(offset,
> >>>>>>>> long)
> >>>>>>>>>>>> <
> >>>>>>>>>>>>>>>>>> 59382.
> >>>>>>>>>>>>>>>>>>> Moreover I think we need to introduce the type for
> >>> computed
> >>>>>>>> columns
> >>>>>>>>>>>>>>>>>> anyway
> >>>>>>>>>>>>>>>>>>> to support functions that infer output type based on
> >>>> expected
> >>>>>>>>>>>> return
> >>>>>>>>>>>>>>>>>> type.
> >>>>>>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
> >>>> SYSTEM_METADATA
> >>>>>>>> would
> >>>>>>>>>>>> have
> >>>>>>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible
> >>> the
> >>>>>>>> planner
> >>>>>>>>>>>>>>>>>> should
> >>>>>>>>>>>>>>>>>>> fail. As far as I know computed columns push down will
> >> be
> >>>>>> part
> >>>>>>>> of
> >>>>>>>>>>>> source
> >>>>>>>>>>>>>>>>>>> rework, won't it? ;)
> >>>>>>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
> >>>>>>>> completely
> >>>>>>>>>>>>>>>>>>> orthogonal. In my current proposal you can also
> >> partition
> >>>> by
> >>>>>> a
> >>>>>>>>>>>> computed
> >>>>>>>>>>>>>>>>>>> column. The difference between using a udf in
> >> partitioned
> >>>> by
> >>>>>> vs
> >>>>>>>>>>>>>>>>>> partitioned
> >>>>>>>>>>>>>>>>>>> by a computed column is that when you partition by a
> >>>> computed
> >>>>>>>>>>>> column
> >>>>>>>>>>>>>>>>>> this
> >>>>>>>>>>>>>>>>>>> column must be also computed when reading the table. If
> >>> you
> >>>>>>>> use a
> >>>>>>>>>>>> udf in
> >>>>>>>>>>>>>>>>>>> the partitioned by, the expression is computed only
> >> when
> >>>>>>>> inserting
> >>>>>>>>>>>> into
> >>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> table.
> >>>>>>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking
> >>> forward
> >>>>>> for
> >>>>>>>>>>>> further
> >>>>>>>>>>>>>>>>>>> suggestions.
> >>>>>>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
> >>>>>>>>>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
> >>>> Reaing
> >>>>>>>>>>>> metadata
> >>>>>>>>>>>>>>>>>> and
> >>>>>>>>>>>>>>>>>>>>>> key-part information from source is an important
> >>> feature
> >>>>>> for
> >>>>>>>>>>>>>>>>>> streaming
> >>>>>>>>>>>>>>>>>>>>>> users.
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
> >>>>>>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of
> >>> introducing
> >>>>>>>> HEADER
> >>>>>>>>>>>>>>>>>>> keyword as
> >>>>>>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
> >>>>>>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
> >>>> Maybe
> >>>>>> we
> >>>>>>>>>>>> should
> >>>>>>>>>>>>>>>>>>> add a
> >>>>>>>>>>>>>>>>>>>>>> section to explain what's the relationship between
> >>> them.
> >>>>>>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION
> >> be
> >>>> used
> >>>>>>>> on
> >>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
> >>>>>>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink
> >>> SQL.
> >>>>>>>> Shall we
> >>>>>>>>>>>>>>>>>> make
> >>>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
> >>>>>>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
> >>>>>>>> (actually, I
> >>>>>>>>>>>>>>>>>>> prefer
> >>>>>>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
> >>>>>>>> properties
> >>>>>>>>>>>>>>>>>>> FLINK-12557)
> >>>>>>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users
> >>>> that
> >>>>>>>> the
> >>>>>>>>>>>>>>>>>> field
> >>>>>>>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>>>>>>>> a rowtime attribute.
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>> Thanks,
> >>>>>>>>>>>>>>>>>>>>>> Jark
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
> >>>>>>>>>>>>>>>>>> [hidden email]>
> >>>>>>>>>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>> Hi,
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
> >>>> enable
> >>>>>>>>>>>> reading
> >>>>>>>>>>>>>>>>>> table
> >>>>>>>>>>>>>>>>>>>>>>> columns from different parts of source records.
> >>> Besides
> >>>>>> the
> >>>>>>>>>>>> main
> >>>>>>>>>>>>>>>>>>> payload
> >>>>>>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose
> >>> additional
> >>>>>>>>>>>>>>>>>> information. It
> >>>>>>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
> >>>>>>>> ingestion
> >>>>>>>>>>>> time
> >>>>>>>>>>>>>>>>>> or a
> >>>>>>>>>>>>>>>>>>>>>>> read and write parts of the record that contain
> >> data
> >>>> but
> >>>>>>>>>>>>>>>>>> additionally
> >>>>>>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
> >>>> etc.),
> >>>>>>>> e.g.
> >>>>>>>>>>>> key
> >>>>>>>>>>>>>>>>>> or
> >>>>>>>>>>>>>>>>>>>>>>> timestamp in Kafka.
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>> We should make it possible to read and write data
> >>> from
> >>>>>> all
> >>>>>>>> of
> >>>>>>>>>>>> those
> >>>>>>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
> >>>>>> partitioning
> >>>>>>>>>>>> data,
> >>>>>>>>>>>>>>>>>> for
> >>>>>>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
> >>>>>> partitioning
> >>>>>>>> when
> >>>>>>>>>>>>>>>>>>> writing
> >>>>>>>>>>>>>>>>>>>>>>> data out.
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>> You can access the FLIP here:
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>
> >>>>>>
> >>>>
> >>>
> >>
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>> Best,
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>> Dawid
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>>>
> >>>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>
> >>>>>>>>
> >>>>>>>
> >>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>>
> >>>
> >>
> >
>
>

[Timo Walther-2]

+1 for:

timestamp INT METADATA [FROM 'my-timestamp-field']

However, I would inverse the default. Because reading is more common
than writing.

Regards,
Timo


On 09.09.20 14:25, Danny Chan wrote:

> “Personally, I still like the computed column design more because it
> allows to have full flexibility to compute the final column”
>
> I have the same feeling, the non-standard syntax "timestamp INT
> SYSTEM_METADATA("ts")" is neither a computed column nor normal column. It
> looks very likely a computed column but it's not (no AS keyword there), we
> should be cautious for such syntax because we use a function as a column
> constraint. No SQL vendor has such a syntax.
>
> Can we just use the SQL keyword as a constraint to mark the column as
> metadata ?
>
> timestamp INT METADATA [FROM 'my-timestamp-field'] [VIRTUAL]
>
> Note that the "FROM 'field name'" is only needed when the name conflicts
> with the declared table column name, when there are no conflicts, we can
> simplify it to:
>
> timestamp INT METADATA
>
> By default, the field is non-virtual and can be read and written, users
> need to mark the column as virtual when it is only readable.
>
> Timo Walther <[hidden email]> 于2020年9月9日周三 下午6:41写道：
>
>> Hi everyone,
>>
>> "key" and "value" in the properties are a special case because they need
>> to configure a format. So key and value are more than just metadata.
>> Jark's example for setting a timestamp would work but as the FLIP
>> discusses, we have way more metadata fields like headers, epoch-leader,
>> etc. Having a property for all of this metadata would mess up the WITH
>> section entirely. Furthermore, we also want to deal with metadata from
>> the formats. Solving this through properties as well would further
>> complicate the property design.
>>
>> Personally, I still like the computed column design more because it
>> allows to have full flexibility to compute the final column:
>>
>> timestamp AS adjustTimestamp(CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3)))
>>
>> Instead of having a helper column and a real column in the table:
>>
>> helperTimestamp AS CAST(SYSTEM_METADATA("ts") AS TIMESTAMP(3))
>> realTimestamp AS adjustTimestamp(helperTimestamp)
>>
>> But I see that the discussion leans towards:
>>
>> timestamp INT SYSTEM_METADATA("ts")
>>
>> Which is fine with me. It is the shortest solution, because we don't
>> need additional CAST. We can discuss the syntax, so that confusion with
>> computed columns can be avoided.
>>
>> timestamp INT USING SYSTEM_METADATA("ts")
>> timestamp INT FROM SYSTEM_METADATA("ts")
>> timestamp INT FROM SYSTEM_METADATA("ts") PERSISTED
>>
>> We use `SYSTEM_TIME` for temporal tables. I think prefixing with SYSTEM
>> makes it clearer that it comes magically from the system.
>>
>> What do you think?
>>
>> Regards,
>> Timo
>>
>>
>>
>> On 09.09.20 11:41, Jark Wu wrote:
>>> Hi Danny,
>>>
>>> This is not Oracle and MySQL computed column syntax, because there is no
>>> "AS" after the type.
>>>
>>> Hi everyone,
>>>
>>> If we want to use "offset INT SYSTEM_METADATA("offset")", then I think we
>>> must further discuss about "PERSISED" or "VIRTUAL" keyword for query-sink
>>> schema problem.
>>> Personally, I think we can use a shorter keyword "METADATA" for
>>> "SYSTEM_METADATA". Because "SYSTEM_METADATA" sounds like a system
>> function
>>> and confuse users this looks like a computed column.
>>>
>>>
>>> Best,
>>> Jark
>>>
>>>
>>>
>>> On Wed, 9 Sep 2020 at 17:23, Danny Chan <[hidden email]> wrote:
>>>
>>>> "offset INT SYSTEM_METADATA("offset")"
>>>>
>>>> This is actually Oracle or MySQL style computed column syntax.
>>>>
>>>> "You are right that one could argue that "timestamp", "headers" are
>>>> something like "key" and "value""
>>>>
>>>> I have the same feeling, both key value and headers timestamp are *real*
>>>> data
>>>> stored in the consumed record, they are not computed or generated.
>>>>
>>>> "Trying to solve everything via properties sounds rather like a hack to
>>>> me"
>>>>
>>>> Things are not that hack if we can unify the routines or the definitions
>>>> (all from the computed column way or all from the table options), i also
>>>> think that it is a hacky that we mix in 2 kinds of syntax for different
>>>> kinds of metadata (read-only and read-write). In this FLIP, we declare
>> the
>>>> Kafka key fields with table options but SYSTEM_METADATA for other
>> metadata,
>>>> that is a hacky thing or something in-consistent.
>>>>
>>>> Kurt Young <[hidden email]> 于2020年9月9日周三 下午4:48写道：
>>>>
>>>>>    I would vote for `offset INT SYSTEM_METADATA("offset")`.
>>>>>
>>>>> I don't think we can stick with the SQL standard in DDL part forever,
>>>>> especially as there are more and more
>>>>> requirements coming from different connectors and external systems.
>>>>>
>>>>> Best,
>>>>> Kurt
>>>>>
>>>>>
>>>>> On Wed, Sep 9, 2020 at 4:40 PM Timo Walther <[hidden email]>
>> wrote:
>>>>>
>>>>>> Hi Jark,
>>>>>>
>>>>>> now we are back at the original design proposed by Dawid :D Yes, we
>>>>>> should be cautious about adding new syntax. But the length of this
>>>>>> discussion shows that we are looking for a good long-term solution. In
>>>>>> this case I would rather vote for a deep integration into the syntax.
>>>>>>
>>>>>> Computed columns are also not SQL standard compliant. And our DDL is
>>>>>> neither, so we have some degree of freedom here.
>>>>>>
>>>>>> Trying to solve everything via properties sounds rather like a hack to
>>>>>> me. You are right that one could argue that "timestamp", "headers" are
>>>>>> something like "key" and "value". However, mixing
>>>>>>
>>>>>> `offset AS SYSTEM_METADATA("offset")`
>>>>>>
>>>>>> and
>>>>>>
>>>>>> `'timestamp.field' = 'ts'`
>>>>>>
>>>>>> looks more confusing to users that an explicit
>>>>>>
>>>>>> `offset AS CAST(SYSTEM_METADATA("offset") AS INT)`
>>>>>>
>>>>>> or
>>>>>>
>>>>>> `offset INT SYSTEM_METADATA("offset")`
>>>>>>
>>>>>> that is symetric for both source and sink.
>>>>>>
>>>>>> What do others think?
>>>>>>
>>>>>> Regards,
>>>>>> Timo
>>>>>>
>>>>>>
>>>>>> On 09.09.20 10:09, Jark Wu wrote:
>>>>>>> Hi everyone,
>>>>>>>
>>>>>>> I think we have a conclusion that the writable metadata shouldn't be
>>>>>>> defined as a computed column, but a normal column.
>>>>>>>
>>>>>>> "timestamp STRING SYSTEM_METADATA('timestamp')" is one of the
>>>>> approaches.
>>>>>>> However, it is not SQL standard compliant, we need to be cautious
>>>>> enough
>>>>>>> when adding new syntax.
>>>>>>> Besides, we have to introduce the `PERSISTED` or `VIRTUAL` keyword to
>>>>>>> resolve the query-sink schema problem if it is read-only metadata.
>>>> That
>>>>>>> adds more stuff to learn for users.
>>>>>>>
>>>>>>> >From my point of view, the "timestamp", "headers" are something like
>>>>>> "key"
>>>>>>> and "value" that stores with the real data. So why not define the
>>>>>>> "timestamp" in the same way with "key" by using a "timestamp.field"
>>>>>>> connector option?
>>>>>>> On the other side, the read-only metadata, such as "offset",
>>>> shouldn't
>>>>> be
>>>>>>> defined as a normal column. So why not use the existing computed
>>>> column
>>>>>>> syntax for such metadata? Then we don't have the query-sink schema
>>>>>> problem.
>>>>>>> So here is my proposal:
>>>>>>>
>>>>>>> CREATE TABLE kafka_table (
>>>>>>>      id BIGINT,
>>>>>>>      name STRING,
>>>>>>>      col1 STRING,
>>>>>>>      col2 STRING,
>>>>>>>      ts TIMESTAMP(3) WITH LOCAL TIME ZONE,    -- ts is a normal field,
>>>> so
>>>>>> can
>>>>>>> be read and written.
>>>>>>>      offset AS SYSTEM_METADATA("offset")
>>>>>>> ) WITH (
>>>>>>>      'connector' = 'kafka',
>>>>>>>      'topic' = 'test-topic',
>>>>>>>      'key.fields' = 'id, name',
>>>>>>>      'key.format' = 'csv',
>>>>>>>      'value.format' = 'avro',
>>>>>>>      'timestamp.field' = 'ts'    -- define the mapping of Kafka
>>>> timestamp
>>>>>>> );
>>>>>>>
>>>>>>> INSERT INTO kafka_table
>>>>>>> SELECT id, name, col1, col2, rowtime FROM another_table;
>>>>>>>
>>>>>>> I think this can solve all the problems without introducing any new
>>>>>> syntax.
>>>>>>> The only minor disadvantage is that we separate the definition
>>>>> way/syntax
>>>>>>> of read-only metadata and read-write fields.
>>>>>>> However, I don't think this is a big problem.
>>>>>>>
>>>>>>> Best,
>>>>>>> Jark
>>>>>>>
>>>>>>>
>>>>>>> On Wed, 9 Sep 2020 at 15:09, Timo Walther <[hidden email]>
>>>> wrote:
>>>>>>>
>>>>>>>> Hi Kurt,
>>>>>>>>
>>>>>>>> thanks for sharing your opinion. I'm totally up for not reusing
>>>>> computed
>>>>>>>> columns. I think Jark was a big supporter of this syntax, @Jark are
>>>>> you
>>>>>>>> fine with this as well? The non-computed column approach was only a
>>>>>>>> "slightly rejected alternative".
>>>>>>>>
>>>>>>>> Furthermore, we would need to think about how such a new design
>>>>>>>> influences the LIKE clause though.
>>>>>>>>
>>>>>>>> However, we should still keep the `PERSISTED` keyword as it
>>>> influences
>>>>>>>> the query->sink schema. If you look at the list of metadata for
>>>>> existing
>>>>>>>> connectors and formats, we currently offer only two writable
>>>> metadata
>>>>>>>> fields. Otherwise, one would need to declare two tables whenever a
>>>>>>>> metadata columns is read (one for the source, one for the sink).
>>>> This
>>>>>>>> can be quite inconvientient e.g. for just reading the topic.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>> Timo
>>>>>>>>
>>>>>>>>
>>>>>>>> On 09.09.20 08:52, Kurt Young wrote:
>>>>>>>>> I also share the concern that reusing the computed column syntax
>>>> but
>>>>>> have
>>>>>>>>> different semantics
>>>>>>>>> would confuse users a lot.
>>>>>>>>>
>>>>>>>>> Besides, I think metadata fields are conceptually not the same with
>>>>>>>>> computed columns. The metadata
>>>>>>>>> field is a connector specific thing and it only contains the
>>>>>> information
>>>>>>>>> that where does the field come
>>>>>>>>> from (during source) or where does the field need to write to
>>>> (during
>>>>>>>>> sink). It's more similar with normal
>>>>>>>>> fields, with assumption that all these fields need going to the
>>>> data
>>>>>>>> part.
>>>>>>>>>
>>>>>>>>> Thus I'm more lean to the rejected alternative that Timo mentioned.
>>>>>> And I
>>>>>>>>> think we don't need the
>>>>>>>>> PERSISTED keyword, SYSTEM_METADATA should be enough.
>>>>>>>>>
>>>>>>>>> During implementation, the framework only needs to pass such
>>>> <field,
>>>>>>>>> metadata field> information to the
>>>>>>>>> connector, and the logic of handling such fields inside the
>>>> connector
>>>>>>>>> should be straightforward.
>>>>>>>>>
>>>>>>>>> Regarding the downside Timo mentioned:
>>>>>>>>>
>>>>>>>>>> The disadvantage is that users cannot call UDFs or parse
>>>> timestamps.
>>>>>>>>>
>>>>>>>>> I think this is fairly simple to solve. Since the metadata field
>>>>> isn't
>>>>>> a
>>>>>>>>> computed column anymore, we can support
>>>>>>>>> referencing such fields in the computed column. For example:
>>>>>>>>>
>>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>>          id BIGINT,
>>>>>>>>>          name STRING,
>>>>>>>>>          timestamp STRING SYSTEM_METADATA("timestamp"),  // get the
>>>>>>>> timestamp
>>>>>>>>> field from metadata
>>>>>>>>>          ts AS to_timestamp(timestamp) // normal computed column,
>>>> parse
>>>>>> the
>>>>>>>>> string to TIMESTAMP type by using the metadata field
>>>>>>>>> ) WITH (
>>>>>>>>>         ...
>>>>>>>>> )
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>> Kurt
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Tue, Sep 8, 2020 at 11:57 PM Timo Walther <[hidden email]>
>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi Leonard,
>>>>>>>>>>
>>>>>>>>>> the only alternative I see is that we introduce a concept that is
>>>>>>>>>> completely different to computed columns. This is also mentioned
>>>> in
>>>>>> the
>>>>>>>>>> rejected alternative section of the FLIP. Something like:
>>>>>>>>>>
>>>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>>>          id BIGINT,
>>>>>>>>>>          name STRING,
>>>>>>>>>>          timestamp INT SYSTEM_METADATA("timestamp") PERSISTED,
>>>>>>>>>>          headers MAP<STRING, BYTES> SYSTEM_METADATA("headers")
>>>>> PERSISTED
>>>>>>>>>> ) WITH (
>>>>>>>>>>         ...
>>>>>>>>>> )
>>>>>>>>>>
>>>>>>>>>> This way we would avoid confusion at all and can easily map
>>>> columns
>>>>> to
>>>>>>>>>> metadata columns. The disadvantage is that users cannot call UDFs
>>>> or
>>>>>>>>>> parse timestamps. This would need to be done in a real computed
>>>>>> column.
>>>>>>>>>>
>>>>>>>>>> I'm happy about better alternatives.
>>>>>>>>>>
>>>>>>>>>> Regards,
>>>>>>>>>> Timo
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 08.09.20 15:37, Leonard Xu wrote:
>>>>>>>>>>> HI, Timo
>>>>>>>>>>>
>>>>>>>>>>> Thanks for driving this FLIP.
>>>>>>>>>>>
>>>>>>>>>>> Sorry but I have a concern about Writing metadata via
>>>>>> DynamicTableSink
>>>>>>>>>> section:
>>>>>>>>>>>
>>>>>>>>>>> CREATE TABLE kafka_table (
>>>>>>>>>>>        id BIGINT,
>>>>>>>>>>>        name STRING,
>>>>>>>>>>>        timestamp AS CAST(SYSTEM_METADATA("timestamp") AS BIGINT)
>>>>>>>> PERSISTED,
>>>>>>>>>>>        headers AS CAST(SYSTEM_METADATA("headers") AS MAP<STRING,
>>>>>> BYTES>)
>>>>>>>>>> PERSISTED
>>>>>>>>>>> ) WITH (
>>>>>>>>>>>        ...
>>>>>>>>>>> )
>>>>>>>>>>> An insert statement could look like:
>>>>>>>>>>>
>>>>>>>>>>> INSERT INTO kafka_table VALUES (
>>>>>>>>>>>        (1, "ABC", 1599133672, MAP('checksum',
>>>> computeChecksum(...)))
>>>>>>>>>>> )
>>>>>>>>>>>
>>>>>>>>>>> The proposed INERT syntax does not make sense to me, because it
>>>>>>>> contains
>>>>>>>>>> computed(generated) column.
>>>>>>>>>>> Both SQL server and Postgresql do not allow to insert value to
>>>>>> computed
>>>>>>>>>> columns even they are persisted, this boke the generated column
>>>>>>>> semantics
>>>>>>>>>> and may confuse user much.
>>>>>>>>>>>
>>>>>>>>>>> For SQL server computed column[1]:
>>>>>>>>>>>> column_name AS computed_column_expression [ PERSISTED [ NOT
>>>> NULL ]
>>>>>>>> ]...
>>>>>>>>>>>> NOTE: A computed column cannot be the target of an INSERT or
>>>>> UPDATE
>>>>>>>>>> statement.
>>>>>>>>>>>
>>>>>>>>>>> For Postgresql generated column[2]:
>>>>>>>>>>>>       height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
>>>>> STORED
>>>>>>>>>>>> NOTE: A generated column cannot be written to directly. In
>>>> INSERT
>>>>> or
>>>>>>>>>> UPDATE commands, a value cannot be specified for a generated
>>>> column,
>>>>>> but
>>>>>>>>>> the keyword DEFAULT may be specified.
>>>>>>>>>>>
>>>>>>>>>>> It shouldn't be allowed to set/update value for generated column
>>>>>> after
>>>>>>>>>> lookup the SQL 2016:
>>>>>>>>>>>> <insert statement> ::=
>>>>>>>>>>>> INSERT INTO <insertion target> <insert columns and source>
>>>>>>>>>>>>
>>>>>>>>>>>> If <contextually typed table value constructor> CTTVC is
>>>>> specified,
>>>>>>>>>> then every <contextually typed row
>>>>>>>>>>>> value constructor element> simply contained in CTTVC whose
>>>>>>>> positionally
>>>>>>>>>> corresponding <column name>
>>>>>>>>>>>> in <insert column list> references a column of which some
>>>>> underlying
>>>>>>>>>> column is a generated column shall
>>>>>>>>>>>> be a <default specification>.
>>>>>>>>>>>> A <default specification> specifies the default value of some
>>>>>>>>>> associated item.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> [1]
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>>>>>>>> <
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://docs.microsoft.com/en-US/sql/t-sql/statements/alter-table-computed-column-definition-transact-sql?view=sql-server-ver15
>>>>>>>>>>>
>>>>>>>>>>> [2]
>>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html
>>>>> <
>>>>>>>>>> https://www.postgresql.org/docs/12/ddl-generated-columns.html>
>>>>>>>>>>>
>>>>>>>>>>>> 在 2020年9月8日，17:31，Timo Walther <[hidden email]> 写道：
>>>>>>>>>>>>
>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>
>>>>>>>>>>>> according to Flink's and Calcite's casting definition in [1][2]
>>>>>>>>>> TIMESTAMP WITH LOCAL TIME ZONE should be castable from BIGINT. If
>>>>> not,
>>>>>>>> we
>>>>>>>>>> will make it possible ;-)
>>>>>>>>>>>>
>>>>>>>>>>>> I'm aware of DeserializationSchema.getProducedType but I think
>>>>> that
>>>>>>>>>> this method is actually misplaced. The type should rather be
>>>> passed
>>>>> to
>>>>>>>> the
>>>>>>>>>> source itself.
>>>>>>>>>>>>
>>>>>>>>>>>> For our Kafka SQL source, we will also not use this method
>>>> because
>>>>>> the
>>>>>>>>>> Kafka source will add own metadata in addition to the
>>>>>>>>>> DeserializationSchema. So DeserializationSchema.getProducedType
>>>> will
>>>>>>>> never
>>>>>>>>>> be read.
>>>>>>>>>>>>
>>>>>>>>>>>> For now I suggest to leave out the `DataType` from
>>>>>>>>>> DecodingFormat.applyReadableMetadata. Also because the format's
>>>>>> physical
>>>>>>>>>> type is passed later in `createRuntimeDecoder`. If necessary, it
>>>> can
>>>>>> be
>>>>>>>>>> computed manually by consumedType + metadata types. We will
>>>> provide
>>>>> a
>>>>>>>>>> metadata utility class for that.
>>>>>>>>>>>>
>>>>>>>>>>>> Regards,
>>>>>>>>>>>> Timo
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> [1]
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://github.com/apache/flink/blob/master/flink-table/flink-table-common/src/main/java/org/apache/flink/table/types/logical/utils/LogicalTypeCasts.java#L200
>>>>>>>>>>>> [2]
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://github.com/apache/calcite/blob/master/core/src/main/java/org/apache/calcite/sql/type/SqlTypeCoercionRule.java#L254
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 08.09.20 10:52, Jark Wu wrote:
>>>>>>>>>>>>> Hi Timo,
>>>>>>>>>>>>> The updated CAST SYSTEM_METADATA behavior sounds good to me. I
>>>>> just
>>>>>>>>>> noticed
>>>>>>>>>>>>> that a BIGINT can't be converted to "TIMESTAMP(3) WITH LOCAL
>>>> TIME
>>>>>>>>>> ZONE".
>>>>>>>>>>>>> So maybe we need to support this, or use "TIMESTAMP(3) WITH
>>>> LOCAL
>>>>>>>> TIME
>>>>>>>>>>>>> ZONE" as the defined type of Kafka timestamp? I think this
>>>> makes
>>>>>>>> sense,
>>>>>>>>>>>>> because it represents the milli-seconds since epoch.
>>>>>>>>>>>>> Regarding "DeserializationSchema doesn't need TypeInfo", I
>>>> don't
>>>>>>>> think
>>>>>>>>>> so.
>>>>>>>>>>>>> The DeserializationSchema implements ResultTypeQueryable, thus
>>>>> the
>>>>>>>>>>>>> implementation needs to return an output TypeInfo.
>>>>>>>>>>>>> Besides, FlinkKafkaConsumer also
>>>>>>>>>>>>> calls DeserializationSchema.getProducedType as the produced
>>>> type
>>>>> of
>>>>>>>> the
>>>>>>>>>>>>> source function [1].
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>> Jark
>>>>>>>>>>>>> [1]:
>>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-kafka-base/src/main/java/org/apache/flink/streaming/connectors/kafka/FlinkKafkaConsumerBase.java#L1066
>>>>>>>>>>>>> On Tue, 8 Sep 2020 at 16:35, Timo Walther <[hidden email]>
>>>>>>>> wrote:
>>>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I updated the FLIP again and hope that I could address the
>>>>>> mentioned
>>>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> @Leonard: Thanks for the explanation. I wasn't aware that
>>>> ts_ms
>>>>>> and
>>>>>>>>>>>>>> source.ts_ms have different semantics. I updated the FLIP and
>>>>>> expose
>>>>>>>>>> the
>>>>>>>>>>>>>> most commonly used properties separately. So frequently used
>>>>>>>>>> properties
>>>>>>>>>>>>>> are not hidden in the MAP anymore:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> debezium-json.ingestion-timestamp
>>>>>>>>>>>>>> debezium-json.source.timestamp
>>>>>>>>>>>>>> debezium-json.source.database
>>>>>>>>>>>>>> debezium-json.source.schema
>>>>>>>>>>>>>> debezium-json.source.table
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> However, since other properties depend on the used
>>>>>> connector/vendor,
>>>>>>>>>> the
>>>>>>>>>>>>>> remaining options are stored in:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> debezium-json.source.properties
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> And accessed with:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> CAST(SYSTEM_METADATA('debezium-json.source.properties') AS
>>>>>>>> MAP<STRING,
>>>>>>>>>>>>>> STRING>)['table']
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Otherwise it is not possible to figure out the value and
>>>> column
>>>>>> type
>>>>>>>>>>>>>> during validation.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> @Jark: You convinced me in relaxing the CAST constraints. I
>>>>> added
>>>>>> a
>>>>>>>>>>>>>> dedicacated sub-section to the FLIP:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> For making the use of SYSTEM_METADATA easier and avoid nested
>>>>>>>> casting
>>>>>>>>>> we
>>>>>>>>>>>>>> allow explicit casting to a target data type:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> rowtime AS CAST(SYSTEM_METADATA("timestamp") AS TIMESTAMP(3)
>>>>> WITH
>>>>>>>>>> LOCAL
>>>>>>>>>>>>>> TIME ZONE)
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> A connector still produces and consumes the data type returned
>>>>> by
>>>>>>>>>>>>>> `listMetadata()`. The planner will insert necessary explicit
>>>>>> casts.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> In any case, the user must provide a CAST such that the
>>>> computed
>>>>>>>>>> column
>>>>>>>>>>>>>> receives a valid data type when constructing the table schema.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> "I don't see a reason why
>>>> `DecodingFormat#applyReadableMetadata`
>>>>>>>>>> needs a
>>>>>>>>>>>>>> DataType argument."
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Correct he DeserializationSchema doesn't need TypeInfo, it is
>>>>>> always
>>>>>>>>>>>>>> executed locally. It is the source that needs TypeInfo for
>>>>>>>> serializing
>>>>>>>>>>>>>> the record to the next operator. And that's this is what we
>>>>>> provide.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> @Danny:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> “SYSTEM_METADATA("offset")` returns the NULL type by default”
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> We can also use some other means to represent an UNKNOWN data
>>>>>> type.
>>>>>>>> In
>>>>>>>>>>>>>> the Flink type system, we use the NullType for it. The
>>>> important
>>>>>>>> part
>>>>>>>>>> is
>>>>>>>>>>>>>> that the final data type is known for the entire computed
>>>>> column.
>>>>>>>> As I
>>>>>>>>>>>>>> mentioned before, I would avoid the suggested option b) that
>>>>> would
>>>>>>>> be
>>>>>>>>>>>>>> similar to your suggestion. The CAST should be enough and
>>>> allows
>>>>>> for
>>>>>>>>>>>>>> complex expressions in the computed column. Option b) would
>>>> need
>>>>>>>>>> parser
>>>>>>>>>>>>>> changes.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>> Timo
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On 08.09.20 06:21, Leonard Xu wrote:
>>>>>>>>>>>>>>> Hi, Timo
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks for you explanation and update,  I have only one
>>>>> question
>>>>>>>> for
>>>>>>>>>>>>>> the latest FLIP.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> About the MAP<STRING, STRING> DataType of key
>>>>>>>>>> 'debezium-json.source', if
>>>>>>>>>>>>>> user want to use the table name metadata, they need to write:
>>>>>>>>>>>>>>> tableName STRING AS
>>>> CAST(SYSTEM_METADATA('debeuim-json.source')
>>>>>> AS
>>>>>>>>>>>>>> MAP<STRING, STRING>)['table']
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> the expression is a little complex for user, Could we only
>>>>>> support
>>>>>>>>>>>>>> necessary metas with simple DataType as following?
>>>>>>>>>>>>>>> tableName STRING AS
>>>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.table') AS
>>>>>>>>>>>>>> STRING),
>>>>>>>>>>>>>>> transactionTime LONG AS
>>>>>>>>>>>>>> CAST(SYSTEM_METADATA('debeuim-json.source.ts_ms') AS BIGINT),
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> In this way, we can simplify the expression, the mainly used
>>>>>>>>>> metadata in
>>>>>>>>>>>>>> changelog format may include
>>>>>>>>>> 'database','table','source.ts_ms','ts_ms' from
>>>>>>>>>>>>>> my side,
>>>>>>>>>>>>>>> maybe we could only support them at first version.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Both Debezium and Canal have above four metadata, and I‘m
>>>>> willing
>>>>>>>> to
>>>>>>>>>>>>>> take some subtasks in next development if necessary.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Debezium:
>>>>>>>>>>>>>>> {
>>>>>>>>>>>>>>>         "before": null,
>>>>>>>>>>>>>>>         "after": {  "id": 101,"name": "scooter"},
>>>>>>>>>>>>>>>         "source": {
>>>>>>>>>>>>>>>           "db": "inventory",                  # 1. database
>>>> name
>>>>>> the
>>>>>>>>>>>>>> changelog belongs to.
>>>>>>>>>>>>>>>           "table": "products",                # 2. table name
>>>> the
>>>>>>>>>> changelog
>>>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>>>           "ts_ms": 1589355504100,             # 3. timestamp
>> of
>>>>> the
>>>>>>>>>> change
>>>>>>>>>>>>>> happened in database system, i.e.: transaction time in
>>>> database.
>>>>>>>>>>>>>>>           "connector": "mysql",
>>>>>>>>>>>>>>>           ….
>>>>>>>>>>>>>>>         },
>>>>>>>>>>>>>>>         "ts_ms": 1589355606100,              # 4. timestamp
>>>> when
>>>>>> the
>>>>>>>>>> debezium
>>>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>>>         "op": "c",
>>>>>>>>>>>>>>>         "transaction": null
>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Canal:
>>>>>>>>>>>>>>> {
>>>>>>>>>>>>>>>         "data": [{  "id": "102", "name": "car battery" }],
>>>>>>>>>>>>>>>         "database": "inventory",      # 1. database name the
>>>>>> changelog
>>>>>>>>>>>>>> belongs to.
>>>>>>>>>>>>>>>         "table": "products",          # 2. table name the
>>>>> changelog
>>>>>>>>>> belongs
>>>>>>>>>>>>>> to.
>>>>>>>>>>>>>>>         "es": 1589374013000,          # 3. execution time of
>>>> the
>>>>>>>> change
>>>>>>>>>> in
>>>>>>>>>>>>>> database system, i.e.: transaction time in database.
>>>>>>>>>>>>>>>         "ts": 1589374013680,          # 4. timestamp when the
>>>>>> cannal
>>>>>>>>>>>>>> processed the changelog.
>>>>>>>>>>>>>>>         "isDdl": false,
>>>>>>>>>>>>>>>         "mysqlType": {},
>>>>>>>>>>>>>>>         ....
>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Best
>>>>>>>>>>>>>>> Leonard
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> 在 2020年9月8日，11:57，Danny Chan <[hidden email]> 写道：
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks Timo ~
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The FLIP was already in pretty good shape, I have only 2
>>>>>> questions
>>>>>>>>>> here:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> 1. “`CAST(SYSTEM_METADATA("offset") AS INT)` would be a
>>>> valid
>>>>>>>>>> read-only
>>>>>>>>>>>>>> computed column for Kafka and can be extracted by the
>>>> planner.”
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> What is the pros we follow the SQL-SERVER syntax here ?
>>>>> Usually
>>>>>> an
>>>>>>>>>>>>>> expression return type can be inferred automatically. But I
>>>>> guess
>>>>>>>>>>>>>> SQL-SERVER does not have function like SYSTEM_METADATA which
>>>>>>>> actually
>>>>>>>>>> does
>>>>>>>>>>>>>> not have a specific return type.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> And why not use the Oracle or MySQL syntax there ?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> column_name [datatype] [GENERATED ALWAYS] AS (expression)
>>>>>>>> [VIRTUAL]
>>>>>>>>>>>>>>>> Which is more straight-forward.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> 2. “SYSTEM_METADATA("offset")` returns the NULL type by
>>>>> default”
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The default type should not be NULL because only NULL
>>>> literal
>>>>>> does
>>>>>>>>>>>>>> that. Usually we use ANY as the type if we do not know the
>>>>>> specific
>>>>>>>>>> type in
>>>>>>>>>>>>>> the SQL context. ANY means the physical value can be any java
>>>>>>>> object.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> [1]
>>>>> https://oracle-base.com/articles/11g/virtual-columns-11gr1
>>>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://dev.mysql.com/doc/refman/5.7/en/create-table-generated-columns.html
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>>> 在 2020年9月4日 +0800 PM4:48，Timo Walther <[hidden email]
>>>>>> ，写道：
>>>>>>>>>>>>>>>>> Hi everyone,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I completely reworked FLIP-107. It now covers the full
>>>> story
>>>>>> how
>>>>>>>> to
>>>>>>>>>>>>>> read
>>>>>>>>>>>>>>>>> and write metadata from/to connectors and formats. It
>>>>> considers
>>>>>>>>>> all of
>>>>>>>>>>>>>>>>> the latest FLIPs, namely FLIP-95, FLIP-132 and FLIP-122. It
>>>>>>>>>> introduces
>>>>>>>>>>>>>>>>> the concept of PERSISTED computed columns and leaves out
>>>>>>>>>> partitioning
>>>>>>>>>>>>>>>>> for now.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Looking forward to your feedback.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>> Timo
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On 04.03.20 09:45, Kurt Young wrote:
>>>>>>>>>>>>>>>>>> Sorry, forgot one question.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> 4. Can we make the value.fields-include more orthogonal?
>>>>> Like
>>>>>>>> one
>>>>>>>>>> can
>>>>>>>>>>>>>>>>>> specify it as "EXCEPT_KEY, EXCEPT_TIMESTAMP".
>>>>>>>>>>>>>>>>>> With current EXCEPT_KEY and EXCEPT_KEY_TIMESTAMP, users
>>>> can
>>>>>> not
>>>>>>>>>>>>>> config to
>>>>>>>>>>>>>>>>>> just ignore timestamp but keep key.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Mar 4, 2020 at 4:42 PM Kurt Young <
>>>> [hidden email]
>>>>>>
>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi Dawid,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I have a couple of questions around key fields, actually
>>>> I
>>>>>> also
>>>>>>>>>> have
>>>>>>>>>>>>>> some
>>>>>>>>>>>>>>>>>>> other questions but want to be focused on key fields
>>>> first.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 1. I don't fully understand the usage of "key.fields". Is
>>>>>> this
>>>>>>>>>>>>>> option only
>>>>>>>>>>>>>>>>>>> valid during write operation? Because for
>>>>>>>>>>>>>>>>>>> reading, I can't imagine how such options can be
>>>> applied. I
>>>>>>>> would
>>>>>>>>>>>>>> expect
>>>>>>>>>>>>>>>>>>> that there might be a SYSTEM_METADATA("key")
>>>>>>>>>>>>>>>>>>> to read and assign the key to a normal field?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 2. If "key.fields" is only valid in write operation, I
>>>> want
>>>>>> to
>>>>>>>>>>>>>> propose we
>>>>>>>>>>>>>>>>>>> can simplify the options to not introducing
>>>> key.format.type
>>>>>> and
>>>>>>>>>>>>>>>>>>> other related options. I think a single "key.field" (not
>>>>>>>> fields)
>>>>>>>>>>>>>> would be
>>>>>>>>>>>>>>>>>>> enough, users can use UDF to calculate whatever key they
>>>>>>>>>>>>>>>>>>> want before sink.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> 3. Also I don't want to introduce "value.format.type" and
>>>>>>>>>>>>>>>>>>> "value.format.xxx" with the "value" prefix. Not every
>>>>>> connector
>>>>>>>>>> has a
>>>>>>>>>>>>>>>>>>> concept
>>>>>>>>>>>>>>>>>>> of key and values. The old parameter "format.type"
>>>> already
>>>>>> good
>>>>>>>>>>>>>> enough to
>>>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>> Kurt
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Tue, Mar 3, 2020 at 10:40 PM Jark Wu <
>>>> [hidden email]>
>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Thanks Dawid,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> I have two more questions.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> SupportsMetadata
>>>>>>>>>>>>>>>>>>>> Introducing SupportsMetadata sounds good to me. But I
>>>> have
>>>>>>>> some
>>>>>>>>>>>>>> questions
>>>>>>>>>>>>>>>>>>>> regarding to this interface.
>>>>>>>>>>>>>>>>>>>> 1) How do the source know what the expected return type
>>>> of
>>>>>>>> each
>>>>>>>>>>>>>> metadata?
>>>>>>>>>>>>>>>>>>>> 2) Where to put the metadata fields? Append to the
>>>>> existing
>>>>>>>>>> physical
>>>>>>>>>>>>>>>>>>>> fields?
>>>>>>>>>>>>>>>>>>>> If yes, I would suggest to change the signature to
>>>>>>>> `TableSource
>>>>>>>>>>>>>>>>>>>> appendMetadataFields(String[] metadataNames, DataType[]
>>>>>>>>>>>>>> metadataTypes)`
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition")
>>>>>>>>>>>>>>>>>>>> Can SYSTEM_METADATA() function be used nested in a
>>>>> computed
>>>>>>>>>> column
>>>>>>>>>>>>>>>>>>>> expression? If yes, how to specify the return type of
>>>>>>>>>>>>>> SYSTEM_METADATA?
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On Tue, 3 Mar 2020 at 17:06, Dawid Wysakowicz <
>>>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> 1. I thought a bit more on how the source would emit
>>>> the
>>>>>>>>>> columns
>>>>>>>>>>>>>> and I
>>>>>>>>>>>>>>>>>>>>> now see its not exactly the same as regular columns. I
>>>>> see
>>>>>> a
>>>>>>>>>> need
>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>>>> elaborate a bit more on that in the FLIP as you asked,
>>>>>> Jark.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> I do agree mostly with Danny on how we should do that.
>>>>> One
>>>>>>>>>>>>>> additional
>>>>>>>>>>>>>>>>>>>>> things I would introduce is an
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> interface SupportsMetadata {
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> boolean supportsMetadata(Set<String> metadataFields);
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> TableSource generateMetadataFields(Set<String>
>>>>>>>> metadataFields);
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> This way the source would have to declare/emit only the
>>>>>>>>>> requested
>>>>>>>>>>>>>>>>>>>>> metadata fields. In order not to clash with user
>>>> defined
>>>>>>>>>> fields.
>>>>>>>>>>>>>> When
>>>>>>>>>>>>>>>>>>>>> emitting the metadata field I would prepend the column
>>>>> name
>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>>> __system_{property_name}. Therefore when requested
>>>>>>>>>>>>>>>>>>>>> SYSTEM_METADATA("partition") the source would append a
>>>>>> field
>>>>>>>>>>>>>>>>>>>>> __system_partition to the schema. This would be never
>>>>>> visible
>>>>>>>>>> to
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> user as it would be used only for the subsequent
>>>> computed
>>>>>>>>>> columns.
>>>>>>>>>>>>>> If
>>>>>>>>>>>>>>>>>>>>> that makes sense to you, I will update the FLIP with
>>>> this
>>>>>>>>>>>>>> description.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> 2. CAST vs explicit type in computed columns
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Here I agree with Danny. It is also the current state
>>>> of
>>>>>> the
>>>>>>>>>>>>>> proposal.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> 3. Partitioning on computed column vs function
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Here I also agree with Danny. I also think those are
>>>>>>>>>> orthogonal. I
>>>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>>>> leave out the STORED computed columns out of the
>>>>>> discussion.
>>>>>>>> I
>>>>>>>>>>>>>> don't see
>>>>>>>>>>>>>>>>>>>>> how do they relate to the partitioning. I already put
>>>>> both
>>>>>> of
>>>>>>>>>> those
>>>>>>>>>>>>>>>>>>>>> cases in the document. We can either partition on a
>>>>>> computed
>>>>>>>>>>>>>> column or
>>>>>>>>>>>>>>>>>>>>> use a udf in a partioned by clause. I am fine with
>>>>> leaving
>>>>>>>> out
>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> partitioning by udf in the first version if you still
>>>>> have
>>>>>>>> some
>>>>>>>>>>>>>>>>>>>> concerns.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> As for your question Danny. It depends which
>>>> partitioning
>>>>>>>>>> strategy
>>>>>>>>>>>>>> you
>>>>>>>>>>>>>>>>>>>> use.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> For the HASH partitioning strategy I thought it would
>>>>> work
>>>>>> as
>>>>>>>>>> you
>>>>>>>>>>>>>>>>>>>>> explained. It would be N = MOD(expr, num). I am not
>>>> sure
>>>>>>>>>> though if
>>>>>>>>>>>>>> we
>>>>>>>>>>>>>>>>>>>>> should introduce the PARTITIONS clause. Usually Flink
>>>>> does
>>>>>>>> not
>>>>>>>>>> own
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> data and the partitions are already an intrinsic
>>>> property
>>>>>> of
>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> underlying source e.g. for kafka we do not create
>>>> topics,
>>>>>> but
>>>>>>>>>> we
>>>>>>>>>>>>>> just
>>>>>>>>>>>>>>>>>>>>> describe pre-existing pre-partitioned topic.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> 4. timestamp vs timestamp.field vs connector.field vs
>>>> ...
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> I am fine with changing it to timestamp.field to be
>>>>>>>> consistent
>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>>> other value.fields and key.fields. Actually that was
>>>> also
>>>>>> my
>>>>>>>>>>>>>> initial
>>>>>>>>>>>>>>>>>>>>> proposal in a first draft I prepared. I changed it
>>>>>> afterwards
>>>>>>>>>> to
>>>>>>>>>>>>>> shorten
>>>>>>>>>>>>>>>>>>>>> the key.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On 03/03/2020 09:00, Danny Chan wrote:
>>>>>>>>>>>>>>>>>>>>>> Thanks Dawid for bringing up this discussion, I think
>>>> it
>>>>>> is
>>>>>>>> a
>>>>>>>>>>>>>> useful
>>>>>>>>>>>>>>>>>>>>> feature ~
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> About how the metadata outputs from source
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> I think it is completely orthogonal, computed column
>>>>> push
>>>>>>>>>> down is
>>>>>>>>>>>>>>>>>>>>> another topic, this should not be a blocker but a
>>>>>> promotion,
>>>>>>>>>> if we
>>>>>>>>>>>>>> do
>>>>>>>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>>>>>>>> have any filters on the computed column, there is no
>>>> need
>>>>>> to
>>>>>>>>>> do any
>>>>>>>>>>>>>>>>>>>>> pushings; the source node just emit the complete record
>>>>>> with
>>>>>>>>>> full
>>>>>>>>>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>>>>> with the declared physical schema, then when generating
>>>>> the
>>>>>>>>>> virtual
>>>>>>>>>>>>>>>>>>>>> columns, we would extract the metadata info and output
>>>> as
>>>>>>>> full
>>>>>>>>>>>>>>>>>>>> columns(with
>>>>>>>>>>>>>>>>>>>>> full schema).
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> About the type of metadata column
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Personally i prefer explicit type instead of CAST,
>>>> they
>>>>>> are
>>>>>>>>>>>>>> symantic
>>>>>>>>>>>>>>>>>>>>> equivalent though, explict type is more
>>>> straight-forward
>>>>>> and
>>>>>>>>>> we can
>>>>>>>>>>>>>>>>>>>> declare
>>>>>>>>>>>>>>>>>>>>> the nullable attribute there.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> About option A: partitioning based on acomputed column
>>>>> VS
>>>>>>>>>> option
>>>>>>>>>>>>>> B:
>>>>>>>>>>>>>>>>>>>>> partitioning with just a function
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>       From the FLIP, it seems that B's partitioning is
>>>>> just
>>>>>> a
>>>>>>>>>> strategy
>>>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>>>> writing data, the partiton column is not included in
>>>> the
>>>>>>>> table
>>>>>>>>>>>>>> schema,
>>>>>>>>>>>>>>>>>>>> so
>>>>>>>>>>>>>>>>>>>>> it's just useless when reading from that.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> - Compared to A, we do not need to generate the
>>>>> partition
>>>>>>>>>> column
>>>>>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>>>> selecting from the table(but insert into)
>>>>>>>>>>>>>>>>>>>>>> - For A we can also mark the column as STORED when we
>>>>> want
>>>>>>>> to
>>>>>>>>>>>>>> persist
>>>>>>>>>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> So in my opition they are orthogonal, we can support
>>>>>> both, i
>>>>>>>>>> saw
>>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>>> MySQL/Oracle[1][2] would suggest to also define the
>>>>>>>> PARTITIONS
>>>>>>>>>>>>>> num, and
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> partitions are managed under a "tablenamespace", the
>>>>>>>> partition
>>>>>>>>>> in
>>>>>>>>>>>>>> which
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> record is stored is partition number N, where N =
>>>>> MOD(expr,
>>>>>>>>>> num),
>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>>> your
>>>>>>>>>>>>>>>>>>>>> design, which partiton the record would persist ?
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> [1]
>>>>>>>>>>>>>>
>>>> https://dev.mysql.com/doc/refman/5.7/en/partitioning-hash.html
>>>>>>>>>>>>>>>>>>>>>> [2]
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://docs.oracle.com/database/121/VLDBG/GUID-F023D3ED-262F-4B19-950A-D3C8F8CDB4F4.htm#VLDBG1270
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>> Danny Chan
>>>>>>>>>>>>>>>>>>>>>> 在 2020年3月2日 +0800 PM6:16，Dawid Wysakowicz <
>>>>>>>>>> [hidden email]
>>>>>>>>>>>>>>>>>>>>> ，写道：
>>>>>>>>>>>>>>>>>>>>>>> Hi Jark,
>>>>>>>>>>>>>>>>>>>>>>> Ad. 2 I added a section to discuss relation to
>>>> FLIP-63
>>>>>>>>>>>>>>>>>>>>>>> Ad. 3 Yes, I also tried to somewhat keep hierarchy of
>>>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>>>> Therefore you have the key.format.type.
>>>>>>>>>>>>>>>>>>>>>>> I also considered exactly what you are suggesting
>>>>>>>> (prefixing
>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>>> connector or kafka). I should've put that into an
>>>>>>>>>> Option/Rejected
>>>>>>>>>>>>>>>>>>>>> alternatives.
>>>>>>>>>>>>>>>>>>>>>>> I agree timestamp, key.*, value.* are connector
>>>>>> properties.
>>>>>>>>>> Why I
>>>>>>>>>>>>>>>>>>>>> wanted to suggest not adding that prefix in the first
>>>>>> version
>>>>>>>>>> is
>>>>>>>>>>>>>> that
>>>>>>>>>>>>>>>>>>>>> actually all the properties in the WITH section are
>>>>>> connector
>>>>>>>>>>>>>>>>>>>> properties.
>>>>>>>>>>>>>>>>>>>>> Even format is in the end a connector property as some
>>>> of
>>>>>> the
>>>>>>>>>>>>>> sources
>>>>>>>>>>>>>>>>>>>> might
>>>>>>>>>>>>>>>>>>>>> not have a format, imo. The benefit of not adding the
>>>>>> prefix
>>>>>>>> is
>>>>>>>>>>>>>> that it
>>>>>>>>>>>>>>>>>>>>> makes the keys a bit shorter. Imagine prefixing all the
>>>>>>>>>> properties
>>>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>>> connector (or if we go with FLINK-12557:
>>>> elasticsearch):
>>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.type: csv
>>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.field: ....
>>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.delimiter: ....
>>>>>>>>>>>>>>>>>>>>>>> elasticsearch.key.format.*: ....
>>>>>>>>>>>>>>>>>>>>>>> I am fine with doing it though if this is a preferred
>>>>>>>>>> approach
>>>>>>>>>>>>>> in the
>>>>>>>>>>>>>>>>>>>>> community.
>>>>>>>>>>>>>>>>>>>>>>> Ad in-line comments:
>>>>>>>>>>>>>>>>>>>>>>> I forgot to update the `value.fields.include`
>>>> property.
>>>>>> It
>>>>>>>>>>>>>> should be
>>>>>>>>>>>>>>>>>>>>> value.fields-include. Which I think you also suggested
>>>> in
>>>>>> the
>>>>>>>>>>>>>> comment,
>>>>>>>>>>>>>>>>>>>>> right?
>>>>>>>>>>>>>>>>>>>>>>> As for the cast vs declaring output type of computed
>>>>>>>> column.
>>>>>>>>>> I
>>>>>>>>>>>>>> think
>>>>>>>>>>>>>>>>>>>>> it's better not to use CAST, but declare a type of an
>>>>>>>>>> expression
>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>>> later
>>>>>>>>>>>>>>>>>>>>> on infer the output type of SYSTEM_METADATA. The reason
>>>>> is
>>>>>> I
>>>>>>>>>> think
>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>>> way
>>>>>>>>>>>>>>>>>>>>> it will be easier to implement e.g. filter push downs
>>>>> when
>>>>>>>>>> working
>>>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> native types of the source, e.g. in case of Kafka's
>>>>>> offset, i
>>>>>>>>>>>>>> think it's
>>>>>>>>>>>>>>>>>>>>> better to pushdown long rather than string. This could
>>>>> let
>>>>>> us
>>>>>>>>>> push
>>>>>>>>>>>>>>>>>>>>> expression like e.g. offset > 12345 & offset < 59382.
>>>>>>>>>> Otherwise we
>>>>>>>>>>>>>> would
>>>>>>>>>>>>>>>>>>>>> have to push down cast(offset, long) > 12345 &&
>>>>>> cast(offset,
>>>>>>>>>> long)
>>>>>>>>>>>>>> <
>>>>>>>>>>>>>>>>>>>> 59382.
>>>>>>>>>>>>>>>>>>>>> Moreover I think we need to introduce the type for
>>>>> computed
>>>>>>>>>> columns
>>>>>>>>>>>>>>>>>>>> anyway
>>>>>>>>>>>>>>>>>>>>> to support functions that infer output type based on
>>>>>> expected
>>>>>>>>>>>>>> return
>>>>>>>>>>>>>>>>>>>> type.
>>>>>>>>>>>>>>>>>>>>>>> As for the computed column push down. Yes,
>>>>>> SYSTEM_METADATA
>>>>>>>>>> would
>>>>>>>>>>>>>> have
>>>>>>>>>>>>>>>>>>>>> to be pushed down to the source. If it is not possible
>>>>> the
>>>>>>>>>> planner
>>>>>>>>>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>>>> fail. As far as I know computed columns push down will
>>>> be
>>>>>>>> part
>>>>>>>>>> of
>>>>>>>>>>>>>> source
>>>>>>>>>>>>>>>>>>>>> rework, won't it? ;)
>>>>>>>>>>>>>>>>>>>>>>> As for the persisted computed column. I think it is
>>>>>>>>>> completely
>>>>>>>>>>>>>>>>>>>>> orthogonal. In my current proposal you can also
>>>> partition
>>>>>> by
>>>>>>>> a
>>>>>>>>>>>>>> computed
>>>>>>>>>>>>>>>>>>>>> column. The difference between using a udf in
>>>> partitioned
>>>>>> by
>>>>>>>> vs
>>>>>>>>>>>>>>>>>>>> partitioned
>>>>>>>>>>>>>>>>>>>>> by a computed column is that when you partition by a
>>>>>> computed
>>>>>>>>>>>>>> column
>>>>>>>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>>>> column must be also computed when reading the table. If
>>>>> you
>>>>>>>>>> use a
>>>>>>>>>>>>>> udf in
>>>>>>>>>>>>>>>>>>>>> the partitioned by, the expression is computed only
>>>> when
>>>>>>>>>> inserting
>>>>>>>>>>>>>> into
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> table.
>>>>>>>>>>>>>>>>>>>>>>> Hope this answers some of your questions. Looking
>>>>> forward
>>>>>>>> for
>>>>>>>>>>>>>> further
>>>>>>>>>>>>>>>>>>>>> suggestions.
>>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> On 02/03/2020 05:18, Jark Wu wrote:
>>>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Thanks Dawid for starting such a great discussion.
>>>>>> Reaing
>>>>>>>>>>>>>> metadata
>>>>>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>>>>>>> key-part information from source is an important
>>>>> feature
>>>>>>>> for
>>>>>>>>>>>>>>>>>>>> streaming
>>>>>>>>>>>>>>>>>>>>>>>> users.
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> In general, I agree with the proposal of the FLIP.
>>>>>>>>>>>>>>>>>>>>>>>> I will leave my thoughts and comments here:
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> 1) +1 to use connector properties instead of
>>>>> introducing
>>>>>>>>>> HEADER
>>>>>>>>>>>>>>>>>>>>> keyword as
>>>>>>>>>>>>>>>>>>>>>>>> the reason you mentioned in the FLIP.
>>>>>>>>>>>>>>>>>>>>>>>> 2) we already introduced PARTITIONED BY in FLIP-63.
>>>>>> Maybe
>>>>>>>> we
>>>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>>>> add a
>>>>>>>>>>>>>>>>>>>>>>>> section to explain what's the relationship between
>>>>> them.
>>>>>>>>>>>>>>>>>>>>>>>> Do their concepts conflict? Could INSERT PARTITION
>>>> be
>>>>>> used
>>>>>>>>>> on
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>>>> PARTITIONED table in this FLIP?
>>>>>>>>>>>>>>>>>>>>>>>> 3) Currently, properties are hierarchical in Flink
>>>>> SQL.
>>>>>>>>>> Shall we
>>>>>>>>>>>>>>>>>>>> make
>>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>>>> new introduced properties more hierarchical?
>>>>>>>>>>>>>>>>>>>>>>>> For example, "timestamp" => "connector.timestamp"?
>>>>>>>>>> (actually, I
>>>>>>>>>>>>>>>>>>>>> prefer
>>>>>>>>>>>>>>>>>>>>>>>> "kafka.timestamp" which is another improvement for
>>>>>>>>>> properties
>>>>>>>>>>>>>>>>>>>>> FLINK-12557)
>>>>>>>>>>>>>>>>>>>>>>>> A single "timestamp" in properties may mislead users
>>>>>> that
>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> field
>>>>>>>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>>>>>>> a rowtime attribute.
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> I also left some minor comments in the FLIP.
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> On Sun, 1 Mar 2020 at 22:30, Dawid Wysakowicz <
>>>>>>>>>>>>>>>>>>>> [hidden email]>
>>>>>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> I would like to propose an improvement that would
>>>>>> enable
>>>>>>>>>>>>>> reading
>>>>>>>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>>>>>>>>>> columns from different parts of source records.
>>>>> Besides
>>>>>>>> the
>>>>>>>>>>>>>> main
>>>>>>>>>>>>>>>>>>>>> payload
>>>>>>>>>>>>>>>>>>>>>>>>> majority (if not all of the sources) expose
>>>>> additional
>>>>>>>>>>>>>>>>>>>> information. It
>>>>>>>>>>>>>>>>>>>>>>>>> can be simply a read-only metadata such as offset,
>>>>>>>>>> ingestion
>>>>>>>>>>>>>> time
>>>>>>>>>>>>>>>>>>>> or a
>>>>>>>>>>>>>>>>>>>>>>>>> read and write parts of the record that contain
>>>> data
>>>>>> but
>>>>>>>>>>>>>>>>>>>> additionally
>>>>>>>>>>>>>>>>>>>>>>>>> serve different purposes (partitioning, compaction
>>>>>> etc.),
>>>>>>>>>> e.g.
>>>>>>>>>>>>>> key
>>>>>>>>>>>>>>>>>>>> or
>>>>>>>>>>>>>>>>>>>>>>>>> timestamp in Kafka.
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> We should make it possible to read and write data
>>>>> from
>>>>>>>> all
>>>>>>>>>> of
>>>>>>>>>>>>>> those
>>>>>>>>>>>>>>>>>>>>>>>>> locations. In this proposal I discuss reading
>>>>>>>> partitioning
>>>>>>>>>>>>>> data,
>>>>>>>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>>>>>>>> completeness this proposal discusses also the
>>>>>>>> partitioning
>>>>>>>>>> when
>>>>>>>>>>>>>>>>>>>>> writing
>>>>>>>>>>>>>>>>>>>>>>>>> data out.
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> I am looking forward to your comments.
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> You can access the FLIP here:
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>
>>>>>>
>>>>>
>>>>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-107%3A+Reading+table+columns+from+different+parts+of+source+records?src=contextnavpagetreemode
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> Dawid
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>>
>