[jira] [Created] (FLINK-13738) NegativeArraySizeException in LongHybridHashTable

[Shang Yuanchun (Jira)]

Robert Metzger created FLINK-13738:
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             Summary: NegativeArraySizeException in LongHybridHashTable
                 Key: FLINK-13738
                 URL: https://issues.apache.org/jira/browse/FLINK-13738
             Project: Flink
          Issue Type: Task
          Components: Table SQL / Runtime
    Affects Versions: 1.9.0
            Reporter: Robert Metzger


Executing this (meaningless) query:
{code:java}
INSERT INTO sinkTable ( SELECT CONCAT( CAST( id AS VARCHAR), CAST( COUNT(*) AS VARCHAR)) as something, 'const' FROM CsvTable, table1  WHERE sometxt LIKE 'a%' AND id = key GROUP BY id ) {code}
leads to the following exception:
{code:java}
Caused by: java.lang.NegativeArraySizeException
 at org.apache.flink.table.runtime.hashtable.LongHybridHashTable.tryDenseMode(LongHybridHashTable.java:216)
 at org.apache.flink.table.runtime.hashtable.LongHybridHashTable.endBuild(LongHybridHashTable.java:105)
 at LongHashJoinOperator$36.endInput1$(Unknown Source)
 at LongHashJoinOperator$36.endInput(Unknown Source)
 at org.apache.flink.streaming.runtime.tasks.OperatorChain.endInput(OperatorChain.java:256)
 at org.apache.flink.streaming.runtime.io.StreamTwoInputSelectableProcessor.checkFinished(StreamTwoInputSelectableProcessor.java:359)
 at org.apache.flink.streaming.runtime.io.StreamTwoInputSelectableProcessor.processInput(StreamTwoInputSelectableProcessor.java:193)
 at org.apache.flink.streaming.runtime.tasks.StreamTask.performDefaultAction(StreamTask.java:276)
 at org.apache.flink.streaming.runtime.tasks.StreamTask.run(StreamTask.java:298)
 at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:403)
 at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:687)
 at org.apache.flink.runtime.taskmanager.Task.run(Task.java:517)
 at java.lang.Thread.run(Thread.java:748){code}
This is the plan:

 
{code:java}
== Abstract Syntax Tree ==
 LogicalSink(name=[sinkTable], fields=[f0, f1])
 +- LogicalProject(something=[CONCAT(CAST($0):VARCHAR(2147483647) CHARACTER SET "UTF-16LE", CAST($1):VARCHAR(2147483647) CHARACTER SET "UTF-16LE" NOT NULL)], EXPR$1=[_UTF-16LE'const'])
 +- LogicalAggregate(group=[
{0}
], agg#0=[COUNT()])
 +- LogicalProject(id=[$1])
 +- LogicalFilter(condition=[AND(LIKE($0, _UTF-16LE'a%'), =($1, CAST($2):BIGINT))])
 +- LogicalJoin(condition=[true], joinType=[inner])
 :- LogicalTableScan(table=[[default_catalog, default_database, CsvTable, source: [CsvTableSource(read fields: sometxt, id)]]])
 +- LogicalTableScan(table=[[default_catalog, default_database, table1, source: [GeneratorTableSource(key, rowtime, payload)]]])
== Optimized Logical Plan ==
 Sink(name=[sinkTable], fields=[f0, f1]): rowcount = 1498810.6659336376, cumulative cost =
{4.459964319978008E8 rows, 1.879799762133187E10 cpu, 4.8E9 io, 8.4E8 network, 1.799524266373455E8 memory}
+- Calc(select=[CONCAT(CAST(id), CAST($f1)) AS something, _UTF-16LE'const' AS EXPR$1]): rowcount = 1498810.6659336376, cumulative cost =
{4.444976213318672E8 rows, 1.8796498810665936E10 cpu, 4.8E9 io, 8.4E8 network, 1.799524266373455E8 memory}
+- HashAggregate(isMerge=[false], groupBy=[id], select=[id, COUNT(*) AS $f1]): rowcount = 1498810.6659336376, cumulative cost =
{4.429988106659336E8 rows, 1.8795E10 cpu, 4.8E9 io, 8.4E8 network, 1.799524266373455E8 memory}
+- Calc(select=[id]): rowcount = 1.575E7, cumulative cost =
{4.415E8 rows, 1.848E10 cpu, 4.8E9 io, 8.4E8 network, 1.2E8 memory}
+- HashJoin(joinType=[InnerJoin], where=[=(id, key0)], select=[id, key0], build=[left]): rowcount = 1.575E7, cumulative cost =
{4.2575E8 rows, 1.848E10 cpu, 4.8E9 io, 8.4E8 network, 1.2E8 memory}
:- Exchange(distribution=[hash[id]]): rowcount = 5000000.0, cumulative cost =
{1.1E8 rows, 8.4E8 cpu, 2.0E9 io, 4.0E7 network, 0.0 memory}
: +- Calc(select=[id], where=[LIKE(sometxt, _UTF-16LE'a%')]): rowcount = 5000000.0, cumulative cost =
{1.05E8 rows, 0.0 cpu, 2.0E9 io, 0.0 network, 0.0 memory}
: +- TableSourceScan(table=[[default_catalog, default_database, CsvTable, source: [CsvTableSource(read fields: sometxt, id)]]], fields=[sometxt, id]): rowcount = 1.0E8, cumulative cost =
{1.0E8 rows, 0.0 cpu, 2.0E9 io, 0.0 network, 0.0 memory}
+- Exchange(distribution=[hash[key0]]): rowcount = 1.0E8, cumulative cost =
{3.0E8 rows, 1.68E10 cpu, 2.8E9 io, 8.0E8 network, 0.0 memory}
+- Calc(select=[CAST(key) AS key0]): rowcount = 1.0E8, cumulative cost =
{2.0E8 rows, 0.0 cpu, 2.8E9 io, 0.0 network, 0.0 memory}
+- TableSourceScan(table=[[default_catalog, default_database, table1, source: [GeneratorTableSource(key, rowtime, payload)]]], fields=[key, rowtime, payload]): rowcount = 1.0E8, cumulative cost =
{1.0E8 rows, 0.0 cpu, 2.8E9 io, 0.0 network, 0.0 memory}
== Physical Execution Plan ==
 Stage 1 : Data Source
 content : collect elements with CollectionInputFormat
Stage 2 : Operator
 content : CsvTableSource(read fields: sometxt, id)
 ship_strategy : REBALANCE
Stage 3 : Operator
 content : SourceConversion(table=[default_catalog.default_database.CsvTable, source: [CsvTableSource(read fields: sometxt, id)]], fields=[sometxt, id])
 ship_strategy : FORWARD
Stage 4 : Operator
 content : Calc(select=[id], where=[(sometxt LIKE _UTF-16LE'a%')])
 ship_strategy : FORWARD
Stage 6 : Data Source
 content : collect elements with CollectionInputFormat
Stage 7 : Operator
 content : SourceConversion(table=[default_catalog.default_database.table1, source: [GeneratorTableSource(key, rowtime, payload)]], fields=[key, rowtime, payload])
 ship_strategy : FORWARD
Stage 8 : Operator
 content : Calc(select=[CAST(key) AS key0])
 ship_strategy : FORWARD
Stage 10 : Operator
 content : HashJoin(joinType=[InnerJoin], where=[(id = key0)], select=[id, key0], build=[left])
 ship_strategy : HASH[id]
Stage 11 : Operator
 content : Calc(select=[id])
 ship_strategy : FORWARD
Stage 12 : Operator
 content : HashAggregate(isMerge=[false], groupBy=[id], select=[id, COUNT(*) AS $f1])
 ship_strategy : FORWARD
Stage 13 : Operator
 content : Calc(select=[(CAST(id) CONCAT CAST($f1)) AS something, _UTF-16LE'const' AS EXPR$1])
 ship_strategy : FORWARD
Stage 14 : Operator
 content : SinkConversionToRow
 ship_strategy : FORWARD
Stage 15 : Operator
 content : Map
 ship_strategy : FORWARD
Stage 16 : Data Sink
 content : Sink: CsvTableSink(f0, f1)
 ship_strategy : FORWARD
{code}
 



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