Sorry for redundant post ...
Hi all, We are working on an implementation of Frequent Subgraph Mining using Flink. At that, the "too few memory segments error" prevents the most promising solution. The problem is not specific for graphs, but all iterative problems where - working and solution sets contain data of different types - the working set may grow, shrink or is replaced for each iteration - the solution set grows for each iteration - the termination criterion is based on data set metrics, e.g. while working set not empty An illustration of our problem workflow, generalized to graph unspecific frequent pattern mining, can be found here: https://github.com/dbs-leipzig/gradoop/blob/master/dev-support/loopWithIntermediateResultMemory.pdf Page 1 shows the most promising solution. We started implementing it using a for loop. However, the "too few memory segments error" makes it untestable. As the iteration body itself is a complex workflow and the number of iterations is arbitrary, unrolling it while reserving operator memory will be a permanent limitation. Increasing limits or physical memory would only delay the problem. The resulting question is: Would it be possible to implement a while-not-empty or at least a for loop, that isn't unrolled and can be executed more memory efficient? Page 2 shows an alternative solution to our problem using the concept of delta iteration. However, Flink delta iteration does neither support broadcasting nor working-set independent intermediate results. Page 3 shows our working solution using two workarounds for those restrictions. However, these workarounds lead to unnecessary memory consumption and redundant expensive computations. So, in the case the answer to the first question is no, a second question: Would it be possible to extend the delta iteration by the support of rich map functions with broadcast sets and the memory of intermediate results? We think, that a while-not-empty loop might be useful for other algorithms too, e.g. variable length path search in graphs. Did we miss Flink features meeting our requirements? Do you think it's worth to create an improvement issue? At that, we'd of course be willing to contribute in the form of development. Best Regards Andre -- ------------------------------------------- PhD Student University of Leipzig Department of Computer Science Database Research Group email: [hidden email] web: dbs.uni-leipzig.de ------------------------------------------- |
Hi Andre,
Thanks for reaching out to the Flink community! I am not sure your analysis is based on correct assumptions about Flink's delta iterations. Flink's delta iterations do support - working and solution sets of different types - worksets that grow and shrink or are completely replaced in each iteration - solution sets that grow (up to the point of the too few memory segments error) - termination via a custom aggregator. So it is max number of iterations, empty workset, and a custom termination criterion. The problem of the "too few memory segments" occurs because the solution set is held in an in-memory hash table. Spilling that table would result in a significant iteration slowdown. One solution is to throw more resources at the problem (more nodes, more RAM). Another work-around is to reduce the amount of managed memory and switch the solution set hash table to "unmanaged". This will keep the result set in a regular Java HashMap but this might cause a OOMError and kill the JVM if it grows too large. AFAIK, there is no way to shrink the solution set at the moment. It might be worth to investigate into that direction. Regarding your questions about the while-not-empty loop. What exactly is the difference to the default delta iteration. It does stop when the workset is empty (in addition to the custom termination criterion). I am not sure if I got all your points. Please let me know, if I got something wrong. Thanks, Fabian 2015-11-03 13:50 GMT+01:00 André Petermann < [hidden email]>: > Sorry for redundant post ... > > > Hi all, > > We are working on an implementation of Frequent Subgraph Mining using > Flink. At that, the "too few memory segments error" prevents the most > promising solution. The problem is not specific for graphs, but all > iterative problems where > > - working and solution sets contain data of different types > - the working set may grow, shrink or is replaced for each iteration > - the solution set grows for each iteration > - the termination criterion is based on data set metrics, > e.g. while working set not empty > > An illustration of our problem workflow, generalized to graph unspecific > frequent pattern mining, can be found here: > > https://github.com/dbs-leipzig/gradoop/blob/master/dev-support/loopWithIntermediateResultMemory.pdf > > Page 1 shows the most promising solution. We started implementing it > using a for loop. However, the "too few memory segments error" makes it > untestable. As the iteration body itself is a complex workflow and the > number of iterations is arbitrary, unrolling it while reserving operator > memory will be a permanent limitation. Increasing limits or physical > memory would only delay the problem. The resulting question is: > > Would it be possible to implement a while-not-empty or at least a for > loop, that isn't unrolled and can be executed more memory efficient? > > Page 2 shows an alternative solution to our problem using the concept of > delta iteration. However, Flink delta iteration does neither support > broadcasting nor working-set independent intermediate results. Page 3 > shows our working solution using two workarounds for those restrictions. > However, these workarounds lead to unnecessary memory consumption and > redundant expensive computations. So, in the case the answer to the > first question is no, a second question: > > Would it be possible to extend the delta iteration by the support of > rich map functions with broadcast sets and the memory of intermediate > results? > > We think, that a while-not-empty loop might be useful for other > algorithms too, e.g. variable length path search in graphs. Did we miss > Flink features meeting our requirements? Do you think it's worth to > create an improvement issue? At that, we'd of course be willing to > contribute in the form of development. > > Best Regards > Andre > > -- > ------------------------------------------- > PhD Student > University of Leipzig > Department of Computer Science > Database Research Group > > email: [hidden email] > web: dbs.uni-leipzig.de > ------------------------------------------- > |
Hi Fabian,
thanks for your fast reply! I created a gist to explain the while-not-empty loop in more detail: https://gist.github.com/p3et/9f6e56cf0b68213e3e2b It is an approach to create a minimal example of the kind of algorithm corresponding to page 1 of the PDF, in particular frequent substrings. Please don't care about the algorithm itself, the actual algorithm is much more complex. However, even this simple case runs only for 7 iterations on my machine before "too few memory segments" is raising. If I understood delta iteration right, it's, necessary to provide a 1:1 mapping between working and solution set items. The empty-case you referred to is the no-more-updates case, but not no-more-items, right? In my example, the working set is completely replaced for each iteration (line 52), with only a parent-child mapping. The solution set is initially empty (line 33) and stores results of all iterations (line 48). I hope this shows the difference to the delta iteration and while-not-empty. Further on, you see the different data types of working and solution sets. I will provide a further Gist for the "second choice" solution using delta iteration. However, what we actually would prefer is to replace the for-loop by something like while(embeddings.isNotEmpty()) including a truly iterative execution. But please let me know if I missed some Flink features already enabling such loops. Thanks, Andre On 03.11.2015 15:47, Fabian Hueske wrote: > Hi Andre, > > Thanks for reaching out to the Flink community! > > I am not sure your analysis is based on correct assumptions about Flink's > delta iterations. > Flink's delta iterations do support > - working and solution sets of different types > - worksets that grow and shrink or are completely replaced in each iteration > - solution sets that grow (up to the point of the too few memory segments > error) > - termination via a custom aggregator. So it is max number of iterations, > empty workset, and a custom termination criterion. > > The problem of the "too few memory segments" occurs because the solution > set is held in an in-memory hash table. > Spilling that table would result in a significant iteration slowdown. One > solution is to throw more resources at the problem (more nodes, more RAM). > Another work-around is to reduce the amount of managed memory and switch > the solution set hash table to "unmanaged". This will keep the result set > in a regular Java HashMap but this might cause a OOMError and kill the JVM > if it grows too large. > AFAIK, there is no way to shrink the solution set at the moment. It might > be worth to investigate into that direction. > > Regarding your questions about the while-not-empty loop. What exactly is > the difference to the default delta iteration. It does stop when the > workset is empty (in addition to the custom termination criterion). > I am not sure if I got all your points. Please let me know, if I got > something wrong. > > Thanks, > Fabian > > 2015-11-03 13:50 GMT+01:00 André Petermann < > [hidden email]>: > >> Sorry for redundant post ... >> >> >> Hi all, >> >> We are working on an implementation of Frequent Subgraph Mining using >> Flink. At that, the "too few memory segments error" prevents the most >> promising solution. The problem is not specific for graphs, but all >> iterative problems where >> >> - working and solution sets contain data of different types >> - the working set may grow, shrink or is replaced for each iteration >> - the solution set grows for each iteration >> - the termination criterion is based on data set metrics, >> e.g. while working set not empty >> >> An illustration of our problem workflow, generalized to graph unspecific >> frequent pattern mining, can be found here: >> >> https://github.com/dbs-leipzig/gradoop/blob/master/dev-support/loopWithIntermediateResultMemory.pdf >> >> Page 1 shows the most promising solution. We started implementing it >> using a for loop. However, the "too few memory segments error" makes it >> untestable. As the iteration body itself is a complex workflow and the >> number of iterations is arbitrary, unrolling it while reserving operator >> memory will be a permanent limitation. Increasing limits or physical >> memory would only delay the problem. The resulting question is: >> >> Would it be possible to implement a while-not-empty or at least a for >> loop, that isn't unrolled and can be executed more memory efficient? >> >> Page 2 shows an alternative solution to our problem using the concept of >> delta iteration. However, Flink delta iteration does neither support >> broadcasting nor working-set independent intermediate results. Page 3 >> shows our working solution using two workarounds for those restrictions. >> However, these workarounds lead to unnecessary memory consumption and >> redundant expensive computations. So, in the case the answer to the >> first question is no, a second question: >> >> Would it be possible to extend the delta iteration by the support of >> rich map functions with broadcast sets and the memory of intermediate >> results? >> >> We think, that a while-not-empty loop might be useful for other >> algorithms too, e.g. variable length path search in graphs. Did we miss >> Flink features meeting our requirements? Do you think it's worth to >> create an improvement issue? At that, we'd of course be willing to >> contribute in the form of development. >> >> Best Regards >> Andre >> >> -- >> ------------------------------------------- >> PhD Student >> University of Leipzig >> Department of Computer Science >> Database Research Group >> >> email: [hidden email] >> web: dbs.uni-leipzig.de >> ------------------------------------------- >> > |
Hi Andre,
On 4 November 2015 at 16:04, André Petermann < [hidden email]> wrote: > Hi Fabian, > > thanks for your fast reply! > > I created a gist to explain the while-not-empty loop in more detail: > https://gist.github.com/p3et/9f6e56cf0b68213e3e2b > > It is an approach to create a minimal example of the kind of algorithm > corresponding to page 1 of the PDF, in particular frequent substrings. > Please don't care about the algorithm itself, the actual algorithm is much > more complex. However, even this simple case runs only for 7 iterations on > my machine before "too few memory segments" is raising. > > If I understood delta iteration right, it's, necessary to provide a 1:1 > mapping between working and solution set items. The empty-case you referred > to is the no-more-updates case, but not no-more-items, right? > I think it's possible to do what you want with a delta iteration. The solution set and workset *don't* need to be of the same type. When you define the delta iteration, e.g. DeltaIteration<ST, WT> iteration = ..., ST is the type of the solution set and WT is the type of the workset. > > In my example, the working set is completely replaced for each iteration > (line 52), with only a parent-child mapping. The solution set is initially > empty (line 33) and stores results of all iterations (line 48). I hope this > shows the difference to the delta iteration and while-not-empty. Further > on, you see the different data types of working and solution sets. > I will provide a further Gist for the "second choice" solution using delta > iteration. However, what we actually would prefer is to replace the > for-loop by something like while(embeddings.isNotEmpty()) including a truly > iterative execution. > The default convergence criterion for a delta iteration is an empty workset. Thus, if you set embeddings as the workset, you have your "while(embeddings.isNotEmpty())" logic. Also, as far as I know, there is no problem with appending new elements to the solution set. So, using allFrequentPatterns as the solution set should be fine. > > But please let me know if I missed some Flink features already enabling > such loops. > > Thanks, > Andre Does this clear things out a bit? Let me know if I misunderstood what you want to do. Cheers, -Vasia. > > > On 03.11.2015 15:47, Fabian Hueske wrote: > >> Hi Andre, >> >> Thanks for reaching out to the Flink community! >> >> I am not sure your analysis is based on correct assumptions about Flink's >> delta iterations. >> Flink's delta iterations do support >> - working and solution sets of different types >> - worksets that grow and shrink or are completely replaced in each >> iteration >> - solution sets that grow (up to the point of the too few memory segments >> error) >> - termination via a custom aggregator. So it is max number of iterations, >> empty workset, and a custom termination criterion. >> >> The problem of the "too few memory segments" occurs because the solution >> set is held in an in-memory hash table. >> Spilling that table would result in a significant iteration slowdown. One >> solution is to throw more resources at the problem (more nodes, more RAM). >> Another work-around is to reduce the amount of managed memory and switch >> the solution set hash table to "unmanaged". This will keep the result set >> in a regular Java HashMap but this might cause a OOMError and kill the JVM >> if it grows too large. >> AFAIK, there is no way to shrink the solution set at the moment. It might >> be worth to investigate into that direction. >> >> Regarding your questions about the while-not-empty loop. What exactly is >> the difference to the default delta iteration. It does stop when the >> workset is empty (in addition to the custom termination criterion). >> I am not sure if I got all your points. Please let me know, if I got >> something wrong. >> >> Thanks, >> Fabian >> >> 2015-11-03 13:50 GMT+01:00 André Petermann < >> [hidden email]>: >> >> Sorry for redundant post ... >>> >>> >>> Hi all, >>> >>> We are working on an implementation of Frequent Subgraph Mining using >>> Flink. At that, the "too few memory segments error" prevents the most >>> promising solution. The problem is not specific for graphs, but all >>> iterative problems where >>> >>> - working and solution sets contain data of different types >>> - the working set may grow, shrink or is replaced for each iteration >>> - the solution set grows for each iteration >>> - the termination criterion is based on data set metrics, >>> e.g. while working set not empty >>> >>> An illustration of our problem workflow, generalized to graph unspecific >>> frequent pattern mining, can be found here: >>> >>> >>> https://github.com/dbs-leipzig/gradoop/blob/master/dev-support/loopWithIntermediateResultMemory.pdf >>> >>> Page 1 shows the most promising solution. We started implementing it >>> using a for loop. However, the "too few memory segments error" makes it >>> untestable. As the iteration body itself is a complex workflow and the >>> number of iterations is arbitrary, unrolling it while reserving operator >>> memory will be a permanent limitation. Increasing limits or physical >>> memory would only delay the problem. The resulting question is: >>> >>> Would it be possible to implement a while-not-empty or at least a for >>> loop, that isn't unrolled and can be executed more memory efficient? >>> >>> Page 2 shows an alternative solution to our problem using the concept of >>> delta iteration. However, Flink delta iteration does neither support >>> broadcasting nor working-set independent intermediate results. Page 3 >>> shows our working solution using two workarounds for those restrictions. >>> However, these workarounds lead to unnecessary memory consumption and >>> redundant expensive computations. So, in the case the answer to the >>> first question is no, a second question: >>> >>> Would it be possible to extend the delta iteration by the support of >>> rich map functions with broadcast sets and the memory of intermediate >>> results? >>> >>> We think, that a while-not-empty loop might be useful for other >>> algorithms too, e.g. variable length path search in graphs. Did we miss >>> Flink features meeting our requirements? Do you think it's worth to >>> create an improvement issue? At that, we'd of course be willing to >>> contribute in the form of development. >>> >>> Best Regards >>> Andre >>> >>> -- >>> ------------------------------------------- >>> PhD Student >>> University of Leipzig >>> Department of Computer Science >>> Database Research Group >>> >>> email: [hidden email] >>> web: dbs.uni-leipzig.de >>> ------------------------------------------- >>> >>> >> |
Hi Vasia!
Many thanks for your reply! Your hints finally enabled me implementing the problems first-choice solution using delta iteration: https://gist.github.com/p3et/12deb7d6321b48e9efab Do you think this could be worth to be contributed as an example within the Flink documentation? The examples I found so far could not help enlightening me how to use delta iteration for this kind of loop (ST != WT, start from empty solution set, ...). Cheers, Andre On 04.11.2015 16:32, Vasiliki Kalavri wrote: > Hi Andre, > > On 4 November 2015 at 16:04, André Petermann < > [hidden email]> wrote: > >> Hi Fabian, >> >> thanks for your fast reply! >> >> I created a gist to explain the while-not-empty loop in more detail: >> https://gist.github.com/p3et/9f6e56cf0b68213e3e2b >> >> It is an approach to create a minimal example of the kind of algorithm >> corresponding to page 1 of the PDF, in particular frequent substrings. >> Please don't care about the algorithm itself, the actual algorithm is much >> more complex. However, even this simple case runs only for 7 iterations on >> my machine before "too few memory segments" is raising. >> >> If I understood delta iteration right, it's, necessary to provide a 1:1 >> mapping between working and solution set items. The empty-case you referred >> to is the no-more-updates case, but not no-more-items, right? >> > > > I think it's possible to do what you want with a delta iteration. > > The solution set and workset *don't* need to be of the same type. When you > define the delta iteration, > e.g. DeltaIteration<ST, WT> iteration = ..., > ST is the type of the solution set and WT is the type of the workset. > > > >> >> In my example, the working set is completely replaced for each iteration >> (line 52), with only a parent-child mapping. The solution set is initially >> empty (line 33) and stores results of all iterations (line 48). I hope this >> shows the difference to the delta iteration and while-not-empty. Further >> on, you see the different data types of working and solution sets. > > >> I will provide a further Gist for the "second choice" solution using delta >> iteration. However, what we actually would prefer is to replace the >> for-loop by something like while(embeddings.isNotEmpty()) including a truly >> iterative execution. >> > > > The default convergence criterion for a delta iteration is an empty > workset. Thus, if you set embeddings as the workset, you have your > "while(embeddings.isNotEmpty())" logic. > Also, as far as I know, there is no problem with appending new elements > to the solution set. So, using allFrequentPatterns as the solution set > should be fine. > > > >> >> But please let me know if I missed some Flink features already enabling >> such loops. >> >> Thanks, >> Andre > > > > Does this clear things out a bit? Let me know if I misunderstood what you > want to do. > > Cheers, > -Vasia. > > > >> >> >> On 03.11.2015 15:47, Fabian Hueske wrote: >> >>> Hi Andre, >>> >>> Thanks for reaching out to the Flink community! >>> >>> I am not sure your analysis is based on correct assumptions about Flink's >>> delta iterations. >>> Flink's delta iterations do support >>> - working and solution sets of different types >>> - worksets that grow and shrink or are completely replaced in each >>> iteration >>> - solution sets that grow (up to the point of the too few memory segments >>> error) >>> - termination via a custom aggregator. So it is max number of iterations, >>> empty workset, and a custom termination criterion. >>> >>> The problem of the "too few memory segments" occurs because the solution >>> set is held in an in-memory hash table. >>> Spilling that table would result in a significant iteration slowdown. One >>> solution is to throw more resources at the problem (more nodes, more RAM). >>> Another work-around is to reduce the amount of managed memory and switch >>> the solution set hash table to "unmanaged". This will keep the result set >>> in a regular Java HashMap but this might cause a OOMError and kill the JVM >>> if it grows too large. >>> AFAIK, there is no way to shrink the solution set at the moment. It might >>> be worth to investigate into that direction. >>> >>> Regarding your questions about the while-not-empty loop. What exactly is >>> the difference to the default delta iteration. It does stop when the >>> workset is empty (in addition to the custom termination criterion). >>> I am not sure if I got all your points. Please let me know, if I got >>> something wrong. >>> >>> Thanks, >>> Fabian >>> >>> 2015-11-03 13:50 GMT+01:00 André Petermann < >>> [hidden email]>: >>> >>> Sorry for redundant post ... >>>> >>>> >>>> Hi all, >>>> >>>> We are working on an implementation of Frequent Subgraph Mining using >>>> Flink. At that, the "too few memory segments error" prevents the most >>>> promising solution. The problem is not specific for graphs, but all >>>> iterative problems where >>>> >>>> - working and solution sets contain data of different types >>>> - the working set may grow, shrink or is replaced for each iteration >>>> - the solution set grows for each iteration >>>> - the termination criterion is based on data set metrics, >>>> e.g. while working set not empty >>>> >>>> An illustration of our problem workflow, generalized to graph unspecific >>>> frequent pattern mining, can be found here: >>>> >>>> >>>> https://github.com/dbs-leipzig/gradoop/blob/master/dev-support/loopWithIntermediateResultMemory.pdf >>>> >>>> Page 1 shows the most promising solution. We started implementing it >>>> using a for loop. However, the "too few memory segments error" makes it >>>> untestable. As the iteration body itself is a complex workflow and the >>>> number of iterations is arbitrary, unrolling it while reserving operator >>>> memory will be a permanent limitation. Increasing limits or physical >>>> memory would only delay the problem. The resulting question is: >>>> >>>> Would it be possible to implement a while-not-empty or at least a for >>>> loop, that isn't unrolled and can be executed more memory efficient? >>>> >>>> Page 2 shows an alternative solution to our problem using the concept of >>>> delta iteration. However, Flink delta iteration does neither support >>>> broadcasting nor working-set independent intermediate results. Page 3 >>>> shows our working solution using two workarounds for those restrictions. >>>> However, these workarounds lead to unnecessary memory consumption and >>>> redundant expensive computations. So, in the case the answer to the >>>> first question is no, a second question: >>>> >>>> Would it be possible to extend the delta iteration by the support of >>>> rich map functions with broadcast sets and the memory of intermediate >>>> results? >>>> >>>> We think, that a while-not-empty loop might be useful for other >>>> algorithms too, e.g. variable length path search in graphs. Did we miss >>>> Flink features meeting our requirements? Do you think it's worth to >>>> create an improvement issue? At that, we'd of course be willing to >>>> contribute in the form of development. >>>> >>>> Best Regards >>>> Andre >>>> >>>> -- >>>> ------------------------------------------- >>>> PhD Student >>>> University of Leipzig >>>> Department of Computer Science >>>> Database Research Group >>>> >>>> email: [hidden email] >>>> web: dbs.uni-leipzig.de >>>> ------------------------------------------- >>>> >>>> >>> > |
Hi Andre,
I'm happy you were able to solve your problem :) Improvements to the documentation are always welcome! To me ST != WT is straight-forward from the javadocs, but I guess it wouldn't hurt to stress it in the docs. Do you think you could simplify your implementation a bit to make for a nice example? It might be a bit too complicated to follow as it is right now. In any case, if you would like to improve the delta iteration docs, please go ahead and open a JIRA. We can discuss the details of what improvements to make over there. Thanks! -Vasia. On 5 November 2015 at 11:41, André Petermann < [hidden email]> wrote: > Hi Vasia! > > Many thanks for your reply! Your hints finally enabled me implementing the > problems first-choice solution using delta iteration: > https://gist.github.com/p3et/12deb7d6321b48e9efab > > Do you think this could be worth to be contributed as an example within > the Flink documentation? The examples I found so far could not help > enlightening me how to use delta iteration for this kind of loop > (ST != WT, start from empty solution set, ...). > > Cheers, > Andre > > > On 04.11.2015 16:32, Vasiliki Kalavri wrote: > >> Hi Andre, >> >> On 4 November 2015 at 16:04, André Petermann < >> [hidden email]> wrote: >> >> Hi Fabian, >>> >>> thanks for your fast reply! >>> >>> I created a gist to explain the while-not-empty loop in more detail: >>> https://gist.github.com/p3et/9f6e56cf0b68213e3e2b >>> >>> It is an approach to create a minimal example of the kind of algorithm >>> corresponding to page 1 of the PDF, in particular frequent substrings. >>> Please don't care about the algorithm itself, the actual algorithm is >>> much >>> more complex. However, even this simple case runs only for 7 iterations >>> on >>> my machine before "too few memory segments" is raising. >>> >>> If I understood delta iteration right, it's, necessary to provide a 1:1 >>> mapping between working and solution set items. The empty-case you >>> referred >>> to is the no-more-updates case, but not no-more-items, right? >>> >>> >> >> I think it's possible to do what you want with a delta iteration. >> >> The solution set and workset *don't* need to be of the same type. When you >> define the delta iteration, >> e.g. DeltaIteration<ST, WT> iteration = ..., >> ST is the type of the solution set and WT is the type of the workset. >> >> >> >> >>> In my example, the working set is completely replaced for each iteration >>> (line 52), with only a parent-child mapping. The solution set is >>> initially >>> empty (line 33) and stores results of all iterations (line 48). I hope >>> this >>> shows the difference to the delta iteration and while-not-empty. Further >>> on, you see the different data types of working and solution sets. >>> >> >> >> I will provide a further Gist for the "second choice" solution using delta >>> iteration. However, what we actually would prefer is to replace the >>> for-loop by something like while(embeddings.isNotEmpty()) including a >>> truly >>> iterative execution. >>> >>> >> >> The default convergence criterion for a delta iteration is an empty >> workset. Thus, if you set embeddings as the workset, you have your >> "while(embeddings.isNotEmpty())" logic. >> Also, as far as I know, there is no problem with appending new elements >> to the solution set. So, using allFrequentPatterns as the solution set >> should be fine. >> >> >> >> >>> But please let me know if I missed some Flink features already enabling >>> such loops. >>> >>> Thanks, >>> Andre >>> >> >> >> >> Does this clear things out a bit? Let me know if I misunderstood what you >> want to do. >> >> Cheers, >> -Vasia. >> >> >> >> >>> >>> On 03.11.2015 15:47, Fabian Hueske wrote: >>> >>> Hi Andre, >>>> >>>> Thanks for reaching out to the Flink community! >>>> >>>> I am not sure your analysis is based on correct assumptions about >>>> Flink's >>>> delta iterations. >>>> Flink's delta iterations do support >>>> - working and solution sets of different types >>>> - worksets that grow and shrink or are completely replaced in each >>>> iteration >>>> - solution sets that grow (up to the point of the too few memory >>>> segments >>>> error) >>>> - termination via a custom aggregator. So it is max number of >>>> iterations, >>>> empty workset, and a custom termination criterion. >>>> >>>> The problem of the "too few memory segments" occurs because the solution >>>> set is held in an in-memory hash table. >>>> Spilling that table would result in a significant iteration slowdown. >>>> One >>>> solution is to throw more resources at the problem (more nodes, more >>>> RAM). >>>> Another work-around is to reduce the amount of managed memory and switch >>>> the solution set hash table to "unmanaged". This will keep the result >>>> set >>>> in a regular Java HashMap but this might cause a OOMError and kill the >>>> JVM >>>> if it grows too large. >>>> AFAIK, there is no way to shrink the solution set at the moment. It >>>> might >>>> be worth to investigate into that direction. >>>> >>>> Regarding your questions about the while-not-empty loop. What exactly is >>>> the difference to the default delta iteration. It does stop when the >>>> workset is empty (in addition to the custom termination criterion). >>>> I am not sure if I got all your points. Please let me know, if I got >>>> something wrong. >>>> >>>> Thanks, >>>> Fabian >>>> >>>> 2015-11-03 13:50 GMT+01:00 André Petermann < >>>> [hidden email]>: >>>> >>>> Sorry for redundant post ... >>>> >>>>> >>>>> >>>>> Hi all, >>>>> >>>>> We are working on an implementation of Frequent Subgraph Mining using >>>>> Flink. At that, the "too few memory segments error" prevents the most >>>>> promising solution. The problem is not specific for graphs, but all >>>>> iterative problems where >>>>> >>>>> - working and solution sets contain data of different types >>>>> - the working set may grow, shrink or is replaced for each iteration >>>>> - the solution set grows for each iteration >>>>> - the termination criterion is based on data set metrics, >>>>> e.g. while working set not empty >>>>> >>>>> An illustration of our problem workflow, generalized to graph >>>>> unspecific >>>>> frequent pattern mining, can be found here: >>>>> >>>>> >>>>> >>>>> https://github.com/dbs-leipzig/gradoop/blob/master/dev-support/loopWithIntermediateResultMemory.pdf >>>>> >>>>> Page 1 shows the most promising solution. We started implementing it >>>>> using a for loop. However, the "too few memory segments error" makes it >>>>> untestable. As the iteration body itself is a complex workflow and the >>>>> number of iterations is arbitrary, unrolling it while reserving >>>>> operator >>>>> memory will be a permanent limitation. Increasing limits or physical >>>>> memory would only delay the problem. The resulting question is: >>>>> >>>>> Would it be possible to implement a while-not-empty or at least a for >>>>> loop, that isn't unrolled and can be executed more memory efficient? >>>>> >>>>> Page 2 shows an alternative solution to our problem using the concept >>>>> of >>>>> delta iteration. However, Flink delta iteration does neither support >>>>> broadcasting nor working-set independent intermediate results. Page 3 >>>>> shows our working solution using two workarounds for those >>>>> restrictions. >>>>> However, these workarounds lead to unnecessary memory consumption and >>>>> redundant expensive computations. So, in the case the answer to the >>>>> first question is no, a second question: >>>>> >>>>> Would it be possible to extend the delta iteration by the support of >>>>> rich map functions with broadcast sets and the memory of intermediate >>>>> results? >>>>> >>>>> We think, that a while-not-empty loop might be useful for other >>>>> algorithms too, e.g. variable length path search in graphs. Did we miss >>>>> Flink features meeting our requirements? Do you think it's worth to >>>>> create an improvement issue? At that, we'd of course be willing to >>>>> contribute in the form of development. >>>>> >>>>> Best Regards >>>>> Andre >>>>> >>>>> -- >>>>> ------------------------------------------- >>>>> PhD Student >>>>> University of Leipzig >>>>> Department of Computer Science >>>>> Database Research Group >>>>> >>>>> email: [hidden email] >>>>> web: dbs.uni-leipzig.de >>>>> ------------------------------------------- >>>>> >>>>> >>>>> >>>> >> |
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