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: peterm...@informatik.uni-leipzig.de web: dbs.uni-leipzig.de -------------------------------------------
