Hi, Regarding the solution set going out of memory, I would like an issue to be filled against it.
Looking into code for CompactingHashTable I see The hash table is internally divided into two parts: The hash index, and the partition buffers that store the actual records. When records are inserted or updated, the hash table appends the records to its corresponding partition, and inserts or updates the entry in the hash index. In the case that the hash table runs out of memory, it compacts a partition by walking through the hash index and copying all reachable elements into a fresh partition. After that, it releases the memory of the partition to compact. It is not clear the expected behaviour when the hash table runs out of memory. If by contrast Spark is working on RDDs and they can be cached in memory or spilled to disk, something similar could be done for all the components currently built in memory and not being spilled to disk to avoid OutOfMemory. What do you think? Best, Ovidiu > On 14 Mar 2016, at 18:48, Ufuk Celebi <u...@apache.org> wrote: > > Probably the limitation is that the number of keys is different in the > real and the synthetic data set respectively. Can you confirm this? > > The solution set for delta iterations is currently implemented as an > in-memory hash table that works on managed memory segments, but is not > spillable. > > – Ufuk > > On Mon, Mar 14, 2016 at 6:30 PM, Ovidiu-Cristian MARCU > <ovidiu-cristian.ma...@inria.fr> wrote: >> >> This problem is surprising as I was able to run PR and CC on a larger graph >> (2bil edges) but with this synthetic graph (1bil edges groups of 10) I ran >> out of memory; regarding configuration (memory and parallelism, other >> internals) I was using the same. >> There is some limitation somewhere I will try to understand more what is >> happening. >> >> Best, >> Ovidiu >> >>> On 14 Mar 2016, at 18:06, Martin Junghanns <m.jungha...@mailbox.org> wrote: >>> >>> Hi, >>> >>> I understand the confusion. So far, I did not run into the problem, but I >>> think this needs to be adressed as all our graph processing abstractions >>> are implemented on top of the delta iteration. >>> >>> According to the previous mailing list discussion, the problem is with the >>> solution set and its missing ability to spill. >>> >>> If this is the still the case, we should open an issue for that. Any >>> further opinions on that? >>> >>> Cheers, >>> Martin >>> >>> >>> On 14.03.2016 17:55, Ovidiu-Cristian MARCU wrote: >>>> Thank you for this alternative. >>>> I don’t understand how the workaround will fix this on systems with >>>> limited memory and maybe larger graph. >>>> >>>> Running Connected Components on the same graph gives the same problem. >>>> >>>> IterationHead(Unnamed Delta Iteration)(82/88) switched to FAILED >>>> java.lang.RuntimeException: Memory ran out. Compaction failed. >>>> numPartitions: 32 minPartition: 31 maxPartition: 32 number of overflow >>>> segments: 417 bucketSize: 827 Overall memory: 149159936 Partition memory: >>>> 65601536 Message: Index: 32, Size: 31 >>>> at >>>> org.apache.flink.runtime.operators.hash.CompactingHashTable.insertRecordIntoPartition(CompactingHashTable.java:469) >>>> at >>>> org.apache.flink.runtime.operators.hash.CompactingHashTable.insertOrReplaceRecord(CompactingHashTable.java:414) >>>> at >>>> org.apache.flink.runtime.operators.hash.CompactingHashTable.buildTableWithUniqueKey(CompactingHashTable.java:325) >>>> at >>>> org.apache.flink.runtime.iterative.task.IterationHeadTask.readInitialSolutionSet(IterationHeadTask.java:212) >>>> at >>>> org.apache.flink.runtime.iterative.task.IterationHeadTask.run(IterationHeadTask.java:273) >>>> at >>>> org.apache.flink.runtime.operators.BatchTask.invoke(BatchTask.java:354) >>>> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:584) >>>> at java.lang.Thread.run(Thread.java:745) >>>> >>>> Best, >>>> Ovidiu >>>> >>>>> On 14 Mar 2016, at 17:36, Martin Junghanns <m.jungha...@mailbox.org> >>>>> wrote: >>>>> >>>>> Hi >>>>> >>>>> I think this is the same issue we had before on the list [1]. Stephan >>>>> recommended the following workaround: >>>>> >>>>>> A possible workaround is to use the option >>>>>> "setSolutionSetUnmanaged(true)" >>>>>> on the iteration. That will eliminate the fragmentation issue, at least. >>>>> >>>>> Unfortunately, you cannot set this when using graph.run(new PageRank(...)) >>>>> >>>>> I created a Gist which shows you how to set this using PageRank >>>>> >>>>> https://gist.github.com/s1ck/801a8ef97ce374b358df >>>>> >>>>> Please let us know if it worked out for you. >>>>> >>>>> Cheers, >>>>> Martin >>>>> >>>>> [1] >>>>> http://mail-archives.apache.org/mod_mbox/flink-user/201508.mbox/%3CCAELUF_ByPAB%2BPXWLemPzRH%3D-awATeSz4sGz4v9TmnvFku3%3Dx3A%40mail.gmail.com%3E >>>>> >>>>> On 14.03.2016 16:55, Ovidiu-Cristian MARCU wrote: >>>>>> Hi, >>>>>> >>>>>> While running PageRank on a synthetic graph I run into this problem: >>>>>> Any advice on how should I proceed to overcome this memory issue? >>>>>> >>>>>> IterationHead(Vertex-centric iteration >>>>>> (org.apache.flink.graph.library.PageRank$VertexRankUpdater@7712cae0 | >>>>>> org.apache.flink.graph.library.PageRank$RankMesseng$ >>>>>> java.lang.RuntimeException: Memory ran out. Compaction failed. >>>>>> numPartitions: 32 minPartition: 24 maxPartition: 25 number of overflow >>>>>> segments: 328 bucketSize: 638 Overall memory: 115539968 Partition >>>>>> memory: 50659328 Message: Index: 25, Size: 24 >>>>>> at >>>>>> org.apache.flink.runtime.operators.hash.CompactingHashTable.insertRecordIntoPartition(CompactingHashTable.java:469) >>>>>> at >>>>>> org.apache.flink.runtime.operators.hash.CompactingHashTable.insertOrReplaceRecord(CompactingHashTable.java:414) >>>>>> at >>>>>> org.apache.flink.runtime.operators.hash.CompactingHashTable.buildTableWithUniqueKey(CompactingHashTable.java:325) >>>>>> at >>>>>> org.apache.flink.runtime.iterative.task.IterationHeadTask.readInitialSolutionSet(IterationHeadTask.java:212) >>>>>> at >>>>>> org.apache.flink.runtime.iterative.task.IterationHeadTask.run(IterationHeadTask.java:273) >>>>>> at >>>>>> org.apache.flink.runtime.operators.BatchTask.invoke(BatchTask.java:354) >>>>>> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:584) >>>>>> at java.lang.Thread.run(Thread.java:745) >>>>>> >>>>>> Thanks! >>>>>> >>>>>> Best, >>>>>> Ovidiu >>>>>> >>>> >>>> >>
