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Posted to issues@flink.apache.org by "Andra Lungu (JIRA)" <ji...@apache.org> on 2015/07/14 23:32:04 UTC
[jira] [Updated] (FLINK-2361) flatMap + distinct gives eroneous
results for big data sets
[ https://issues.apache.org/jira/browse/FLINK-2361?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Andra Lungu updated FLINK-2361:
-------------------------------
Summary: flatMap + distinct gives eroneous results for big data sets (was: flatMap + distict gives eroneous results for big data sets)
> flatMap + distinct gives eroneous results for big data sets
> -----------------------------------------------------------
>
> Key: FLINK-2361
> URL: https://issues.apache.org/jira/browse/FLINK-2361
> Project: Flink
> Issue Type: Bug
> Components: Gelly
> Affects Versions: 0.10
> Reporter: Andra Lungu
>
> When running the simple Connected Components algorithm (currently in Gelly) on the twitter follower graph, with 1, 100 or 10000 iterations, I get the following error:
> Caused by: java.lang.Exception: Target vertex '657282846' does not exist!.
> at org.apache.flink.graph.spargel.VertexCentricIteration$VertexUpdateUdfSimpleVV.coGroup(VertexCentricIteration.java:300)
> at org.apache.flink.runtime.operators.CoGroupWithSolutionSetSecondDriver.run(CoGroupWithSolutionSetSecondDriver.java:220)
> at org.apache.flink.runtime.operators.RegularPactTask.run(RegularPactTask.java:496)
> at org.apache.flink.runtime.iterative.task.AbstractIterativePactTask.run(AbstractIterativePactTask.java:139)
> at org.apache.flink.runtime.iterative.task.IterationTailPactTask.run(IterationTailPactTask.java:107)
> at org.apache.flink.runtime.operators.RegularPactTask.invoke(RegularPactTask.java:362)
> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:559)
> at java.lang.Thread.run(Thread.java:722)
> Now this is very bizzare as the DataSet of vertices is produced from the DataSet of edges... Which means there cannot be a an edge with an invalid target id... The method calls flatMap to isolate the src and trg ids and distinct to ensure their uniqueness.
> The algorithm works fine for smaller data sets...
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