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https://issues.apache.org/jira/browse/FLINK-1442?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14305468#comment-14305468
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ASF GitHub Bot commented on FLINK-1442:
---------------------------------------
Github user hsaputra commented on a diff in the pull request:
https://github.com/apache/flink/pull/344#discussion_r24097257
--- Diff:
flink-runtime/src/test/scala/org/apache/flink/runtime/testingUtils/TestingMemoryArchivist.scala
---
@@ -31,10 +31,11 @@ trait TestingMemoryArchivist extends ActorLogMessages {
def receiveTestingMessages: Receive = {
case RequestExecutionGraph(jobID) =>
- graphs.get(jobID) match {
- case Some(executionGraph) => sender ! ExecutionGraphFound(jobID,
executionGraph)
- case None => sender ! ExecutionGraphNotFound(jobID)
+ val executionGraph = getGraph(jobID)
+ if (executionGraph != null) {
--- End diff --
I like @tillrohrmann to use Option as alternative to null. In Java land,
Guava's Optional could be use to do similar thing (which I think will be part
of Java8)
> Archived Execution Graph consumes too much memory
> -------------------------------------------------
>
> Key: FLINK-1442
> URL: https://issues.apache.org/jira/browse/FLINK-1442
> Project: Flink
> Issue Type: Bug
> Components: JobManager
> Affects Versions: 0.9
> Reporter: Stephan Ewen
> Assignee: Max Michels
>
> The JobManager archives the execution graphs, for analysis of jobs. The
> graphs may consume a lot of memory.
> Especially the execution edges in all2all connection patterns are extremely
> many and add up in memory consumption.
> The execution edges connect all parallel tasks. So for a all2all pattern
> between n and m tasks, there are n*m edges. For parallelism of multiple 100
> tasks, this can easily reach 100k objects and more, each with a set of
> metadata.
> I propose the following to solve that:
> 1. Clear all execution edges from the graph (majority of the memory
> consumers) when it is given to the archiver.
> 2. Have the map/list of the archived graphs behind a soft reference, to it
> will be removed under memory pressure before the JVM crashes. That may remove
> graphs from the history early, but is much preferable to the JVM crashing, in
> which case the graph is lost as well...
> 3. Long term: The graph should be archived somewhere else. Somthing like the
> History server used by Hadoop and Hive would be a good idea.
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