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https://issues.apache.org/jira/browse/FLINK-1442?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14304846#comment-14304846
]
ASF GitHub Bot commented on FLINK-1442:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/344#discussion_r24072128
--- Diff:
flink-runtime/src/main/scala/org/apache/flink/runtime/jobmanager/MemoryArchivist.scala
---
@@ -25,48 +25,82 @@ import org.apache.flink.runtime.jobgraph.JobID
import org.apache.flink.runtime.messages.ArchiveMessages._
import org.apache.flink.runtime.messages.JobManagerMessages._
+import scala.collection.mutable.LinkedHashMap
+import scala.ref.SoftReference
+
class MemoryArchivist(private val max_entries: Int) extends Actor with
ActorLogMessages with
ActorLogging {
/**
* Map of execution graphs belonging to recently started jobs with the
time stamp of the last
- * received job event.
+ * received job event. The insert order is preserved through a
LinkedHashMap.
*/
- val graphs = collection.mutable.HashMap[JobID, ExecutionGraph]()
- val lru = collection.mutable.Queue[JobID]()
+ val graphs = LinkedHashMap[JobID, SoftReference[ExecutionGraph]]()
override def receiveWithLogMessages: Receive = {
+ /* Receive Execution Graph to archive */
case ArchiveExecutionGraph(jobID, graph) => {
- graphs.update(jobID, graph)
+ // wrap graph inside a soft reference
+ graphs.update(jobID, new SoftReference(graph))
+
+ // clear all execution edges of the graph
+ val iter = graph.getAllExecutionVertices().iterator()
+ while (iter.hasNext) {
+ iter.next().clearExecutionEdges()
+ }
+
cleanup(jobID)
}
case RequestArchivedJobs => {
- sender ! ArchivedJobs(graphs.values)
+ sender ! ArchivedJobs(getAllGraphs())
}
case RequestJob(jobID) => {
- graphs.get(jobID) match {
- case Some(graph) => sender ! JobFound(jobID, graph)
- case None => sender ! JobNotFound(jobID)
+ getGraph(jobID) match {
+ case graph: ExecutionGraph => sender ! JobFound(jobID, graph)
+ case _ => sender ! JobNotFound(jobID)
}
}
case RequestJobStatus(jobID) => {
- graphs.get(jobID) match {
- case Some(eg) => sender ! CurrentJobStatus(jobID, eg.getState)
- case None => sender ! JobNotFound(jobID)
+ getGraph(jobID) match {
+ case graph: ExecutionGraph => sender ! CurrentJobStatus(jobID,
graph.getState)
+ case _ => sender ! JobNotFound(jobID)
}
}
}
- def cleanup(jobID: JobID): Unit = {
- if (!lru.contains(jobID)) {
- lru.enqueue(jobID)
+ /**
+ * Gets all graphs that have not been garbage collected.
+ * @return An iterable with all valid ExecutionGraphs
+ */
+ def getAllGraphs() = graphs.values.flatMap(ref => ref.get match {
+ case Some(graph) => Seq(graph)
+ case _ => Seq()
+ })
+
+ /**
+ * Gets a graph with a jobID if it has not been garbage collected.
+ * @param jobID
+ * @return ExecutionGraph or null
+ */
+ def getGraph(jobID: JobID) = graphs.get(jobID) match {
--- End diff --
Why are we doing that? Why not working on the Option type? I don't like
null.
> 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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