Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/344#discussion_r24072836
--- 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()
+ }
--- End diff --
Why not using Scala power: graph.getAllExecutionVertices.asScala.foreach {
_.clearExecutionEdges } with import
scala.collection.JavaConvertes.iterableAsScalaIterableConverter in scope.
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