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From markhamstra <>
Subject [GitHub] spark pull request: [SPARK-6183][Deploy] Skip bad workers when re-...
Date Sun, 08 Mar 2015 03:28:38 GMT
Github user markhamstra commented on a diff in the pull request:
    --- Diff: core/src/main/scala/org/apache/spark/deploy/master/Master.scala ---
    @@ -467,7 +467,9 @@ private[spark] class Master(
        * two executors on the same worker).
       def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = {
    -    worker.memoryFree >= app.desc.memoryPerSlave && !worker.hasExecutor(app)
    +    worker.memoryFree >= app.desc.memoryPerSlave &&
    +    !worker.hasExecutor(app) &&
    +    !app.removedExecutors.exists(_.worker == worker)
    --- End diff --
    I agree with @mateiz, and Spark Streaming is by no means the only Spark application for
which a common use case is to have the entire cluster essentially running a single long-lived
application.  To have transiently failing Executors perpetually banned from doing work for
that long-lived application is not acceptable.

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