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https://issues.apache.org/jira/browse/SPARK-13198?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15136773#comment-15136773
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Sean Owen commented on SPARK-13198:
-----------------------------------
I don't think stop() is relevant here. There's not an active attempt to free up
resources once the app is done. It's assumed the driver JVM is shutting down.
Yes, the question was whether it had tried to do a full GC, and sounds like it
has done, OK.
Still if you're just finding there is a bunch of left over bookkeeping info for
executors, probably from all the old contexts, I think that's "normal" or at
least "not a problem as Spark is intended to be used"
> sc.stop() does not clean up on driver, causes Java heap OOM.
> ------------------------------------------------------------
>
> Key: SPARK-13198
> URL: https://issues.apache.org/jira/browse/SPARK-13198
> Project: Spark
> Issue Type: Bug
> Components: Mesos
> Affects Versions: 1.6.0
> Reporter: Herman Schistad
> Attachments: Screen Shot 2016-02-04 at 16.31.28.png, Screen Shot
> 2016-02-04 at 16.31.40.png, Screen Shot 2016-02-04 at 16.31.51.png, Screen
> Shot 2016-02-08 at 09.30.59.png, Screen Shot 2016-02-08 at 09.31.10.png,
> Screen Shot 2016-02-08 at 10.03.04.png, gc.log
>
>
> When starting and stopping multiple SparkContext's linearly eventually the
> driver stops working with a "io.netty.handler.codec.EncoderException:
> java.lang.OutOfMemoryError: Java heap space" error.
> Reproduce by running the following code and loading in ~7MB parquet data each
> time. The driver heap space is not changed and thus defaults to 1GB:
> {code:java}
> def main(args: Array[String]) {
> val conf = new SparkConf().setMaster("MASTER_URL").setAppName("")
> conf.set("spark.mesos.coarse", "true")
> conf.set("spark.cores.max", "10")
> for (i <- 1 until 100) {
> val sc = new SparkContext(conf)
> val sqlContext = new SQLContext(sc)
> val events = sqlContext.read.parquet("hdfs://locahost/tmp/something")
> println(s"Context ($i), number of events: " + events.count)
> sc.stop()
> }
> }
> {code}
> The heap space fills up within 20 loops on my cluster. Increasing the number
> of cores to 50 in the above example results in heap space error after 12
> contexts.
> Dumping the heap reveals many equally sized "CoarseMesosSchedulerBackend"
> objects (see attachments). Digging into the inner objects tells me that the
> `executorDataMap` is where 99% of the data in said object is stored. I do
> believe though that this is beside the point as I'd expect this whole object
> to be garbage collected or freed on sc.stop().
> Additionally I can see in the Spark web UI that each time a new context is
> created the number of the "SQL" tab increments by one (i.e. last iteration
> would have SQL99). After doing stop and creating a completely new context I
> was expecting this number to be reset to 1 ("SQL").
> I'm submitting the jar file with `spark-submit` and no special flags. The
> cluster is running Mesos 0.23. I'm running Spark 1.6.0.
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