ConcurrentModificationException when using Spark c

Question

I'm trying to run a Spark-based application on an Azure HDInsight on-demand cluster, and am seeing lots of SparkExceptions (caused by ConcurrentModificationExceptions) being logged. The application runs without these errors when I start a local Spark instance.

I've seen reports of similar errors when using accumulators and my code is indeed using a CollectionAccumulator, however I have placed synchronized blocks everywhere I use it, and it makes no difference. The accumulator-related code looks like this:

class MySparkClass(sc : SparkContext) {
    val myAccumulator = sc.collectionAccumulator[MyRecord]

    override def add(record: MyRecord) = {
        synchronized {
            myAccumulator.add(record)
        }
    }

    override def endOfBatch() = {
        synchronized {
            myAccumulator.value.asScala.foreach((record: MyRecord) => {
                processIt(record)
            })
        }
    }
}

The exceptions don't cause the application to fail, however when endOfBatch is called and the code tries to read values out of the accumulator it is empty and processIt is never called.

We are using HDInsight version 3.6 with Spark version 2.3.0

18/11/26 11:04:37 WARN Executor: Issue communicating with driver in heartbeater
org.apache.spark.SparkException: Exception thrown in awaitResult: 
    at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:205)
    at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
    at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:92)
    at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$reportHeartBeat(Executor.scala:785)
    at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply$mcV$sp(Executor.scala:814)
    at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply(Executor.scala:814)
    at org.apache.spark.executor.Executor$$anon$2$$anonfun$run$1.apply(Executor.scala:814)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1988)
    at org.apache.spark.executor.Executor$$anon$2.run(Executor.scala:814)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.util.ConcurrentModificationException
    at java.util.ArrayList.writeObject(ArrayList.java:770)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:1140)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.defaultWriteObject(ObjectOutputStream.java:441)
    at java.util.Collections$SynchronizedCollection.writeObject(Collections.java:2081)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:1140)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1378)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1174)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1378)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1174)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
    at org.apache.spark.rpc.netty.RequestMessage.serialize(NettyRpcEnv.scala:565)
    at org.apache.spark.rpc.netty.NettyRpcEnv.ask(NettyRpcEnv.scala:231)
    at org.apache.spark.rpc.netty.NettyRpcEndpointRef.ask(NettyRpcEnv.scala:523)
    at org.apache.spark.rpc.RpcEndpointRef.askSync(RpcEndpointRef.scala:91)
    ... 13 more

The following code is a more self-contained example that reproduces the problem. MyRecord is a simple case class containing only numeric values. The code runs without error locally, but on an HDInsight cluster it produces the error above.

object MainDemo {
    def main(args: Array[String]) {
        val sparkContext = SparkSession.builder.master("local[4]").getOrCreate().sparkContext
        val myAccumulator = sparkContext.collectionAccumulator[MyRecord]

        sparkContext.binaryFiles("/my/files/here").foreach(_ => {
            for(i <- 1 to 100000) {
                val record = MyRecord(i, 0, 0)
                myAccumulator.add(record)
            }
        })

        myAccumulator.value.asScala.foreach((record: MyRecord) => {
            // we expect this to be called once for each record that we 'add' above,
            // but it is never called
            println(record)
        })
    }
}

Answer 1

I doubt if having synchronized block really helps. CustomeAccumulators or all other accumulator are not thread-safe. They do not really have to since the DAGScheduler.updateAccumulators method that the spark driver uses to update the values of accumulators after a task completes (successfully or with a failure) is only executed on a single thread that runs scheduling loop. Besides that, they are write-only data structures for workers that have their own local accumulator reference whereas accessing the value of an accumulator is only allowed by the driver. And when you say that it works in local mode because it is single JVM but in cluster mode, they are different JVM and java instance, PRC calls are being triggered to enable the communication.

How your MyRecord object looks like and if you just end your line with .value rather having an iterator over it will help. Just try.

myAccumulator.value

Answer 2

It makes sense to read the accumulator only after some action on the RDD has been called (collect or count).

Also you don't need to synchronize on the accumulator since an independent copy of it will be allocated per partition.