[jira] [Created] (SPARK-22465) Cogroup of two disproportionate RDDs could lead into 2G limit BUG

["Amit Kumar (JIRA)" <ji...@apache.org>]

Amit Kumar created SPARK-22465:
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             Summary: Cogroup of two disproportionate RDDs could lead into 2G limit BUG
                 Key: SPARK-22465
                 URL: https://issues.apache.org/jira/browse/SPARK-22465
             Project: Spark
          Issue Type: Bug
          Components: Spark Core
    Affects Versions: 2.2.0, 2.1.2, 2.1.1, 2.1.0, 2.0.2, 2.0.1, 2.0.0, 1.6.3, 1.6.2, 1.6.1, 1.6.0, 1.5.2, 1.5.1, 1.5.0, 1.4.1, 1.4.0, 1.3.1, 1.3.0, 1.2.2, 1.2.1, 1.2.0, 1.1.1, 1.1.0, 1.0.2, 1.0.1, 1.0.0
            Reporter: Amit Kumar
            Priority: Critical


While running my spark pipeline, it failed with the following exception
{noformat}
2017-11-03 04:49:09,776 [Executor task launch worker for task 58670] ERROR org.apache.spark.executor.Executor  - Exception in task 630.0 in stage 28.0 (TID 58670)
java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
	at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:869)
	at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:103)
	at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:91)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1303)
	at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:105)
	at org.apache.spark.storage.BlockManager.getLocalValues(BlockManager.scala:469)
	at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:705)
	at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:99)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:324)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
{noformat}


After debugging I found that the issue lies with how spark handles cogroup of two RDDs.
Here is the relevant code from apache spark
{noformat}
 /**
   * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
   * list of values for that key in `this` as well as `other`.
   */
  def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
    cogroup(other, defaultPartitioner(self, other))
  }


/**
   * Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
   *
   * If any of the RDDs already has a partitioner, choose that one.
   *
   * Otherwise, we use a default HashPartitioner. For the number of partitions, if
   * spark.default.parallelism is set, then we'll use the value from SparkContext
   * defaultParallelism, otherwise we'll use the max number of upstream partitions.
   *
   * Unless spark.default.parallelism is set, the number of partitions will be the
   * same as the number of partitions in the largest upstream RDD, as this should
   * be least likely to cause out-of-memory errors.
   *
   * We use two method parameters (rdd, others) to enforce callers passing at least 1 RDD.
   */
  def defaultPartitioner(rdd: RDD[_], others: RDD[_]*): Partitioner = {
    val rdds = (Seq(rdd) ++ others)
    val hasPartitioner = rdds.filter(_.partitioner.exists(_.numPartitions > 0))
    if (hasPartitioner.nonEmpty) {
      hasPartitioner.maxBy(_.partitions.length).partitioner.get
    } else {
      if (rdd.context.conf.contains("spark.default.parallelism")) {
        new HashPartitioner(rdd.context.defaultParallelism)
      } else {
        new HashPartitioner(rdds.map(_.partitions.length).max)
      }
    }
  }

{noformat}

Given this  suppose we have two  pair RDDs.
RDD1 : A small RDD which fewer data and partitions
RDD2: A huge RDD which has loads of data and partitions

Now in the code if we were to have a cogroup
{noformat}
val RDD3 = RDD1.cogroup(RDD2)
{noformat}

there is a case where this could lead to the SPARK-6235 Bug which is If RDD1 has a partitioner when it is being called into a cogroup. This is because the cogroups partitions are then decided by the partitioner and could lead to the huge RDD2 being shuffled into a small number of partitions.

One way is probably to add a safety check here that would ignore the partitioner if the number of partitions on the two RDDs are very different in magnitude.







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