How to balance my data across the partitions?

Question

Edit: The answer helps, but I described my solution in: memoryOverhead issue in Spark.

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I have an RDD with 202092 partitions, which reads a dataset cre

Answer 1

The memory overhead limit exceeding issue I think is due to DirectMemory buffers used during fetch. I think it's fixed in 2.0.0. (We had the same issue, but stopped digging much deeper when we found that upgrading to 2.0.0 resolved it. Unfortunately I don't have Spark issue numbers to back me up.)

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The uneven partitions after repartition are surprising. Contrast with https://github.com/apache/spark/blob/v2.0.0/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L443. Spark even generates random keys in repartition, so it is not done with a hash that could be biased.

I tried your example and get the exact same results with Spark 1.6.2 and Spark 2.0.0. But not from Scala spark-shell:

scala> val data = sc.parallelize(1 to 3, 3).mapPartitions { it => (1 to it.next * 1000).iterator }
data: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[6] at mapPartitions at <console>:24

scala> data.mapPartitions { it => Iterator(it.toSeq.size) }.collect.toSeq
res1: Seq[Int] = WrappedArray(1000, 2000, 3000)

scala> data.repartition(3).mapPartitions { it => Iterator(it.toSeq.size) }.collect.toSeq
res2: Seq[Int] = WrappedArray(1999, 2001, 2000)

scala> data.repartition(6).mapPartitions { it => Iterator(it.toSeq.size) }.collect.toSeq
res3: Seq[Int] = WrappedArray(999, 1000, 1000, 1000, 1001, 1000)

scala> data.repartition(12).mapPartitions { it => Iterator(it.toSeq.size) }.collect.toSeq
res4: Seq[Int] = WrappedArray(500, 501, 501, 501, 501, 500, 499, 499, 499, 499, 500, 500)

Such beautiful partitions!

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_{(Sorry this is not a full answer. I just wanted to share my findings so far.)}