有没有办法忽略Spark中元素很少的RDD分区上的处理？

Question

I have an RDD and I need to apply a computation on each partition (using .mapPartition ) but only if the current partition of data have more than X elements.

Example: The number of elements in each partition of the RDD is:

80, 9, 0, 0, 0, 3, 60

I want to process only over the partitions with more than 50 elements.

Is this even possible?

Answer 1

Can be also done lazily without pre-calculating sizes. Filtering to partitions with at least two elements in this example

import org.apache.spark.Partitioner

object DemoPartitioner extends Partitioner {
  override def numPartitions: Int = 3
  override def getPartition(key: Any): Int = key match {
    case num: Int => num
  }
}

sc.parallelize(Seq((0, "a"), (0, "a"), (0, "a"), (1, "b"), (2, "c"), (2, "c")))
  .partitionBy(DemoPartitioner) // create 3 partitions of sizes 3,1,2
  .mapPartitions { it =>
    val firstElements = it.take(2).toSeq
    if (firstElements.size < 2) {
      Iterator.empty
    } else {
      firstElements.iterator ++ it
    }
  }.foreach(println)

Output:

(2,c)
(2,c)
(0,a)
(0,a)
(0,a)

So partition 1 with just a single element was skipped