Spark性能优化-coalesce(n)

有时用Spark 运行Job 的时候，输出可能会出现一些空或者小内容。这时重新将输出的Partition 进行重新调整，可以减少RDD中Patition的数目。 
两种方式： 
1. coalesce(numPartitions:Int, shuffle:Boolean = false) 
2. repartition(numPartitions:Int)

，repartition只是coalesce 简化版

/**
 * Return a new RDD that has exactly numPartitions partitions.
 *
 * Can increase or decrease the level of parallelism in this RDD. Internally, this uses
 * a shuffle to redistribute data.
 *
 * If you are decreasing the number of partitions in this RDD, consider using `coalesce`,
 * which can avoid performing a shuffle.
如果你想减少patitions 用coalesce
 */

def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
  coalesce(numPartitions, shuffle = true)
}

/**
 * Return a new RDD that is reduced into `numPartitions` partitions.
 *
 * This results in a narrow dependency, e.g. if you go from 1000 partitions
 * to 100 partitions, there will not be a shuffle, instead each of the 100
 * new partitions will claim 10 of the current partitions.
 *
 * However, if you're doing a drastic coalesce, e.g. to numPartitions = 1,
 * this may result in your computation taking place on fewer nodes than
 * you like (e.g. one node in the case of numPartitions = 1). To avoid this,
 * you can pass shuffle = true. This will add a shuffle step, but means the
 * current upstream partitions will be executed in parallel (per whatever
 * the current partitioning is).
 *
 * Note: With shuffle = true, you can actually coalesce to a larger number
 * of partitions. This is useful if you have a small number of partitions,
 * say 100, potentially with a few partitions being abnormally large. Calling
 * coalesce(1000, shuffle = true) will result in 1000 partitions with the
 * data distributed using a hash partitioner.

shuffle为收缩 默认采用 partitionCoalescer进行分区
 */
def coalesce(numPartitions: Int, shuffle: Boolean = false,
             partitionCoalescer: Option[PartitionCoalescer] = Option.empty)
            (implicit ord: Ordering[T] = null)
    : RDD[T] = withScope {
  require(numPartitions > 0, s"Number of partitions ($numPartitions) must be positive.")
  if (shuffle) {
    /** Distributes elements evenly across output partitions, starting from a random partition. */
//index 所在分区， items 
    val distributePartition = (index: Int, items: Iterator[T]) => {
      var position = (new Random(index)).nextInt(numPartitions)
      items.map { t =>
        // Note that the hash code of the key will just be the key itself. The HashPartitioner
        // will mod it with the number of total partitions.
        position = position + 1
        (position, t)
      }
    } : Iterator[(Int, T)]

    // include a shuffle step so that our upstream tasks are still distributed
    new CoalescedRDD(
//如果扩展 newShuffledRDD 这个核心操作distributePartition看 对现有分区进行重新划分看上面函数distributePartition 。
      new ShuffledRDD[Int, T, T](mapPartitionsWithIndex(distributePartition),
      new HashPartitioner(numPartitions)),
      numPartitions,
      partitionCoalescer).values
  } else {
//shuffle为false
    new CoalescedRDD(this, numPartitions, partitionCoalescer)
  }
}