spark中stage的划分依据action算子进行，每一次action（reduceByKey等）算子都会触发一次shuffle过程，该过程涉及到数据的重新分区。spark中的分区器包括HashPartitioner及RangePartitioner两种。HashPartitioner根据key进行分区，当某一个key对应的数据较多时会出现数据倾斜的情况，又因为每一个partition对应一个task，数据较多的task会耗费较多的时间，影响spark任务运行的时间。此时，可以使用RangePartitioner分区器，RangePartitioner基于水塘抽样算法，可以在不知道整体数据量的情况下，等概率地取到每条数据。

一、HashPartitioner

/**
 * A [[org.apache.spark.Partitioner]] that implements hash-based partitioning using
 * Java's `Object.hashCode`.
 *
 * Java arrays have hashCodes that are based on the arrays' identities rather than their contents,
 * so attempting to partition an RDD[Array[_]] or RDD[(Array[_], _)] using a HashPartitioner will
 * produce an unexpected or incorrect result.
 */
class HashPartitioner(partitions: Int) extends Partitioner {
  require(partitions >= 0, s"Number of partitions ($partitions) cannot be negative.")

  def numPartitions: Int = partitions

  def getPartition(key: Any): Int = key match {
    case null => 0
    case _ => Utils.nonNegativeMod(key.hashCode, numPartitions)
  }

  override def equals(other: Any): Boolean = other match {
    case h: HashPartitioner =>
      h.numPartitions == numPartitions
    case _ =>
      false
  }

  override def hashCode: Int = numPartitions
}

 /* Calculates 'x' modulo 'mod', takes to consideration sign of x,
  * i.e. if 'x' is negative, than 'x' % 'mod' is negative too
  * so function return (x % mod) + mod in that case.
  */
  def nonNegativeMod(x: Int, mod: Int): Int = {
    val rawMod = x % mod
    rawMod + (if (rawMod < 0) mod else 0)
  }

HashPartitioner主要根据RDD的key进行分区，当key为null时，对应的partitionId为0，当key不为null时，partitionId计算过程为：先将key的hashcode值对分区个数numPartitions取余，当余数小于0时，将余数与numPartitions相加，否则与0相加。很明显，相同key的数据一定会分到同一个分区中，可能导致数据倾斜，进而影响spark运行速度。

二、RangePartitioner

HashPartitioner分区可能导致每个分区中数据量的不均匀。而RangePartitioner分区则尽量保证每个分区中数据量的均匀，将一定范围内的数映射到某一个分区内。分区与分区之间数据是有序的，但分区内的元素是不能保证顺序的。

1、水塘抽样算法原理

对于一长度为n（大到无法加载到内存中）的数组N，如何等概率地从中取出k个元素，组成数组R？
水塘抽样算法做法如下：首先，去数组N前k个元素放入数组R中；然后遍历数组N中剩余元素，对于数组N中第i个元素N[i-1]（i大于k），随机生成一个数rand，若rand<k，则将N[i-1]替换掉数组R中第rand个元素，否则，保持原样。可以得知，取得数组N中每一元素的概率均为k/n。对于数组N中前k个元素而言，由于第一次就将其取出，因此在后续迭代过程中只需保持原样即可，概率为 $\frac{ k}{k+1 }*\frac{ k+1}{k+2 }*...\frac{n-1}{n }=\frac{ k}{n}\$ ，对于数组N中剩余的n-k个元素，只需在遍历到其所在的位置时替换掉现有元素中的一个并在后续步骤中保持原样，概率为 $\frac{ k}{i }*\frac{i}{i+1}*...\frac{n-1}{n }=\frac{ k}{n}\$ 。

2、RangePartitioner

// An array of upper bounds for the first (partitions - 1) partitions
  private var rangeBounds: Array[K] = {
    if (partitions <= 1) {
      Array.empty
    } else {
      // This is the sample size we need to have roughly balanced output partitions, capped at 1M.
      // Cast to double to avoid overflowing ints or longs
      val sampleSize = math.min(samplePointsPerPartitionHint.toDouble * partitions, 1e6)
      // Assume the input partitions are roughly balanced and over-sample a little bit.
      val sampleSizePerPartition = math.ceil(3.0 * sampleSize / rdd.partitions.length).toInt
      val (numItems, sketched) = RangePartitioner.sketch(rdd.map(_._1), sampleSizePerPartition)
      if (numItems == 0L) {
        Array.empty
      } else {
        // If a partition contains much more than the average number of items, we re-sample from it
        // to ensure that enough items are collected from that partition.
        val fraction = math.min(sampleSize / math.max(numItems, 1L), 1.0)
        val candidates = ArrayBuffer.empty[(K, Float)]
        val imbalancedPartitions = mutable.Set.empty[Int]
        sketched.foreach { case (idx, n, sample) =>
          if (fraction * n > sampleSizePerPartition) {
            imbalancedPartitions += idx
          } else {
            // The weight is 1 over the sampling probability.
            val weight = (n.toDouble / sample.length).toFloat
            for (key <- sample) {
              candidates += ((key, weight))
            }
          }
        }
        if (imbalancedPartitions.nonEmpty) {
          // Re-sample imbalanced partitions with the desired sampling probability.
          val imbalanced = new PartitionPruningRDD(rdd.map(_._1), imbalancedPartitions.contains)
          val seed = byteswap32(-rdd.id - 1)
          val reSampled = imbalanced.sample(withReplacement = false, fraction, seed).collect()
          val weight = (1.0 / fraction).toFloat
          candidates ++= reSampled.map(x => (x, weight))
        }
        RangePartitioner.determineBounds(candidates, math.min(partitions, candidates.size))
      }
    }
  }

  /**
   * Sketches the input RDD via reservoir sampling on each partition.
   *
   * @param rdd the input RDD to sketch
   * @param sampleSizePerPartition max sample size per partition
   * @return (total number of items, an array of (partitionId, number of items, sample))
   */
  def sketch[K : ClassTag](
      rdd: RDD[K],
      sampleSizePerPartition: Int): (Long, Array[(Int, Long, Array[K])]) = {
    val shift = rdd.id
    // val classTagK = classTag[K] // to avoid serializing the entire partitioner object
    val sketched = rdd.mapPartitionsWithIndex { (idx, iter) =>
      val seed = byteswap32(idx ^ (shift << 16))
      val (sample, n) = SamplingUtils.reservoirSampleAndCount(
        iter, sampleSizePerPartition, seed)
      Iterator((idx, n, sample))
    }.collect()
    val numItems = sketched.map(_._2).sum
    (numItems, sketched)
  }

private[spark] object SamplingUtils {

  /**
   * Reservoir sampling implementation that also returns the input size.
   *
   * @param input input size
   * @param k reservoir size
   * @param seed random seed
   * @return (samples, input size)
   */
  def reservoirSampleAndCount[T: ClassTag](
      input: Iterator[T],
      k: Int,
      seed: Long = Random.nextLong())
    : (Array[T], Long) = {
    val reservoir = new Array[T](k)
    // Put the first k elements in the reservoir.
    var i = 0
    while (i < k && input.hasNext) {
      val item = input.next()
      reservoir(i) = item
      i += 1
    }

    // If we have consumed all the elements, return them. Otherwise do the replacement.
    if (i < k) {
      // If input size < k, trim the array to return only an array of input size.
      val trimReservoir = new Array[T](i)
      System.arraycopy(reservoir, 0, trimReservoir, 0, i)
      (trimReservoir, i)
    } else {
      // If input size > k, continue the sampling process.
      var l = i.toLong
      val rand = new XORShiftRandom(seed)
      while (input.hasNext) {
        val item = input.next()
        l += 1
        // There are k elements in the reservoir, and the l-th element has been
        // consumed. It should be chosen with probability k/l. The expression
        // below is a random long chosen uniformly from [0,l)
        val replacementIndex = (rand.nextDouble() * l).toLong
        if (replacementIndex < k) {
          reservoir(replacementIndex.toInt) = item
        }
      }
      (reservoir, l)
    }
  }

RangePartitioner分区执行原理：
1、计算总体的数据抽样大小sampleSize，计算规则是：至少每个分区抽取20个数据或者最多1M的数据量。
2、根据sampleSize和分区数量计算每个分区的数据抽样样本数量最大值sampleSizePerPartition
3、根据以上两个值进行水塘抽样，返回RDD的总数据量，分区ID和每个分区的采样数据。
4、计算出数据量较大的分区通过RDD.sample进行重新抽样。
5、通过抽样数组 candidates: ArrayBuffer[(K, wiegth)]计算出分区边界的数组BoundsArray
6、在取数据时，如果分区数小于128则直接获取，如果大于128则通过二分法，获取当前Key属于那个区间，返回对应的BoundsArray下标即为partitionsID

spark源码解析之partitioner

spark源码解析之partitioner

一、HashPartitioner

二、RangePartitioner

1、水塘抽样算法原理

2、RangePartitioner