spark自定义分区

目录

一、需求

二、代码展示

三、数据展示

四、结果展示

五、三种分区方式介绍

一、需求

       防止大量数据倾斜，自定义Partition的函数，map阶段使用元祖（int ， String）int 去模做Hash，均匀分配到不同的Partion中。后续演化：自定义map的key值，key值为一个随机的范围数。

二、代码展示

两个类：defineSparkPartition.scala UsedefineSparkPartition.scala
注意事项：
(1)不要使用flatMap()方法
(2)只有Key-Value类型的RDD才有分区的，非Key-Value类型的RDD分区的值是None
(3)每个RDD的分区ID范围：0~numPartitions-1，决定这个值是属于那个分区的。

import org.apache.spark.Partitioner

/**
  * Created by yuhui
  */
class  defineSparkPartition(numParts: Int) extends Partitioner {

  /**
    * 这个方法需要返回你想要创建分区的个数
    */
  override def numPartitions: Int = numParts

  /**
    *
    * 这个函数需要对输入的key做计算，然后返回该key的分区ID，范围一定是0到numPartitions-1；    *
    * @param key
    * @return
    */
  override def getPartition(key: Any): Int = {
    val domain = new java.net.URL(key.toString).getHost()
    domain match {
      case "blog.csdn.net" => 1 % numPartitions
      case "news.cctv.com" => 2 % numPartitions
      case "news.china.com" => 3 % numPartitions
      case _ =>4 % numPartitions
    }
  }

  /**
    * 这个是Java标准的判断相等的函数，之所以要求用户实现这个函数是因为Spark内部会比较两个RDD的分区是否一样。
    * @param other
    * @return
    */
  override def equals(other: Any): Boolean = other match {
    case mypartition: defineSparkPartition =>
      mypartition.numPartitions == numPartitions
    case _ =>
      false
  }
  override def hashCode: Int = numPartitions

}

import org.apache.spark.{SparkConf, SparkContext}

/**
  * Created by yuhui
  */
object UsedefineSparkPartition {

  def main(args: Array[String]) {

    val conf=new SparkConf()
      .setMaster("local[2]")
      .setAppName("UsedefineSparkPartition")
   
    val sc=new SparkContext(conf)

    //读取本地文件
    val lines=sc.textFile("D:/word.txt")

    val splitMap=lines.map(line=>(line.split(",")(0),line.split(",")(1))).map(word=>(word._1,word._2))//注意：RDD一定要是key-value

    //保存到本地文件
    splitMap.partitionBy(new defineSparkPartition(4)).saveAsTextFile("D:/partrion/test")

    sc.stop()

  }

}

三、数据展示

​​http://blog.csdn.net/silentwolfyh/article/details/76993419,blog.csdn.net​​​ http://blog.csdn.net/silentwolfyh/article/details/76860369,blog.csdn.net
http://blog.csdn.net/silentwolfyh/article/details/77571596,blog.csdn.net
http://blog.csdn.net/silentwolfyh/article/details/77188905,blog.csdn.net
http://news.cctv.com/2017/09/18/ARTIEX7bcZI2cYUqrsEC2DLf170918.shtml,news.cctv.com
http://news.cctv.com/2017/09/18/ARTI4McIqsaFV6115br9eiRJ170918.shtml,news.cctv.com
http://news.cctv.com/2017/09/18/ARTfdabrnntvV6115br9eiRJ170918.shtml,news.cctv.com
http://news.china.com/domestic/945/20170919/31463894.html,news.china.com
http://news.china.com/domestic/945/20170919/31464711.html,news.china.com
http://news.china.com/domestic/945/20170919/31464711.html,news.china.com
https://www.baidu.com/,www.baidu.com
http://news.163.com/17/0918/22/CULBLQUT0001899N.html,news.163.com
http://news.163.com/17/0919/06/CUM7EVQI0001899N.html,news.163.com
http://news.163.com/17/0919/03/CULRN5180001875P.html,news.163.com

四、结果展示

part-00000

(https://www.baidu.com/,www.baidu.com)
(http://news.163.com/17/0918/22/CULBLQUT0001899N.html,news.163.com)
(http://news.163.com/17/0919/06/CUM7EVQI0001899N.html,news.163.com)
(http://news.163.com/17/0919/03/CULRN5180001875P.html,news.163.com)

part-00001

(http://blog.csdn.net/silentwolfyh/article/details/76993419,blog.csdn.net)
(http://blog.csdn.net/silentwolfyh/article/details/76860369,blog.csdn.net)
(http://blog.csdn.net/silentwolfyh/article/details/77571596,blog.csdn.net)
(http://blog.csdn.net/silentwolfyh/article/details/77188905,blog.csdn.net)

part-00002

(http://news.cctv.com/2017/09/18/ARTIEX7bcZI2cYUqrsEC2DLf170918.shtml,news.cctv.com)
(http://news.cctv.com/2017/09/18/ARTI4McIqsaFV6115br9eiRJ170918.shtml,news.cctv.com)
(http://news.cctv.com/2017/09/18/ARTfdabrnntvV6115br9eiRJ170918.shtml,news.cctv.com)

part-00003

(http://news.china.com/domestic/945/20170919/31463894.html,news.china.com)
(http://news.china.com/domestic/945/20170919/31464711.html,news.china.com)
(http://news.china.com/domestic/945/20170919/31464711.html,news.china.com)

五、三种分区方式介绍

defaultPartitioner.scala

/**
   * 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 bySize = (Seq(rdd) ++ others).sortBy(_.partitions.size).reverse
    for (r <- bySize if r.partitioner.isDefined && r.partitioner.get.numPartitions > 0) {
      return r.partitioner.get
    }
    if (rdd.context.conf.contains("spark.default.parallelism")) {
      new HashPartitioner(rdd.context.defaultParallelism)
    } else {
      new HashPartitioner(bySize.head.partitions.size)
    }
  }

HashPartitioner分区的原理：对于给定的key，计算其hashCode，并除于分区的个数取余，如果余数小于0，则用余数+分区的个数，最后返回的值就是这个key所属的分区ID。实现如下：

/**
 * 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
}

HashPartitioner分区弊端：可能导致每个分区中数据量的不均匀，极端情况下会导致某些分区拥有RDD的全部数据。

RangePartitioner分区优势：尽量保证每个分区中数据量的均匀，而且分区与分区之间是有序的，一个分区中的元素肯定都是比另一个分区内的元素小或者大；

但是分区内的元素是不能保证顺序的。简单的说就是将一定范围内的数映射到某一个分区内。

RangePartitioner作用：将一定范围内的数映射到某一个分区内，在实现中，分界的算法尤为重要。算法对应的函数是rangeBounds。

代码如下：

/**
 * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly
 * equal ranges. The ranges are determined by sampling the content of the RDD passed in.
 *
 * Note that the actual number of partitions created by the RangePartitioner might not be the same
 * as the `partitions` parameter, in the case where the number of sampled records is less than
 * the value of `partitions`.
 */
class RangePartitioner[K : Ordering : ClassTag, V](
    partitions: Int,
    rdd: RDD[_ <: Product2[K, V]],
    private var ascending: Boolean = true)
  extends Partitioner {

  // We allow partitions = 0, which happens when sorting an empty RDD under the default settings.
  require(partitions >= 0, s"Number of partitions cannot be negative but found $partitions.")

  private var ordering = implicitly[Ordering[K]]

  // 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.
      val sampleSize = math.min(20.0 * partitions, 1e6)
      // Assume the input partitions are roughly balanced and over-sample a little bit.
      val sampleSizePerPartition = math.ceil(3.0 * sampleSize / rdd.partitions.size).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.size).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, partitions)
      }
    }
  }

  def numPartitions: Int = rangeBounds.length + 1

  private var binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K]

  def getPartition(key: Any): Int = {
    val k = key.asInstanceOf[K]
    var partition = 0
    if (rangeBounds.length <= 128) {
      // If we have less than 128 partitions naive search
      while (partition < rangeBounds.length && ordering.gt(k, rangeBounds(partition))) {
        partition += 1
      }
    } else {
      // Determine which binary search method to use only once.
      partition = binarySearch(rangeBounds, k)
      // binarySearch either returns the match location or -[insertion point]-1
      if (partition < 0) {
        partition = -partition-1
      }
      if (partition > rangeBounds.length) {
        partition = rangeBounds.length
      }
    }
    if (ascending) {
      partition
    } else {
      rangeBounds.length - partition
    }
  }

  override def equals(other: Any): Boolean = other match {
    case r: RangePartitioner[_, _] =>
      r.rangeBounds.sameElements(rangeBounds) && r.ascending == ascending
    case _ =>
      false
  }

  override def hashCode(): Int = {
    val prime = 31
    var result = 1
    var i = 0
    while (i < rangeBounds.length) {
      result = prime * result + rangeBounds(i).hashCode
      i += 1
    }
    result = prime * result + ascending.hashCode
    result
  }

  @throws(classOf[IOException])
  private def writeObject(out: ObjectOutputStream): Unit = Utils.tryOrIOException {
    val sfactory = SparkEnv.get.serializer
    sfactory match {
      case js: JavaSerializer => out.defaultWriteObject()
      case _ =>
        out.writeBoolean(ascending)
        out.writeObject(ordering)
        out.writeObject(binarySearch)

        val ser = sfactory.newInstance()
        Utils.serializeViaNestedStream(out, ser) { stream =>
          stream.writeObject(scala.reflect.classTag[Array[K]])
          stream.writeObject(rangeBounds)
        }
    }
  }

  @throws(classOf[IOException])
  private def readObject(in: ObjectInputStream): Unit = Utils.tryOrIOException {
    val sfactory = SparkEnv.get.serializer
    sfactory match {
      case js: JavaSerializer => in.defaultReadObject()
      case _ =>
        ascending = in.readBoolean()
        ordering = in.readObject().asInstanceOf[Ordering[K]]
        binarySearch = in.readObject().asInstanceOf[(Array[K], K) => Int]

        val ser = sfactory.newInstance()
        Utils.deserializeViaNestedStream(in, ser) { ds =>
          implicit val classTag = ds.readObject[ClassTag[Array[K]]]()
          rangeBounds = ds.readObject[Array[K]]()
        }
    }
  }
}