如何进行spark join的源码分析

这期内容当中小编将会给大家带来有关如何进行spark join的源码分析，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。

import org.apache.spark.rdd.RDDimport org.apache.spark.{SparkConf, SparkContext}object JoinDemo {  def main(args: Array[String]): Unit = {    val conf = new SparkConf().setAppName(this.getClass.getCanonicalName.init).setMaster("local[*]")    val sc = new SparkContext(conf)    sc.setLogLevel("WARN")    val random = scala.util.Random    val col1 = Range(1, 50).map(idx => (random.nextInt(10), s"user$idx"))    val col2 = Array((0, "BJ"), (1, "SH"), (2, "GZ"), (3, "SZ"), (4, "TJ"), (5, "CQ"), (6, "HZ"), (7, "NJ"), (8, "WH"), (0, "CD"))    val rdd1: RDD[(Int, String)] = sc.makeRDD(col1)    val rdd2: RDD[(Int, String)] = sc.makeRDD(col2)    val rdd3: RDD[(Int, (String, String))] = rdd1.join(rdd2)    println (rdd3.dependencies)    val rdd4: RDD[(Int, (String, String))] = rdd1.partitionBy(new HashPartitioner(3)).join(rdd2.partitionBy(newHashPartitioner(3)))    println(rdd4.dependencies)    sc.stop()  }}

1.两个打印语句：List(org.apache.spark.OneToOneDependency@63acf8f6) List(org.apache.spark.OneToOneDependency@d9a498)对应的依赖：rdd3对应的是宽依赖，rdd4对应的是窄依赖原因：1）参考webUI由DAG图可以看出，第一个join和之前的清晰划分成单独的Satge。可以看出这个是宽依赖。第二个join，partitionBy之后再进行join并没有单独划分成一个stage，由此可见是一个窄依赖。

rdd3的join

rdd4的join

2）代码解析： a.首先是默认的join方法，这里使用了一个默认分区器

  /**   * Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each   * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and   * (k, v2) is in `other`. Performs a hash join across the cluster.   */  def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = self.withScope {    join(other, defaultPartitioner(self, other))  }

b.默认分区器，对于第一个join会返回一个以电脑core总数为分区数量的HashPartitioner.第二个join会返回我们设定的HashPartitioner(分区数目3)

  def defaultPartitioner(rdd: RDD[_], others: RDD[_]*): Partitioner = {    val rdds = (Seq(rdd) ++ others)    val hasPartitioner = rdds.filter(_.partitioner.exists(_.numPartitions > 0))    val hasMaxPartitioner: Option[RDD[_]] = if (hasPartitioner.nonEmpty) {      Some(hasPartitioner.maxBy(_.partitions.length))    } else {      None    }    val defaultNumPartitions = if (rdd.context.conf.contains("spark.default.parallelism")) {      rdd.context.defaultParallelism    } else {      rdds.map(_.partitions.length).max    }    // If the existing max partitioner is an eligible one, or its partitions number is larger    // than the default number of partitions, use the existing partitioner.    if (hasMaxPartitioner.nonEmpty && (isEligiblePartitioner(hasMaxPartitioner.get, rdds) ||        defaultNumPartitions < hasMaxPartitioner.get.getNumPartitions)) {      hasMaxPartitioner.get.partitioner.get    } else {      new HashPartitioner(defaultNumPartitions)    }  }

c.走到了实际执行的join方法，里面flatMapValues是一个窄依赖，所以说如果有宽依赖应该在cogroup算子中

  /**   * Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each   * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and   * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD.   */  def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = self.withScope {    this.cogroup(other, partitioner).flatMapValues( pair =>      for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w)    )  }

d.进入cogroup方法中，核心是CoGroupedRDD，根据两个需要join的rdd和一个分区器。由于第一个join的时候，两个rdd都没有分区器，所以在这一步，两个rdd需要先根据传入的分区器进行一次shuffle，因此第一个join是宽依赖。第二个join此时已经分好区了，不需要再再进行shuffle了。所以第二个是窄依赖

  /**   * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the   * list of values for that key in `this` as well as `other`.   */  def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner)      : RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {    if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {      throw new SparkException("HashPartitioner cannot partition array keys.")    }    val cg = new CoGroupedRDD[K](Seq(self, other), partitioner)    cg.mapValues { case Array(vs, w1s) =>      (vs.asInstanceOf[Iterable[V]], w1s.asInstanceOf[Iterable[W]])    }  }

e.两个都打印出OneToOneDependency，是因为在CoGroupedRDD里面，getDependencies方法里面，如果rdd有partitioner就都会返回OneToOneDependency(rdd)。

  override def getDependencies: Seq[Dependency[_]] = {    rdds.map { rdd: RDD[_] =>      if (rdd.partitioner == Some(part)) {        logDebug("Adding one-to-one dependency with " + rdd)        new OneToOneDependency(rdd)      } else {        logDebug("Adding shuffle dependency with " + rdd)        new ShuffleDependency[K, Any, CoGroupCombiner](          rdd.asInstanceOf[RDD[_ <: Product2[K, _]]], part, serializer)      }    }  }

join什么时候是宽依赖什么时候是窄依赖？由上述分析可以知道，如果需要join的两个表，本身已经有分区器，且分区的数目相同，此时，相同的key在同一个分区内。就是窄依赖。反之，如果两个需要join的表中没有分区器或者分区数量不同，在join的时候需要shuffle，那么就是宽依赖

上述就是小编为大家分享的如何进行spark join的源码分析了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注行业资讯频道。