参考文章：

https://blog.csdn.net/qq_20641565/article/details/76216417

今天模拟实现 broadcastJoin 的时候突然意识到了这个点，对 Spark 的 Cache 做个总结。

问题

     在Spark中有时候我们很多地方都会用到同一个RDD, 按照常规的做法的话,那么每个地方遇到Action操作的时候都会对同一个算子计算多次。这样会造成效率低下的问题 !!!! 

常见 transform , action 算子 =>

https://blog.csdn.net/u010003835/article/details/106341908

例如:

val rdd1 = sc.textFile("xxx")

rdd1.xxxxx.xxxx.collect

rdd1.xxx.xxcollect

方法

   上面就是两个代码都用到了rdd1这个RDD,如果程序执行的话,那么sc.textFile(“xxx”)就要被执行两次,  可以把rdd1的结果进行cache到内存中,使用如下方法

val rdd1 = sc.textFile("xxx")

val rdd2 = rdd1.cache

rdd2.xxxxx.xxxx.collect

rdd2.xxx.xxcollect

示例

例如 如下Demo

package com.spark.test.offline.skewed_dataimport org.apache.spark.SparkConf
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.sql.types.{StructField, _}
import org.apache.spark.sql.{Row, SparkSession}import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import scala.util.Random/*** Created by szh on 2020/6/5.*/
object JOINSkewedData2 {def main(args: Array[String]): Unit = {val sparkConf = new SparkConfsparkConf.setAppName("JOINSkewedData").set("spark.sql.autoBroadcastJoinThreshold", "1048576") //1M broadcastJOIN//.set("spark.sql.autoBroadcastJoinThreshold", "104857600") //100M broadcastJOIN.set("spark.sql.shuffle.partitions", "3")if (args.length > 0 && args(0).equals("ide")) {sparkConf.setMaster("local[3]")}val spark = SparkSession.builder().config(sparkConf).getOrCreate()val sparkContext = spark.sparkContextsparkContext.setLogLevel("WARN")//sparkContext.setCheckpointDir("")val userArr = new ArrayBuffer[(Int, String)]()val nameArr = Array[String]("sun", "zhen", "hua", "kk", "cc")val threshold = 1000000for (i <- 1 to threshold) {var id = 10if (i < (threshold * 0.9)) {id = 1} else {id = i}val name = nameArr(Random.nextInt(5))userArr.+=((id, name))}val rddA = sparkContext.parallelize(userArr)//spark.sql("CACHE TABLE userA")//-----------------------------------------//---------------------------------------val arrList = new ArrayBuffer[(Int, Int)]for (i <- 1 to (threshold * 0.1).toInt) {val id = ival salary = Random.nextInt(100)arrList.+=((id, salary))}val rddB = sparkContext.parallelize(arrList)val broadData: Broadcast[Array[(Int, Int)]] = sparkContext.broadcast(rddB.collect())import scala.util.control._val resultRdd = rddA.mapPartitions(arr => {val broadVal = broadData.valuevar rowArr = new ArrayBuffer[Row]()val broadMap = new mutable.HashMap[Int, Int]()while (arr.hasNext) {val x = arr.nextval loop = new Breaksvar rRow: Row = null//var rRow: Option[Row] = Noneloop.breakable(for (tmpVal <- broadVal) {if (tmpVal._1 == x._1) {rRow = Row(tmpVal._1, x._2, tmpVal._2)//println(rRow)loop.break}})if (rRow != null) {rowArr.+=(rRow)rRow = null}}println(rowArr.size)rowArr.iterator})//      .filter(x => {//        x match {//          case None => false//          case _ => true//        }//      })val resultStruct = StructType(Array(StructField("uid", IntegerType, nullable = true), StructField("name", StringType, nullable = true), StructField("salary", IntegerType, nullable = true)))spark.createDataFrame(resultRdd, resultStruct).createOrReplaceTempView("resultB")val resultDF = spark.sql("SELECT uid, name, salary FROM resultB")//resultDF.checkpoint()resultDF.cache()resultDF.foreach(x => {val i = 1})println(resultDF.count())resultDF.show()resultDF.explain(true)Thread.sleep(60 * 10 * 1000)sparkContext.stop()}}

注意其中 

    resultDF.foreach(x => {
      val i = 1
    })

    println(resultDF.count())

    resultDF.show()

foreach， count ， show  是 3个 Action 操作 !!

不对 resultDF 进行 cache， 整个任务的执行时间 如下图 ：

对 resultDF 进行 cache， 整个任务的执行时间 如下图 ：

对比上图，可以清楚的看到没有进行 cache, count 对上游又重新计算了一遍多了20多秒 !!!!!