数据倾斜是指我们在并行进行数据处理的时候，由于数据散列引起Spark的单个Partition的分布不均，导致大量的数据集中分布到一台或者几台计算节点上，导致处理速度远低于平均计算速度，从而拖延导致整个计算过程过慢，影响整个计算性能。

数据倾斜带来的问题

单个或者多个Task长尾执行，拖延整个任务运行时间，导致整体耗时过大。单个Task处理数据过多，很容易导致OOM。

数据倾斜的产生原因

数据倾斜一般是发生在 shuffle 类的算子、SQL函数导致，具体如以下：

类型	RDD	SQL
去重	distinct	distinct
聚合	groupByKey、reduceByKey、aggregateByKey	group by
关联	join、left join、right join	join、left join、right join

通过Spark web ui event timeline观察明显长尾任务：

数据倾斜大Key定位

RDD进行抽取：

val cscTopKey: Array[(Int, Row)] = sampleSKew(sparkSession,"default.tab_spark","id")println(cscTopKey.mkString("
"))   def sampleSKew( sparkSession: SparkSession, tableName: String, keyColumn: String ): Array[(Int, Row)] = {    val df: DataFrame = sparkSession.sql("select " + keyColumn + " from " + tableName)    val top10Key: Array[(Int, Row)] = df      .select(keyColumn).sample(withReplacement = false, 0.1).rdd      .map(k => (k, 1)).reduceByKey(_ + _)      .map(k => (k._2, k._1)).sortByKey(ascending = false)      .take(10)    top10Key  }

SQL进行抽取：

SELECT  id,conut(1) as cnFROM  default.tab_spark_test_3GROUP BY id  ORDER BY cn DESCLIMIT 100;###结果集100000,2000012100001,1600012100002,1

单表数据倾斜优化

为了减少 shuffle 数据量以及 reduce 端的压力，通常 Spark SQL 在 map 端会做一个partial aggregate（通常叫做预聚合或者偏聚合），即在 shuffle 前将同一分区内所属同 key 的记录先进行一个预结算，再将结果进行 shuffle，发送到 reduce 端做一个汇总，类似 MR 的提前Combiner，所以执行计划中 HashAggregate 通常成对出现。但是这种也会出现问题，如果key重复的量级特别大，Combiner也是解决不了本质问题。

解决方案：

sparkSession.udf.register("random_prefix", ( value: Int, num: Int ) => randomPrefixUDF(value, num))sparkSession.udf.register("remove_random_prefix", ( value: String ) => removeRandomPrefixUDF(value))     //t1 增加前缀，t2按照加盐的key进行聚，t3去除加盐，聚合    val sql =      """        |select        |  id,        |  sum(sell) totalSell        |from        |  (        |    select        |      remove_random_prefix(random_id) id,        |      sell        |    from        |      (        |        select        |          random_id,        |          sum(pic) sell        |        from        |          (        |            select        |              random_prefix(id, 6) random_id,        |              pic        |            from        |              default.tab_spark_test_3        |          ) t1        |        group by random_id        |      ) t2        |  ) t3        |group by        |   id      """.stripMargin      def randomPrefixUDF( value: Int, num: Int ): String = {    new Random().nextInt(num).toString + "_" + value  } def removeRandomPrefixUDF( value: String ): String = {    value.toString.split("_")(1)  }

表关联数据倾斜优化

1、适用场景

适用于 join 时出现数据倾斜。

2、解决逻辑

a.将存在倾斜的表，根据抽样结果，拆分为倾斜 key（skew 表）和没有倾斜 key（common）的两个数据集；

b.将 skew 表的 key 全部加上随机前缀，然后对另外一个不存在严重数据倾斜的数据集（old 表）整体与随机前缀集作笛卡尔乘积（即将数据量扩大 N 倍，得到 new 表）。

c.打散的 skew 表 join 扩容的 new 表

union common 表 join old 表

以下为打散大 key 和扩容小表的实现思路：

1、打散大表：实际就是数据一进一出进行处理，对大 key 前拼上随机前缀实现打散；

2、扩容小表：实际就是将 DataFrame 中每一条数据，转成一个集合，并往这个集合里循环添加 10 条数据，最后使用 flatmap 压平此集合，达到扩容的效果。

 /**   * 打散大表  扩容小表 解决数据倾斜   *   * @param sparkSession   */  def scatterBigAndExpansionSmall(sparkSession: SparkSession): Unit = {    import sparkSession.implicits._    val saleCourse = sparkSession.sql("select *from sparktuning.sale_course")    val coursePay = sparkSession.sql("select * from sparktuning.course_pay")      .withColumnRenamed("discount", "pay_discount")      .withColumnRenamed("createtime", "pay_createtime")    val courseShoppingCart = sparkSession.sql("select * from sparktuning.course_shopping_cart")      .withColumnRenamed("discount", "cart_discount")      .withColumnRenamed("createtime", "cart_createtime")     // TODO 1、拆分 倾斜的key    val commonCourseShoppingCart: Dataset[Row] = courseShoppingCart.filter(item => item.getAs[Long]("courseid") != 101 && item.getAs[Long]("courseid") != 103)    val skewCourseShoppingCart: Dataset[Row] = courseShoppingCart.filter(item => item.getAs[Long]("courseid") == 101 || item.getAs[Long]("courseid") == 103)     //TODO 2、将倾斜的key打散  打散36份    val newCourseShoppingCart = skewCourseShoppingCart.mapPartitions((partitions: Iterator[Row]) => {      partitions.map(item => {        val courseid = item.getAs[Long]("courseid")        val randInt = Random.nextInt(36)        CourseShoppingCart(courseid, item.getAs[String]("orderid"),          item.getAs[String]("coursename"), item.getAs[String]("cart_discount"),          item.getAs[String]("sellmoney"), item.getAs[String]("cart_createtime"),          item.getAs[String]("dt"), item.getAs[String]("dn"), randInt + "_" + courseid)      })    })    //TODO 3、小表进行扩容 扩大36倍    val newSaleCourse = saleCourse.flatMap(item => {      val list = new ArrayBuffer[SaleCourse]()      val courseid = item.getAs[Long]("courseid")      val coursename = item.getAs[String]("coursename")      val status = item.getAs[String]("status")      val pointlistid = item.getAs[Long]("pointlistid")      val majorid = item.getAs[Long]("majorid")      val chapterid = item.getAs[Long]("chapterid")      val chaptername = item.getAs[String]("chaptername")      val edusubjectid = item.getAs[Long]("edusubjectid")      val edusubjectname = item.getAs[String]("edusubjectname")      val teacherid = item.getAs[Long]("teacherid")      val teachername = item.getAs[String]("teachername")      val coursemanager = item.getAs[String]("coursemanager")      val money = item.getAs[String]("money")      val dt = item.getAs[String]("dt")      val dn = item.getAs[String]("dn")      for (i <- 0 until 36) {        list.append(SaleCourse(courseid, coursename, status, pointlistid, majorid, chapterid, chaptername, edusubjectid,          edusubjectname, teacherid, teachername, coursemanager, money, dt, dn, i + "_" + courseid))      }      list    })     // TODO 4、倾斜的大key 与  扩容后的表 进行join    val df1: DataFrame = newSaleCourse      .join(newCourseShoppingCart.drop("courseid").drop("coursename"), Seq("rand_courseid", "dt", "dn"), "right")      .join(coursePay, Seq("orderid", "dt", "dn"), "left")      .select("courseid", "coursename", "status", "pointlistid", "majorid", "chapterid", "chaptername", "edusubjectid"        , "edusubjectname", "teacherid", "teachername", "coursemanager", "money", "orderid", "cart_discount", "sellmoney",        "cart_createtime", "pay_discount", "paymoney", "pay_createtime", "dt", "dn")      // TODO 5、没有倾斜大key的部分 与 原来的表 进行join    val df2: DataFrame = saleCourse      .join(commonCourseShoppingCart.drop("coursename"), Seq("courseid", "dt", "dn"), "right")      .join(coursePay, Seq("orderid", "dt", "dn"), "left")      .select("courseid", "coursename", "status", "pointlistid", "majorid", "chapterid", "chaptername", "edusubjectid"        , "edusubjectname", "teacherid", "teachername", "coursemanager", "money", "orderid", "cart_discount", "sellmoney",        "cart_createtime", "pay_discount", "paymoney", "pay_createtime", "dt", "dn")     // TODO 6、将 倾斜key join后的结果 与 普通key join后的结果，uinon起来    df1      .union(df2)      .write.mode(SaveMode.Overwrite).insertInto("sparktuning.salecourse_detail")  }