Spark算子：RDD基本转换操作–coalesce、repartition_spark coalesce函数用法

1. coalesce

def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null): RDD[T]

该函数用于将RDD进行重分区，使用HashPartitioner。第一个参数为重分区的数目，第二个为是否进行shuffle，默认为false.

代码测试如下：

scala> var data = sc.textFile("example.txt")
data: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[53] at textFile at :21
 
scala> data.collect
res1: Array[String] = Array(hello world, hello spark, hello hive, hi spark)
 
scala> data.partitions.size
res2: Int = 2 //RDD data默认有两个分区
 
scala> var rdd1 = data.coalesce(1)
rdd1: org.apache.spark.rdd.RDD[String] = CoalescedRDD[2] at coalesce at :23
 
scala> rdd1.partitions.size
res3: Int = 1 //rdd1的分区数为1
 
scala> var rdd1 = data.coalesce(4)
rdd1: org.apache.spark.rdd.RDD[String] = CoalescedRDD[3] at coalesce at :23
 
 
scala> rdd1.partitions.size
res4: Int = 2 //如果重分区的数目大于原来的分区数，那么必须指定shuffle参数为true，否则，分区数不变
 
scala> var rdd1 = data.coalesce(4,true)
rdd1: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[7] at coalesce at :23
 
scala> rdd1.partitions.size
res5: Int = 4

2. repartition

def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T]

该函数其实就是coalesce函数第二个参数为true的实现

代码测试如下：

scala> var rdd2 = data.repartition(1)
rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[11] at repartition at :23
 
scala> rdd2.partitions.size
res6: Int = 1
 
scala> var rdd2 = data.repartition(4)
rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[15] at repartition at :23
 
scala> rdd2.partitions.size
res7: Int = 4