1. coalesce(联合,合并,接合,发音cola-les)
2. repartition
1.coalesce
1. 示例代码
package spark.examples import org.apache.spark.{SparkContext, SparkConf} import org.apache.spark.SparkContext._ object SparkRDDCoalesce { def main(args : Array[String]) { val conf = new SparkConf().setAppName("SparkRDDDistinct").setMaster("local"); val sc = new SparkContext(conf); val rdd1 = sc.parallelize(List(1,8,2,1,4,2,7,6,2,3,1,19,21, 66,74,22,21,72,78,102), 8) val pairs = rdd1.coalesce(3, true); pairs.saveAsTextFile("file:///D:/coalesce-0-" + System.currentTimeMillis()); val pairs2 = rdd1.coalesce(3, false); pairs2.saveAsTextFile("file:///D:/coalesce-1-" + System.currentTimeMillis()); println(pairs.toDebugString) } }
1.1 依赖关系
(3) MappedRDD[4] at coalesce at SparkRDDCoalesce.scala:12 [] | CoalescedRDD[3] at coalesce at SparkRDDCoalesce.scala:12 [] | ShuffledRDD[2] at coalesce at SparkRDDCoalesce.scala:12 [] +-(8) MapPartitionsRDD[1] at coalesce at SparkRDDCoalesce.scala:12 [] | ParallelCollectionRDD[0] at parallelize at SparkRDDCoalesce.scala:11 []
1.2 计算结果
1.2.1 shuffle为true
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part-00000
4
7
6
1
21
21
78
part-00001
1
2
2
19
66
102
part-00002
8
1
2
3
74
22
72
1.2.2 shuffle为false
part-00000
1
8
2
1
4
part-00001
2
7
6
2
3
1
19
part-00002
21
66
74
22
21
72
78
102
2. RDD依赖图
3.源代码
/** * Return a new RDD that is reduced into `numPartitions` partitions. * * This results in a narrow dependency, e.g. if you go from 1000 partitions * to 100 partitions, there will not be a shuffle, instead each of the 100 * new partitions will claim 10 of the current partitions. * * However, if you're doing a drastic coalesce, e.g. to numPartitions = 1, * this may result in your computation taking place on fewer nodes than * you like (e.g. one node in the case of numPartitions = 1). To avoid this, * you can pass shuffle = true. This will add a shuffle step, but means the * current upstream partitions will be executed in parallel (per whatever * the current partitioning is). * * Note: With shuffle = true, you can actually coalesce to a larger number * of partitions. This is useful if you have a small number of partitions, * say 100, potentially with a few partitions being abnormally large. Calling * coalesce(1000, shuffle = true) will result in 1000 partitions with the * data distributed using a hash partitioner. */ def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null) : RDD[T] = { if (shuffle) { /** Distributes elements evenly across output partitions, starting from a random partition. */ val distributePartition = (index: Int, items: Iterator[T]) => { var position = (new Random(index)).nextInt(numPartitions) items.map { t => ///将items转换为(递增的Key,item)形式 // Note that the hash code of the key will just be the key itself. The HashPartitioner // will mod it with the number of total partitions. position = position + 1 ///整数的hashCode为其本身?是的,参见Java的Integer#hashCode方法 (position, t) } } : Iterator[(Int, T)] // include a shuffle step so that our upstream tasks are still distributed new CoalescedRDD( new ShuffledRDD[Int, T, T](mapPartitionsWithIndex(distributePartition), new HashPartitioner(numPartitions)), numPartitions).values } else { ///如果shuffle,则直接构造CoalescedRDD new CoalescedRDD(this, numPartitions) } }
2. repartition
/** * Return a new RDD that has exactly numPartitions partitions. * * Can increase or decrease the level of parallelism in this RDD. Internally, this uses * a shuffle to redistribute data. * * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, * which can avoid performing a shuffle. */ def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = { coalesce(numPartitions, shuffle = true) }
可见repartition使用了shuffle为true的coalesce,主要用于对partition进行扩容(扩大partition),如果是窄化partition,考虑使用coalesce以避免使用shuffle(言外之意,是使用shuffle为false版本的coalesce)