Github user jose-torres commented on a diff in the pull request: https://github.com/apache/spark/pull/21560#discussion_r197930245 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousCoalesceRDD.scala --- @@ -0,0 +1,108 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.streaming.continuous + +import org.apache.spark._ +import org.apache.spark.rdd.{CoalescedRDDPartition, RDD} +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.UnsafeRow +import org.apache.spark.sql.execution.streaming.continuous.shuffle._ +import org.apache.spark.util.ThreadUtils + +case class ContinuousCoalesceRDDPartition(index: Int) extends Partition { + // This flag will be flipped on the executors to indicate that the threads processing + // partitions of the write-side RDD have been started. These will run indefinitely + // asynchronously as epochs of the coalesce RDD complete on the read side. + private[continuous] var writersInitialized: Boolean = false +} + +/** + * RDD for continuous coalescing. Asynchronously writes all partitions of `prev` into a local + * continuous shuffle, and then reads them in the task thread using `reader`. + */ +class ContinuousCoalesceRDD( + context: SparkContext, + numPartitions: Int, + readerQueueSize: Int, + epochIntervalMs: Long, + readerEndpointName: String, + prev: RDD[InternalRow]) + extends RDD[InternalRow](context, Nil) { + + override def getPartitions: Array[Partition] = Array(ContinuousCoalesceRDDPartition(0)) + + val readerRDD = new ContinuousShuffleReadRDD( + sparkContext, + numPartitions, + readerQueueSize, + prev.getNumPartitions, + epochIntervalMs, + Seq(readerEndpointName)) + + private lazy val threadPool = ThreadUtils.newDaemonFixedThreadPool( + prev.getNumPartitions, + this.name) + + override def compute(split: Partition, context: TaskContext): Iterator[InternalRow] = { + assert(split.index == 0) + // lazy initialize endpoint so writer can send to it + readerRDD.partitions(0).asInstanceOf[ContinuousShuffleReadPartition].endpoint + + if (!split.asInstanceOf[ContinuousCoalesceRDDPartition].writersInitialized) { + val rpcEnv = SparkEnv.get.rpcEnv + val outputPartitioner = new HashPartitioner(1) --- End diff -- Repartition would normally imply distributed execution, which isn't happening here.
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