Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/21560#discussion_r196586217
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousCoalesceRDD.scala
---
@@ -0,0 +1,93 @@
+/*
+ * 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._
+
+case class ContinuousCoalesceRDDPartition(index: Int) extends Partition {
+ 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(var reader: ContinuousShuffleReadRDD, var
prev: RDD[InternalRow])
+ extends RDD[InternalRow](reader.context, Nil) {
+
+ override def getPartitions: Array[Partition] =
Array(ContinuousCoalesceRDDPartition(0))
+
+ override def compute(split: Partition, context: TaskContext):
Iterator[InternalRow] = {
+ assert(split.index == 0)
+ // lazy initialize endpoint so writer can send to it
+
reader.partitions(0).asInstanceOf[ContinuousShuffleReadPartition].endpoint
+
+ if
(!split.asInstanceOf[ContinuousCoalesceRDDPartition].writersInitialized) {
+ val rpcEnv = SparkEnv.get.rpcEnv
+ val outputPartitioner = new HashPartitioner(1)
+ val endpointRefs = reader.endpointNames.map { endpointName =>
+ rpcEnv.setupEndpointRef(rpcEnv.address, endpointName)
+ }
+
+ val threads = prev.partitions.map { prevSplit =>
+ new Thread() {
--- End diff --
maybe use a thread pool (using `org...spark.util.ThreadUtils`) with a name
to track threads. Then the cached threads in threadpool can be reused across
epochs.
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