Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/12154#discussion_r58758160
--- Diff:
streaming/src/main/scala/org/apache/spark/streaming/scheduler/ExecutorAllocationManager.scala
---
@@ -0,0 +1,217 @@
+/*
+ * 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.streaming.scheduler
+
+import scala.util.Random
+
+import org.apache.spark.{ExecutorAllocationClient, SparkConf}
+import org.apache.spark.internal.Logging
+import org.apache.spark.streaming.util.RecurringTimer
+import org.apache.spark.util.{Clock, Utils}
+
+/**
+ * Class that manages executor allocated to a StreamingContext, and
dynamically request or kill
+ * executors based on the statistics of the streaming computation. At a
high level, the policy is:
+ * - Use StreamingListener interface get batch processing times of
completed batches
+ * - Periodically take the average batch completion times and compare with
the batch interval
+ * - If (avg. proc. time / batch interval) >= scaling up ratio, then
request more executors
+ * - If (avg. proc. time / batch interval) <= scaling down ratio, then try
to kill a executor that
+ * is not running a receiver
+ */
+private[streaming] class ExecutorAllocationManager(
+ client: ExecutorAllocationClient,
+ receiverTracker: ReceiverTracker,
+ conf: SparkConf,
+ batchDurationMs: Long,
+ clock: Clock) extends StreamingListener with Logging {
+
+ import ExecutorAllocationManager._
+
+ private val scalingIntervalSecs = conf.getTimeAsSeconds(
+ SCALING_INTERVAL_KEY,
+ s"${SCALING_INTERVAL_DEFAULT_SECS}s")
+ private val scalingUpRatio = conf.getDouble(SCALING_UP_RATIO_KEY,
SCALING_UP_RATIO_DEFAULT)
+ private val scalingDownRatio = conf.getDouble(SCALING_DOWN_RATIO_KEY,
SCALING_DOWN_RATIO_DEFAULT)
+ private val minNumExecutors = conf.getInt(
+ MIN_EXECUTORS_KEY,
+ math.max(1, receiverTracker.numReceivers))
+ private val maxNumExecutors = conf.getInt(MAX_EXECUTORS_KEY,
Integer.MAX_VALUE)
+ private val timer = new RecurringTimer(clock, scalingIntervalSecs * 1000,
+ _ => manageAllocation(), "streaming-executor-allocation-manager")
+
+ @volatile private var batchProcTimeSum = 0L
+ @volatile private var batchProcTimeCount = 0
+
+ validateSettings()
+
+ def start(): Unit = {
+ timer.start()
+ logInfo(s"ExecutorAllocationManager started with " +
+ s"ratios = [$scalingUpRatio, $scalingDownRatio] and interval =
$scalingIntervalSecs sec")
+ }
+
+ def stop(): Unit = {
+ timer.stop(interruptTimer = true)
+ logInfo("ExecutorAllocationManager stopped")
+ }
+
+ private def manageAllocation(): Unit = synchronized {
+ logInfo(s"Managing executor allocation with ratios = [$scalingUpRatio,
$scalingDownRatio]")
+ if (batchProcTimeCount > 0) {
+ val averageBatchProcTime = batchProcTimeSum / batchProcTimeCount
+ val ratio = averageBatchProcTime.toDouble / batchDurationMs
+ logInfo(s"Average: $averageBatchProcTime, ratio = $ratio" )
+ if (ratio >= scalingUpRatio) {
+ logDebug("Requesting executors")
+ val numNewExecutors = math.max(math.round(ratio).toInt, 1)
+ requestExecutors(numNewExecutors)
+ } else if (ratio <= scalingDownRatio) {
+ logDebug("Killing executors")
+ killExecutor()
+ }
+ }
+ batchProcTimeSum = 0
+ batchProcTimeCount = 0
+ }
+
+ private def requestExecutors(numNewExecutors: Int): Unit = {
+ require(numNewExecutors >= 1)
+ val allExecIds = client.getExecutorIds()
+ logDebug(s"Executors (${allExecIds.size}) = ${allExecIds}")
+ val targetTotalExecutors =
+ math.max(math.min(maxNumExecutors, allExecIds.size +
numNewExecutors), minNumExecutors)
--- End diff --
there is some approximation here. I am assuming that the scaling interval
will be high enough that each readjustment will not overlap with each other. we
can make this more advanced in the future.
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