Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/2746#discussion_r19452054
--- Diff:
core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala ---
@@ -0,0 +1,413 @@
+/*
+ * 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
+
+import scala.collection.mutable
+
+import org.apache.spark.scheduler._
+
+/**
+ * An agent that dynamically allocates and removes executors based on the
workload.
+ *
+ * The add policy depends on whether there are backlogged tasks waiting to
be scheduled. If
+ * the scheduler queue is not drained in N seconds, then new executors are
added. If the queue
+ * persists for another M seconds, then more executors are added and so
on. The number added
+ * in each round increases exponentially from the previous round until an
upper bound on the
+ * number of executors has been reached.
+ *
+ * The rationale for the exponential increase is twofold: (1) Executors
should be added slowly
+ * in the beginning in case the number of extra executors needed turns out
to be small. Otherwise,
+ * we may add more executors than we need just to remove them later. (2)
Executors should be added
+ * quickly over time in case the maximum number of executors is very high.
Otherwise, it will take
+ * a long time to ramp up under heavy workloads.
+ *
+ * The remove policy is simpler: If an executor has been idle for K
seconds, meaning it has not
+ * been scheduled to run any tasks, then it is removed.
+ *
+ * There is no retry logic in either case because we make the assumption
that the cluster manager
+ * will eventually fulfill all requests it receives asynchronously.
+ *
+ * The relevant Spark properties include the following:
+ *
+ * spark.dynamicAllocation.enabled - Whether this feature is enabled
+ * spark.dynamicAllocation.minExecutors - Lower bound on the number of
executors
+ * spark.dynamicAllocation.maxExecutors - Upper bound on the number of
executors
+ *
+ * spark.dynamicAllocation.schedulerBacklogTimeout (M) -
+ * If there are backlogged tasks for this duration, add new executors
+ *
+ * spark.dynamicAllocation.sustainedSchedulerBacklogTimeout (N) -
+ * If the backlog is sustained for this duration, add more executors
+ * This is used only after the initial backlog timeout is exceeded
+ *
+ * spark.dynamicAllocation.executorIdleTimeout (K) -
+ * If an executor has been idle for this duration, remove it
+ */
+private[spark] class ExecutorAllocationManager(sc: SparkContext) extends
Logging {
+ import ExecutorAllocationManager._
+
+ private val conf = sc.conf
+
+ // Lower and upper bounds on the number of executors. These are required.
+ private val minNumExecutors =
conf.getInt("spark.dynamicAllocation.minExecutors", -1)
+ private val maxNumExecutors =
conf.getInt("spark.dynamicAllocation.maxExecutors", -1)
+ if (minNumExecutors < 0 || maxNumExecutors < 0) {
+ throw new SparkException("spark.dynamicAllocation.{min/max}Executors
must be set!")
+ }
+ if (minNumExecutors > maxNumExecutors) {
+ throw new SparkException("spark.dynamicAllocation.minExecutors must " +
--- End diff --
I would echo back the actual values - in some cases users might be confused
where the configurations are coming from.
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