Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/2746#discussion_r18743600
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/ExecutorScalingManager.scala ---
@@ -0,0 +1,324 @@
+/*
+ * 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.scheduler
+
+import java.util.{Timer, TimerTask}
+
+import scala.collection.mutable
+
+import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
+
+/**
+ * An agent that dynamically scales the number of executors based on the
workload.
+ *
+ * The add policy depends on the number of pending tasks. If the queue of
pending tasks has not
+ * been drained for 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, meaning it
has not been scheduled
+ * to run any tasks, for K seconds, then it is removed. This requires
starting a timer on each
+ * executor instead of just starting a global one as in the add case.
+ *
+ * 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.addExecutorThreshold - How long before new
executors are added (N)
+ * spark.dynamicAllocation.addExecutorInterval - How often to add new
executors (M)
+ * spark.dynamicAllocation.removeExecutorThreshold - How long before an
executor is removed (K)
+ *
+ * Synchronization: Because the schedulers in Spark are single-threaded,
contention only arises
+ * if the application itself runs multiple jobs concurrently. Under normal
circumstances, however,
+ * synchronizing each method on this class should not be expensive
assuming biased locking is
+ * enabled in the JVM (on by default for Java 6+). Tighter locks are also
used where possible.
+ *
+ * Note: This is part of a larger implementation (SPARK-3174) and
currently does not actually
+ * request to add or remove executors. The mechanism to actually do this
will be added separately,
+ * e.g. in SPARK-3822 for Yarn.
+ */
+private[scheduler] class ExecutorScalingManager(scheduler:
TaskSchedulerImpl) extends Logging {
--- End diff --
All of those sound good to me. The second one if I had to choose.
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