Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/1707#discussion_r15772625
--- Diff:
core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala ---
@@ -0,0 +1,122 @@
+/*
+ * 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.shuffle
+
+import scala.collection.mutable
+
+import org.apache.spark.{Logging, SparkException, SparkConf}
+
+/**
+ * Allocates a pool of memory to task threads for use in shuffle
operations. Each disk-spilling
+ * collection (ExternalAppendOnlyMap or ExternalSorter) used by these
tasks can acquire memory
+ * from this pool and release it as it spills data out. When a task ends,
all its memory will be
+ * released by the Executor.
+ *
+ * This class tries to ensure that each thread gets a reasonable share of
memory, instead of some
+ * thread ramping up to a large amount first and then causing others to
spill to disk repeatedly.
+ * If there are N threads, it ensures that each thread can acquire at
least 1 / 2N of the memory
+ * before it has to spill, and at most 1 / N. Because N varies
dynamically, we keep track of the
+ * set of active threads and redo the calculations of 1 / 2N and 1 / N in
waiting threads whenever
+ * this set changes. This is all done by synchronizing access on "this" to
mutate state and using
+ * wait() and notifyAll() to signal changes.
+ */
+private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging
{
+ private val threadMemory = new mutable.HashMap[Long, Long]() //
threadId -> memory bytes
+
+ def this(conf: SparkConf) = this(ShuffleMemoryManager.getMaxMemory(conf))
+
+ /**
+ * Try to acquire up to numBytes memory for the current thread, or
return 0 if the pool cannot
+ * allocate any memory to it. This call may block until there is enough
free memory in some
+ * situations, to make sure each thread has a chance to ramp up to at
least 1 / 2N of the total
+ * memory pool (where N is the # of active threads) before it is forced
to spill.
+ */
+ def tryToAcquire(numBytes: Long): Long = synchronized {
+ val threadId = Thread.currentThread().getId
+ assert(numBytes > 0, "invalid number of bytes requested: " + numBytes)
+
+ // Add this thread to the threadMemory map just so we can keep an
accurate count of the number
+ // of active threads, to let other threads ramp down their memory in
calls to tryToAcquire
+ if (!threadMemory.contains(threadId)) {
+ threadMemory(threadId) = 0L
+ notifyAll() // Will later cause waiting threads to wake up and
check numThreads again
+ }
+
+ // Keep looping until we're either sure that we don't want to grant
this request (because this
+ // thread would have more than 1 / numActiveThreads of the memory) or
we have enough free
+ // memory to give it (we always let each thread get at least 1 / (2 *
numActiveThreads)).
+ while (true) {
+ val numActiveThreads = threadMemory.keys.size
+ val curMem = threadMemory(threadId)
+ val freeMemory = maxMemory - threadMemory.values.sum
+
+ // How much we can grant this thread; don't let it grow to more than
1 / numActiveThreads
+ val maxToGrant = math.min(numBytes, (maxMemory / numActiveThreads) -
curMem)
+
+ if (curMem < maxMemory / (2 * numActiveThreads)) {
+ // We want to let each thread get at least 1 / (2 *
numActiveThreads) before blocking;
+ // if we can't give it this much now, wait for other threads to
free up memory
+ if (freeMemory >= math.min(maxToGrant, maxMemory / (2 *
numActiveThreads) - curMem)) {
+ val toGrant = math.min(maxToGrant, freeMemory)
+ threadMemory(threadId) += toGrant
+ return toGrant
+ } else {
+ wait()
--- End diff --
Looks like this case exists because a new thread may join when all other
threads have collectively occupied all of the memory. Maybe add a comment here
that describes this scenario?
(At first I thought this could never happen because each thread is
guaranteed to not go over 1/N, and hence there is always 1/2N memory available.
However, this assumes the number of threads is static, which is not true here)
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