Github user aarondav commented on a diff in the pull request:
https://github.com/apache/spark/pull/1707#discussion_r15726524
--- Diff:
core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala ---
@@ -0,0 +1,114 @@
+/*
+ * 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 org.apache.spark.{SparkException, SparkConf}
+import scala.collection.mutable
+
+/**
+ * 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) {
+ private val threadMemory = new mutable.HashMap[Long, Long]() //
threadId -> memory bytes
+
+ def this(conf: SparkConf) = this(ShuffleMemoryManager.getMaxMemory(conf))
+
+ /**
+ * Try to acquire numBytes memory for the current thread, or return
false if the pool cannot
+ * allocate this much 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
a reasonable share of
+ * the available memory before being forced to spill.
+ */
+ def tryToAcquire(numBytes: Long): Boolean = synchronized {
+ val threadId = Thread.currentThread().getId
+
+ // 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
+ threadMemory.getOrElseUpdate(threadId, 0L)
+
+ // 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)
+ if (curMem + numBytes > maxMemory / numActiveThreads) {
+ // We'd get more than 1 / numActiveThreads of the total memory;
don't allow that
+ return false
+ }
+ val bytesFree = maxMemory - threadMemory.values.sum
+ if (bytesFree >= numBytes) {
+ // Grant the request
+ threadMemory(threadId) = curMem + numBytes
+ // Notify other waiting threads because the # active of threads
may have increased, so
+ // they may cancel their current waits
+ notifyAll()
+ return true
+ } else if (curMem + numBytes <= maxMemory / (2 * numActiveThreads)) {
+ // This thread has so little memory that we want it to block and
acquire a bigger
+ // amount instead of cancelling the request. Wait on "this" for a
thread to call notify.
+ // Before doing the wait, however, also notify other current
waiters in case our thread
+ // becoming active just pushed them over the limit to give up
their own waits.
+ notifyAll()
+ wait()
--- End diff --
Perhaps add a log message prior to this to indicate why some of Spark is
blocking.
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