Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/7734#discussion_r35719677
--- Diff:
core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala ---
@@ -19,95 +19,100 @@ package org.apache.spark.shuffle
import scala.collection.mutable
-import org.apache.spark.{Logging, SparkException, SparkConf}
+import org.apache.spark.{Logging, SparkException, SparkConf, TaskContext}
/**
- * Allocates a pool of memory to task threads for use in shuffle
operations. Each disk-spilling
+ * Allocates a pool of memory to tasks 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
+ * This class tries to ensure that each task gets a reasonable share of
memory, instead of some
+ * task ramping up to a large amount first and then causing others to
spill to disk repeatedly.
+ * If there are N tasks, it ensures that each tasks 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
+ * set of active tasks and redo the calculations of 1 / 2N and 1 / N in
waiting tasks 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
+ private val taskMemory = new mutable.HashMap[Long, Long]() //
taskAttemptId -> memory bytes
def this(conf: SparkConf) = this(ShuffleMemoryManager.getMaxMemory(conf))
+ private def currentTaskAttemptId(): Long = {
+ Option(TaskContext.get()).map(_.taskAttemptId()).getOrElse(-1L)
--- End diff --
add a line explaining why the default value is needed
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