Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/8012#discussion_r36475645
--- Diff:
core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala ---
@@ -34,11 +37,14 @@ import org.apache.spark.{Logging, SparkException,
SparkConf, TaskContext}
* 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.
+ *
+ * Use `ShuffleMemoryManager.create()` factory method to create a new
instance.
*/
-private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging
{
- private val taskMemory = new mutable.HashMap[Long, Long]() //
taskAttemptId -> memory bytes
+private[spark]
+class ShuffleMemoryManager protected (val maxMemory: Long, val
pageSizeBytes: Long)
+ extends Logging {
--- End diff --
I usually do
```
private[spark] class ShuffleMemoryManager ptoected(
val maxMemory: Long,
val pageSizeBytes: Long)
extends Logging {
```
not a big deal...
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