viirya commented on a change in pull request #26624:
URL: https://github.com/apache/spark/pull/26624#discussion_r419808850
##########
File path: core/src/main/scala/org/apache/spark/util/ThreadUtils.scala
##########
@@ -17,21 +17,106 @@
package org.apache.spark.util
+import java.util
import java.util.concurrent._
import java.util.concurrent.locks.ReentrantLock
+import com.google.common.util.concurrent.{MoreExecutors, ThreadFactoryBuilder}
import scala.concurrent.{Awaitable, ExecutionContext,
ExecutionContextExecutor, Future}
import scala.concurrent.duration.{Duration, FiniteDuration}
import scala.language.higherKinds
import scala.util.control.NonFatal
-import com.google.common.util.concurrent.ThreadFactoryBuilder
-
import org.apache.spark.SparkException
import org.apache.spark.rpc.RpcAbortException
private[spark] object ThreadUtils {
+ object MDCAwareThreadPoolExecutor {
+ def newCachedThreadPool(threadFactory: ThreadFactory): ThreadPoolExecutor
= {
+ // The values needs to be synced with `Executors.newCachedThreadPool`
+ new MDCAwareThreadPoolExecutor(
+ 0,
+ Integer.MAX_VALUE,
+ 60L,
+ TimeUnit.SECONDS,
+ new SynchronousQueue[Runnable],
+ threadFactory)
+ }
+
+ def newFixedThreadPool(nThreads: Int, threadFactory: ThreadFactory):
ThreadPoolExecutor = {
+ // The values needs to be synced with `Executors.newFixedThreadPool`
+ new MDCAwareThreadPoolExecutor(
+ nThreads,
+ nThreads,
+ 0L,
+ TimeUnit.MILLISECONDS,
+ new LinkedBlockingQueue[Runnable],
+ threadFactory)
+ }
+
+ def newSingleThreadExecutor(threadFactory: ThreadFactory): ExecutorService
= {
+ // The values needs to be synced with `Executors.newSingleThreadExecutor`
+ Executors.unconfigurableExecutorService(
+ new MDCAwareThreadPoolExecutor(
Review comment:
The finalize method helps to shutdown the underlying thread pool. I'm
not sure if we rely on this or not in Spark. If you are confident on this
change, then it should be fine.
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