Github user mwws commented on a diff in the pull request:
https://github.com/apache/spark/pull/8760#discussion_r42199742
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala ---
@@ -0,0 +1,243 @@
+/*
+ * 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.scheduler
+
+import java.util.concurrent.TimeUnit
+
+import scala.collection.mutable
+
+import org.apache.spark.SparkConf
+import org.apache.spark.Success
+import org.apache.spark.TaskEndReason
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.util.SystemClock
+import org.apache.spark.util.ThreadUtils
+import org.apache.spark.util.Utils
+
+
+/**
+ * BlacklistTracker is design to track problematic executors and node on
application level.
+ * It is shared by all TaskSet, so that once a new TaskSet coming, it
could be benefit from
+ * previous experience of other TaskSet.
+ *
+ * Once task finished, the callback method in TaskSetManager should update
+ * executorIdToFailureStatus Map.
+ */
+private[spark] class BlacklistTracker(sparkConf: SparkConf) extends
WithCache{
+ // maintain a ExecutorId --> FailureStatus HashMap
+ private val executorIdToFailureStatus: mutable.HashMap[String,
FailureStatus] = mutable.HashMap()
+
+ // Apply Strategy pattern here to change different blacklist detection
logic
+ private val strategy = BlacklistStrategy(sparkConf)
+
+ // A daemon thread to expire blacklist executor periodically
+ private val scheduler =
ThreadUtils.newDaemonSingleThreadScheduledExecutor(
+ "spark-scheduler-blacklist-expire-timer")
+
+ private val clock = new SystemClock()
+
+ private val enableBlacklistSpeculate = sparkConf.getBoolean(
+ "spark.scheduler.blacklist.speculate", false)
+
+ private val recoverPeriod = sparkConf.getLong(
+ "spark.scheduler.blacklist.recoverPeriod", 60L)
+
+ def start(): Unit = {
+ val scheduleTask = new Runnable() {
+ override def run(): Unit = {
+
Utils.logUncaughtExceptions(expireExecutorsInBlackList(executorIdToFailureStatus))
+ }
+ }
+ scheduler.scheduleAtFixedRate(scheduleTask, 0L, recoverPeriod,
TimeUnit.SECONDS)
+ }
+
+ def stop(): Unit = {
+ scheduler.shutdown()
+ scheduler.awaitTermination(10, TimeUnit.SECONDS)
+ }
+
+ // The actual implementation is delegated to strategy
+ private def expireExecutorsInBlackList(
+ executorIdToFailureStatus: mutable.HashMap[String, FailureStatus]):
Unit = synchronized {
+ strategy.expireExecutorsInBlackList(executorIdToFailureStatus)
+
+ invalidAllCache()
+ }
+
+ def updateFailureExecutors(info: TaskInfo, reason: TaskEndReason) : Unit
= synchronized {
+ reason match {
+ // If task succeeding, remove related record from
executorIdToFailureStatus
+ case Success =>
+ removeFailureExecutorsForTaskId(info.executorId, info.taskId)
+
+ // If task failing, update latest failure time and failedTaskIds
+ case _ =>
+ val executorId = info.executorId
+ executorIdToFailureStatus.get(executorId) match {
+ case Some(failureStatus) =>
+ failureStatus.updatedTime = clock.getTimeMillis()
+ val failedTimes =
failureStatus.numFailuresPerTask.getOrElse(info.taskId, 0) + 1
+ failureStatus.numFailuresPerTask.update(info.taskId,
failedTimes)
+ case None =>
+ val failedTasks = mutable.HashMap(info.taskId -> 1)
+ val failureStatus = new FailureStatus(
+ clock.getTimeMillis(),
+ info.host,
+ failedTasks)
--- End diff --
Sure, I will follow the idiomatic way in Spark. The intention for splitting
into different lines is just to make length check happy.
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