Github user tgravescs commented on a diff in the pull request: https://github.com/apache/spark/pull/14079#discussion_r71524840 --- Diff: core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala --- @@ -0,0 +1,221 @@ +/* + * 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.atomic.AtomicReference + +import scala.collection.mutable.{HashMap, HashSet} + +import org.apache.spark.SparkConf +import org.apache.spark.internal.Logging +import org.apache.spark.internal.config +import org.apache.spark.util.Clock +import org.apache.spark.util.SystemClock +import org.apache.spark.util.Utils + +/** + * BlacklistTracker is designed to track problematic executors and nodes. It supports blacklisting + * specific (executor, task) pairs within a stage, blacklisting entire executors and nodes for a + * stage, and blacklisting executors and nodes across an entire application (with a periodic + * expiry). + * + * The tracker needs to deal with a variety of workloads, eg.: bad user code, which may lead to many + * task failures, but that should not count against individual executors; many small stages, which + * may prevent a bad executor for having many failures within one stage, but still many failures + * over the entire application; "flaky" executors, that don't fail every task, but are still + * faulty; etc. + * + * THREADING: As with most helpers of TaskSchedulerImpl, this is not thread-safe. Though it is + * called by multiple threads, callers must already have a lock on the TaskSchedulerImpl. The + * one exception is [[nodeBlacklist()]], which can be called without holding a lock. + */ +private[scheduler] class BlacklistTracker ( + conf: SparkConf, + clock: Clock = new SystemClock()) extends Logging { + + private val MAX_FAILURES_PER_EXEC = conf.get(config.MAX_FAILURES_PER_EXEC) + private val MAX_FAILED_EXEC_PER_NODE = conf.get(config.MAX_FAILED_EXEC_PER_NODE) + val EXECUTOR_RECOVERY_MILLIS = BlacklistTracker.getBlacklistExpiryTime(conf) + + // a count of failed tasks for each executor. Only counts failures after tasksets complete + // successfully + private val executorIdToFailureCount: HashMap[String, Int] = HashMap() + private val executorIdToBlacklistExpiryTime: HashMap[String, Long] = new HashMap() + private val nodeIdToBlacklistExpiryTime: HashMap[String, Long] = new HashMap() + private val _nodeBlacklist: AtomicReference[Set[String]] = new AtomicReference(Set()) + private var nextExpiryTime: Long = Long.MaxValue + + def start(): Unit = {} + + def stop(): Unit = {} + + def expireExecutorsInBlacklist(): Unit = { + val now = clock.getTimeMillis() + // quickly check if we've got anything to expire from blacklist -- if not, avoid doing any work + if (now > nextExpiryTime) { + val execsToClear = executorIdToBlacklistExpiryTime.filter(_._2 < now).keys + if (execsToClear.nonEmpty) { + logInfo(s"Removing executors $execsToClear from blacklist during periodic recovery") + execsToClear.foreach { exec => executorIdToBlacklistExpiryTime.remove(exec) } + } + if (executorIdToBlacklistExpiryTime.nonEmpty) { + nextExpiryTime = executorIdToBlacklistExpiryTime.map{_._2}.min + } else { + nextExpiryTime = Long.MaxValue + } + val nodesToClear = nodeIdToBlacklistExpiryTime.filter(_._2 < now).keys + if (nodesToClear.nonEmpty) { + logInfo(s"Removing nodes $nodesToClear from blacklist during periodic recovery") + nodesToClear.foreach { node => nodeIdToBlacklistExpiryTime.remove(node) } + // make a copy of the blacklisted nodes so nodeBlacklist() is threadsafe + _nodeBlacklist.set(nodeIdToBlacklistExpiryTime.keySet.toSet) + } + } + } + + def taskSetSucceeded( + failuresByExec: HashMap[String, FailureStatus], + scheduler: TaskSchedulerImpl): Unit = { + // if any tasks failed, we count them towards the overall failure count for the executor at + // this point. + failuresByExec.foreach { case (exec, newFailures) => + val prevFailures = executorIdToFailureCount.getOrElse(exec, 0) + val newTotal = prevFailures + newFailures.totalFailures + + if (newTotal >= MAX_FAILURES_PER_EXEC) { + logInfo(s"Blacklisting executor $exec because it has $newTotal" + + s" task failures in successful task sets") + val now = clock.getTimeMillis() + val expiryTime = now + EXECUTOR_RECOVERY_MILLIS + executorIdToBlacklistExpiryTime.put(exec, expiryTime) + executorIdToFailureCount.remove(exec) + if (expiryTime < nextExpiryTime) { + nextExpiryTime = expiryTime + } + + val node = scheduler.getHostForExecutor(exec) + val execs = scheduler.getExecutorsAliveOnHost(node).getOrElse(Set()) --- End diff -- actually I guess that could be bad in the case of like YARN preemption, I haven't seen the scheduler take that into account does it? I know the yarn allocator does for executor failures but I assume scheduler just see executor lost and fails the tasks as normal?
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