Github user tgravescs commented on a diff in the pull request: https://github.com/apache/spark/pull/14079#discussion_r76683789 --- Diff: core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala --- @@ -0,0 +1,395 @@ +/* + * 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.{ArrayBuffer, 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, SystemClock, Utils} + +/** + * BlacklistTracker is designed to track problematic executors and nodes. It supports blacklisting + * executors and nodes across an entire application (with a periodic expiry). TaskSetManagers add + * additional blacklisting of executors and nodes for individual tasks and stages which works in + * concert with the blacklisting here. + * + * The tracker needs to deal with a variety of workloads, eg.: + * + * * bad user code -- this may lead to many task failures, but that should not count against + * individual executors + * * many small stages -- this may prevent a bad executor for having many failures within one + * stage, but still many failures over the entire application + * * "flaky" executors -- they don't fail every task, but are still faulty enough to merit + * blacklisting + * + * See the design doc on SPARK-8425 for a more in-depth discussion. + * + * 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 { + + BlacklistTracker.validateBlacklistConfs(conf) + 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 BLACKLIST_TIMEOUT_MILLIS = BlacklistTracker.getBlacklistTimeout(conf) + + /** + * A map from executorId to information on task failures. Tracks the time of each task failure, + * so that we can avoid blacklisting executors due to failures that are very far apart. We do not + * actively remove from this as soon as tasks hit their timeouts, to avoid the time it would take + * to do so. But it will not grow too large, because as soon as an executor gets too many + * failures, we blacklist the executor and remove its entry here. + */ + private[scheduler] val executorIdToFailureList: HashMap[String, ExecutorFailureList] = + new HashMap() + val executorIdToBlacklistStatus: HashMap[String, BlacklistedExecutor] = new HashMap() + val nodeIdToBlacklistExpiryTime: HashMap[String, Long] = new HashMap() + /** + * An immutable copy of the set of nodes that are currently blacklisted. Kept in an + * AtomicReference to make [[nodeBlacklist()]] thread-safe. + */ + private val _nodeBlacklist: AtomicReference[Set[String]] = new AtomicReference(Set()) + /** + * Time when the next blacklist will expire. Used as a + * shortcut to avoid iterating over all entries in the blacklist when none will have expired. + */ + private[scheduler] var nextExpiryTime: Long = Long.MaxValue + /** + * Mapping from nodes to all of the executors that have been blacklisted on that node. We do *not* + * remove from this when executors are removed from spark, so we can track when we get multiple + * successive blacklisted executors on one node. Nonetheless, it will not grow too large because + * there cannot be many blacklisted executors on one node, before we stop requesting more + * executors on that node, and we periodically clean up the list of blacklisted executors. + */ + val nodeToFailedExecs: HashMap[String, HashSet[String]] = new HashMap() + + def applyBlacklistTimeout(): 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) { + // Apply the timeout to blacklisted nodes and executors + val execsToUnblacklist = executorIdToBlacklistStatus.filter(_._2.expiryTime < now).keys + if (execsToUnblacklist.nonEmpty) { + // Un-blacklist any executors that have been blacklisted longer than the blacklist timeout. + logInfo(s"Removing executors $execsToUnblacklist from blacklist because the blacklist " + + s"has timed out") + execsToUnblacklist.foreach { exec => + val status = executorIdToBlacklistStatus.remove(exec).get + val failedExecsOnNode = nodeToFailedExecs(status.node) + failedExecsOnNode.remove(exec) + if (failedExecsOnNode.isEmpty) { + nodeToFailedExecs.remove(status.node) + } + } + } + val nodesToUnblacklist = nodeIdToBlacklistExpiryTime.filter(_._2 < now).keys + if (nodesToUnblacklist.nonEmpty) { + // Un-blacklist any nodes that have been blacklisted longer than the blacklist timeout. + logInfo(s"Removing nodes $nodesToUnblacklist from blacklist because the blacklist " + + s"has timed out") + nodesToUnblacklist.foreach { node => nodeIdToBlacklistExpiryTime.remove(node) } + _nodeBlacklist.set(nodeIdToBlacklistExpiryTime.keySet.toSet) + } + updateNextExpiryTime() + } + } + + private def updateNextExpiryTime(): Unit = { + if (executorIdToBlacklistStatus.nonEmpty) { + nextExpiryTime = executorIdToBlacklistStatus.map{_._2.expiryTime}.min + } else { + nextExpiryTime = Long.MaxValue + } + } + + + def updateBlacklistForSuccessfulTaskSet( + stageId: Int, + stageAttemptId: Int, + failuresByExec: HashMap[String, ExecutorFailuresInTaskSet]): Unit = { + // if any tasks failed, we count them towards the overall failure count for the executor at + // this point. + val now = clock.getTimeMillis() + val expiryTime = now + BLACKLIST_TIMEOUT_MILLIS + failuresByExec.foreach { case (exec, failuresInTaskSet) => + val allFailuresOnOneExecutor = + executorIdToFailureList.getOrElseUpdate(exec, new ExecutorFailureList) + // Apply the timeout to individual tasks. This is to prevent one-off failures that are very + // spread out in time (and likely have nothing to do with problems on the executor) from + // triggering blacklisting. However, note that we do *not* remove executors and nodes from + // the blacklist as we expire individual task failures -- each have their own timeout. Eg., + // suppose: + // * timeout = 10, maxFailuresPerExec = 2 + // * Task 1 fails on exec 1 at time 0 + // * Task 2 fails on exec 1 at time 5 + // --> exec 1 is blacklisted from time 5 - 15. + // This is to simplify the implementation, as well as keep the behavior easier to understand + // for the end user. + allFailuresOnOneExecutor.dropFailuresWithTimeoutBefore(now) + allFailuresOnOneExecutor.addFailures(stageId, stageAttemptId, failuresInTaskSet) + val newTotal = allFailuresOnOneExecutor.numUniqueTaskFailures + + if (newTotal >= MAX_FAILURES_PER_EXEC) { + logInfo(s"Blacklisting executor id: $exec because it has $newTotal" + + s" task failures in successful task sets") + val node = failuresInTaskSet.node + executorIdToBlacklistStatus.put(exec, BlacklistedExecutor(node, expiryTime)) + executorIdToFailureList.remove(exec) + updateNextExpiryTime() + + // In addition to blacklisting the executor, we also update the data for failures on the + // node, and potentially put the entire node into a blacklist as well. + val blacklistedExecsOnNode = nodeToFailedExecs.getOrElseUpdate(node, HashSet[String]()) + blacklistedExecsOnNode += exec + if (blacklistedExecsOnNode.size >= MAX_FAILED_EXEC_PER_NODE) { + logInfo(s"Blacklisting node $node because it has ${blacklistedExecsOnNode.size} " + + s"executors blacklisted: ${blacklistedExecsOnNode}") + nodeIdToBlacklistExpiryTime.put(node, expiryTime) + _nodeBlacklist.set(nodeIdToBlacklistExpiryTime.keySet.toSet) + } + } + } + } + + def isExecutorBlacklisted(executorId: String): Boolean = { + executorIdToBlacklistStatus.contains(executorId) + } + + /** + * Get the full set of nodes that are blacklisted. Unlike other methods in this class, this *IS* + * thread-safe -- no lock required on a taskScheduler. + */ + def nodeBlacklist(): Set[String] = { + _nodeBlacklist.get() + } + + def isNodeBlacklisted(node: String): Boolean = { + nodeIdToBlacklistExpiryTime.contains(node) + } + + def handleRemovedExecutor(executorId: String): Unit = { + // We intentionally do not clean up executors that are already blacklisted in nodeToFailedExecs, + // so that if another executor on the same node gets blacklisted, we can blacklist the entire + // node. We also can't clean up executorIdToBlacklistStatus, so we can eventually remove + // the executor after the timeout. Despite not clearing those structures here, we don't expect + // they will grow too big since you won't get too many executors on one node, and the timeout + // will clear it up periodically in any case. + executorIdToFailureList -= executorId + } +} + + +private[scheduler] object BlacklistTracker extends Logging { + + private val DEFAULT_TIMEOUT = "1h" + + /** + * Returns true if the blacklist is enabled, based on checking the configuration in the following + * order: + * 1. Is it specifically enabled or disabled? + * 2. Is it enabled via the legacy timeout conf? + * 3. Use the default for the spark-master: + * - off for local mode + * - on for distributed modes (including local-cluster) + */ + def isBlacklistEnabled(conf: SparkConf): Boolean = { + conf.get(config.BLACKLIST_ENABLED) match { + case Some(isEnabled) => + isEnabled + case None => + // if they've got a non-zero setting for the legacy conf, always enable the blacklist, + // otherwise, use the default based on the cluster-mode (off for local-mode, on otherwise). + val legacyKey = config.BLACKLIST_LEGACY_TIMEOUT_CONF.key + conf.get(config.BLACKLIST_LEGACY_TIMEOUT_CONF) match { + case Some(legacyTimeout) => + if (legacyTimeout == 0) { + logWarning(s"Turning off blacklisting due to legacy configuaration:" + + s" $legacyKey == 0") + false + } else { + // mostly this is necessary just for tests, since real users that want the blacklist + // will get it anyway by default + logWarning(s"Turning on blacklisting due to legacy configuration:" + + s" $legacyKey > 0") + true + } + case None => + // local-cluster is *not* considered local for these purposes, we still want the + // blacklist enabled by default + !Utils.isLocalMaster(conf) + } + } + } + + def getBlacklistTimeout(conf: SparkConf): Long = { + conf.get(config.BLACKLIST_TIMEOUT_CONF).getOrElse { + conf.get(config.BLACKLIST_LEGACY_TIMEOUT_CONF).getOrElse { + Utils.timeStringAsMs(DEFAULT_TIMEOUT) + } + } + } + + /** + * Verify that blacklist configurations are consistent; if not, throw an exception. Should only + * be called if blacklisting is enabled. + * + * The configuration for the blacklist is expected to adhere to a few invariants. Default + * values follow these rules of course, but users may unwittingly change one configuration + * without making the corresponding adjustment elsewhere. This ensures we fail-fast when + * there are such misconfigurations. + */ + def validateBlacklistConfs(conf: SparkConf): Unit = { + + def mustBePos(k: String, v: String): Unit = { + throw new IllegalArgumentException(s"$k was $v, but must be > 0.") + } + + // undocumented escape hatch for validation -- just for tests that want to run in an "unsafe" + // configuration. + if (!conf.get("spark.blacklist.testing.skipValidation", "false").toBoolean) { + + Seq( + config.MAX_TASK_ATTEMPTS_PER_EXECUTOR, + config.MAX_TASK_ATTEMPTS_PER_NODE, + config.MAX_FAILURES_PER_EXEC_STAGE, + config.MAX_FAILED_EXEC_PER_NODE_STAGE, + config.MAX_FAILURES_PER_EXEC, + config.MAX_FAILED_EXEC_PER_NODE + ).foreach { config => + val v = conf.get(config) + if (v <= 0) { + mustBePos(config.key, v.toString) + } + } + + val timeout = getBlacklistTimeout(conf) + if (timeout <= 0) { + // first, figure out where the timeout came from, to include the right conf in the message. + conf.get(config.BLACKLIST_TIMEOUT_CONF) match { + case Some(t) => + mustBePos(config.BLACKLIST_TIMEOUT_CONF.key, timeout.toString) + case None => + mustBePos(config.BLACKLIST_LEGACY_TIMEOUT_CONF.key, timeout.toString) + } + } + + val maxTaskFailures = conf.getInt("spark.task.maxFailures", 4) + val maxNodeAttempts = conf.get(config.MAX_TASK_ATTEMPTS_PER_NODE) + + if (maxTaskFailures <= maxNodeAttempts) { + throw new IllegalArgumentException(s"${config.MAX_TASK_ATTEMPTS_PER_NODE.key} " + + s"( = ${maxNodeAttempts}) was <= spark.task.maxFailures " + + s"( = ${maxTaskFailures} ). Though blacklisting is enabled, with this configuration, " + + s"Spark will not be robust to one failed disk. Increase " + + s"${config.MAX_TASK_ATTEMPTS_PER_NODE.key} or spark.task.maxFailures, or disable " + + s"blacklisting with ${config.BLACKLIST_ENABLED.key}") + } + } + + } +} + +/** Failures for one executor, within one taskset */ +private[scheduler] final class ExecutorFailuresInTaskSet(val node: String) { --- End diff -- I don't see parameter "node" is every used here
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