Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/14079#discussion_r77005603
--- 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 --
its used in `BlacklistTracker.updateBlacklistForSuccessfulTaskSet()`.
(Since the executor might already be gone by the time we get to that method, we
need to save the node from the original taskInfo.)
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