Github user kayousterhout commented on a diff in the pull request:
https://github.com/apache/spark/pull/14079#discussion_r77273256
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala ---
@@ -0,0 +1,393 @@
+/*
+ * 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")
--- End diff --
"for those executors has timed out"? (just to make it obvious that other
executors may not have been un-blacklisted")
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