Github user kayousterhout commented on a diff in the pull request:
https://github.com/apache/spark/pull/14079#discussion_r72502080
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/BlacklistTracker.scala ---
@@ -0,0 +1,214 @@
+/*
+ * 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, SystemClock, 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] = new
HashMap()
+ private val executorIdToBlacklistStatus: HashMap[String,
BlacklistedExecutor] = 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
+ // for blacklisted executors, the node it is on. 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.
+ val nodeToFailedExecs: HashMap[String, HashSet[String]] = new HashMap()
+
+ 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 =
executorIdToBlacklistStatus.filter(_._2.expiryTime < now).keys
+ if (execsToClear.nonEmpty) {
+ logInfo(s"Removing executors $execsToClear from blacklist during
periodic recovery")
--- End diff --
Removing nodes $nodesToClear from blacklist because the blacklist has
expired (or "timed out")?
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