Github user mccheah commented on a diff in the pull request:
https://github.com/apache/spark/pull/21366#discussion_r190367677
--- Diff:
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsEventHandler.scala
---
@@ -0,0 +1,229 @@
+/*
+ * 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.cluster.k8s
+
+import java.util.concurrent.{Future, LinkedBlockingQueue,
ScheduledExecutorService, TimeUnit}
+import java.util.concurrent.atomic.{AtomicInteger, AtomicLong}
+
+import io.fabric8.kubernetes.api.model.{Pod, PodBuilder}
+import io.fabric8.kubernetes.client.KubernetesClient
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+
+import org.apache.spark.{SparkConf, SparkException}
+import org.apache.spark.deploy.k8s.Config._
+import org.apache.spark.deploy.k8s.Constants._
+import org.apache.spark.deploy.k8s.KubernetesConf
+import org.apache.spark.internal.Logging
+import org.apache.spark.scheduler.ExecutorExited
+import org.apache.spark.util.Utils
+
+private[spark] class ExecutorPodsEventHandler(
+ conf: SparkConf,
+ executorBuilder: KubernetesExecutorBuilder,
+ kubernetesClient: KubernetesClient,
+ eventProcessorExecutor: ScheduledExecutorService) extends Logging {
+
+ import ExecutorPodsEventHandler._
+
+ private val EXECUTOR_ID_COUNTER = new AtomicLong(0L)
+
+ private val totalExpectedExecutors = new AtomicInteger(0)
+
+ private val eventQueue = new LinkedBlockingQueue[Seq[Pod]]()
+
+ private val podAllocationSize =
conf.get(KUBERNETES_ALLOCATION_BATCH_SIZE)
+
+ private val podAllocationDelay =
conf.get(KUBERNETES_ALLOCATION_BATCH_DELAY)
+
+ private val kubernetesDriverPodName = conf
+ .get(KUBERNETES_DRIVER_POD_NAME)
+ .getOrElse(throw new SparkException("Must specify the driver pod
name"))
+
+ private val driverPod = kubernetesClient.pods()
+ .withName(kubernetesDriverPodName)
+ .get()
+
+ // Use sets of ids instead of counters to be able to handle duplicate
events.
+
+ // Executor IDs that have been requested from Kubernetes but are not
running yet.
+ private val pendingExecutors = mutable.Set.empty[Long]
+
+ // We could use CoarseGrainedSchedulerBackend#totalRegisteredExecutors
here for tallying the
+ // executors that are running. But, here we choose instead to maintain
all state within this
+ // class from the persecptive of the k8s API. Therefore whether or not
this scheduler loop
+ // believes an executor is running is dictated by the K8s API rather
than Spark's RPC events.
+ // We may need to consider where these perspectives may differ and which
perspective should
+ // take precedence.
+ private val runningExecutors = mutable.Set.empty[Long]
+
+ private var eventProcessorFuture: Future[_] = _
+
+ def start(applicationId: String, schedulerBackend:
KubernetesClusterSchedulerBackend): Unit = {
+ require(eventProcessorFuture == null, "Cannot start event processing
twice.")
+ logInfo(s"Starting Kubernetes executor pods event handler for
application with" +
+ s" id $applicationId.")
+ val eventProcessor = new Runnable {
+ override def run(): Unit = {
+ Utils.tryLogNonFatalError {
+ processEvents(applicationId, schedulerBackend)
+ }
+ }
+ }
+ eventProcessorFuture = eventProcessorExecutor.scheduleWithFixedDelay(
+ eventProcessor, 0L, podAllocationDelay, TimeUnit.MILLISECONDS)
+ }
+
+ def stop(): Unit = {
+ if (eventProcessorFuture != null) {
+ eventProcessorFuture.cancel(true)
+ eventProcessorFuture = null
+ }
+ }
+
+ private def processEvents(
+ applicationId: String, schedulerBackend:
KubernetesClusterSchedulerBackend) {
+ val currentEvents = new
java.util.ArrayList[Seq[Pod]](eventQueue.size())
+ eventQueue.drainTo(currentEvents)
+ currentEvents.asScala.flatten.foreach { updatedPod =>
+ val execId =
updatedPod.getMetadata.getLabels.get(SPARK_EXECUTOR_ID_LABEL).toLong
+ val podPhase = updatedPod.getStatus.getPhase.toLowerCase
+ if (isDeleted(updatedPod)) {
+ removeExecutorFromSpark(schedulerBackend, updatedPod, execId)
+ } else {
+ updatedPod.getStatus.getPhase.toLowerCase match {
+ case "running" =>
+ // If clause is for resililence to out of order operations -
executor must be
+ // pending and first reach running. Without this check you may
e.g. process a
+ // deletion event followed by some arbitrary modification
event - we want the
+ // deletion event to "stick".
+ if (pendingExecutors.contains(execId)) {
+ pendingExecutors.remove(execId)
+ runningExecutors.add(execId)
+ }
+ // TODO (SPARK-24135) - handle more classes of errors
+ case "error" | "failed" | "succeeded" =>
+ // If deletion failed on a previous try, we can try again if
resync informs us the pod
+ // is still around.
+ // Delete as best attempt - duplicate deletes will throw an
exception but the end state
+ // of getting rid of the pod is what matters.
+ if (!isDeleted(updatedPod)) {
+ Utils.tryLogNonFatalError {
+ kubernetesClient
+ .pods()
+ .withName(updatedPod.getMetadata.getName)
+ .delete()
+ }
+ }
+ removeExecutorFromSpark(schedulerBackend, updatedPod, execId)
+ }
+ }
+ }
+
+ val currentRunningExecutors = runningExecutors.size
--- End diff --
Furthermore I don't think it's just a matter of using thread safe data
structures and access to the hash sets, but one also has to think carefully
about ordering and atomicity, etc.
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