Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/19468#discussion_r146979317
--- Diff:
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/KubernetesClusterSchedulerBackend.scala
---
@@ -0,0 +1,440 @@
+/*
+ * 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.io.Closeable
+import java.net.InetAddress
+import java.util.concurrent.{ConcurrentHashMap, ExecutorService,
ScheduledExecutorService, TimeUnit}
+import java.util.concurrent.atomic.{AtomicInteger, AtomicLong,
AtomicReference}
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+import scala.concurrent.{ExecutionContext, Future}
+
+import io.fabric8.kubernetes.api.model._
+import io.fabric8.kubernetes.client.{KubernetesClient,
KubernetesClientException, Watcher}
+import io.fabric8.kubernetes.client.Watcher.Action
+
+import org.apache.spark.SparkException
+import org.apache.spark.deploy.k8s.config._
+import org.apache.spark.deploy.k8s.constants._
+import org.apache.spark.rpc.{RpcAddress, RpcEndpointAddress, RpcEnv}
+import org.apache.spark.scheduler.{ExecutorExited, SlaveLost,
TaskSchedulerImpl}
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
+import org.apache.spark.util.Utils
+
+private[spark] class KubernetesClusterSchedulerBackend(
+ scheduler: TaskSchedulerImpl,
+ rpcEnv: RpcEnv,
+ executorPodFactory: ExecutorPodFactory,
+ kubernetesClient: KubernetesClient,
+ allocatorExecutor: ScheduledExecutorService,
+ requestExecutorsService: ExecutorService)
+ extends CoarseGrainedSchedulerBackend(scheduler, rpcEnv) {
+
+ import KubernetesClusterSchedulerBackend._
+
+ private val EXECUTOR_ID_COUNTER = new AtomicLong(0L)
+ private val RUNNING_EXECUTOR_PODS_LOCK = new Object
+ // Indexed by executor IDs and guarded by RUNNING_EXECUTOR_PODS_LOCK.
+ private val runningExecutorsToPods = new mutable.HashMap[String, Pod]
+ // Indexed by executor pod names and guarded by
RUNNING_EXECUTOR_PODS_LOCK.
+ private val runningPodsToExecutors = new mutable.HashMap[String, String]
+ private val executorPodsByIPs = new ConcurrentHashMap[String, Pod]()
+ private val podsWithKnownExitReasons = new ConcurrentHashMap[String,
ExecutorExited]()
+ private val disconnectedPodsByExecutorIdPendingRemoval = new
ConcurrentHashMap[String, Pod]()
+
+ private val kubernetesNamespace = conf.get(KUBERNETES_NAMESPACE)
+
+ private val kubernetesDriverPodName = conf
+ .get(KUBERNETES_DRIVER_POD_NAME)
+ .getOrElse(throw new SparkException("Must specify the driver pod
name"))
+ private implicit val requestExecutorContext =
ExecutionContext.fromExecutorService(
+ requestExecutorsService)
+
+ private val driverPod = try {
+ kubernetesClient.pods()
+ .inNamespace(kubernetesNamespace)
+ .withName(kubernetesDriverPodName)
+ .get()
+ } catch {
+ case throwable: Throwable =>
+ logError(s"Executor cannot find driver pod.", throwable)
+ throw new SparkException(s"Executor cannot find driver pod",
throwable)
+ }
+
+ override val minRegisteredRatio =
+ if
(conf.getOption("spark.scheduler.minRegisteredResourcesRatio").isEmpty) {
+ 0.8
+ } else {
+ super.minRegisteredRatio
+ }
+
+ private val executorWatchResource = new AtomicReference[Closeable]
+ protected val totalExpectedExecutors = new AtomicInteger(0)
+
+ private val driverUrl = RpcEndpointAddress(
+ conf.get("spark.driver.host"),
+ conf.getInt("spark.driver.port", DEFAULT_DRIVER_PORT),
+ CoarseGrainedSchedulerBackend.ENDPOINT_NAME).toString
+
+ private val initialExecutors = getInitialTargetExecutorNumber()
+
+ private val podAllocationInterval =
conf.get(KUBERNETES_ALLOCATION_BATCH_DELAY)
+ require(podAllocationInterval > 0, s"Allocation batch delay " +
+ s"${KUBERNETES_ALLOCATION_BATCH_DELAY} " +
+ s"is ${podAllocationInterval}, should be a positive integer")
+
+ private val podAllocationSize =
conf.get(KUBERNETES_ALLOCATION_BATCH_SIZE)
+ require(podAllocationSize > 0, s"Allocation batch size " +
+ s"${KUBERNETES_ALLOCATION_BATCH_SIZE} " +
+ s"is ${podAllocationSize}, should be a positive integer")
+
+ private val allocatorRunnable = new Runnable {
+
+ // Maintains a map of executor id to count of checks performed to
learn the loss reason
+ // for an executor.
+ private val executorReasonCheckAttemptCounts = new
mutable.HashMap[String, Int]
+
+ override def run(): Unit = {
+ handleDisconnectedExecutors()
+ RUNNING_EXECUTOR_PODS_LOCK.synchronized {
+ if (totalRegisteredExecutors.get() < runningExecutorsToPods.size) {
+ logDebug("Waiting for pending executors before scaling")
+ } else if (totalExpectedExecutors.get() <=
runningExecutorsToPods.size) {
+ logDebug("Maximum allowed executor limit reached. Not scaling up
further.")
+ } else {
+ val nodeToLocalTaskCount = getNodesWithLocalTaskCounts
+ for (i <- 0 until math.min(
+ totalExpectedExecutors.get - runningExecutorsToPods.size,
podAllocationSize)) {
+ val (executorId, pod) =
allocateNewExecutorPod(nodeToLocalTaskCount)
+ runningExecutorsToPods.put(executorId, pod)
+ runningPodsToExecutors.put(pod.getMetadata.getName, executorId)
+ logInfo(
+ s"Requesting a new executor, total executors is now
${runningExecutorsToPods.size}")
+ }
+ }
+ }
+ }
+
+ def handleDisconnectedExecutors(): Unit = {
+ // For each disconnected executor, synchronize with the loss reasons
that may have been found
+ // by the executor pod watcher. If the loss reason was discovered by
the watcher,
+ // inform the parent class with removeExecutor.
+ disconnectedPodsByExecutorIdPendingRemoval.keys().asScala.foreach {
case (executorId) =>
+ val executorPod =
disconnectedPodsByExecutorIdPendingRemoval.get(executorId)
+ val knownExitReason = Option(podsWithKnownExitReasons.remove(
+ executorPod.getMetadata.getName))
+ knownExitReason.fold {
+ removeExecutorOrIncrementLossReasonCheckCount(executorId)
+ } { executorExited =>
+ logDebug(s"Removing executor $executorId with loss reason " +
executorExited.message)
+ removeExecutor(executorId, executorExited)
+ // We keep around executors that have exit conditions caused by
the application. This
+ // allows them to be debugged later on. Otherwise, mark them as
to be deleted from the
+ // the API server.
+ if (!executorExited.exitCausedByApp) {
+ deleteExecutorFromClusterAndDataStructures(executorId)
+ }
+ }
+ }
+ }
+
+ def removeExecutorOrIncrementLossReasonCheckCount(executorId: String):
Unit = {
+ val reasonCheckCount =
executorReasonCheckAttemptCounts.getOrElse(executorId, 0)
+ if (reasonCheckCount >= MAX_EXECUTOR_LOST_REASON_CHECKS) {
+ removeExecutor(executorId, SlaveLost("Executor lost for unknown
reasons."))
+ deleteExecutorFromClusterAndDataStructures(executorId)
+ } else {
+ executorReasonCheckAttemptCounts.put(executorId, reasonCheckCount
+ 1)
+ }
+ }
+
+ def deleteExecutorFromClusterAndDataStructures(executorId: String):
Unit = {
+ disconnectedPodsByExecutorIdPendingRemoval.remove(executorId)
+ executorReasonCheckAttemptCounts -= executorId
+ RUNNING_EXECUTOR_PODS_LOCK.synchronized {
+ runningExecutorsToPods.remove(executorId).map { pod =>
+ kubernetesClient.pods().delete(pod)
+ runningPodsToExecutors.remove(pod.getMetadata.getName)
+ }.getOrElse(logWarning(s"Unable to remove pod for unknown executor
$executorId"))
+ }
+ }
+ }
+
+ private def getInitialTargetExecutorNumber(defaultNumExecutors: Int =
1): Int = {
+ if (Utils.isDynamicAllocationEnabled(conf)) {
+ val minNumExecutors =
conf.getInt("spark.dynamicAllocation.minExecutors", 0)
+ val initialNumExecutors =
Utils.getDynamicAllocationInitialExecutors(conf)
+ val maxNumExecutors =
conf.getInt("spark.dynamicAllocation.maxExecutors", 1)
+ require(initialNumExecutors >= minNumExecutors &&
initialNumExecutors <= maxNumExecutors,
+ s"initial executor number $initialNumExecutors must between min
executor number " +
+ s"$minNumExecutors and max executor number $maxNumExecutors")
+
+ initialNumExecutors
+ } else {
+ conf.getInt("spark.executor.instances", defaultNumExecutors)
+ }
+
+ }
+
+ override def applicationId(): String = conf.get("spark.app.id",
super.applicationId())
+
+ override def sufficientResourcesRegistered(): Boolean = {
+ totalRegisteredExecutors.get() >= initialExecutors * minRegisteredRatio
+ }
+
+ override def start(): Unit = {
+ super.start()
+ executorWatchResource.set(
+ kubernetesClient
+ .pods()
+ .withLabel(SPARK_APP_ID_LABEL, applicationId())
+ .watch(new ExecutorPodsWatcher()))
+
+ allocatorExecutor.scheduleWithFixedDelay(
+ allocatorRunnable, 0L, podAllocationInterval, TimeUnit.SECONDS)
+
+ if (!Utils.isDynamicAllocationEnabled(conf)) {
+ doRequestTotalExecutors(initialExecutors)
+ }
+ }
+
+ override def stop(): Unit = {
+ // stop allocation of new resources and caches.
+ allocatorExecutor.shutdown()
+
+ // send stop message to executors so they shut down cleanly
+ super.stop()
+
+ // then delete the executor pods
+ // TODO investigate why Utils.tryLogNonFatalError() doesn't work in
this context.
+ // When using Utils.tryLogNonFatalError some of the code fails but
without any logs or
+ // indication as to why.
+ try {
+ RUNNING_EXECUTOR_PODS_LOCK.synchronized {
+
runningExecutorsToPods.values.foreach(kubernetesClient.pods().delete(_))
+ runningExecutorsToPods.clear()
+ runningPodsToExecutors.clear()
+ }
+ executorPodsByIPs.clear()
+ val resource = executorWatchResource.getAndSet(null)
+ if (resource != null) {
+ resource.close()
+ }
+ } catch {
+ case e: Throwable => logError("Uncaught exception while shutting
down controllers.", e)
+ }
+ try {
+ logInfo("Closing kubernetes client")
+ kubernetesClient.close()
+ } catch {
+ case e: Throwable => logError("Uncaught exception closing Kubernetes
client.", e)
+ }
+ }
+
+ /**
+ * @return A map of K8s cluster nodes to the number of tasks that could
benefit from data
+ * locality if an executor launches on the cluster node.
+ */
+ private def getNodesWithLocalTaskCounts() : Map[String, Int] = {
+ val nodeToLocalTaskCount = mutable.Map[String, Int]() ++
+ KubernetesClusterSchedulerBackend.this.synchronized {
+ hostToLocalTaskCount
+ }
+ for (pod <- executorPodsByIPs.values().asScala) {
+ // Remove cluster nodes that are running our executors already.
+ // TODO: This prefers spreading out executors across nodes. In case
users want
+ // consolidating executors on fewer nodes, introduce a flag. See the
spark.deploy.spreadOut
+ // flag that Spark standalone has:
https://spark.apache.org/docs/latest/spark-standalone.html
+ nodeToLocalTaskCount.remove(pod.getSpec.getNodeName).nonEmpty ||
+ nodeToLocalTaskCount.remove(pod.getStatus.getHostIP).nonEmpty ||
+ nodeToLocalTaskCount.remove(
+
InetAddress.getByName(pod.getStatus.getHostIP).getCanonicalHostName).nonEmpty
+ }
+ nodeToLocalTaskCount.toMap[String, Int]
+ }
+
+ /**
+ * Allocates a new executor pod
+ *
+ * @param nodeToLocalTaskCount A map of K8s cluster nodes to the number
of tasks that could
+ * benefit from data locality if an
executor launches on the cluster
+ * node.
+ * @return A tuple of the new executor name and the Pod data structure.
+ */
+ private def allocateNewExecutorPod(nodeToLocalTaskCount: Map[String,
Int]): (String, Pod) = {
+ val executorId = EXECUTOR_ID_COUNTER.incrementAndGet().toString
+ val executorPod = executorPodFactory.createExecutorPod(
+ executorId,
+ applicationId(),
+ driverUrl,
+ conf.getExecutorEnv,
+ driverPod,
+ nodeToLocalTaskCount)
+ try {
+ (executorId, kubernetesClient.pods.create(executorPod))
+ } catch {
+ case throwable: Throwable =>
+ logError("Failed to allocate executor pod.", throwable)
+ throw throwable
+ }
+ }
+
+ override def doRequestTotalExecutors(requestedTotal: Int):
Future[Boolean] = Future[Boolean] {
+ totalExpectedExecutors.set(requestedTotal)
+ true
+ }
+
+ override def doKillExecutors(executorIds: Seq[String]): Future[Boolean]
= Future[Boolean] {
+ RUNNING_EXECUTOR_PODS_LOCK.synchronized {
+ for (executor <- executorIds) {
+ val maybeRemovedExecutor = runningExecutorsToPods.remove(executor)
+ maybeRemovedExecutor.foreach { executorPod =>
+ kubernetesClient.pods().delete(executorPod)
+ disconnectedPodsByExecutorIdPendingRemoval.put(executor,
executorPod)
+ runningPodsToExecutors.remove(executorPod.getMetadata.getName)
+ }
+ if (maybeRemovedExecutor.isEmpty) {
+ logWarning(s"Unable to remove pod for unknown executor
$executor")
+ }
+ }
+ }
+ true
+ }
+
+ def getExecutorPodByIP(podIP: String): Option[Pod] = {
+ // Note: Per
https://github.com/databricks/scala-style-guide#concurrency, we don't
+ // want to be switching to scala.collection.concurrent.Map on
+ // executorPodsByIPs.
--- End diff --
Please remove this comment, or refer to
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