Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/19468#discussion_r153349685
--- Diff:
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/KubernetesClusterSchedulerBackend.scala
---
@@ -0,0 +1,432 @@
+/*
+ * 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 javax.annotation.concurrent.GuardedBy
+
+import io.fabric8.kubernetes.api.model._
+import io.fabric8.kubernetes.client.{KubernetesClient,
KubernetesClientException, Watcher}
+import io.fabric8.kubernetes.client.Watcher.Action
+import scala.collection.JavaConverters._
+import scala.collection.mutable
+import scala.concurrent.{ExecutionContext, Future}
+
+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,
SchedulerBackendUtils}
+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
+ @GuardedBy("RUNNING_EXECUTOR_PODS_LOCK")
+ private val runningExecutorsToPods = new mutable.HashMap[String, Pod]
+ 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 = kubernetesClient.pods()
+ .inNamespace(kubernetesNamespace)
+ .withName(kubernetesDriverPodName)
+ .get()
+
+ protected override val minRegisteredRatio =
+ if
(conf.getOption("spark.scheduler.minRegisteredResourcesRatio").isEmpty) {
+ 0.8
+ } else {
+ super.minRegisteredRatio
+ }
+
+ private val executorWatchResource = new AtomicReference[Closeable]
+ private 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 =
SchedulerBackendUtils.getInitialTargetExecutorNumber(conf)
+
+ private val podAllocationInterval =
conf.get(KUBERNETES_ALLOCATION_BATCH_DELAY)
+
+ private val podAllocationSize =
conf.get(KUBERNETES_ALLOCATION_BATCH_SIZE)
+
+ 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()
+
+ val executorsToAllocate = mutable.Map[String, Pod]()
+ val currentTotalRegisteredExecutors = totalRegisteredExecutors.get
+ val currentTotalExpectedExecutors = totalExpectedExecutors.get
+ val currentNodeToLocalTaskCount = getNodesWithLocalTaskCounts()
+ RUNNING_EXECUTOR_PODS_LOCK.synchronized {
+ if (currentTotalRegisteredExecutors < runningExecutorsToPods.size)
{
+ logDebug("Waiting for pending executors before scaling")
+ } else if (currentTotalExpectedExecutors <=
runningExecutorsToPods.size) {
+ logDebug("Maximum allowed executor limit reached. Not scaling up
further.")
+ } else {
+ for (i <- 0 until math.min(
+ currentTotalExpectedExecutors - runningExecutorsToPods.size,
podAllocationSize)) {
+ val executorId = EXECUTOR_ID_COUNTER.incrementAndGet().toString
+ val executorPod = executorPodFactory.createExecutorPod(
+ executorId,
+ applicationId(),
+ driverUrl,
+ conf.getExecutorEnv,
+ driverPod,
+ currentNodeToLocalTaskCount)
+ executorsToAllocate(executorId) = executorPod
+ logInfo(
+ s"Requesting a new executor, total executors is now
${runningExecutorsToPods.size}")
+ }
+ }
+ }
+
+ val allocatedExecutors = executorsToAllocate.mapValues { pod =>
+ Utils.tryLog {
+ kubernetesClient.pods().create(pod)
+ }
+ }
+
+ RUNNING_EXECUTOR_PODS_LOCK.synchronized {
+ allocatedExecutors.map {
+ case (executorId, attemptedAllocatedExecutor) =>
+ attemptedAllocatedExecutor.map { successfullyAllocatedExecutor
=>
+ runningExecutorsToPods.put(executorId,
successfullyAllocatedExecutor)
+ }
+ }
+ }
+ }
+
+ 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.asScala.foreach {
+ case (executorId, executorPod) =>
+ val knownExitReason = Option(podsWithKnownExitReasons.remove(
+ executorPod.getMetadata.getName))
+ knownExitReason.fold {
+ removeExecutorOrIncrementLossReasonCheckCount(executorId)
+ } { executorExited =>
+ logWarning(s"Removing executor $executorId with loss reason "
+ executorExited.message)
+ removeExecutor(executorId, executorExited)
+ // We don't delete the pod running the executor that has an
exit condition caused by
+ // the application from the Kubernetes API server. This allows
users to debug later on
+ // through commands such as "kubectl logs <pod name>" and
+ // "kubectl describe pod <pod name>". Note that exited
containers have terminated and
+ // therefore won't take CPU and memory resources.
+ // Otherwise, the executor pod is marked to be deleted from
the API server.
+ if (executorExited.exitCausedByApp) {
--- End diff --
Sounds great, as long as spark driver memory is not getting impacted by
having these around; it should be fine.
Thanks for clarifying !
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