spark KubernetesClusterSchedulerBackend 源码

spark KubernetesClusterSchedulerBackend 代码

文件路径：/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/KubernetesClusterSchedulerBackend.scala

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 * 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
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 * 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
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package org.apache.spark.scheduler.cluster.k8s

import java.util.concurrent.{ScheduledExecutorService, TimeUnit}
import java.util.concurrent.atomic.AtomicInteger

import scala.concurrent.Future

import io.fabric8.kubernetes.api.model.Pod
import io.fabric8.kubernetes.api.model.PodBuilder
import io.fabric8.kubernetes.client.KubernetesClient

import org.apache.spark.SparkContext
import org.apache.spark.deploy.k8s.{KubernetesConf, KubernetesUtils}
import org.apache.spark.deploy.k8s.Config._
import org.apache.spark.deploy.k8s.Constants._
import org.apache.spark.deploy.k8s.submit.KubernetesClientUtils
import org.apache.spark.deploy.security.HadoopDelegationTokenManager
import org.apache.spark.internal.config.SCHEDULER_MIN_REGISTERED_RESOURCES_RATIO
import org.apache.spark.resource.ResourceProfile
import org.apache.spark.rpc.{RpcAddress, RpcCallContext}
import org.apache.spark.scheduler.{ExecutorDecommissionInfo, ExecutorKilled, ExecutorLossReason,
  TaskSchedulerImpl}
import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, SchedulerBackendUtils}
import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RegisterExecutor
import org.apache.spark.util.{ThreadUtils, Utils}

private[spark] class KubernetesClusterSchedulerBackend(
    scheduler: TaskSchedulerImpl,
    sc: SparkContext,
    kubernetesClient: KubernetesClient,
    executorService: ScheduledExecutorService,
    snapshotsStore: ExecutorPodsSnapshotsStore,
    podAllocator: AbstractPodsAllocator,
    lifecycleEventHandler: ExecutorPodsLifecycleManager,
    watchEvents: ExecutorPodsWatchSnapshotSource,
    pollEvents: ExecutorPodsPollingSnapshotSource)
    extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) {
  private val appId = KubernetesConf.getKubernetesAppId()

  protected override val minRegisteredRatio =
    if (conf.get(SCHEDULER_MIN_REGISTERED_RESOURCES_RATIO).isEmpty) {
      0.8
    } else {
      super.minRegisteredRatio
    }

  private val initialExecutors = SchedulerBackendUtils.getInitialTargetExecutorNumber(conf)

  private val shouldDeleteDriverService = conf.get(KUBERNETES_DRIVER_SERVICE_DELETE_ON_TERMINATION)

  private val shouldDeleteExecutors = conf.get(KUBERNETES_DELETE_EXECUTORS)

  private val defaultProfile = scheduler.sc.resourceProfileManager.defaultResourceProfile

  private val namespace = conf.get(KUBERNETES_NAMESPACE)

  // Allow removeExecutor to be accessible by ExecutorPodsLifecycleEventHandler
  private[k8s] def doRemoveExecutor(executorId: String, reason: ExecutorLossReason): Unit = {
    removeExecutor(executorId, reason)
  }

  private def setUpExecutorConfigMap(driverPod: Option[Pod]): Unit = {
    val configMapName = KubernetesClientUtils.configMapNameExecutor
    val resolvedExecutorProperties =
      Map(KUBERNETES_NAMESPACE.key -> namespace)
    val confFilesMap = KubernetesClientUtils
      .buildSparkConfDirFilesMap(configMapName, conf, resolvedExecutorProperties) ++
      resolvedExecutorProperties
    val labels =
      Map(SPARK_APP_ID_LABEL -> applicationId(), SPARK_ROLE_LABEL -> SPARK_POD_EXECUTOR_ROLE)
    val configMap = KubernetesClientUtils.buildConfigMap(configMapName, confFilesMap, labels)
    KubernetesUtils.addOwnerReference(driverPod.orNull, Seq(configMap))
    kubernetesClient.configMaps().inNamespace(namespace).resource(configMap).create()
  }

  /**
   * Get an application ID associated with the job.
   * This returns the string value of spark.app.id if set, otherwise
   * the locally-generated ID.
   *
   * @return The application ID
   */
  override def applicationId(): String = {
    conf.getOption("spark.app.id").getOrElse(appId)
  }

  override def start(): Unit = {
    super.start()
    // Must be called before setting the executors
    podAllocator.start(applicationId(), this)
    val initExecs = Map(defaultProfile -> initialExecutors)
    podAllocator.setTotalExpectedExecutors(initExecs)
    lifecycleEventHandler.start(this)
    watchEvents.start(applicationId())
    pollEvents.start(applicationId())
    if (!conf.get(KUBERNETES_EXECUTOR_DISABLE_CONFIGMAP)) {
      setUpExecutorConfigMap(podAllocator.driverPod)
    }
  }

  override def stop(): Unit = {
    // When `CoarseGrainedSchedulerBackend.stop` throws `SparkException`,
    // K8s cluster scheduler should log and proceed in order to delete the K8s cluster resources.
    Utils.tryLogNonFatalError {
      super.stop()
    }

    Utils.tryLogNonFatalError {
      snapshotsStore.stop()
    }

    Utils.tryLogNonFatalError {
      watchEvents.stop()
    }

    Utils.tryLogNonFatalError {
      pollEvents.stop()
    }

    if (shouldDeleteDriverService) {
      Utils.tryLogNonFatalError {
        kubernetesClient
          .services()
          .inNamespace(namespace)
          .withLabel(SPARK_APP_ID_LABEL, applicationId())
          .delete()
      }
    }

    if (conf.get(KUBERNETES_DRIVER_OWN_PVC)) {
      Utils.tryLogNonFatalError {
        kubernetesClient
          .persistentVolumeClaims()
          .inNamespace(namespace)
          .withLabel(SPARK_APP_ID_LABEL, applicationId())
          .delete()
      }
    }

    if (shouldDeleteExecutors) {

      podAllocator.stop(applicationId())

      if (!conf.get(KUBERNETES_EXECUTOR_DISABLE_CONFIGMAP)) {
        Utils.tryLogNonFatalError {
          kubernetesClient
            .configMaps()
            .inNamespace(namespace)
            .withLabel(SPARK_APP_ID_LABEL, applicationId())
            .withLabel(SPARK_ROLE_LABEL, SPARK_POD_EXECUTOR_ROLE)
            .delete()
        }
      }
    }

    Utils.tryLogNonFatalError {
      ThreadUtils.shutdown(executorService)
    }

    Utils.tryLogNonFatalError {
      kubernetesClient.close()
    }
  }

  override def doRequestTotalExecutors(
      resourceProfileToTotalExecs: Map[ResourceProfile, Int]): Future[Boolean] = {
    podAllocator.setTotalExpectedExecutors(resourceProfileToTotalExecs)
    Future.successful(true)
  }

  override def sufficientResourcesRegistered(): Boolean = {
    totalRegisteredExecutors.get() >= initialExecutors * minRegisteredRatio
  }

  override def getExecutorIds(): Seq[String] = synchronized {
    super.getExecutorIds()
  }

  private def labelDecommissioningExecs(execIds: Seq[String]) = {
    // Only kick off the labeling task if we have a label.
    conf.get(KUBERNETES_EXECUTOR_DECOMMISSION_LABEL).foreach { label =>
      val labelTask = new Runnable() {
        override def run(): Unit = Utils.tryLogNonFatalError {
          kubernetesClient.pods()
            .inNamespace(namespace)
            .withLabel(SPARK_APP_ID_LABEL, applicationId())
            .withLabel(SPARK_ROLE_LABEL, SPARK_POD_EXECUTOR_ROLE)
            .withLabelIn(SPARK_EXECUTOR_ID_LABEL, execIds: _*)
            .resources()
            .forEach { podResource =>
              podResource.edit({ p: Pod =>
                new PodBuilder(p).editOrNewMetadata()
                  .addToLabels(label,
                    conf.get(KUBERNETES_EXECUTOR_DECOMMISSION_LABEL_VALUE).getOrElse(""))
                  .endMetadata()
                  .build()})
          }
        }
      }
      executorService.execute(labelTask)
    }
  }

  override def decommissionExecutors(
      executorsAndDecomInfo: Array[(String, ExecutorDecommissionInfo)],
      adjustTargetNumExecutors: Boolean,
      triggeredByExecutor: Boolean): Seq[String] = {
    // If decommissioning is triggered by the executor the K8s cluster manager has already
    // picked the pod to evict so we don't need to update the labels.
    if (!triggeredByExecutor) {
      labelDecommissioningExecs(executorsAndDecomInfo.map(_._1))
    }
    super.decommissionExecutors(executorsAndDecomInfo, adjustTargetNumExecutors,
      triggeredByExecutor)
  }

  override def doKillExecutors(executorIds: Seq[String]): Future[Boolean] = {
    // If we've decided to remove some executors we should tell Kubernetes that we don't care.
    labelDecommissioningExecs(executorIds)

    // Tell the executors to exit themselves.
    executorIds.foreach { id =>
      removeExecutor(id, ExecutorKilled)
    }

    // Give some time for the executors to shut themselves down, then forcefully kill any
    // remaining ones. This intentionally ignores the configuration about whether pods
    // should be deleted; only executors that shut down gracefully (and are then collected
    // by the ExecutorPodsLifecycleManager) will respect that configuration.
    val killTask = new Runnable() {
      override def run(): Unit = Utils.tryLogNonFatalError {
        val running = kubernetesClient
          .pods()
          .inNamespace(namespace)
          .withField("status.phase", "Running")
          .withLabel(SPARK_APP_ID_LABEL, applicationId())
          .withLabel(SPARK_ROLE_LABEL, SPARK_POD_EXECUTOR_ROLE)
          .withLabelIn(SPARK_EXECUTOR_ID_LABEL, executorIds: _*)

        if (!running.list().getItems().isEmpty()) {
          logInfo(s"Forcefully deleting ${running.list().getItems().size()} pods " +
            s"(out of ${executorIds.size}) that are still running after graceful shutdown period.")
          running.delete()
        }
      }
    }
    executorService.schedule(killTask, conf.get(KUBERNETES_DYN_ALLOC_KILL_GRACE_PERIOD),
      TimeUnit.MILLISECONDS)

    // Return an immediate success, since we can't confirm or deny that executors have been
    // actually shut down without waiting too long and blocking the allocation thread, which
    // waits on this future to complete, blocking further allocations / deallocations.
    //
    // This relies a lot on the guarantees of Spark's RPC system, that a message will be
    // delivered to the destination unless there's an issue with the connection, in which
    // case the executor will shut itself down (and the driver, separately, will just declare
    // it as "lost"). Coupled with the allocation manager keeping track of which executors are
    // pending release, returning "true" here means that eventually all the requested executors
    // will be removed.
    //
    // The cleanup timer above is just an optimization to make sure that stuck executors don't
    // stick around in the k8s server. Normally it should never delete any pods at all.
    Future.successful(true)
  }

  override def createDriverEndpoint(): DriverEndpoint = {
    new KubernetesDriverEndpoint()
  }

  val execId = new AtomicInteger(0)

  override protected def createTokenManager(): Option[HadoopDelegationTokenManager] = {
    Some(new HadoopDelegationTokenManager(conf, sc.hadoopConfiguration, driverEndpoint))
  }

  override protected def isExecutorExcluded(executorId: String, hostname: String): Boolean = {
    podAllocator.isDeleted(executorId)
  }

  private class KubernetesDriverEndpoint extends DriverEndpoint {
    private def generateExecID(context: RpcCallContext): PartialFunction[Any, Unit] = {
      case x: GenerateExecID =>
        val newId = execId.incrementAndGet().toString
        context.reply(newId)
        // Generally this should complete quickly but safer to not block in-case we're in the
        // middle of an etcd fail over or otherwise slower writes.
        val labelTask = new Runnable() {
          override def run(): Unit = Utils.tryLogNonFatalError {
            // Label the pod with it's exec ID
            kubernetesClient.pods()
              .inNamespace(namespace)
              .withName(x.podName)
              .edit({p: Pod => new PodBuilder(p).editMetadata()
                .addToLabels(SPARK_EXECUTOR_ID_LABEL, newId)
                .endMetadata()
                .build()})
          }
        }
        executorService.execute(labelTask)
    }
    private def ignoreRegisterExecutorAtStoppedContext: PartialFunction[Any, Unit] = {
      case _: RegisterExecutor if sc.isStopped => // No-op
    }

    override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] =
      generateExecID(context).orElse(
        ignoreRegisterExecutorAtStoppedContext.orElse(
          super.receiveAndReply(context)))

    override def onDisconnected(rpcAddress: RpcAddress): Unit = {
      // Don't do anything besides disabling the executor - allow the Kubernetes API events to
      // drive the rest of the lifecycle decisions
      // TODO what if we disconnect from a networking issue? Probably want to mark the executor
      // to be deleted eventually.
      addressToExecutorId.get(rpcAddress).foreach(disableExecutor)
    }
  }

}

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