Github user mccheah commented on a diff in the pull request:
https://github.com/apache/spark/pull/16061#discussion_r90365964
--- Diff:
kubernetes/src/main/scala/org/apache/spark/scheduler/cluster/kubernetes/KubernetesClusterSchedulerBackend.scala
---
@@ -0,0 +1,222 @@
+/*
+ * 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.kubernetes
+
+import collection.JavaConverters._
+import io.fabric8.kubernetes.api.model.PodBuilder
+import io.fabric8.kubernetes.api.model.extensions.JobBuilder
+import io.fabric8.kubernetes.client.{ConfigBuilder,
DefaultKubernetesClient}
+import org.apache.spark.internal.config._
+import org.apache.spark.scheduler._
+import org.apache.spark.scheduler.cluster._
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.{SparkConf, SparkContext, SparkException}
+import org.apache.spark.rpc.RpcEndpointAddress
+import org.apache.spark.scheduler.TaskSchedulerImpl
+import org.apache.spark.util.Utils
+
+import scala.collection.mutable
+import scala.util.Random
+import scala.concurrent.Future
+
+private[spark] class KubernetesClusterSchedulerBackend(
+ scheduler:
TaskSchedulerImpl,
+ sc: SparkContext)
+ extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) {
+
+ val client = new DefaultKubernetesClient()
+
+ val DEFAULT_NUMBER_EXECUTORS = 2
+ val sparkExecutorName = s"spark-executor-${Random.alphanumeric take 5
mkString("")}".toLowerCase()
+
+ // TODO: do these need mutex guarding?
+ // key is executor id, value is pod name
+ var executorToPod = mutable.Map.empty[String, String] // active executors
+ var shutdownToPod = mutable.Map.empty[String, String] // pending shutdown
+ var executorID = 0
+
+ val sparkImage = conf.get("spark.kubernetes.sparkImage")
+ val clientJarUri = conf.get("spark.executor.jar")
+ val ns = conf.get(
+ "spark.kubernetes.namespace",
+ KubernetesClusterScheduler.defaultNameSpace)
+ val dynamicExecutors = Utils.isDynamicAllocationEnabled(conf)
+
+ // executor back-ends take their configuration this way
+ if (dynamicExecutors) {
+ conf.setExecutorEnv("spark.dynamicAllocation.enabled", "true")
+ conf.setExecutorEnv("spark.shuffle.service.enabled", "true")
+ }
+
+ override def start(): Unit = {
+ super.start()
+ createExecutorPods(getInitialTargetExecutorNumber(sc.getConf))
+ }
+
+ override def stop(): Unit = {
+ // Kill all executor pods indiscriminately
+ killExecutorPods(executorToPod.toVector)
+ killExecutorPods(shutdownToPod.toVector)
+ super.stop()
+ }
+
+ // Dynamic allocation interfaces
+ override def doRequestTotalExecutors(requestedTotal: Int):
Future[Boolean] = {
+ logInfo(s"Received doRequestTotalExecutors: $requestedTotal")
+ val n = executorToPod.size
+ val delta = requestedTotal - n
+ if (delta > 0) {
+ logInfo(s"Adding $delta new executors")
+ createExecutorPods(delta)
+ } else if (delta < 0) {
+ val d = -delta
+ val idle = executorToPod.toVector.filter { case (id, _) =>
!scheduler.isExecutorBusy(id) }
+ if (idle.length > 0) {
+ logInfo(s"Shutting down ${idle.length} idle executors")
+ shutdownExecutors(idle.take(d))
+ }
+ val r = math.max(0, d - idle.length)
+ if (r > 0) {
+ logInfo(s"Shutting down $r non-idle executors")
+ shutdownExecutors(executorToPod.toVector.slice(n - r, n))
+ }
+ }
+ // TODO: are there meaningful failure modes here?
+ Future.successful(true)
+ }
+
+ override def doKillExecutors(executorIds: Seq[String]): Future[Boolean]
= {
+ logInfo(s"Received doKillExecutors")
+ killExecutorPods(executorIds.map { id => (id, executorToPod(id)) })
+ Future.successful(true)
+ }
+
+ private def createExecutorPods(n: Int) {
+ for (i <- 1 to n) {
+ executorID += 1
+ executorToPod += ((executorID.toString,
createExecutorPod(executorID)))
+ }
+ }
+
+ def shutdownExecutors(idPodPairs: Seq[(String, String)]) {
+ val active = getExecutorIds.toSet
+
+ // Check for any finished shutting down and kill the pods
+ val shutdown = shutdownToPod.toVector.filter { case (e, _) =>
!active.contains(e) }
+ killExecutorPods(shutdown)
+
+ // Now request shutdown for the new ones.
+ // Move them from executor list to list pending shutdown
+ for ((id, pod) <- idPodPairs) {
+ try {
+ // TODO: 'ask' returns a future - can it be used to check eventual
success?
+ Option(driverEndpoint).foreach(_.ask[Boolean](RemoveExecutor(id,
ExecutorKilled)))
+ executorToPod -= id
+ shutdownToPod += ((id, pod))
+ } catch {
+ case e: Exception => logError(s"Error shutting down executor $id",
e)
+ }
+ }
+ }
+
+ private def killExecutorPods(idPodPairs: Seq[(String, String)]) {
+ for ((id, pod) <- idPodPairs) {
+ try {
+ client.pods().inNamespace(ns).withName(pod).delete()
+ executorToPod -= id
+ shutdownToPod -= id
+ } catch {
+ case e: Exception => logError(s"Error killing executor pod $pod",
e)
+ }
+ }
+ }
+
+ def getInitialTargetExecutorNumber(conf: SparkConf,
+ numExecutors: Int =
+ DEFAULT_NUMBER_EXECUTORS): Int = {
+ if (dynamicExecutors) {
+ val minNumExecutors = conf.get(DYN_ALLOCATION_MIN_EXECUTORS)
+ val initialNumExecutors =
+ Utils.getDynamicAllocationInitialExecutors(conf)
+ val maxNumExecutors = conf.get(DYN_ALLOCATION_MAX_EXECUTORS)
+ require(
+ initialNumExecutors >= minNumExecutors && initialNumExecutors <=
maxNumExecutors,
+ s"initial executor number $initialNumExecutors must between min
executor number " +
+ s"$minNumExecutors and max executor number $maxNumExecutors")
+
+ initialNumExecutors
+ } else {
+ conf.get(EXECUTOR_INSTANCES).getOrElse(numExecutors)
+ }
+ }
+
+ def createExecutorPod(executorNum: Int): String = {
+ // create a single k8s executor pod.
+ val labelMap = Map("type" -> "spark-executor")
+ val podName = s"$sparkExecutorName-$executorNum"
+
+ val submitArgs = mutable.ArrayBuffer.empty[String]
+
+ if (conf.getBoolean("spark.dynamicAllocation.enabled", false)) {
+ submitArgs ++= Vector(
+ "dynamic-executors")
+ }
+
+ submitArgs ++=
Vector("org.apache.spark.executor.CoarseGrainedExecutorBackend",
+ "--driver-url", s"$driverURL",
+ "--executor-id", s"$executorNum",
+ "--hostname", "localhost",
+ "--app-id", "1", // TODO: change app-id per application and pass
from driver.
+ "--cores", "1")
+
+ var pod = new PodBuilder()
+ .withNewMetadata()
+ .withLabels(labelMap.asJava)
+ .withName(podName)
+ .endMetadata()
+ .withNewSpec()
+ .withRestartPolicy("OnFailure")
+
+ .addNewContainer().withName("spark-executor").withImage(sparkImage)
+ .withImagePullPolicy("IfNotPresent")
+ .withCommand("/opt/executor.sh")
--- End diff --
Actually I think I was mistaken here - `spark-class` eventually calls to
[this
class](https://github.com/apache/spark/blob/master/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java#L65)
which then forks the actual process running the passed-in class. We can
probably then indeed take advantage of `SPARK_JAVA_OPTS` and
`SPARK_EXECUTOR_MEMORY`, etc. Arguments like executor-id which are passed as
command line arguments and not JVM options should probably still be set as
environment variables on the container though and the Dockerfile handles
formatting the command line arguments sent to `spark-class`.
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