Github user mccheah commented on a diff in the pull request: https://github.com/apache/spark/pull/19468#discussion_r147019793 --- Diff: resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodFactory.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 scala.collection.JavaConverters._ + +import io.fabric8.kubernetes.api.model._ + +import org.apache.spark.{SparkConf, SparkException} +import org.apache.spark.deploy.k8s.ConfigurationUtils +import org.apache.spark.deploy.k8s.config._ +import org.apache.spark.deploy.k8s.constants._ +import org.apache.spark.util.Utils + +/** + * Configures executor pods. Construct one of these with a SparkConf to set up properties that are + * common across all executors. Then, pass in dynamic parameters into createExecutorPod. + */ +private[spark] trait ExecutorPodFactory { + def createExecutorPod( + executorId: String, + applicationId: String, + driverUrl: String, + executorEnvs: Seq[(String, String)], + driverPod: Pod, + nodeToLocalTaskCount: Map[String, Int]): Pod +} + +private[spark] class ExecutorPodFactoryImpl(sparkConf: SparkConf) + extends ExecutorPodFactory { + + import ExecutorPodFactoryImpl._ + + private val executorExtraClasspath = sparkConf.get( + org.apache.spark.internal.config.EXECUTOR_CLASS_PATH) + private val executorJarsDownloadDir = sparkConf.get(INIT_CONTAINER_JARS_DOWNLOAD_LOCATION) + + private val executorLabels = ConfigurationUtils.parsePrefixedKeyValuePairs( + sparkConf, + KUBERNETES_EXECUTOR_LABEL_PREFIX, + "executor label") + require( + !executorLabels.contains(SPARK_APP_ID_LABEL), + s"Custom executor labels cannot contain $SPARK_APP_ID_LABEL as it is reserved for Spark.") + require( + !executorLabels.contains(SPARK_EXECUTOR_ID_LABEL), + s"Custom executor labels cannot contain $SPARK_EXECUTOR_ID_LABEL as it is reserved for" + + s" Spark.") + + private val executorAnnotations = + ConfigurationUtils.parsePrefixedKeyValuePairs( + sparkConf, + KUBERNETES_EXECUTOR_ANNOTATION_PREFIX, + "executor annotation") + private val nodeSelector = + ConfigurationUtils.parsePrefixedKeyValuePairs( + sparkConf, + KUBERNETES_NODE_SELECTOR_PREFIX, + "node selector") + + private val executorDockerImage = sparkConf.get(EXECUTOR_DOCKER_IMAGE) + private val dockerImagePullPolicy = sparkConf.get(DOCKER_IMAGE_PULL_POLICY) + private val executorPort = sparkConf.getInt("spark.executor.port", DEFAULT_STATIC_PORT) + private val blockmanagerPort = sparkConf + .getInt("spark.blockmanager.port", DEFAULT_BLOCKMANAGER_PORT) + private val kubernetesDriverPodName = sparkConf + .get(KUBERNETES_DRIVER_POD_NAME) + .getOrElse(throw new SparkException("Must specify the driver pod name")) + + private val executorPodNamePrefix = sparkConf.get(KUBERNETES_EXECUTOR_POD_NAME_PREFIX) + + private val executorMemoryMiB = sparkConf.get(org.apache.spark.internal.config.EXECUTOR_MEMORY) + private val executorMemoryString = sparkConf.get( + org.apache.spark.internal.config.EXECUTOR_MEMORY.key, + org.apache.spark.internal.config.EXECUTOR_MEMORY.defaultValueString) + + private val memoryOverheadMiB = sparkConf + .get(KUBERNETES_EXECUTOR_MEMORY_OVERHEAD) + .getOrElse(math.max((MEMORY_OVERHEAD_FACTOR * executorMemoryMiB).toInt, + MEMORY_OVERHEAD_MIN_MIB)) + private val executorMemoryWithOverhead = executorMemoryMiB + memoryOverheadMiB + + private val executorCores = sparkConf.getDouble("spark.executor.cores", 1d) + private val executorLimitCores = sparkConf.getOption(KUBERNETES_EXECUTOR_LIMIT_CORES.key) + + override def createExecutorPod( + executorId: String, + applicationId: String, + driverUrl: String, + executorEnvs: Seq[(String, String)], + driverPod: Pod, + nodeToLocalTaskCount: Map[String, Int]): Pod = { + val name = s"$executorPodNamePrefix-exec-$executorId" + + // hostname must be no longer than 63 characters, so take the last 63 characters of the pod + // name as the hostname. This preserves uniqueness since the end of name contains + // executorId and applicationId + val hostname = name.substring(Math.max(0, name.length - 63)) + val resolvedExecutorLabels = Map( + SPARK_EXECUTOR_ID_LABEL -> executorId, + SPARK_APP_ID_LABEL -> applicationId, + SPARK_ROLE_LABEL -> SPARK_POD_EXECUTOR_ROLE) ++ + executorLabels + val executorMemoryQuantity = new QuantityBuilder(false) + .withAmount(s"${executorMemoryMiB}Mi") + .build() + val executorMemoryLimitQuantity = new QuantityBuilder(false) + .withAmount(s"${executorMemoryWithOverhead}Mi") + .build() + val executorCpuQuantity = new QuantityBuilder(false) + .withAmount(executorCores.toString) + .build() + val executorExtraClasspathEnv = executorExtraClasspath.map { cp => + new EnvVarBuilder() + .withName(ENV_EXECUTOR_EXTRA_CLASSPATH) + .withValue(cp) + .build() + } + val executorExtraJavaOptionsEnv = sparkConf + .get(org.apache.spark.internal.config.EXECUTOR_JAVA_OPTIONS) + .map { opts => + val delimitedOpts = Utils.splitCommandString(opts) + delimitedOpts.zipWithIndex.map { + case (opt, index) => + new EnvVarBuilder().withName(s"$ENV_JAVA_OPT_PREFIX$index").withValue(opt).build() + } + }.getOrElse(Seq.empty[EnvVar]) + val executorEnv = (Seq( + (ENV_EXECUTOR_PORT, executorPort.toString), + (ENV_DRIVER_URL, driverUrl), + // Executor backend expects integral value for executor cores, so round it up to an int. + (ENV_EXECUTOR_CORES, math.ceil(executorCores).toInt.toString), + (ENV_EXECUTOR_MEMORY, executorMemoryString), + (ENV_APPLICATION_ID, applicationId), + (ENV_EXECUTOR_ID, executorId), + (ENV_MOUNTED_CLASSPATH, s"$executorJarsDownloadDir/*")) ++ executorEnvs) + .map(env => new EnvVarBuilder() + .withName(env._1) + .withValue(env._2) + .build() + ) ++ Seq( + new EnvVarBuilder() + .withName(ENV_EXECUTOR_POD_IP) + .withValueFrom(new EnvVarSourceBuilder() + .withNewFieldRef("v1", "status.podIP") + .build()) + .build() --- End diff -- Kubernetes networking is a pretty interesting topic in and of itself, and I foresee part of the challenge of the project will be dealing with these unexpected and unpredictable situations where Spark's assumptions about networking won't hold in the Kubernetes setting. My two cents is that we can only make headway on this project by using what we know to be true about Kubernetes networking - the fact that we can't use hostnames for all executors and have to communicate over IP addresses - and to then tackle networking bugs as they come up. On both production clusters and our integration test environment, we have caught some of these issues as they have come up and patched them accordingly already, and we can continue to do so. @mridulm - is this a reasonable way forward, or how would you suggest we proceed otherwise? @foxish do you have any thoughts on this discussion thread?
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