Github user mccheah commented on a diff in the pull request: https://github.com/apache/spark/pull/21366#discussion_r193934305 --- Diff: resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/ExecutorPodsSnapshotsStoreImpl.scala --- @@ -0,0 +1,95 @@ +/* + * 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.util.concurrent.{ExecutorService, ScheduledExecutorService, TimeUnit} + +import com.google.common.collect.Lists +import io.fabric8.kubernetes.api.model.Pod +import io.reactivex.disposables.Disposable +import io.reactivex.functions.Consumer +import io.reactivex.schedulers.Schedulers +import io.reactivex.subjects.PublishSubject +import javax.annotation.concurrent.GuardedBy +import scala.collection.JavaConverters._ +import scala.collection.mutable + +import org.apache.spark.util.{ThreadUtils, Utils} + +private[spark] class ExecutorPodsSnapshotsStoreImpl( + bufferSnapshotsExecutor: ScheduledExecutorService, + executeSubscriptionsExecutor: ExecutorService) + extends ExecutorPodsSnapshotsStore { + + private val SNAPSHOT_LOCK = new Object() + + private val snapshotsObservable = PublishSubject.create[ExecutorPodsSnapshot]() + private val observedDisposables = mutable.Buffer.empty[Disposable] + + @GuardedBy("SNAPSHOT_LOCK") + private var currentSnapshot = ExecutorPodsSnapshot() + + override def addSubscriber( + processBatchIntervalMillis: Long) + (subscriber: ExecutorPodsSnapshot => Unit): Unit = { + observedDisposables += snapshotsObservable + // Group events in the time window given by the caller. These buffers are then sent + // to the caller's lambda at the given interval, with the pod updates that occurred + // in that given interval. + .buffer( + processBatchIntervalMillis, + TimeUnit.MILLISECONDS, + // For testing - specifically use the given scheduled executor service to trigger + // buffer boundaries. Allows us to inject a deterministic scheduler here. + Schedulers.from(bufferSnapshotsExecutor)) + // Trigger an event cycle immediately. Not strictly required to be fully correct, but + // in particular the pod allocator should try to request executors immediately instead + // of waiting for one pod allocation delay. + .startWith(Lists.newArrayList(ExecutorPodsSnapshot())) + // Force all triggered events - both the initial event above and the buffered ones in + // the following time windows - to execute asynchronously to this call's thread. + .observeOn(Schedulers.from(executeSubscriptionsExecutor)) + .subscribe(toReactivexConsumer { snapshots: java.util.List[ExecutorPodsSnapshot] => + Utils.tryLogNonFatalError { + snapshots.asScala.foreach(subscriber) + } + }) + } + + override def stop(): Unit = { + observedDisposables.foreach(_.dispose()) + snapshotsObservable.onComplete() + ThreadUtils.shutdown(bufferSnapshotsExecutor) + ThreadUtils.shutdown(executeSubscriptionsExecutor) + } + + override def updatePod(updatedPod: Pod): Unit = SNAPSHOT_LOCK.synchronized { + currentSnapshot = currentSnapshot.withUpdate(updatedPod) --- End diff -- So the watch only creates new snapshots by applying some "diff" according to the next event to the previous snapshot. One downside of this data model is that we end up buffering multiple collections of pods in the observable stream which can all perhaps only differ by a single pod per update. Thus we end up temporarily storing redundant information in the snapshots. But the observable buffers are ephemeral and will be periodically processed by the periodic iterations of the subscribers. I wouldn't mind thinking about a more optimal representation here.
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