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+/*
+ * 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
+
+import org.apache.spark.scheduler.cluster.Pool
+import org.apache.spark.scheduler.cluster.SchedulingMode.SchedulingMode
+/**
+ * Low-level task scheduler interface, implemented by both ClusterScheduler and LocalScheduler.
+ * These schedulers get sets of tasks submitted to them from the DAGScheduler for each stage,
+ * and are responsible for sending the tasks to the cluster, running them, retrying if there
+ * are failures, and mitigating stragglers. They return events to the DAGScheduler through
+ * the TaskSchedulerListener interface.
+ */
+private[spark] trait TaskScheduler {
+
+ def rootPool: Pool
+
+ def schedulingMode: SchedulingMode
+
+ def start(): Unit
+
+ // Invoked after system has successfully initialized (typically in spark context).
+ // Yarn uses this to bootstrap allocation of resources based on preferred locations, wait for slave registerations, etc.
+ def postStartHook() { }
+
+ // Disconnect from the cluster.
+ def stop(): Unit
+
+ // Submit a sequence of tasks to run.
+ def submitTasks(taskSet: TaskSet): Unit
+
+ // Set a listener for upcalls. This is guaranteed to be set before submitTasks is called.
+ def setListener(listener: TaskSchedulerListener): Unit
+
+ // Get the default level of parallelism to use in the cluster, as a hint for sizing jobs.
+ def defaultParallelism(): Int
+}