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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 spark.scheduler.cluster
import spark.{SparkContext, Utils}
/**
* A backend interface for cluster scheduling systems that allows plugging in different ones under
* ClusterScheduler. We assume a Mesos-like model where the application gets resource offers as
* machines become available and can launch tasks on them.
*/
private[spark] trait SchedulerBackend {
def start(): Unit
def stop(): Unit
def reviveOffers(): Unit
def defaultParallelism(): Int
// Memory used by each executor (in megabytes)
protected val executorMemory: Int = SparkContext.executorMemoryRequested
// TODO: Probably want to add a killTask too
}
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