| /* |
| * 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 java.util.Properties |
| |
| import org.apache.spark.JobArtifactSet |
| import org.apache.spark.util.CallSite |
| |
| /** |
| * A running job in the DAGScheduler. Jobs can be of two types: a result job, which computes a |
| * ResultStage to execute an action, or a map-stage job, which computes the map outputs for a |
| * ShuffleMapStage before any downstream stages are submitted. The latter is used for adaptive |
| * query planning, to look at map output statistics before submitting later stages. We distinguish |
| * between these two types of jobs using the finalStage field of this class. |
| * |
| * Jobs are only tracked for "leaf" stages that clients directly submitted, through DAGScheduler's |
| * submitJob or submitMapStage methods. However, either type of job may cause the execution of |
| * other earlier stages (for RDDs in the DAG it depends on), and multiple jobs may share some of |
| * these previous stages. These dependencies are managed inside DAGScheduler. |
| * |
| * @param jobId A unique ID for this job. |
| * @param finalStage The stage that this job computes (either a ResultStage for an action or a |
| * ShuffleMapStage for submitMapStage). |
| * @param callSite Where this job was initiated in the user's program (shown on UI). |
| * @param listener A listener to notify if tasks in this job finish or the job fails. |
| * @param artifacts A set of artifacts that this job has may use. |
| * @param properties Scheduling properties attached to the job, such as fair scheduler pool name. |
| */ |
| private[spark] class ActiveJob( |
| val jobId: Int, |
| val finalStage: Stage, |
| val callSite: CallSite, |
| val listener: JobListener, |
| val artifacts: JobArtifactSet, |
| val properties: Properties) { |
| |
| /** |
| * Number of partitions we need to compute for this job. Note that result stages may not need |
| * to compute all partitions in their target RDD, for actions like first() and lookup(). |
| */ |
| val numPartitions = finalStage match { |
| case r: ResultStage => r.partitions.length |
| case m: ShuffleMapStage => m.numPartitions |
| } |
| |
| /** Which partitions of the stage have finished */ |
| val finished = Array.fill[Boolean](numPartitions)(false) |
| |
| var numFinished = 0 |
| } |