spark RDDBarrier 源码
spark RDDBarrier 代码
文件路径：/core/src/main/scala/org/apache/spark/rdd/RDDBarrier.scala
/*
* 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.rdd
import scala.reflect.ClassTag
import org.apache.spark.TaskContext
import org.apache.spark.annotation.{Experimental, Since}
/**
* :: Experimental ::
* Wraps an RDD in a barrier stage, which forces Spark to launch tasks of this stage together.
* [[org.apache.spark.rdd.RDDBarrier]] instances are created by
* [[org.apache.spark.rdd.RDD#barrier]].
*/
@Experimental
@Since("2.4.0")
class RDDBarrier[T: ClassTag] private[spark] (rdd: RDD[T]) {
/**
* :: Experimental ::
* Returns a new RDD by applying a function to each partition of the wrapped RDD,
* where tasks are launched together in a barrier stage.
* The interface is the same as [[org.apache.spark.rdd.RDD#mapPartitions]].
* Please see the API doc there.
* @see [[org.apache.spark.BarrierTaskContext]]
*/
@Experimental
@Since("2.4.0")
def mapPartitions[S: ClassTag](
f: Iterator[T] => Iterator[S],
preservesPartitioning: Boolean = false): RDD[S] = rdd.withScope {
val cleanedF = rdd.sparkContext.clean(f)
new MapPartitionsRDD(
rdd,
(context: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(iter),
preservesPartitioning,
isFromBarrier = true
)
}
/**
* :: Experimental ::
* Returns a new RDD by applying a function to each partition of the wrapped RDD, while tracking
* the index of the original partition. And all tasks are launched together in a barrier stage.
* The interface is the same as [[org.apache.spark.rdd.RDD#mapPartitionsWithIndex]].
* Please see the API doc there.
* @see [[org.apache.spark.BarrierTaskContext]]
*/
@Experimental
@Since("3.0.0")
def mapPartitionsWithIndex[S: ClassTag](
f: (Int, Iterator[T]) => Iterator[S],
preservesPartitioning: Boolean = false): RDD[S] = rdd.withScope {
val cleanedF = rdd.sparkContext.clean(f)
new MapPartitionsRDD(
rdd,
(_: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(index, iter),
preservesPartitioning,
isFromBarrier = true
)
}
// TODO: [SPARK-25247] add extra conf to RDDBarrier, e.g., timeout.
}
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