spark WholeTextFileRDD 源码
spark WholeTextFileRDD 代码
文件路径：/core/src/main/scala/org/apache/spark/rdd/WholeTextFileRDD.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 org.apache.hadoop.conf.{Configurable, Configuration}
import org.apache.hadoop.io.{Text, Writable}
import org.apache.hadoop.mapreduce.InputSplit
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
import org.apache.hadoop.mapreduce.task.JobContextImpl
import org.apache.spark.{Partition, SparkContext}
import org.apache.spark.input.WholeTextFileInputFormat
/**
* An RDD that reads a bunch of text files in, and each text file becomes one record.
*/
private[spark] class WholeTextFileRDD(
sc : SparkContext,
inputFormatClass: Class[_ <: WholeTextFileInputFormat],
keyClass: Class[Text],
valueClass: Class[Text],
conf: Configuration,
minPartitions: Int)
extends NewHadoopRDD[Text, Text](sc, inputFormatClass, keyClass, valueClass, conf) {
override def getPartitions: Array[Partition] = {
val conf = getConf
// setMinPartitions below will call FileInputFormat.listStatus(), which can be quite slow when
// traversing a large number of directories and files. Parallelize it.
conf.setIfUnset(FileInputFormat.LIST_STATUS_NUM_THREADS,
Runtime.getRuntime.availableProcessors().toString)
val inputFormat = inputFormatClass.getConstructor().newInstance()
inputFormat match {
case configurable: Configurable =>
configurable.setConf(conf)
case _ =>
}
val jobContext = new JobContextImpl(conf, jobId)
inputFormat.setMinPartitions(jobContext, minPartitions)
val rawSplits = inputFormat.getSplits(jobContext).toArray
val result = new Array[Partition](rawSplits.size)
for (i <- 0 until rawSplits.size) {
result(i) = new NewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
}
result
}
}
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