/** * 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.hadoop.mapred.lib; import java.io.IOException; import java.util.ArrayList; import org.apache.hadoop.classification.InterfaceAudience; import org.apache.hadoop.classification.InterfaceStability; import org.apache.hadoop.fs.FileStatus; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapred.FileInputFormat; import org.apache.hadoop.mapred.FileSplit; import org.apache.hadoop.mapred.InputSplit; import org.apache.hadoop.mapred.JobConf; import org.apache.hadoop.mapred.JobConfigurable; import org.apache.hadoop.mapred.LineRecordReader; import org.apache.hadoop.mapred.RecordReader; import org.apache.hadoop.mapred.Reporter; /** * NLineInputFormat which splits N lines of input as one split. * * In many "pleasantly" parallel applications, each process/mapper * processes the same input file (s), but with computations are * controlled by different parameters.(Referred to as "parameter sweeps"). * One way to achieve this, is to specify a set of parameters * (one set per line) as input in a control file * (which is the input path to the map-reduce application, * where as the input dataset is specified * via a config variable in JobConf.). * * The NLineInputFormat can be used in such applications, that splits * the input file such that by default, one line is fed as * a value to one map task, and key is the offset. * i.e. (k,v) is (LongWritable, Text). * The location hints will span the whole mapred cluster. */ @InterfaceAudience.Public @InterfaceStability.Stable public class NLineInputFormat extends FileInputFormat<LongWritable, Text> implements JobConfigurable { private int N = 1; public RecordReader<LongWritable, Text> getRecordReader( InputSplit genericSplit, JobConf job, Reporter reporter) throws IOException { reporter.setStatus(genericSplit.toString()); return new LineRecordReader(job, (FileSplit) genericSplit); } /** * Logically splits the set of input files for the job, splits N lines * of the input as one split. * * @see org.apache.hadoop.mapred.FileInputFormat#getSplits(JobConf, int) */ public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException { ArrayList<FileSplit> splits = new ArrayList<FileSplit>(); for (FileStatus status : listStatus(job)) { for (org.apache.hadoop.mapreduce.lib.input.FileSplit split : org.apache.hadoop.mapreduce.lib.input. NLineInputFormat.getSplitsForFile(status, job, N)) { splits.add(new FileSplit(split)); } } return splits.toArray(new FileSplit[splits.size()]); } public void configure(JobConf conf) { N = conf.getInt("mapreduce.input.lineinputformat.linespermap", 1); } /** * NLineInputFormat uses LineRecordReader, which always reads * (and consumes) at least one character out of its upper split * boundary. So to make sure that each mapper gets N lines, we * move back the upper split limits of each split * by one character here. * @param fileName Path of file * @param begin the position of the first byte in the file to process * @param length number of bytes in InputSplit * @return FileSplit */ protected static FileSplit createFileSplit(Path fileName, long begin, long length) { return (begin == 0) ? new FileSplit(fileName, begin, length - 1, new String[] {}) : new FileSplit(fileName, begin - 1, length, new String[] {}); } }