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Apache Hadoop MapReduce Examples
/**
* 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.examples;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.Random;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapred.ClusterStatus;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* This program uses map/reduce to just run a distributed job where there is
* no interaction between the tasks and each task write a large unsorted
* random binary sequence file of BytesWritable.
* In order for this program to generate data for terasort with 10-byte keys
* and 90-byte values, have the following config:
*
* <?xml version="1.0"?>
* <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
* <configuration>
* <property>
* <name>mapreduce.randomwriter.minkey</name>
* <value>10</value>
* </property>
* <property>
* <name>mapreduce.randomwriter.maxkey</name>
* <value>10</value>
* </property>
* <property>
* <name>mapreduce.randomwriter.minvalue</name>
* <value>90</value>
* </property>
* <property>
* <name>mapreduce.randomwriter.maxvalue</name>
* <value>90</value>
* </property>
* <property>
* <name>mapreduce.randomwriter.totalbytes</name>
* <value>1099511627776</value>
* </property>
* </configuration>
*
* Equivalently, {@link RandomWriter} also supports all the above options
* and ones supported by {@link GenericOptionsParser} via the command-line.
*/
public class RandomWriter extends Configured implements Tool {
public static final String TOTAL_BYTES = "mapreduce.randomwriter.totalbytes";
public static final String BYTES_PER_MAP =
"mapreduce.randomwriter.bytespermap";
public static final String MAPS_PER_HOST =
"mapreduce.randomwriter.mapsperhost";
public static final String MAX_VALUE = "mapreduce.randomwriter.maxvalue";
public static final String MIN_VALUE = "mapreduce.randomwriter.minvalue";
public static final String MIN_KEY = "mapreduce.randomwriter.minkey";
public static final String MAX_KEY = "mapreduce.randomwriter.maxkey";
/**
* User counters
*/
static enum Counters { RECORDS_WRITTEN, BYTES_WRITTEN }
/**
* A custom input format that creates virtual inputs of a single string
* for each map.
*/
static class RandomInputFormat extends InputFormat {
/**
* Generate the requested number of file splits, with the filename
* set to the filename of the output file.
*/
public List getSplits(JobContext job) throws IOException {
List result = new ArrayList();
Path outDir = FileOutputFormat.getOutputPath(job);
int numSplits =
job.getConfiguration().getInt(MRJobConfig.NUM_MAPS, 1);
for(int i=0; i < numSplits; ++i) {
result.add(new FileSplit(new Path(outDir, "dummy-split-" + i), 0, 1,
(String[])null));
}
return result;
}
/**
* Return a single record (filename, "") where the filename is taken from
* the file split.
*/
static class RandomRecordReader extends RecordReader {
Path name;
Text key = null;
Text value = new Text();
public RandomRecordReader(Path p) {
name = p;
}
public void initialize(InputSplit split,
TaskAttemptContext context)
throws IOException, InterruptedException {
}
public boolean nextKeyValue() {
if (name != null) {
key = new Text();
key.set(name.getName());
name = null;
return true;
}
return false;
}
public Text getCurrentKey() {
return key;
}
public Text getCurrentValue() {
return value;
}
public void close() {}
public float getProgress() {
return 0.0f;
}
}
public RecordReader createRecordReader(InputSplit split,
TaskAttemptContext context) throws IOException, InterruptedException {
return new RandomRecordReader(((FileSplit) split).getPath());
}
}
static class RandomMapper extends Mapper {
private long numBytesToWrite;
private int minKeySize;
private int keySizeRange;
private int minValueSize;
private int valueSizeRange;
private Random random = new Random();
private BytesWritable randomKey = new BytesWritable();
private BytesWritable randomValue = new BytesWritable();
private void randomizeBytes(byte[] data, int offset, int length) {
for(int i=offset + length - 1; i >= offset; --i) {
data[i] = (byte) random.nextInt(256);
}
}
/**
* Given an output filename, write a bunch of random records to it.
*/
public void map(WritableComparable key,
Writable value,
Context context) throws IOException,InterruptedException {
int itemCount = 0;
while (numBytesToWrite > 0) {
int keyLength = minKeySize +
(keySizeRange != 0 ? random.nextInt(keySizeRange) : 0);
randomKey.setSize(keyLength);
randomizeBytes(randomKey.getBytes(), 0, randomKey.getLength());
int valueLength = minValueSize +
(valueSizeRange != 0 ? random.nextInt(valueSizeRange) : 0);
randomValue.setSize(valueLength);
randomizeBytes(randomValue.getBytes(), 0, randomValue.getLength());
context.write(randomKey, randomValue);
numBytesToWrite -= keyLength + valueLength;
context.getCounter(Counters.BYTES_WRITTEN).increment(keyLength + valueLength);
context.getCounter(Counters.RECORDS_WRITTEN).increment(1);
if (++itemCount % 200 == 0) {
context.setStatus("wrote record " + itemCount + ". " +
numBytesToWrite + " bytes left.");
}
}
context.setStatus("done with " + itemCount + " records.");
}
/**
* Save the values out of the configuaration that we need to write
* the data.
*/
@Override
public void setup(Context context) {
Configuration conf = context.getConfiguration();
numBytesToWrite = conf.getLong(BYTES_PER_MAP,
1*1024*1024*1024);
minKeySize = conf.getInt(MIN_KEY, 10);
keySizeRange =
conf.getInt(MAX_KEY, 1000) - minKeySize;
minValueSize = conf.getInt(MIN_VALUE, 0);
valueSizeRange =
conf.getInt(MAX_VALUE, 20000) - minValueSize;
}
}
/**
* This is the main routine for launching a distributed random write job.
* It runs 10 maps/node and each node writes 1 gig of data to a DFS file.
* The reduce doesn't do anything.
*
* @throws IOException
*/
public int run(String[] args) throws Exception {
if (args.length == 0) {
System.out.println("Usage: writer ");
ToolRunner.printGenericCommandUsage(System.out);
return 2;
}
Path outDir = new Path(args[0]);
Configuration conf = getConf();
JobClient client = new JobClient(conf);
ClusterStatus cluster = client.getClusterStatus();
int numMapsPerHost = conf.getInt(MAPS_PER_HOST, 10);
long numBytesToWritePerMap = conf.getLong(BYTES_PER_MAP,
1*1024*1024*1024);
if (numBytesToWritePerMap == 0) {
System.err.println("Cannot have" + BYTES_PER_MAP + " set to 0");
return -2;
}
long totalBytesToWrite = conf.getLong(TOTAL_BYTES,
numMapsPerHost*numBytesToWritePerMap*cluster.getTaskTrackers());
int numMaps = (int) (totalBytesToWrite / numBytesToWritePerMap);
if (numMaps == 0 && totalBytesToWrite > 0) {
numMaps = 1;
conf.setLong(BYTES_PER_MAP, totalBytesToWrite);
}
conf.setInt(MRJobConfig.NUM_MAPS, numMaps);
Job job = new Job(conf);
job.setJarByClass(RandomWriter.class);
job.setJobName("random-writer");
FileOutputFormat.setOutputPath(job, outDir);
job.setOutputKeyClass(BytesWritable.class);
job.setOutputValueClass(BytesWritable.class);
job.setInputFormatClass(RandomInputFormat.class);
job.setMapperClass(RandomMapper.class);
job.setReducerClass(Reducer.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
System.out.println("Running " + numMaps + " maps.");
// reducer NONE
job.setNumReduceTasks(0);
Date startTime = new Date();
System.out.println("Job started: " + startTime);
int ret = job.waitForCompletion(true) ? 0 : 1;
Date endTime = new Date();
System.out.println("Job ended: " + endTime);
System.out.println("The job took " +
(endTime.getTime() - startTime.getTime()) /1000 +
" seconds.");
return ret;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new RandomWriter(), args);
System.exit(res);
}
}