hadoop实例 RandomWriter

参考文献：http://www.hadooper.cn/dct/page/65778

1.概述

RandomWriter（随机写）例子利用 Map/Reduce把 数据随机的写到dfs中。每个map输入单个文件名，然后随机写BytesWritable的键和值到DFS顺序文件。map没有产生任何输出，所以reduce没有执行。产生的数据是可以配置的。配置变量如下

名字	默认值	描述
test.randomwriter.maps_per_host	10	每个节点运行的map任务数
test.randomwrite.bytes_per_map	1073741824	每个map任务产生的数据量
test.randomwrite.min_key	10	minimum size of the key in bytes
test.randomwrite.max_key	1000	maximum size of the key in bytes
test.randomwrite.min_value	0	minimum size of the value
test.randomwrite.max_value	20000	maximum size of the value

test.randomwriter.maps_per_host表示每个工作节点（datanode）上运行map的次数。默认情况下，只有一个数据节点，那么就有10个map，每个map的数据量为1G，因此要将10G数据写入到hdfs中。我配置的试验环境中只有2个工作节点，不过我希望每个工作节点只有1个map任务。

test.randomwrite.bytes_per_map我原本以为是随机写输出的测试文件的大小，默认为1G=1*1024*1024*1024，但是我将这个数据改成1*1024*1024以后，输出的测试文件还是1G，这让我很不解。（PS:2011-11-2，今天知道这个参数表示没个map任务产生的数据量，如果将其改为1*1024*1024，那么就表示没个map任务产生的数据量为1MB。）（PS：2011-11-3，修改参数test.randomwrite.bytes_per_map并不能更改每个map任务产生的数据量，还是1G，不管我将这个参数设定为什么值。不过修改参数：test.randomwriter.maps_per_host是有效的。测试发现将该参数设为1和2都测试通过。问题：在哪里修改test.randomwrite.bytes_per_map才能真正修改map任务产生的数据量。！）

2.代码实例

其中test.randomwrite.bytes_per_map=1*1024*1024，test.randomwriter.maps_per_host=1。

/** * 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.Date; 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.FileOutputFormat; import org.apache.hadoop.mapred.FileSplit; import org.apache.hadoop.mapred.InputFormat; import org.apache.hadoop.mapred.InputSplit; import org.apache.hadoop.mapred.JobClient; import org.apache.hadoop.mapred.JobConf; import org.apache.hadoop.mapred.MapReduceBase; import org.apache.hadoop.mapred.Mapper; import org.apache.hadoop.mapred.OutputCollector; import org.apache.hadoop.mapred.RecordReader; import org.apache.hadoop.mapred.Reporter; import org.apache.hadoop.mapred.SequenceFileOutputFormat; import org.apache.hadoop.mapred.lib.IdentityReducer; 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: * <xmp> * <?xml version="1.0"?> * <?xml-stylesheet type="text/xsl" href="configuration.xsl"?> * <configuration> * <property> * <name>test.randomwrite.min_key</name> * <value>10</value> * </property> * <property> * <name>test.randomwrite.max_key</name> * <value>10</value> * </property> * <property> * <name>test.randomwrite.min_value</name> * <value>90</value> * </property> * <property> * <name>test.randomwrite.max_value</name> * <value>90</value> * </property> * <property> * <name>test.randomwrite.total_bytes</name> * <value>1099511627776</value> * </property> * </configuration></xmp> * * 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 { /** * 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 implements InputFormat<Text, Text> { /** * Generate the requested number of file splits, with the filename * set to the filename of the output file. */ public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException { InputSplit[] result = new InputSplit[numSplits]; Path outDir = FileOutputFormat.getOutputPath(job); for(int i=0; i < result.length; ++i) { result[i] = 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 implements RecordReader<Text, Text> { Path name; public RandomRecordReader(Path p) { name = p; } public boolean next(Text key, Text value) { if (name != null) { key.set(name.getName()); name = null; return true; } return false; } public Text createKey() { return new Text(); } public Text createValue() { return new Text(); } public long getPos() { return 0; } public void close() {} public float getProgress() { return 0.0f; } } public RecordReader<Text, Text> getRecordReader(InputSplit split, JobConf job, Reporter reporter) throws IOException { return new RandomRecordReader(((FileSplit) split).getPath()); } } static class Map extends MapReduceBase implements Mapper<WritableComparable, Writable, BytesWritable, BytesWritable> { 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, OutputCollector<BytesWritable, BytesWritable> output, Reporter reporter) throws IOException { 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()); output.collect(randomKey, randomValue); numBytesToWrite -= keyLength + valueLength; reporter.incrCounter(Counters.BYTES_WRITTEN, keyLength + valueLength); reporter.incrCounter(Counters.RECORDS_WRITTEN, 1); if (++itemCount % 200 == 0) { reporter.setStatus("wrote record " + itemCount + ". " + numBytesToWrite + " bytes left."); } } reporter.setStatus("done with " + itemCount + " records."); } /** * Save the values out of the configuaration that we need to write * the data. */ @Override public void configure(JobConf job) { numBytesToWrite = job.getLong("test.randomwrite.bytes_per_map", 1*1024*1024); minKeySize = job.getInt("test.randomwrite.min_key", 10); keySizeRange = job.getInt("test.randomwrite.max_key", 1000) - minKeySize; minValueSize = job.getInt("test.randomwrite.min_value", 0); valueSizeRange = job.getInt("test.randomwrite.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 <out-dir>"); ToolRunner.printGenericCommandUsage(System.out); return -1; } Path outDir = new Path(args[0]); JobConf job = new JobConf(getConf()); job.setJarByClass(RandomWriter.class); job.setJobName("random-writer"); FileOutputFormat.setOutputPath(job, outDir); job.setOutputKeyClass(BytesWritable.class); job.setOutputValueClass(BytesWritable.class); job.setInputFormat(RandomInputFormat.class); job.setMapperClass(Map.class); job.setReducerClass(IdentityReducer.class); job.setOutputFormat(SequenceFileOutputFormat.class); JobClient client = new JobClient(job); ClusterStatus cluster = client.getClusterStatus(); int numMapsPerHost = job.getInt("test.randomwriter.maps_per_host", 1); long numBytesToWritePerMap = job.getLong("test.randomwrite.bytes_per_map", 1*1024*1024); if (numBytesToWritePerMap == 0) { System.err.println("Cannot have test.randomwrite.bytes_per_map set to 0"); return -2; } long totalBytesToWrite = job.getLong("test.randomwrite.total_bytes", numMapsPerHost*numBytesToWritePerMap*cluster.getTaskTrackers()); int numMaps = (int) (totalBytesToWrite / numBytesToWritePerMap); if (numMaps == 0 && totalBytesToWrite > 0) { numMaps = 1; job.setLong("test.randomwrite.bytes_per_map", totalBytesToWrite); } job.setNumMapTasks(numMaps); System.out.println("Running " + numMaps + " maps."); // reducer NONE job.setNumReduceTasks(0); Date startTime = new Date(); System.out.println("Job started: " + startTime); JobClient.runJob(job); Date endTime = new Date(); System.out.println("Job ended: " + endTime); System.out.println("The job took " + (endTime.getTime() - startTime.getTime()) /1000 + " seconds."); return 0; } public static void main(String[] args) throws Exception { int res = ToolRunner.run(new Configuration(), new RandomWriter(), args); System.exit(res); } } 输出信息：

11/10/17 13:27:46 WARN conf.Configuration: DEPRECATED: hadoop-site.xml found in the classpath. Usage of hadoop-site.xml is deprecated. Instead use core-site.xml, mapred-site.xml and hdfs-site.xml to override properties of core-default.xml, mapred-default.xml and hdfs-default.xml respectively Running 2 maps. Job started: Mon Oct 17 13:27:47 CST 2011 11/10/17 13:27:47 INFO mapred.JobClient: Running job: job_201110171322_0001 11/10/17 13:27:48 INFO mapred.JobClient: map 0% reduce 0% 11/10/17 13:29:58 INFO mapred.JobClient: map 50% reduce 0% 11/10/17 13:30:05 INFO mapred.JobClient: map 100% reduce 0% 11/10/17 13:30:07 INFO mapred.JobClient: Job complete: job_201110171322_0001 11/10/17 13:30:07 INFO mapred.JobClient: Counters: 8 11/10/17 13:30:07 INFO mapred.JobClient: Job Counters 11/10/17 13:30:07 INFO mapred.JobClient: Launched map tasks=3 11/10/17 13:30:07 INFO mapred.JobClient: org.apache.hadoop.examples.RandomWriter$Counters 11/10/17 13:30:07 INFO mapred.JobClient: BYTES_WRITTEN=2147504078 11/10/17 13:30:07 INFO mapred.JobClient: RECORDS_WRITTEN=204528 11/10/17 13:30:07 INFO mapred.JobClient: FileSystemCounters 11/10/17 13:30:07 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=2154580318 11/10/17 13:30:07 INFO mapred.JobClient: Map-Reduce Framework 11/10/17 13:30:07 INFO mapred.JobClient: Map input records=2 11/10/17 13:30:07 INFO mapred.JobClient: Spilled Records=0 11/10/17 13:30:07 INFO mapred.JobClient: Map input bytes=0 11/10/17 13:30:07 INFO mapred.JobClient: Map output records=204528 Job ended: Mon Oct 17 13:30:07 CST 2011 The job took 140 seconds. 在hdfs上产生了两个文件，在/home/hadoop/rand目录下，分别是part-00000（1Gb,r3）和part-00001（1Gb,r3）