Java examples for Big Data:Hadoop
Generate the official terasort input data set.
/**//from w ww . j a v a2 s.co m * 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.terasort; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.WritableUtils; import org.apache.hadoop.mapred.FileOutputFormat; 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.util.Tool; import org.apache.hadoop.util.ToolRunner; /** * Generate the official terasort input data set. * The user specifies the number of rows and the output directory and this * class runs a map/reduce program to generate the data. * The format of the data is: * <ul> * <li>(10 bytes key) (10 bytes rowid) (78 bytes filler) \r \n * <li>The keys are random characters from the set ' ' .. '~'. * <li>The rowid is the right justified row id as a int. * <li>The filler consists of 7 runs of 10 characters from 'A' to 'Z'. * </ul> * * <p> * To run the program: * <b>bin/hadoop jar hadoop-examples-*.jar teragen 10000000000 in-dir</b> */ public class TeraGen extends Configured implements Tool { /** * An input format that assigns ranges of longs to each mapper. */ static class RangeInputFormat implements InputFormat<LongWritable, NullWritable> { /** * An input split consisting of a range on numbers. */ static class RangeInputSplit implements InputSplit { long firstRow; long rowCount; public RangeInputSplit() { } public RangeInputSplit(long offset, long length) { firstRow = offset; rowCount = length; } public long getLength() throws IOException { return 0; } public String[] getLocations() throws IOException { return new String[] {}; } public void readFields(DataInput in) throws IOException { firstRow = WritableUtils.readVLong(in); rowCount = WritableUtils.readVLong(in); } public void write(DataOutput out) throws IOException { WritableUtils.writeVLong(out, firstRow); WritableUtils.writeVLong(out, rowCount); } } /** * A record reader that will generate a range of numbers. */ static class RangeRecordReader implements RecordReader<LongWritable, NullWritable> { long startRow; long finishedRows; long totalRows; public RangeRecordReader(RangeInputSplit split) { startRow = split.firstRow; finishedRows = 0; totalRows = split.rowCount; } public void close() throws IOException { // NOTHING } public LongWritable createKey() { return new LongWritable(); } public NullWritable createValue() { return NullWritable.get(); } public long getPos() throws IOException { return finishedRows; } public float getProgress() throws IOException { return finishedRows / (float) totalRows; } public boolean next(LongWritable key, NullWritable value) { if (finishedRows < totalRows) { key.set(startRow + finishedRows); finishedRows += 1; return true; } else { return false; } } } public RecordReader<LongWritable, NullWritable> getRecordReader( InputSplit split, JobConf job, Reporter reporter) throws IOException { return new RangeRecordReader((RangeInputSplit) split); } /** * Create the desired number of splits, dividing the number of rows * between the mappers. */ public InputSplit[] getSplits(JobConf job, int numSplits) { long totalRows = getNumberOfRows(job); long rowsPerSplit = totalRows / numSplits; System.out.println("Generating " + totalRows + " using " + numSplits + " maps with step of " + rowsPerSplit); InputSplit[] splits = new InputSplit[numSplits]; long currentRow = 0; for (int split = 0; split < numSplits - 1; ++split) { splits[split] = new RangeInputSplit(currentRow, rowsPerSplit); currentRow += rowsPerSplit; } splits[numSplits - 1] = new RangeInputSplit(currentRow, totalRows - currentRow); return splits; } } static long getNumberOfRows(JobConf job) { return job.getLong("terasort.num-rows", 0); } static void setNumberOfRows(JobConf job, long numRows) { job.setLong("terasort.num-rows", numRows); } static class RandomGenerator { private long seed = 0; private static final long mask32 = (1l << 32) - 1; /** * The number of iterations separating the precomputed seeds. */ private static final int seedSkip = 128 * 1024 * 1024; /** * The precomputed seed values after every seedSkip iterations. * There should be enough values so that a 2**32 iterations are * covered. */ private static final long[] seeds = new long[] { 0L, 4160749568L, 4026531840L, 3892314112L, 3758096384L, 3623878656L, 3489660928L, 3355443200L, 3221225472L, 3087007744L, 2952790016L, 2818572288L, 2684354560L, 2550136832L, 2415919104L, 2281701376L, 2147483648L, 2013265920L, 1879048192L, 1744830464L, 1610612736L, 1476395008L, 1342177280L, 1207959552L, 1073741824L, 939524096L, 805306368L, 671088640L, 536870912L, 402653184L, 268435456L, 134217728L, }; /** * Start the random number generator on the given iteration. * @param initalIteration the iteration number to start on */ RandomGenerator(long initalIteration) { int baseIndex = (int) ((initalIteration & mask32) / seedSkip); seed = seeds[baseIndex]; for (int i = 0; i < initalIteration % seedSkip; ++i) { next(); } } RandomGenerator() { this(0); } long next() { seed = (seed * 3141592621l + 663896637) & mask32; return seed; } } /** * The Mapper class that given a row number, will generate the appropriate * output line. */ public static class SortGenMapper extends MapReduceBase implements Mapper<LongWritable, NullWritable, Text, Text> { private Text key = new Text(); private Text value = new Text(); private RandomGenerator rand; private byte[] keyBytes = new byte[12]; private byte[] spaces = " ".getBytes(); private byte[][] filler = new byte[26][]; { for (int i = 0; i < 26; ++i) { filler[i] = new byte[10]; for (int j = 0; j < 10; ++j) { filler[i][j] = (byte) ('A' + i); } } } /** * Add a random key to the text * @param rowId */ private void addKey() { for (int i = 0; i < 3; i++) { long temp = rand.next() / 52; keyBytes[3 + 4 * i] = (byte) (' ' + (temp % 95)); temp /= 95; keyBytes[2 + 4 * i] = (byte) (' ' + (temp % 95)); temp /= 95; keyBytes[1 + 4 * i] = (byte) (' ' + (temp % 95)); temp /= 95; keyBytes[4 * i] = (byte) (' ' + (temp % 95)); } key.set(keyBytes, 0, 10); } /** * Add the rowid to the row. * @param rowId */ private void addRowId(long rowId) { byte[] rowid = Integer.toString((int) rowId).getBytes(); int padSpace = 10 - rowid.length; if (padSpace > 0) { value.append(spaces, 0, 10 - rowid.length); } value.append(rowid, 0, Math.min(rowid.length, 10)); } /** * Add the required filler bytes. Each row consists of 7 blocks of * 10 characters and 1 block of 8 characters. * @param rowId the current row number */ private void addFiller(long rowId) { int base = (int) ((rowId * 8) % 26); for (int i = 0; i < 7; ++i) { value.append(filler[(base + i) % 26], 0, 10); } value.append(filler[(base + 7) % 26], 0, 8); } public void map(LongWritable row, NullWritable ignored, OutputCollector<Text, Text> output, Reporter reporter) throws IOException { long rowId = row.get(); if (rand == null) { // we use 3 random numbers per a row rand = new RandomGenerator(rowId * 3); } addKey(); value.clear(); addRowId(rowId); addFiller(rowId); output.collect(key, value); } } /** * @param args the cli arguments */ public int run(String[] args) throws IOException { JobConf job = (JobConf) getConf(); setNumberOfRows(job, Long.parseLong(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.setJobName("TeraGen"); job.setJarByClass(TeraGen.class); job.setMapperClass(SortGenMapper.class); job.setNumReduceTasks(0); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); job.setInputFormat(RangeInputFormat.class); job.setOutputFormat(TeraOutputFormat.class); JobClient.runJob(job); return 0; } public static void main(String[] args) throws Exception { int res = ToolRunner.run(new JobConf(), new TeraGen(), args); System.exit(res); } }