Java tutorial
/** * 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 avro.mr; import java.io.IOException; import java.util.StringTokenizer; import org.apache.avro.Schema; import org.apache.avro.Schema.Type; import org.apache.avro.mapred.AvroWrapper; import org.apache.avro.mapred.Pair; import org.apache.avro.mapreduce.AvroJob; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; /** * The classic WordCount example modified to output Avro Pair<CharSequence, * Integer> records instead of text. * * @link http://avro.apache.org/docs/current/mr.html */ public class MapReduceAvroWordCount extends Configured implements Tool { public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); context.write(word, one); } } } public static class Reduce extends Reducer<Text, IntWritable, AvroWrapper<Pair<CharSequence, Integer>>, NullWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable value : values) { sum += value.get(); } context.write(new AvroWrapper<Pair<CharSequence, Integer>>( new Pair<CharSequence, Integer>(key.toString(), sum)), NullWritable.get()); } } public int run(String[] args) throws Exception { if (args.length != 2) { System.err.println("Usage: AvroWordCount <input path> <output path>"); return -1; } Job job = Job.getInstance(getConf()); job.setJarByClass(MapReduceAvroWordCount.class); job.setJobName("wordcount"); // We call setOutputSchema first so we can override the configuration // parameters it sets AvroJob.setOutputKeySchema(job, Pair.getPairSchema(Schema.create(Type.STRING), Schema.create(Type.INT))); job.setOutputValueClass(NullWritable.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setInputFormatClass(TextInputFormat.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setSortComparatorClass(Text.Comparator.class); FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); return 0; } public static void main(String[] args) throws Exception { int res = ToolRunner.run(new Configuration(), new MapReduceAvroWordCount(), args); System.exit(res); } }