/**
* 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.zebra.mapreduce;
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.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.zebra.mapreduce.BasicTableOutputFormat;
import org.apache.hadoop.zebra.mapreduce.TableInputFormat;
import org.apache.hadoop.zebra.parser.ParseException;
import org.apache.hadoop.zebra.schema.Schema;
import org.apache.hadoop.zebra.types.TypesUtils;
import org.apache.pig.data.Tuple;
import java.io.IOException;
import java.util.Iterator;
/**
* <code>TableMapReduceExample<code> is a map-reduce example for Table Input/Output Format.
* <p/>
* Schema for Table is set to two columns containing Word of type <i>string</i> and Count of type <i>int</i> using <code> BasicTableOutputFormat.setSchema(jobConf, "word:string, count:int"); </code>
* <p/>
* Hint for creation of Column Groups is specified using
* <code> BasicTableOutputFormat.setStorageHint(jobConf, "[word];[count]"); </code>
* . Here we have two column groups.
* <p/>
* Input file should contain rows of word and count, separated by a space. For
* example:
*
* <pre>
* this 2
* is 1
* a 5
* test 2
* hello 1
* world 3
* </pre>
* <p/>
* <p>
* Second job reads output from the first job which is in Table Format. Here we
* specify <i>count</i> as projection column. Table Input Format projects in put
* row which has both word and count into a row containing only the count column
* and hands it to map.
* <p/>
* Reducer sums the counts and produces a sum of counts which should match total
* number of words in original text.
*/
public class TableMapReduceExample extends Configured implements Tool {
static class Map extends Mapper<LongWritable, Text, BytesWritable, Tuple> {
private BytesWritable bytesKey;
private Tuple tupleRow;
/**
* Map method for reading input.
*/
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// value should contain "word count"
String[] wordCount = value.toString().split(" ");
if (wordCount.length != 2) {
// LOG the error
throw new IOException("Value does not contain two fields:" + value);
}
byte[] word = wordCount[0].getBytes();
bytesKey.set(word, 0, word.length);
tupleRow.set(0, new String(word));
tupleRow.set(1, Integer.parseInt(wordCount[1]));
context.write( bytesKey, tupleRow );
}
/**
* Configuration of the job. Here we create an empty Tuple Row.
*/
@Override
public void setup(Context context) {
bytesKey = new BytesWritable();
try {
Schema outSchema = BasicTableOutputFormat.getSchema( context );
tupleRow = TypesUtils.createTuple(outSchema);
} catch (IOException e) {
throw new RuntimeException(e);
} catch (ParseException e) {
throw new RuntimeException(e);
}
}
}
static class ProjectionMap extends
Mapper<BytesWritable, Tuple, Text, IntWritable> {
private final static Text all = new Text("All");
/**
* Map method which gets count column after projection.
*
* @throws IOException
*/
@Override
public void map(BytesWritable key, Tuple value, Context context)
throws IOException, InterruptedException {
context.write( all, new IntWritable((Integer) value.get(0)) );
}
}
public static class ProjectionReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
/**
* Reduce method which implements summation. Acts as both reducer and
* combiner.
*
* @throws IOException
*/
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
Iterator<IntWritable> iterator = values.iterator();
while (iterator.hasNext()) {
sum += iterator.next().get();
}
context.write(key, new IntWritable(sum));
}
}
/**
* Where jobs and their settings and sequence is set.
*
* @param args
* arguments with exception of Tools understandable ones.
*/
public int run(String[] args) throws Exception {
if (args == null || args.length != 3) {
System.out
.println("usage: TableMapReduceExample input_path_for_text_file output_path_for_table output_path_for_text_file");
System.exit(-1);
}
/*
* First MR Job creating a Table with two columns
*/
Job job = new Job();
job.setJobName("TableMapReduceExample");
Configuration conf = job.getConfiguration();
conf.set("table.output.tfile.compression", "none");
// Input settings
job.setInputFormatClass(TextInputFormat.class);
job.setMapperClass(Map.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
// Output settings
job.setOutputFormatClass(BasicTableOutputFormat.class);
BasicTableOutputFormat.setOutputPath( job, new Path(args[1]) );
// set the logical schema with 2 columns
BasicTableOutputFormat.setSchema( job, "word:string, count:int" );
// for demo purposes, create 2 physical column groups
BasicTableOutputFormat.setStorageHint( job, "[word];[count]" );
// set map-only job.
job.setNumReduceTasks(0);
// Run Job
job.submit();
/*
* Second MR Job for Table Projection of count column
*/
Job projectionJob = new Job();
projectionJob.setJobName("TableProjectionMapReduceExample");
conf = projectionJob.getConfiguration();
// Input settings
projectionJob.setMapperClass(ProjectionMap.class);
projectionJob.setInputFormatClass(TableInputFormat.class);
TableInputFormat.setProjection(job, "count");
TableInputFormat.setInputPaths(job, new Path(args[1]));
projectionJob.setMapOutputKeyClass(Text.class);
projectionJob.setMapOutputValueClass(IntWritable.class);
// Output settings
projectionJob.setOutputFormatClass(TextOutputFormat.class);
FileOutputFormat.setOutputPath(projectionJob, new Path(args[2]));
projectionJob.setReducerClass(ProjectionReduce.class);
projectionJob.setCombinerClass(ProjectionReduce.class);
// Run Job
projectionJob.submit();
return 0;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new TableMapReduceExample(),
args);
System.exit(res);
}
}