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* to you under the Apache License, Version 2.0 (the
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* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.flink.hadoopcompatibility.mapreduce.example;
import org.apache.flink.api.java.aggregation.Aggregations;
import org.apache.flink.api.common.functions.RichFlatMapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;
import org.apache.hadoop.fs.Path;
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.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.hadoopcompatibility.mapreduce.HadoopInputFormat;
import org.apache.flink.hadoopcompatibility.mapreduce.HadoopOutputFormat;
/**
* Implements a word count which takes the input file and counts the number of
* occurrences of each word in the file and writes the result back to disk.
*
* This example shows how to use Hadoop Input Formats, how to convert Hadoop Writables to
* common Java types for better usage in a Flink job and how to use Hadoop Output Formats.
*/
@SuppressWarnings("serial")
public class WordCount {
public static void main(String[] args) throws Exception {
if (args.length < 2) {
System.err.println("Usage: WordCount <input path> <result path>");
return;
}
final String inputPath = args[0];
final String outputPath = args[1];
final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
env.setDegreeOfParallelism(1);
// Set up the Hadoop Input Format
Job job = Job.getInstance();
HadoopInputFormat<LongWritable, Text> hadoopInputFormat = new HadoopInputFormat<LongWritable, Text>(new TextInputFormat(), LongWritable.class, Text.class, job);
TextInputFormat.addInputPath(job, new Path(inputPath));
// Create a Flink job with it
DataSet<Tuple2<LongWritable, Text>> text = env.createInput(hadoopInputFormat);
// Tokenize the line and convert from Writable "Text" to String for better handling
DataSet<Tuple2<String, Integer>> words = text.flatMap(new Tokenizer());
// Sum up the words
DataSet<Tuple2<String, Integer>> result = words.groupBy(0).aggregate(Aggregations.SUM, 1);
// Convert String back to Writable "Text" for use with Hadoop Output Format
DataSet<Tuple2<Text, IntWritable>> hadoopResult = result.map(new HadoopDatatypeMapper());
// Set up Hadoop Output Format
HadoopOutputFormat<Text, IntWritable> hadoopOutputFormat = new HadoopOutputFormat<Text, IntWritable>(new TextOutputFormat<Text, IntWritable>(), job);
hadoopOutputFormat.getConfiguration().set("mapreduce.output.textoutputformat.separator", " ");
hadoopOutputFormat.getConfiguration().set("mapred.textoutputformat.separator", " "); // set the value for both, since this test
// is being executed with both types (hadoop1 and hadoop2 profile)
TextOutputFormat.setOutputPath(job, new Path(outputPath));
// Output & Execute
hadoopResult.output(hadoopOutputFormat);
env.execute("Word Count");
}
/**
* Splits a line into words and converts Hadoop Writables into normal Java data types.
*/
public static final class Tokenizer extends RichFlatMapFunction<Tuple2<LongWritable, Text>, Tuple2<String, Integer>> {
@Override
public void flatMap(Tuple2<LongWritable, Text> value, Collector<Tuple2<String, Integer>> out) {
// normalize and split the line
String line = value.f1.toString();
String[] tokens = line.toLowerCase().split("\\W+");
// emit the pairs
for (String token : tokens) {
if (token.length() > 0) {
out.collect(new Tuple2<String, Integer>(token, 1));
}
}
}
}
/**
* Converts Java data types to Hadoop Writables.
*/
public static final class HadoopDatatypeMapper extends RichMapFunction<Tuple2<String, Integer>, Tuple2<Text, IntWritable>> {
@Override
public Tuple2<Text, IntWritable> map(Tuple2<String, Integer> value) throws Exception {
return new Tuple2<Text, IntWritable>(new Text(value.f0), new IntWritable(value.f1));
}
}
}