This topic describes an example of running WordCount in MapReduce.
Preparations
- Prepare the JAR package of the test program. In this topic, the JAR package is named
mapreduce-examples.jar and stored in the local path data\resources.
- Create test tables.
create table wc_in (key string, value string); create table wc_out(key string, cnt bigint);
- Add test resources.
add jar data\resources\mapreduce-examples.jar -f;
- Create test tables.
- Prepare test tables and resources for WordCount.
- Use Tunnel to import data.
tunnel upload data wc_in;
The following data is imported to the wc_in table:hello,odps
Procedure
Run WordCount on the MaxCompute client.
jar -resources mapreduce-examples.jar -classpath data\resources\mapreduce-examples.jar
com.aliyun.odps.mapred.open.example.WordCount wc_in wc_out
Expected result
The job runs normally. The following data is returned in the wc_out table:
+------------+------------+
| key | cnt |
+------------+------------+
| hello | 1 |
| odps | 1 |
+------------+------------+
Sample code
For information about Project Object Model (POM) dependencies, see Usage notes.
package com.aliyun.odps.mapred.open.example;
import java.io.IOException;
import java.util.Iterator;
import com.aliyun.odps.data.Record;
import com.aliyun.odps.data.TableInfo;
import com.aliyun.odps.mapred.JobClient;
import com.aliyun.odps.mapred.MapperBase;
import com.aliyun.odps.mapred.ReducerBase;
import com.aliyun.odps.mapred.conf.JobConf;
import com.aliyun.odps.mapred.utils.InputUtils;
import com.aliyun.odps.mapred.utils.OutputUtils;
import com.aliyun.odps.mapred.utils.SchemaUtils;
public class WordCount {
public static class TokenizerMapper extends MapperBase {
private Record word;
private Record one;
@Override
public void setup(TaskContext context) throws IOException {
word = context.createMapOutputKeyRecord();
one = context.createMapOutputValueRecord();
one.set(new Object[] { 1L });
System.out.println("TaskID:" + context.getTaskID().toString());
}
@Override
public void map(long recordNum, Record record, TaskContext context)
throws IOException {
for (int i = 0; i < record.getColumnCount(); i++) {
word.set(new Object[] { record.get(i).toString() });
context.write(word, one);
}
}
}
/**
* A combiner class that combines map output by sum them.
**/
public static class SumCombiner extends ReducerBase {
private Record count;
@Override
public void setup(TaskContext context) throws IOException {
count = context.createMapOutputValueRecord();
}
/** The combiner implements the same interface as that of the reducer. The combiner allows you to immediately run a local reduce job on the mapper to reduce the output of the mapper. */
@Override
public void reduce(Record key, Iterator<Record> values, TaskContext context)
throws IOException {
long c = 0;
while (values.hasNext()) {
Record val = values.next();
c += (Long) val.get(0);
}
count.set(0, c);
context.write(key, count);
}
}
/**
* A reducer class that just emits the sum of the input values.
**/
public static class SumReducer extends ReducerBase {
private Record result = null;
@Override
public void setup(TaskContext context) throws IOException {
result = context.createOutputRecord();
}
@Override
public void reduce(Record key, Iterator<Record> values, TaskContext context)
throws IOException {
long count = 0;
while (values.hasNext()) {
Record val = values.next();
count += (Long) val.get(0);
}
result.set(0, key.get(0));
result.set(1, count);
context.write(result);
}
}
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("Usage: WordCount <in_table> <out_table>");
System.exit(2);
}
JobConf job = new JobConf();
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(SumCombiner.class);
job.setReducerClass(SumReducer.class);
/** Configure the schema that defines the intermediate output of the mapper as key-value pairs. The intermediate output of the mapper exists as records. */
job.setMapOutputKeySchema(SchemaUtils.fromString("word:string"));
job.setMapOutputValueSchema(SchemaUtils.fromString("count:bigint"));
/** Configure information about input and output tables. */
InputUtils.addTable(TableInfo.builder().tableName(args[0]).build(), job);
OutputUtils.addTable(TableInfo.builder().tableName(args[1]).build(), job);
JobClient.runJob(job);
}
}