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复杂的MapReduce处理中，往往需要将复杂的处理过程，分解成多个简单的Job来执行，第1个Job的输出做为第2个Job的输入，相互之间有一定依赖关系。下面以求平均值为例 1. 求Sum
2. 求Count
3. 计算平均数
[hadoop@h71 q1]$ vi ceshi.txt
2
8
8
3
2
3
5
3
0
2
7
[hadoop@h71 q1]$ hadoop fs -put ceshi.txt /input
每1个步骤看成一个Job，其中Job3必须等待Job1、Job2完成，并将Job1、Job2的输出结果做为输入，下面的代码演示了如何将这3个Job串起来
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
public class Avg2 {
public static boolean deleteFile(Configuration conf, String remoteFilePath, boolean recursive) throws IOException {
FileSystem fs = FileSystem.get(conf);
boolean result = fs.delete(new Path(remoteFilePath), recursive);
fs.close();
return result;
}
public static boolean deleteFile(Configuration conf, String remoteFilePath) throws IOException {
return deleteFile(conf, remoteFilePath, true);
}
private static final Text TEXT_SUM = new Text("SUM");
private static final Text TEXT_COUNT = new Text("COUNT");
private static final Text TEXT_AVG = new Text("AVG");
//计算Sum
public static class SumMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
public long sum = 0;
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
sum += Long.parseLong(value.toString());
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(TEXT_SUM, new LongWritable(sum));
}
}
public static class SumReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
public long sum = 0;
public void reduce(Text key, Iterable<LongWritable> values, Context context)
throws IOException, InterruptedException {
for (LongWritable v : values) {
sum += v.get();
}
context.write(TEXT_SUM, new LongWritable(sum));
}
}
//计算Count
public static class CountMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
public long count = 0;
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
count += 1;
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(TEXT_COUNT, new LongWritable(count));
}
}
public static class CountReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
public long count = 0;
public void reduce(Text key, Iterable<LongWritable> values, Context context)
throws IOException, InterruptedException {
for (LongWritable v : values) {
count += v.get();
}
context.write(TEXT_COUNT, new LongWritable(count));
}
}
//计算Avg
public static class AvgMapper extends Mapper<LongWritable, Text, LongWritable, LongWritable> {
public long count = 0;
public long sum = 0;
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] v = value.toString().split("\t");
if (v[0].equals("COUNT")) {
count = Long.parseLong(v[1]);
} else if (v[0].equals("SUM")) {
sum = Long.parseLong(v[1]);
}
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(new LongWritable(sum), new LongWritable(count));
}
}
public static class AvgReducer extends Reducer<LongWritable, LongWritable, Text, DoubleWritable> {
public long sum = 0;
public long count = 0;
public void reduce(LongWritable key, Iterable<LongWritable> values, Context context)
throws IOException, InterruptedException {
sum += key.get();
for (LongWritable v : values) {
count += v.get();
}
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(TEXT_AVG, new DoubleWritable(new Double(sum) / count));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String inputPath = "/input/ceshi.txt";
String maxOutputPath = "/output/sum/";
String countOutputPath = "/output/count/";
String avgOutputPath = "/output/avg/";
//删除输出目录(可选,省得多次运行时,总是报OUTPUT目录已存在)
Avg2.deleteFile(conf, maxOutputPath);
Avg2.deleteFile(conf, countOutputPath);
Avg2.deleteFile(conf, avgOutputPath);
Job job1 = Job.getInstance(conf, "Sum");
job1.setJarByClass(Avg2.class);
job1.setMapperClass(SumMapper.class);
job1.setCombinerClass(SumReducer.class);
job1.setReducerClass(SumReducer.class);
job1.setOutputKeyClass(Text.class);
job1.setOutputValueClass(LongWritable.class);
FileInputFormat.addInputPath(job1, new Path(inputPath));
FileOutputFormat.setOutputPath(job1, new Path(maxOutputPath));
Job job2 = Job.getInstance(conf, "Count");
job2.setJarByClass(Avg2.class);
job2.setMapperClass(CountMapper.class);
job2.setCombinerClass(CountReducer.class);
job2.setReducerClass(CountReducer.class);
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(LongWritable.class);
FileInputFormat.addInputPath(job2, new Path(inputPath));
FileOutputFormat.setOutputPath(job2, new Path(countOutputPath));
Job job3 = Job.getInstance(conf, "Average");
job3.setJarByClass(Avg2.class);
job3.setMapperClass(AvgMapper.class);
job3.setReducerClass(AvgReducer.class);
job3.setMapOutputKeyClass(LongWritable.class);
job3.setMapOutputValueClass(LongWritable.class);
job3.setOutputKeyClass(Text.class);
job3.setOutputValueClass(DoubleWritable.class);
//将job1及job2的输出为做job3的输入
FileInputFormat.addInputPath(job3, new Path(maxOutputPath));
FileInputFormat.addInputPath(job3, new Path(countOutputPath));
FileOutputFormat.setOutputPath(job3, new Path(avgOutputPath));
//提交job1及job2,并等待完成
if (job1.waitForCompletion(true) && job2.waitForCompletion(true)) {
System.exit(job3.waitForCompletion(true) ? 0 : 1);
}
}
}
在Linux中执行该代码：
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/javac Avg2.java
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/jar cvf xx.jar Avg2*class
[hadoop@h71 q1]$ hadoop jar xx.jar Avg2
[hadoop@h71 q1]$ hadoop fs -lsr /output
drwxr-xr-x - hadoop supergroup 0 2017-03-18 22:45 /output/avg
-rw-r--r-- 2 hadoop supergroup 0 2017-03-18 22:45 /output/avg/_SUCCESS
-rw-r--r-- 2 hadoop supergroup 22 2017-03-18 22:45 /output/avg/part-r-00000
drwxr-xr-x - hadoop supergroup 0 2017-03-18 22:44 /output/count
-rw-r--r-- 2 hadoop supergroup 0 2017-03-18 22:44 /output/count/_SUCCESS
-rw-r--r-- 2 hadoop supergroup 9 2017-03-18 22:44 /output/count/part-r-00000
drwxr-xr-x - hadoop supergroup 0 2017-03-18 22:44 /output/sum
-rw-r--r-- 2 hadoop supergroup 0 2017-03-18 22:44 /output/sum/_SUCCESS
-rw-r--r-- 2 hadoop supergroup 7 2017-03-18 22:44 /output/sum/part-r-00000
[hadoop@h71 q1]$ hadoop fs -cat /output/sum/part-r-00000
SUM 43
[hadoop@h71 q1]$ hadoop fs -cat /output/count/part-r-00000
COUNT 11
[hadoop@h71 q1]$ hadoop fs -cat /output/avg/part-r-00000
AVG 3.909090909090909
参考自：http://www.cnblogs.com/yjmyzz/p/4540469.html
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