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。以上一篇中的求平均数为例，可以分解成三个步骤：
1. 求Sum
2. 求Count
3. 计算平均数
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
package yjmyzz.mr.job.link; import org.apache.hadoop.conf.Configuration;
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 yjmyzz.util.HDFSUtil; import java.io.IOException; public class Avg2 { 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/duplicate.txt";
String maxOutputPath = "/output/max/";
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)
HDFSUtil.deleteFile(conf, maxOutputPath);
HDFSUtil.deleteFile(conf, countOutputPath);
HDFSUtil.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);
} } }
输入文本在上一篇可以找到，上面这段代码的主要思路：
1. Sum和Count均采用相同的输入/input/duplicate.txt，然后将各自的处理结果分别输出到/output/max/及/output/count/下
2. Avg从/output/max及/output/count获取结果做为输入，然后根据Key值不同，拿到sum和count的值，最终计算并输出到/output/avg/下