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小编给大家分享一下hadoop中mapreduce如何实现串联执行，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！
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import java.io.IOException;
import java.util.Iterator;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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.jobcontrol.ControlledJob;
import org.apache.hadoop.mapreduce.lib.jobcontrol.JobControl;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class PickMain {
private static final Log LOG = LogFactory.getLog(PickMain.class);
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
/*
* Configuration conf = new Configuration();
Job job1 = Job.getInstance(conf);
job1.setJarByClass(PickMain.class);
job1.setMapperClass(FindMapper.class);
job1.setReducerClass(FindReducer.class);
job1.setOutputKeyClass(Text.class);
job1.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job1, new Path(args[0]));
FileOutputFormat.setOutputPath(job1, new Path(args[1]));
boolean flag1 = job1.waitForCompletion(true);
//下面这种方法也可以实现串联执行job
if(flag1) {
Job job2 = Job.getInstance(conf);
job2.setJarByClass(PickMain.class);
job2.setMapperClass(SecondFindMapper.class);
job2.setReducerClass(SecondFindReducer.class);
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job2, new Path(args[1]));
FileOutputFormat.setOutputPath(job2, new Path(args[2]));
boolean flag2 = job2.waitForCompletion(true);
System.out.println(flag2?0:1);
if(flag2) {
LOG.info("The job is done!");
System.exit(0);
}else {
LOG.info("The Second job is wrong!");
System.exit(1);
}
}else {
LOG.info("The firt job is Running Wrong job break!");
System.exit(1);
}
*/
//下面通过使用ContolledJob和JobControl来实现提交多个作业
Configuration conf = new Configuration();
Job job1 = Job.getInstance(conf);
job1.setJarByClass(PickMain.class);
job1.setMapperClass(FindMapper.class);
job1.setReducerClass(FindReducer.class);
job1.setOutputKeyClass(Text.class);
job1.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job1, new Path(args[0]));
FileOutputFormat.setOutputPath(job1, new Path(args[1]));
Configuration conf2 = new Configuration();
Job job2 = Job.getInstance(conf2);
job2.setJarByClass(PickMain.class);
job2.setMapperClass(SecondFindMapper.class);
job2.setReducerClass(SecondFindReducer.class);
job2.setOutputKeyClass(Text.class);
job2.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job2, new Path(args[1]));
FileOutputFormat.setOutputPath(job2, new Path(args[2]));
//创建ControlledJob对job进行包装
ControlledJob cjob1 = new ControlledJob(conf);
ControlledJob cjob2 = new ControlledJob(conf2);
cjob1.setJob(job1);
cjob2.setJob(job2);
//设置依赖关系，这个时候只有等到job1执行完成后job2才会执行
cjob2.addDependingJob(cjob1);
//JobControl该类相当于一个job控制器，它是一个线程，需要通过线程启动
JobControl jc = new JobControl("my_jobcontrol");
jc.addJob(cjob1);
jc.addJob(cjob2);
Thread th = new Thread(jc);
th.start();
//等到所有的job都执行完成后在退出
while(!jc.allFinished()) {
Thread.sleep(5000);
}
System.exit(0);
}
}
class FindMapper extends Mapper{
Text m1 = new Text();
Text m2 = new Text();
@Override
protected void map(LongWritable key, Text value, Mapper.Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] tmp1 = line.split(":");
String outval = tmp1[0];
String[] outkeys = tmp1[1].split(",");
for(int i = 0 ; i{
StringBuilder sb = new StringBuilder();
NullWritable nul = NullWritable.get();
Text outval = new Text();
String spector = ":";
@Override
protected void reduce(Text txt, Iterable txtiter, Reducer.Context context)
throws IOException, InterruptedException {
sb.delete(0, sb.length());
sb.append(txt.toString());
Iterator it = txtiter.iterator();
while(it.hasNext()) {
sb.append(spector+it.next().toString());
}
outval.set(sb.toString());
context.write(outval, nul);
}
}
class SecondFindMapper extends Mapper{
Text keyout = new Text();
Text valueout = new Text();
@Override
protected void map(LongWritable key, Text value, Mapper.Context context)
throws IOException, InterruptedException {
String[] fs = value.toString().split(":");
valueout.set(fs[0]);
if(fs.length>0) {
for(int i = 1;i(int)fs[j].toCharArray()[0]) {
keyout.set(fs[j]+"-"+fs[i]);
}else {
keyout.set(fs[i]+"-"+fs[j]);
}
context.write(keyout, valueout);
}
}
}
}
}
class SecondFindReducer extends Reducer{
StringBuilder sb = new StringBuilder();
Text outvalue = new Text();
@Override
protected void reduce(Text key, Iterable iter, Reducer.Context context)
throws IOException, InterruptedException {
sb.delete(0, sb.length());
Iterator it = iter.iterator();
if(it.hasNext()) {
sb.append(it.next().toString());
}
while(it.hasNext()) {
sb.append(","+it.next().toString());
}
outvalue.set(sb.toString());
context.write(key, outvalue);
}
} 以上是“hadoop中mapreduce如何实现串联执行”这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注创新互联行业资讯频道！
