hadoop 多job执行的三种方法

If you need to split your Map Reduce jar file in two jobs in order to get two different output file, one from each reducers of the two jobs.

I mean that the first job has to produce an output file that will be the input for the second job in chain.

I will provide some ways following to solve this problem, so you can execute your job one by one automationly.

 

1. Cascading jobs

   Create the JobConf object "job1" for the first job and set all the parameters with "input" as inputdirectory and "temp" as output directory. Execute this job: JobClient.run(job1).

   Immediately below it, create the JobConf object "job2" for the second job and set all the parameters with "temp" as inputdirectory and "output" as output directory. Execute this job:JobClient.run(job2).

2. Two JobConf objects

   Create two JobConf objects and set all the parameters in them just like (1) except that you don't use JobClient.run.

   Then create two Job objects with jobconfs as parameters:

   Job job1=new Job(jobconf1); Job job2=new Job(jobconf2);

   Using the jobControl object, you specify the job dependencies and then run the jobs:

   JobControl jbcntrl=new JobControl("jbcntrl");
   jbcntrl.addJob(job1);
   jbcntrl.addJob(job2);
   job2.addDependingJob(job1);
   jbcntrl.run();
   

3. ChainMapper and ChainReducer

   If you need a structure somewhat like Map+ | Reduce | Map*, you can use the ChainMapper and ChainReducer classes that come with Hadoop version 0.19 and onwards. Note that in this case, you can use only one reducer but any number of mappers before or after it.

 

参考：

http://stackoverflow.com/questions/3059736/map-reduce-chainmapper-and-chainreducer