Hadoop is not an API for orchestrating mapreduce jobs- fortunately, there is no need for such an API. Each mapreduce job can simple be run like a normal java class.
So, to run multiple mapreduce jobs?
Easy- you create a main() method in a single class which runs each job individually by invoking each job separately, using the waitForCompletion() method which blocks until a job completes.
..this method will block until each individual job completes.
On Nov 23, 2012, at 5:22 PM, Sean McNamara <[EMAIL PROTECTED]> wrote:
> It's not clear to me how to stitch together multiple map reduce jobs. Without using cascading or something else like it, is the method basically to write to a intermediate spot, and have the next stage read from there?
> If so, how are jobs responsible for cleaning up the temp/intermediate data they create? What happens if stage 1 completes, and state 2 doesn't, do the stage 1 files get left around?
> Does anyone have some insight they could share?