The Scheduler and the Overseer
Jobs, (previsouly harness), are the glue that ties transformations together and let them interract.
>>> job = Job()
Jobs have a few purposes:
- Manage the graph. and their input/output channels and connections.
>>> # Add a transform. Each transform has its own thread. You should avoid using the lower level method ``add()`` >>> # unless you perfectly understand the underlying mechanisms. >>> job.add_chain(t1, t2, t3)
- Manage threads and work units. Each transform is contained in a thread that will live from the job start to whatever means that the contained transform is now “dead”. The job will dispatch work between those threads, and monitor their status.
>>> # Show thread status >>> print '\n'.join(map(repr, h.get_threads())) (1, - Extract-1 in=1 out=3) (2, - SimpleTransform-2 in=3 out=3) (3, - Log-3 in=3 out=3)
The format of the tuples shown is the following:
(id, state name statistics)
Id is a simple numeric identifier that indexes the transform and associated thread. State is either “+” for “alive thread” or “-” for “finished/dead thread”. Name is the thread name, most often built using the transform name and a thread id. Statistics is the number of lines that got read or written to input / output on this transform.
- Manage execution. Once configured, your ETL process will be runnable by calling the job instance.
>>> # Call the job == run the ETL process >>> job()
- class rdc.etl.harness.base.IHarness[source]¶
ETL harness interface.
The harness is basically the executable stuff that will actually run a job.
- class rdc.etl.job.Job(debug=False, profile=False)[source]¶
- add_chain(*transforms, **kwargs)¶
Main helper method to add chains of transforms to this harness. You can plug the whole chain from and to other transforms by specifying input and output parameters.
The transforms provided should not be bound yet.
>>> h = ThreadedHarness() >>> t1, t2, t3 = Transform(), Transform(), Transform() >>> h.add_chain(t1, t2, t3)
Returns attached threads.
Returns attached transorms.