Introduce support for Dask backend #104
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This PR introduces the Dask module to support PyRDF analysis execution through a dask.distributed scheduler. The connection to the scheduler is either started remotely or locally depending on whether the user provides a scheduler address in the configuration of the Dask instance The execution of the graph is done through the dask.delayed mechanism that wraps both the mapper and reducer functions. Data ranges are mapped and the results are recursively reduced until there is only one list of merged action results. A call to `dask.distributed.Future.compute` returns the final result to the user. A new entry has been added to the options of PyRDF.use accordingly.
eguiraud
approved these changes
Nov 11, 2020
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| def __init__(self, config={}): |
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needs docs for what parameters can be passed as configuration
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Sure! I'll make more tests and finalize the PR with docs in the end 👍
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Thank you! Yeah it's a very basic first implementation, I wanted to push it to also let others try it for now. I'll make sure to add more tests and docs before merging |
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This PR introduces the Dask module to support PyRDF analysis execution through a dask.distributed scheduler. The connection to the scheduler is either started remotely or locally depending on whether the user provides a scheduler address in the configuration of the Dask instance
The execution of the graph is done through the dask.delayed mechanism that wraps both the mapper and reducer functions. Data ranges are mapped and the results are recursively reduced until there is only one list of merged action results. A call to
dask.distributed.Future.computereturns the final result to the user.A new entry has been added to the options of PyRDF.use accordingly.
TODO: