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wesm / pandas2

Design documents and code for the pandas 2.0 effort.
https://pandas-dev.github.io/pandas2/
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DESIGN: out-of-core / parallelism #39

Open jreback opened 8 years ago

jreback commented 8 years ago

parallel .apply, see here

xref dask & distributed

This issue is a placeholder for discussion w.r.t. how much pandas 2.0 should be in charge of out-of-core / parallel operations.

For example in IO operations (see #38), it makes sense to do parallel reading, where pandas can simply front for dask in-line with out the user directly knowning that this is happening. Sure, a more sophisticated user can control this (maybe thru some limited options, or by directly using dask), but the default should simply be it working (in appropriate scenarios) as parallel reads.

Once could argue that .apply and .groupby are similar scenarios (again assume that the dataset is 'big' enough), and we have a GIL releasing function.

We also can include the current use of numexpr for .query evaluation.

So we are hiding dask / numexpr (or potentially other parallel / out-of-core processing libraries) from the user directly. Things, just work, and are faster.

So how far should we take this? Certainly users can directly use these libraries, but it could be a potential win to include dask as an optional dependency (even though it depends on pandas), and dispatch computation for out-of-core / parallelism as approriate.

jreback commented 8 years ago

cc @mrocklin

FrancescAlted commented 8 years ago

For what is worth, this support of multiple engines is implemented by bcolz already; namely, 'numexpr' (the default), 'python' and 'dask'. 'numexpr' works generally faster than the other two, but 'python' and 'dask' can bring more weaponry for doing different tasks. Here there are some benchmarks using the different engines on a modern CPU (Intel Skylake):

https://github.com/Blosc/movielens-bench/blob/master/querying-ep14.ipynb # uses numexpr (default) https://github.com/Blosc/movielens-bench/blob/master/querying-ep14-python.ipynb https://github.com/Blosc/movielens-bench/blob/master/querying-ep14-dask.ipynb

And in case it could serve as inspiration, here it is the implementation of the different engines (vm's in bcolz jargon) for the generic computational engine in bcolz:

https://github.com/Blosc/bcolz/blob/master/bcolz/chunked_eval.py#L177