mirror of
https://github.com/thegeeklab/ansible-later.git
synced 2024-11-16 01:50:39 +00:00
63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
|
from toolz.itertoolz import partition_all
|
||
|
|
from toolz.compatibility import reduce, map
|
||
|
|
from toolz.utils import no_default
|
||
|
|
|
||
|
|
|
||
|
|
def fold(binop, seq, default=no_default, map=map, chunksize=128, combine=None):
|
||
|
|
"""
|
||
|
|
Reduce without guarantee of ordered reduction.
|
||
|
|
|
||
|
|
inputs:
|
||
|
|
|
||
|
|
``binop`` - associative operator. The associative property allows us to
|
||
|
|
leverage a parallel map to perform reductions in parallel.
|
||
|
|
``seq`` - a sequence to be aggregated
|
||
|
|
``default`` - an identity element like 0 for ``add`` or 1 for mul
|
||
|
|
|
||
|
|
``map`` - an implementation of ``map``. This may be parallel and
|
||
|
|
determines how work is distributed.
|
||
|
|
``chunksize`` - Number of elements of ``seq`` that should be handled
|
||
|
|
within a single function call
|
||
|
|
``combine`` - Binary operator to combine two intermediate results.
|
||
|
|
If ``binop`` is of type (total, item) -> total
|
||
|
|
then ``combine`` is of type (total, total) -> total
|
||
|
|
Defaults to ``binop`` for common case of operators like add
|
||
|
|
|
||
|
|
Fold chunks up the collection into blocks of size ``chunksize`` and then
|
||
|
|
feeds each of these to calls to ``reduce``. This work is distributed
|
||
|
|
with a call to ``map``, gathered back and then refolded to finish the
|
||
|
|
computation. In this way ``fold`` specifies only how to chunk up data but
|
||
|
|
leaves the distribution of this work to an externally provided ``map``
|
||
|
|
function. This function can be sequential or rely on multithreading,
|
||
|
|
multiprocessing, or even distributed solutions.
|
||
|
|
|
||
|
|
If ``map`` intends to serialize functions it should be prepared to accept
|
||
|
|
and serialize lambdas. Note that the standard ``pickle`` module fails
|
||
|
|
here.
|
||
|
|
|
||
|
|
Example
|
||
|
|
-------
|
||
|
|
|
||
|
|
>>> # Provide a parallel map to accomplish a parallel sum
|
||
|
|
>>> from operator import add
|
||
|
|
>>> fold(add, [1, 2, 3, 4], chunksize=2, map=map)
|
||
|
|
10
|
||
|
|
"""
|
||
|
|
if combine is None:
|
||
|
|
combine = binop
|
||
|
|
|
||
|
|
chunks = partition_all(chunksize, seq)
|
||
|
|
|
||
|
|
# Evaluate sequence in chunks via map
|
||
|
|
if default == no_default:
|
||
|
|
results = map(lambda chunk: reduce(binop, chunk), chunks)
|
||
|
|
else:
|
||
|
|
results = map(lambda chunk: reduce(binop, chunk, default), chunks)
|
||
|
|
|
||
|
|
results = list(results) # TODO: Support complete laziness
|
||
|
|
|
||
|
|
if len(results) == 1: # Return completed result
|
||
|
|
return results[0]
|
||
|
|
else: # Recurse to reaggregate intermediate results
|
||
|
|
return fold(combine, results, map=map, chunksize=chunksize)
|