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Description:

resourcebackeddaskarray 0.1.0

resource-backed-dask-array

ResourceBackedDaskArray is an experimental Dask array subclass
that opens/closes a resource when computing (but only once per compute call).
installation
pip install resource-backed-dask-array

motivation for this package
Consider the following class that simulates a file reader capable of returning a
dask array (using
dask.array.map_blocks)
The file handle must be in an open state in order to read a chunk, otherwise
it segfaults (or otherwise errors)
import dask.array as da
import numpy as np

class FileReader:

def __init__(self):
self._closed = False

def close(self):
"""close the imaginary file"""
self._closed = True

@property
def closed(self):
return self._closed

def __enter__(self):
if self.closed:
self._closed = False # open
return self

def __exit__(self, *_):
self.close()

def to_dask(self) -> da.Array:
"""Method that returns a dask array for this file."""
return da.map_blocks(
self._dask_block,
chunks=((1,) * 4, 4, 4),
dtype=float,
)

def _dask_block(self):
"""simulate getting a single chunk of the file."""
if self.closed:
raise RuntimeError("Segfault!")
return np.random.rand(1, 4, 4)

As long as the file stays open, everything works fine:
>>> fr = FileReader()
>>> dsk_ary = fr.to_dask()
>>> dsk_ary.compute().shape
(4, 4, 4)

However, if one closes the file, the dask array returned
from to_dask will now fail:
>>> fr.close()
>>> dsk_ary.compute() # RuntimeError: Segfault!

A "quick-and-dirty" solution here might be to force the _dask_block method to
temporarily reopen the file if it finds the file in the closed state, but if the
file-open process takes any amount of time, this could incur significant
overhead as it opens-and-closes for every chunk in the array.
usage
ResourceBackedDaskArray.from_array
This library attempts to provide a solution to the above problem with a
ResourceBackedDaskArray object. This manages the opening/closing of
an underlying resource whenever .compute() is called – and does so only once for all chunks in a single compute task graph.
>>> from resource_backed_dask_array import resource_backed_dask_array
>>> safe_dsk_ary = resource_backed_dask_array(dsk_ary, fr)
>>> safe_dsk_ary.compute().shape
(4, 4, 4)

>>> fr.closed # leave it as we found it
True

The second argument passed to from_array must be a resuable context manager
that additionally provides a closed attribute (like io.IOBase). In other words,
it must implement the following protocol:

it must have an __enter__ method that opens the underlying resource
it must have an __exit__ method that closes the resource and optionally handles exceptions
it must have a closed attribute that reports whether or not the resource is closed.

In the example above, the FileReader class itself implemented this protocol, and so was suitable as the second argument to ResourceBackedDaskArray.from_array above.
Important Caveats
This was created for single-process (and maybe just single-threaded?)
use cases where dask's out-of-core lazy loading is still very desireable. Usage
with dask.distributed is untested and may very well fail. Using stateful objects (such as the reusable context manager used here) in multi-threaded/processed tasks is error prone.