dask-array 0.2.0

Dask array with query optimization

dask-array

Dask array with query optimization

Motivation

Dask Array is powerful, but requires expertise to drive effectively.

The dask-array project reimplements dask.array, but represents your calculation at a level where it can be optimized intelligently before executed. This allows the project to reorder and replace calculations to provide the same result but with a more efficient path.

Installation

pip install dask-array

Usage

This project looks and feels like Dask Array

import dask_array as da

x = da.ones((1000, 1000), chunks=(100, 100))

y = x + x.T
result = y[:100, :100]

But when you go to compute, your calculation gets rewritten to be more efficient. This is apparent if you look at the query structure of the underlying array.

>>> result.pprint()
Operation             Shape    Bytes   Chunks
  Getitem          (100, 100)   78 kiB  100×100
  └ Add          (1000, 1000)  7.6 MiB  100×100
    ├ Ones       (1000, 1000)  7.6 MiB  100×100
    └ Transpose  (1000, 1000)  7.6 MiB  100×100
      └ Ones     (1000, 1000)  7.6 MiB  100×100

This calculation starts from a large array expression, then takes a small slice. It is more efficient to push that slice into the expression before building the task graph.

The optimize function rewrites things automatically.

>>> result.optimize().pprint()
Operation            Shape   Bytes   Chunks
  FusedBlockwise  (100, 100)  78 kiB  100×100
  └ Ones          (100, 100)  78 kiB  100×100

You don't need to call optimize though. Dask compute/persist machinery will do this for you. You just need to change your import.

# import dask.array as da
import dask_array as da

Native Frisky acceleration

The normal Dask scheduler path is pure Python and works without a Rust toolchain. On platforms with a native wheel, dask-array also includes a Rust accelerator that Frisky can use to submit compact task records instead of materializing large Python task graphs. This is automatic when computing with a Frisky scheduler; otherwise computations stay on the standard Dask path.

Xarray

Dask-array can replace the default dask.array by registering itself as an Xarray chunk manager.

from dask_array.xarray import register
register()

After this all xarray calculations will benefit from query optimization.