Custom Graphs

There may be times that you want to do parallel computing, but your application doesn’t fit neatly into something like dask.array or dask.bag. In these cases, you can interact directly with the dask schedulers. These schedulers operate well as standalone modules.

This separation provides a release valve for complex situations and allows advanced projects additional opportunities for parallel execution, even if those projects have an internal representation for their computations. As dask schedulers improve or expand to distributed memory, code written to use dask schedulers will advance as well.


"Dask graph for data pipeline"

As discussed in the motivation and specification sections, the schedulers take a task graph which is a dict of tuples of functions, and a list of desired keys from that graph.

Here is a mocked out example building a graph for a traditional clean and analyze pipeline:

def load(filename):

def clean(data):

def analyze(sequence_of_data):

def store(result):
    with open(..., 'w') as f:

dsk = {'load-1': (load, ''),
       'load-2': (load, ''),
       'load-3': (load, ''),
       'clean-1': (clean, 'load-1'),
       'clean-2': (clean, 'load-2'),
       'clean-3': (clean, 'load-3'),
       'analyze': (analyze, ['clean-%d' % i for i in [1, 2, 3]]),
       'store': (store, 'analyze')}

from dask.multiprocessing import get
get(dsk, 'store')  # executes in parallel