This work is supported by Continuum Analytics and the XDATA Program as part of the Blaze Project

tl;dr: We demonstrate a prototype distributed computing library and discuss data locality.

Distributed Computing

Here’s a new prototype library for distributed computing. It could use some critical feedback.

This blogpost uses distributed on a toy example. I won’t talk about the design here, but the docs should be a quick and informative read. I recommend the quickstart in particular.

We’re going to do a simple computation a few different ways on a cluster of four nodes. The computation will be

1. Make a 1000 random numpy arrays, each of size 1 000 000
2. Compute the sum of each array
3. Compute the total sum of the sums

We’ll do this directly with a distributed Pool and again with a dask graph.

Start up a Cluster

I have a cluster of four m3.xlarges on EC2

ssh node1
dcenter

ssh node2
dworkder node1:8787

ssh node3
dworkder node1:8787

ssh node4
dworkder node1:8787


Notes on how I set up my cluster.

Pool

On the client side we spin up a distributed Pool and point it to the center node.

>>> from distributed import Pool
>>> pool = Pool('node1:8787')


Then we create a bunch of random numpy arrays:

>>> import numpy as np
>>> arrays = pool.map(np.random.random, [1000000] * 1000)


Our result is a list of proxy objects that point back to individual numpy arrays on the worker computers. We don’t move data until we need to. (Though we could call .get() on this to collect the numpy array from the worker.)

>>> arrays[0]
RemoteData<center=10.141.199.202:8787, key=3e446310-6...>


Further computations on this data happen on the cluster, on the worker nodes that hold the data already.

>>> sums = pool.map(np.sum, arrays)


This avoids costly data transfer times. Data transfer will happen when necessary though, as when we compute the final sum. This forces communication because all of the intermediate sums must move to one node for the final addition.

>>> total = pool.apply(np.sum, args=(sums,))
>>> total.get()  # finally transfer result to local machine
499853416.82058007


Now we do the same computation all at once by manually constructing a dask graph (beware, this can get gnarly, friendlier approaches exist below.)

>>> dsk = dict()
>>> for i in range(1000):
...     dsk[('x', i)] = (np.random.random, 1000000)
...     dsk[('sum', i)] = (np.sum, ('x', i))

>>> dsk['total'] = (sum, [('sum', i) for i in range(1000)])

>>> get('node1', 8787, dsk, 'total')
500004095.00759566


Apparently not everyone finds dask dictionaries to be pleasant to write by hand. You could also use this with dask.imperative or dask.array.

def get2(dsk, keys):
""" Make get scheduler that hardcodes the IP and Port """
return get('node1', 8787, dsk, keys)

>>> from dask.imperative import do
>>> arrays = [do(np.random.random)(1000000) for i in range(1000)]
>>> sums = [do(np.sum)(x) for x in arrays]
>>> total = do(np.sum)(sums)

>>> total.compute(get=get2)
499993637.00844824


>>> import dask.array as da
>>> x = da.random.random(1000000*1000, chunks=(1000000,))
>>> x.sum().compute(get=get2)
500000250.44921482


The dask approach was smart enough to delete all of the intermediates that it didn’t need. It could have run intelligently on far more data than even our cluster could hold. With the pool we manage data ourselves manually.

>>> from distributed import delete
>>> delete(('node0', 8787), arrays)


Mix and Match

We can also mix these abstractions and put the results from the pool into dask graphs.

>>> arrays = pool.map(np.random.random, [1000000] * 1000)
>>> dsk = {('sum', i): (np.sum, x) for i, x in enumerate(arrays)}
>>> dsk['total'] = (sum, [('sum', i) for i in range(1000)])


Discussion

The Pool and get user interfaces are independent from each other but both use the same underlying network and both build off of the same codebase. With distributed I wanted to build a system that would allow me to experiment easily. I’m mostly happy with the result so far.

One non-trivial theme here is data-locality. We keep intermediate results on the cluster and schedule jobs on computers that already have the relevant data if possible. The workers can communicate with each other if necessary so that any worker can do any job, but we try to arrange jobs so that workers don’t have to communicate if not necessary.

Another non-trivial aspect is that the high level dask.array example works without any tweaking of dask. Dask’s separation of schedulers from collections means that existing dask.array code (or dask.dataframe, dask.bag, dask.imperative code) gets to evolve as we experiment with new fancier schedulers.

Finally, I hope that the cluster setup here feels pretty minimal. You do need some way to run a command on a bunch of machines but most people with clusters have some mechanism to do that, even if its just ssh as I did above. My hope is that distributed lowers the bar for non-trivial cluster computing in Python.

Disclaimer

Everything here is very experimental. The library itself is broken and unstable. It was made in the last few weeks and hasn’t been used on anything serious. Please adjust expectations accordingly and provide critical feedback.