Writing the “related work” for a project called “distributed”, is a Sisyphean task. We’ll list a few notable projects that you’ve probably already heard of down below.
You may also find the dask comparison with spark of interest.
Big Data World¶
- The venerable Hadoop provides batch processing with the MapReduce programming paradigm. Python users typically use Hadoop Streaming or MRJob.
- Spark builds on top of HDFS systems with a nicer API and in-memory processing. Python users typically use PySpark.
- Storm provides streaming computation. Python users typically use streamparse.
This is a woefully inadequate representation of the excellent work blossoming in this space. A variety of projects have come into this space and rival or complement the projects above. Still, most “Big Data” processing hype probably centers around the three projects above, or their derivatives.
There are dozens of Python projects for distributed computing. Here we list a few of the more prominent projects that we see in active use today.
Ad hoc computation¶
In relation to these projects
- Supports data-local computation like Hadoop and Spark
- Uses a task graph with data dependencies abstraction like Luigi
- In support of ad-hoc applications, like IPython Parallel and Scoop
In depth comparison to particular projects¶
IPython Parallel is a distributed computing framework from the IPython
project. It uses a centralized hub to farm out jobs to several
processes running on remote workers. It communicates over ZeroMQ sockets and
centralizes communication through the central hub.
IPython parallel has been around for a while and, while not particularly fancy, is quite stable and robust.
IPython Parallel offers parallel
map and remote
apply functions that
route computations to remote workers
>>> view = Client(...)[:] >>> results = view.map(func, sequence) >>> result = view.apply(func, *args, **kwargs) >>> future = view.apply_async(func, *args, **kwargs)
It also provides direct execution of code in the remote process and collection of data from the remote namespace.
>>> view.execute('x = 1 + 2') >>> view['x'] [3, 3, 3, 3, 3, 3]
Distributed and IPython Parallel are similar in that they provide
apply/submit abstractions over distributed worker processes running Python.
Both manage the remote namespaces of those worker processes.
They are dissimilar in terms of their maturity, how worker nodes communicate to each other, and in the complexity of algorithms that they enable.
The primary advantages of
distributed over IPython Parallel include
- Peer-to-peer communication between workers
- Dynamic task scheduling
Distributed workers share data in a peer-to-peer fashion, without having to
send intermediate results through a central bottleneck. This allows
distributed to be more effective for more complex algorithms and to manage
larger datasets in a more natural manner. IPython parallel does not provide a
mechanism for workers to communicate with each other, except by using the
central node as an intermediary for data transfer or by relying on some other
medium, like a shared file system. Data transfer through the central node can
easily become a bottleneck and so IPython parallel has been mostly helpful in
embarrassingly parallel work (the bulk of applications) but has not been used
extensively for more sophisticated algorithms that require non-trivial
The distributed client includes a dynamic task scheduler capable of managing
deep data dependencies between tasks. The IPython parallel docs include a
recipe for executing task graphs with data dependencies. This same idea is
core to all of
distributed, which uses a dynamic task scheduler for all
distributed.Future objects can be used within
submit/map/get calls before they have completed.
>>> x = client.submit(f, 1) # returns a future >>> y = client.submit(f, 2) # returns a future >>> z = client.submit(add, x, y) # consumes futures
The ability to use futures cheaply within
enables the construction of very sophisticated data pipelines with simple code.
Additionally, distributed can serve as a full dask task scheduler, enabling
support for distributed arrays, dataframes, machine learning pipelines, and any
other application build on dask graphs. The dynamic task schedulers within
distributed are adapted from the dask task schedulers and so are fairly
IPython Parallel Advantages
IPython Parallel has the following advantages over
- Maturity: IPython Parallel has been around for a while.
- Explicit control over the worker processes: IPython parallel allows you to execute arbitrary statements on the workers, allowing it to serve in system administration tasks.
- Deployment help: IPython Parallel has mechanisms built-in to aid deployment on SGE, MPI, etc.. Distributed does not have any such sugar, though is fairly simple to set up by hand.
- Various other advantages: Over the years IPython parallel has accrued a
variety of helpful features like IPython interaction magics,
Futureobjects within calls to
submit/map. When chaining computations, it is preferable to submit Future objects directly rather than wait on them before submission.
Futureobjects, not concrete results. The
map()method returns immediately.
- Despite sharing a similar API,
Futureobjects cannot always be substituted for
concurrent.futures.Futureobjects, especially when using
- Distributed generally does not support callbacks.
If you need full compatibility with the
API, use the object returned by the