Orkan is a pipeline parallelization library, written in Python.
Orkan is a pipeline parallelization library, written in Python.
Making use of the multicore capabilities of ones machine in Python is often not as easy as it should be. Orkan aims to provide a plain API to utilze those underused CPUs of yours in cases you need some extra horse power for your computation.
Code Repo: https://github.com/tobigue/Orkan
A pipeline is a chain of computations, in which the output of one computation is the input to the next. Orkan allows pipeline processing of a finite number of elements, but also the processing of an infinite stream of elements. The processing of different modules in the pipeline can be parallelized, as well as multiple workers for each module.
Taking its cue from the terminology of Storm, Orkan adopts the concept of spouts and bolts. In Orkan:
Spouts are the processes which feed elements into the Pipeline. They are defined as functions accepting a callback function, which is used to pass an element into the pipeline. Examples for spouts are functions listening for input via HTTP requests, crawling the internet, reading large files and sending off chunks for further processing or just feeding the elements of an iterable into the pipeline:
big_numbers = [ 112272535095293, 112582705942171, 112272535095293, 115280095190773, 115797848077099, 1099726899285419] * 5 def put_primes_spout(callback): for n in big_numbers: callback(n)
Bolts are the processes inside the pipeline which do the further processing. They are defined as functions which accept an element from the previous processing step and pass on a (possibly modified) element to the next module in the pipeline (or the list of results) with a callback function:
import math def is_prime_bolt(n, callback): """From http://docs.python.org/dev/library/concurrent.futures.html""" if n % 2 == 0: callback((n, False)) sqrt_n = int(math.floor(math.sqrt(n))) for i in range(3, sqrt_n + 1, 2): if n % i == 0: callback((n, False)) callback((n, True))
For convenience of using “normal” functions, you can also specify bolts which do not expect a callback function. In this case, the return value of the function is passed to the next module in the pipeline:
import math def is_prime_bolt(n): """From http://docs.python.org/dev/library/concurrent.futures.html""" if n % 2 == 0: return n, False sqrt_n = int(math.floor(math.sqrt(n))) for i in range(3, sqrt_n + 1, 2): if n % i == 0: return n, False return n, True
Note that spouts and bolts will be started in seperate python processes. That means, their in- and output elements have to be pickable and they should not interact with non-threadsafe elements in the main process. The passing of elements between the different modules of the pipeline is realized using threadsafe Queues.
This is how you set up and start a simple pipeline using the spout and bolt defined above:
from orkan import Pipeline pipeline = Pipeline([(put_primes_spout, 1)], [(is_prime_bolt, 2)]) result = list(pipeline.start())
The pipeline is defined by passing a list of spouts and a list of bolts. Each element in a list is a tuple of the function to be executed and the number of workers to be spawned for this function. Note that if you run more than one worker for a function the order of result elements might not correspond to the order of the respective input elements. If you need to relate the result elements to the input elements, you should pass the input elements along the pipeline (e.g. by returning a tuple in each bolt).
By default a pipeline is started with maximal n parallel processes, where n is the number of CPUs in your machine. That means, in the example above on a dual core machine at first one spout and one bolt are running in parallel. As soon as the spout finishes an additional bolt worker is spawned. On a quad core machine all three workers will run in parallel from the beginning.
You can change this by passing a value for n_jobs to start():
# this example corresponds to non-parallel processing pipeline = Pipeline([(put_primes_spout, 1)], [(is_prime_bolt, 1)]) result = list(pipeline.start(n_jobs=1))
Note that in case of an infinite input stream of data you will need at least one worker for every spout/bolt, as no worker will ever finish and thus won’t free a slot for a new worker further down in the pipeline. I also is a good idea to not pass on any information with the last bolt of an infinitely running pipeline, as otherwise you probably will run out of memory at some point.
You should test your spouts and bolts before using in the pipeline, as error messages are not always propagated back to the main process.
The examples will use the following simple spout and bolts:
def s(callback): """Simple spout that puts some random numbers into the Pipeline.""" for _ in range(10): n = int(random.random() * 1000000) callback(n) def b1(n): """Simple bolt that doubles the passed element (via return).""" return n * 2 def b2(n, callback): """Simple bolt that halves the passed element (via callback).""" callback(n / 2) def v(n, callback): """Simple bolt for an inifinte stream of incoming data, that prints the result at the end of the Pipeline and does not pass anything on.""" print n
pipeline = Pipeline([(s, 1)], [(b1, 1), (b2, 1)]) results = list(pipeline.start(n_jobs=1)) """ s | b1 | b2 | result """
Parallel processing of pipeline modules:
pipeline = Pipeline([(s, 1)], [(b1, 1), (b2, 1)]) results = list(pipeline.start(n_jobs=4)) s----b1----b2 | result
Parallel workers for the b1 bolt:
pipeline = Pipeline([(s, 1)], [(b1, 2), (b2, 1)]) results = list(pipeline.start(n_jobs=4)) """ .-b1-------. s--| |--b2 '-------b1-' | result """
More workers than processes (b2 workers will wait for spouts to finish):
pipeline = Pipeline([(s, 2)], [(b1, 2), (b2, 2)]) results = list(pipeline.start(n_jobs=4)) """ s-------. .-b1-------. |--| |-+ s-' '-------b1-' | .-b2-------. | +-| |--------------+ | '-------b2-' | result """
Infinite Input Stream
Endless stream of input data done right:
def s2(callback): """Simple spout that produces an infinite stream of random numbers.""" while 1: n = int(random.random() * 1000000) callback(n) pipeline = Pipeline([(s2, 1)], [(b1, 1), (v, 1)]) results = list(pipeline.start(n_jobs=4)) """ s2---b1----v """
Endless stream of input data done wrong (v workers will never start):
pipeline = Pipeline([(s, 2)], [(b1, 2), (v, 2)]) results = list(pipeline.start(n_jobs=4)) """ s2------. .-b1-------. |--| |---#! s2-' '-------b1-' """
Testing requires having the nose library (pip install nose). After installation, the package can be tested by executing from outside the source directory:
nosetests --exe -v
- Does not work on Windows
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