Parallel and concurrent iterators
A library for easy iterator-based concurrency and parallelism.
What is this?
Have you ever been working with
map or a generator and gotten annoyed with how slow lazy evaluation made some tasks?
Have you ever wondered "could I run this generator in the background?"
This library exists as the answer to that question.
It implements asynchronous preloading generators and parallel
Both the preloaders and the parallel
map implementations support
threading as backends.
How do I use it?
Here's a simple example of preloading a generator:
from iterlib import thread_preload gen = (x**2 for x in range(100000)) preloaded_gen = thread_preload(gen, buffer_size=100)
That's it! The generator will now preload up to 100 items in the background. When you call
next(preloaded_gen), either directly or indirectly through a
for statement, it will return values from the preloaded queue.
Preloading generators has a significant limitation: it's impossible to use more than one background executor because access to iterators requires synchronization. However, most generators tend to be
maps over other iterators, which opens an opportunity. We can't parallelize reads from an iterator, but we can parallelize function calls.
Use one of
process_map when you know your generator is a map:
from iterlib import thread_map gen = [x for x in range(100000)] mapped_gen = thread_map(lambda x: x**2, buffer_size=100, num_workers=4)
This will create an
mapped_gen. When you call
iter(mapped_gen), a generator will be created in the background that will preload up to 100 samples per worker (so 400 total in this example).
Careful: These functions has different semantics than the regular Python
map! If you
mapover an indexable collection (like a list or numpy array) the returned
ItemizedMapwill also be an indexable collection that lazily evaluates the
mapfor each element you access! Only when
iteris called (in a for loop or directly) will it return an asynchronous generator.
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