April 2016
Intermediate to advanced
170 pages
3h 48m
English
Content preview from Distributed Computing with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Multiple processes
Traditionally, the way Python programmers have worked around the GIL and its effect on CPU-bound threads has been to use multiple processes instead of multiple threads. This approach (multiprocessing) has some disadvantages, which mostly boil down to having to launch multiple instances of the Python interpreter with all the startup time and memory usage penalties that this implies.
At the same time, however, using multiple processes to execute tasks in parallel has some nice properties. Multiple processes have their own memory space and implement a share-nothing architecture, making it easy to reason about data-access patterns. They also allow us to (more) easily transition from a single-machine architecture to a distributed ...
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