multiprocessing.
Understanding the Concept
multiprocessing.Pool distributes calls across reusable worker processes. Its map method collects ordered results and is convenient for independent CPU tasks.
Use a saved file and the __main__ guard; worker processes have different startup overheads.
How to reason about concurrent execution
Workers may start and finish in a different order on every run. Focus on ownership of data, how work and errors move between workers, and what happens when one worker stops early. Locks protect invariants, queues exchange messages, and explicit shutdown logic prevents blocked or orphaned workers.
Concurrency adds overhead and failure modes. Begin with a working sequential solution, choose bounded worker counts and explicit timeouts, then measure whether overlap or parallel execution improves the target workload.
Worked Example
Read the code from the first line to the last. Identify which values are inputs, which operation is being demonstrated and what should happen before running it.
from multiprocessing import Pool
def cube(number):
return number ** 3
if __name__ == "__main__":
with Pool(processes=2) as pool:
print(pool.map(cube, [1, 2, 3]))Walkthrough: What Each Part Does
- The imports make multiprocessing available. These are standard-library building blocks unless the example explicitly names an external tool.
- The definition cube creates reusable behavior. Its body runs when called, awaited or instantiated; defining it alone does not run the body of an ordinary function.
- The with or async with scope controls lifetime. Setup takes place on entry, and cleanup runs on exit even when an exception interrupts the body.
- The last lines coordinate completion and inspect results. Actual worker scheduling may differ between executions.
This demonstration focuses on one practical behavior. Before changing several lines, vary one input or one configuration choice and check whether the result changes for the reason you expected.
The example uses terminal, platform, external tool or process behavior that was not exercised automatically. Run it only in a suitable environment after reviewing the shown permissions and paths.
Important Behavior and Edge Cases
Using lambdas or nested functions with spawn, which commonly are not picklable.
Use a saved file and the __main__ guard; worker processes have different startup overheads.
Practice Exercise
Compare map with imap for large sequences and describe why streaming changes memory behavior.
Write down the result you expect before running your changed code. Check at least one normal case and one boundary or failure case. If your example starts workers or opens resources, verify how they are cleaned up.
Further reading: official documentation and API reference.
Common Beginner Mistakes
- Using lambdas or nested functions with spawn, which commonly are not picklable.
- Forgetting to handle worker errors and shutdown, even when the normal completion path appears correct.
Key Takeaways
- multiprocessing.
- Use a saved file and the __main__ guard; worker processes have different startup overheads.
- Practice: Compare map with imap for large sequences and describe why streaming changes memory behavior.