ProcessPoolExecutor sends picklable callables and inputs to separate processes.
Understanding the Concept
ProcessPoolExecutor sends picklable callables and inputs to separate processes. It can use several CPU cores for independent CPU-bound computations.
Run this from a saved .py file; interactive notebooks and stdin execution vary across platforms.
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 concurrent.futures import ProcessPoolExecutor
def square(number):
return number * number
if __name__ == "__main__":
with ProcessPoolExecutor(max_workers=2) as executor:
print(list(executor.map(square, [3, 5, 7])))Walkthrough: What Each Part Does
- The imports make concurrent.futures available. These are standard-library building blocks unless the example explicitly names an external tool.
- The definition square 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
Defining the worker as a lambda or a local nested function, which commonly fails with process serialization.
Run this from a saved .py file; interactive notebooks and stdin execution vary across platforms.
Practice Exercise
Create a module file containing the function and run it under the __main__ guard.
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
- Defining the worker as a lambda or a local nested function, which commonly fails with process serialization.
- Forgetting to handle worker errors and shutdown, even when the normal completion path appears correct.
Key Takeaways
- ProcessPoolExecutor sends picklable callables and inputs to separate processes.
- Run this from a saved .py file; interactive notebooks and stdin execution vary across platforms.
- Practice: Create a module file containing the function and run it under the __main__ guard.