submit schedules one callable with its arguments and returns a Future immediately; map applies one callable to many inputs and yields results in input order.
Understanding the Concept
submit schedules one callable with its arguments and returns a Future immediately; map applies one callable to many inputs and yields results in input order.
Choose map for bulk homogeneous work and submit for independently managed tasks.
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 ThreadPoolExecutor
with ThreadPoolExecutor(max_workers=2) as executor:
single = executor.submit(abs, -6)
values = list(executor.map(abs, [-2, -7, 4]))
print(single.result(timeout=1), values)Walkthrough: What Each Part Does
- The central operation is executor.submit(abs, -6). Read its actual arguments: those values determine what is tested, logged, measured or scheduled. Then locate the line that inspects its result or handles a failure.
- The imports make concurrent.futures available. These are standard-library building blocks unless the example explicitly names an external tool.
- 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 coordination call waits for a result or a completion condition. Predict what would happen if the worker failed or never became ready.
- 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.
Example Output
6 [2, 7, 4]The output above was captured by running the example in the available Python environment. The order of concurrent events, durations and platform-dependent values can legitimately differ on your computer.
Important Behavior and Edge Cases
Treating executor.map as unordered just because workers run concurrently.
Choose map for bulk homogeneous work and submit for independently managed tasks.
Practice Exercise
Return the input identifier with each result and compare ordering under submit and map.
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
- Treating executor.map as unordered just because workers run concurrently.
- Forgetting to handle worker errors and shutdown, even when the normal completion path appears correct.
Key Takeaways
- submit schedules one callable with its arguments and returns a Future immediately; map applies one callable to many inputs and yields results in input order.
- Choose map for bulk homogeneous work and submit for independently managed tasks.
- Practice: Return the input identifier with each result and compare ordering under submit and map.