Threads share one process and memory; processes have separate address spaces; asyncio tasks cooperate through await points on an event loop.
Understanding the Concept
Threads share one process and memory; processes have separate address spaces; asyncio tasks cooperate through await points on an event loop. Each has distinct overhead, communication and cancellation behavior.
Choose a model based on bottlenecks, not merely the number of 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
def decorate(text):
return text.upper()
with ThreadPoolExecutor(max_workers=2) as pool:
print(list(pool.map(decorate, ["a", "b", "c"])))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 decorate 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.
Example Output
['A', 'B', 'C']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
Using asyncio to accelerate long blocking code that never awaits.
Choose a model based on bottlenecks, not merely the number of tasks.
Practice Exercise
Compare how errors and shared state would be handled under the three models.
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 asyncio to accelerate long blocking code that never awaits.
- Forgetting to handle worker errors and shutdown, even when the normal completion path appears correct.
Key Takeaways
- Threads share one process and memory; processes have separate address spaces; asyncio tasks cooperate through await points on an event loop.
- Choose a model based on bottlenecks, not merely the number of tasks.
- Practice: Compare how errors and shared state would be handled under the three models.