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How to run CPU-bound work without blocking asyncio
Do not call a CPU-heavy synchronous function directly inside a coroutine: it occupies the event-loop thread while it runs. Python’s asyncio development guide recommends moving blocking CPU-bound work out of that thread. For process-based work, submit the function to a ProcessPoolExecutor through loop.run_in_executor(), then await the result.
import asyncio
from concurrent.futures import ProcessPoolExecutor
# Keep worker functions at module scope so child processes can import them.
def cpu_bound(value):
return value * value
async def main():
with ProcessPoolExecutor() as pool:
loop = asyncio.get_running_loop()
result = await loop.run_in_executor(pool, cpu_bound, 12)
print(result)
if __name__ == "__main__":
asyncio.run(main())The guard matters: the event-loop documentation uses it in its process-pool example, and multiprocessing-based execution needs a guarded program entry point. Keep submitted functions and their arguments and return values importable and picklable. A function defined only in an interactive REPL or a lambda is not a reliable process-pool target; a submitted callable must also not call executor or future methods on that same pool, which can deadlock. These requirements are detailed in the concurrent.futures documentation.
This pattern is for synchronous CPU work. Keep asyncio coordination—such as awaiting results and scheduling callbacks—in the parent process. Coroutines and callbacks cannot be scheduled directly from a separate multiprocessing process; use the executor integration or explicit interprocess communication instead, as described in the asyncio development guide.
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Which multiprocessing start method should you use?
Start methods determine how workers are created and what they inherit. The behavior below reflects Python 3.14 documentation consulted on October 7, 2026; older Python releases can have different defaults. On Python 3.14, forkserver is the default on supported POSIX platforms, including Linux. If your application specifically requires fork, request it rather than relying on the default.
| Method | How it starts workers | Practical implications |
|---|---|---|
forkserver |
A server process forks workers when requested. | Python 3.14’s POSIX default. The server is generally single-threaded and avoids inheriting unnecessary resources from the parent. |
spawn |
Starts a fresh interpreter and passes the resources needed to run the child. | Slower to start than fork or forkserver. The child must import the main module and unpickle the target and arguments. |
fork |
Duplicates the parent interpreter and inherits its resources. | Safely forking a multithreaded process is problematic. Since Python 3.14, fork is not the default on any platform. |
These behaviors and the version change are documented in multiprocessing — Process-based parallelism. If a start method is an application requirement, specify it locally rather than silently assuming the platform default:
import multiprocessing as mp
from concurrent.futures import ProcessPoolExecutor
context = mp.get_context("spawn")
pool = ProcessPoolExecutor(mp_context=context)Choose the method based on the application’s threading, startup, and inheritance needs—not a universal rule that one method is always fastest or safest. If you are writing a library, let the application using it supply the multiprocessing context instead of imposing a global choice. Synchronization objects created under one context may not be compatible with processes using another context; see the multiprocessing documentation.
How to evaluate performance without assuming a speedup
Processes can use multiple processors and avoid the GIL limitation described in Python’s multiprocessing introduction, but they add startup and communication costs. In particular, spawn has comparatively slow startup, and sending large amounts of data between processes is discouraged. Manager-based shared state is flexible, but slower than shared memory. The documentation describes these trade-offs; it does not publish a general speedup, benchmark dataset, or break-even task size.
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Measure the actual workload before choosing a pool design or worker count. Compare a sequential baseline with the candidate process configurations using the same machine, inputs, and representative workload. Record:
- Python version, selected start method, worker count, machine, and workload characteristics.
- End-to-end latency and throughput, with pool startup reported separately from steady-state work.
- How much data is serialized and transferred between processes.
- Whether event-loop responsiveness remains acceptable while the CPU work runs.
These are practical measurement recommendations derived from the documented startup and communication costs, not a benchmark protocol prescribed by Python. Do not transfer a result from a different workload to your own without testing it.
How to make process lifecycle and failures reliable
Process integration adds failure modes beyond coroutine completion. Treat data movement, worker exit, and cleanup as part of correctness.
Keep interprocess communication bounded
Multiprocessing queues and pipes serialize values. Avoid unnecessary shared state and large transfers; prefer sending the smallest useful inputs and results. The multiprocessing documentation explains the communication options and their costs.
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Drain queued output before joining producers
A producer that has put data on a multiprocessing queue can wait for its feeder thread to flush buffered output. If the parent joins that producer before consuming the queued data, the program can deadlock. Read the required output before joining the process that produced it.
Join children and shut them down orderly
On POSIX, a finished process that has not been joined can remain a zombie, so explicitly join processes you start. Avoid using termination as routine cleanup: Python warns that terminating a process while it is using a lock, semaphore, pipe, or queue can leave that shared resource broken or unavailable to other processes. Prefer an orderly shutdown path, especially when workers use shared resources.
Surface worker failures and choose retries deliberately
ProcessPoolExecutor raises BrokenProcessPool if a worker terminates abnormally. Surface that failure to the application, determine which work can safely be retried, and decide whether the pool should be closed or recreated. Retry safety depends on the operation: if a task can produce an external side effect, do not assume that repeating it is harmless. The exception behavior is documented in concurrent.futures.
Set worker lifetime only when you need it
The executor’s max_tasks_per_child option can replace workers after a configured number of tasks. By default, workers have no task limit. If this option is set without an explicit multiprocessing context, it selects spawn; it is incompatible with fork. Account for the resulting context and worker-replacement behavior when configuring or diagnosing the pool.
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What should process-pool tests cover?
Use an async-aware test framework for coroutine behavior. Python’s unittest documentation describes unittest.IsolatedAsyncioTestCase, which accepts coroutine test methods, creates an event loop for each test, and cancels remaining tasks at the end.
Then add integration tests that exercise a real process pool rather than only mocking the executor. Cover the cases your application supports:
- Successful completion using the worker, arguments, and result shapes the application actually submits.
- A worker exception and, where relevant, abnormal worker exit, including how the application reports the failure.
- Cancellation and shutdown behavior, including whether the parent completes its cleanup path.
- Queue output draining, process joining, and cleanup of resources used by workers.
- Each supported start context, if the application claims to support more than one.
Run tests from an importable module and include representative serialized inputs and outputs; otherwise a test may miss failures caused by process startup or pickling. A test under one start context does not establish compatibility with another, given the documented context-specific constraints. Keep performance measurements separate from correctness tests and report whether startup is included.
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