The Tool Desk
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Why one hung API call can stall a benchmark
A benchmark that fans out many requests needs a policy for requests that stop making progress. Without an explicit limit, the run may depend on the HTTP client’s defaults. Those defaults vary: HTTPX documents a timeout after five seconds of network inactivity, while aiohttp’s stable guide documents a 300-second total timeout and a 30-second socket-connect timeout. These are library defaults, not recommended values for every workload; check the documentation for the version actually installed.
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Timeouts also measure different things. A whole-operation deadline limits total elapsed time, while an inactivity timeout can allow a request to continue as long as data keeps arriving. A useful benchmark design distinguishes the request’s total budget from limits on connection setup, sending, receiving, or waiting for a pooled connection.
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Set an API call timeout at the right boundary
HTTPX: configure the client or request
HTTPX allows timeout configuration at client level or per request. Its documented default is five seconds of network inactivity, not a five-second cap on the entire operation. If phases need different budgets, configure connect, read, write, and pool timeouts explicitly. See the HTTPX timeout documentation.
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aiohttp: choose total and phase limits
aiohttp uses ClientTimeout, configurable for a session or an individual request. Its fields distinguish total duration, connection or pool acquisition, socket connection, and the interval allowed between received data chunks. The stable quickstart documents defaults of 300 seconds total and 30 seconds for socket connection; verify these against the installed version rather than assuming they apply universally. See the aiohttp timeout documentation.
Choose the budget from the workload
There is no evidence-based universal timeout for this benchmark: the API, request type, latency objective, and client are unspecified. Choose a request budget that reflects the service-level expectation and the slow responses the benchmark is meant to measure. A limit that is too short will misclassify legitimate slow responses; one that is too long can make failures dominate the total run time.
Use asyncio timeouts without hiding cancellation
In Python 3.11 and later, asyncio.timeout() provides a timeout context for an awaited operation. asyncio.wait_for() is another option: when its limit expires, it cancels the awaited operation and raises TimeoutError. Neither should be treated as a guaranteed hard wall-clock cutoff. wait_for() waits for cancellation to finish, so the elapsed time can exceed the stated limit while the coroutine performs cleanup.
Cancellation is part of how asyncio implements timeouts. Use try/finally to release local resources, and generally allow CancelledError to propagate after cleanup. Python’s 3.13 task documentation warns: “The asyncio components that enable structured concurrency, like asyncio.TaskGroup and asyncio.timeout(), are implemented using cancellation internally and might misbehave if a coroutine swallows asyncio.CancelledError.” See Python 3.13 asyncio task documentation.
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The following conceptual example records each operation’s elapsed time and separates timeout from other errors. Adapt the timeout mechanism and exception boundaries to the Python version and HTTP client in use:
import asyncio
import time
async def one_run(client, request, request_budget_seconds):
started = time.monotonic()
try:
async with asyncio.timeout(request_budget_seconds):
response = await client.send(request)
response.raise_for_status()
return {"status": "ok", "elapsed": time.monotonic() - started}
except TimeoutError:
return {"status": "timeout", "elapsed": time.monotonic() - started}
except asyncio.CancelledError:
# Let cancellation reach the caller after any needed cleanup.
raise
except Exception as exc:
return {
"status": "error",
"error_type": type(exc).__name__,
"elapsed": time.monotonic() - started,
}Do not catch cancellation and convert it into a successful result. If cleanup is required, put it in a finally block and re-raise cancellation afterward.
Keep one failed request from stopping all benchmark runs
The right concurrency behavior depends on whether runs are independent. If the benchmark should report every run, catch and serialize expected request failures within each worker, as in the example above. Then a timeout or HTTP error becomes that run’s outcome rather than an exception that invalidates the whole batch.
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asyncio.TaskGroup and asyncio.gather() have different failure behavior:
- TaskGroup: if a child task raises an exception, the group cancels its remaining scheduled tasks. This is useful for dependent work or intentional fail-fast behavior, but a benchmark collecting independent results should contain expected per-run failures inside the workers.
- gather(): by default, an exception from one awaitable is propagated, but the other awaitables are not automatically cancelled and may keep running. Keep task references and deliberately collect results or exceptions so the benchmark can account for all work.
These behaviors are documented in the asyncio task documentation. Pick the policy deliberately: fail fast when later work depends on earlier work; isolate failures when each run is an independent measurement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Bound the whole benchmark as well as each request
A per-request timeout prevents one operation from waiting without limit, but it does not guarantee that the complete benchmark finishes within a target duration. Bound the overall run separately when the harness has a wall-clock deadline. If that deadline expires, preserve partial results and mark unfinished work as cancelled or incomplete rather than reporting the batch as successful.
Keep the two limits distinct in configuration and reporting: the request budget answers how long one API operation may take, while the benchmark deadline answers how long the full workload may run. An overall deadline can cancel in-flight requests, so cleanup and cancellation handling still matter.
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Each run should produce a distinguishable outcome, with elapsed time captured using a monotonic clock. At minimum, retain separate categories for:
- successful responses;
- HTTP errors;
- timeouts;
- cancellations, including work cut short by the overall deadline;
- other request or client errors.
Keep enough context to interpret results, such as the run identifier, elapsed duration, and error type. A retry or catch-all handler that turns failed requests into apparent successes can make the benchmark misleading. Retry behavior should be specific to the endpoint: do not automatically retry requests that may have caused a side effect unless the API provides an idempotency or deduplication strategy.
Quick Recap
A practical decision checklist
- Identify the HTTP client and inspect the defaults for its installed version.
- Set explicit request-level limits, covering the phases that matter for the client.
- Choose whether timeout scope means total operation time, network inactivity, or both.
- Set a separate overall benchmark deadline if the harness must finish by a fixed time.
- Choose fail-fast or per-run error containment based on whether tasks are dependent.
- Record each run’s status and elapsed time; preserve partial results if the batch deadline expires.
- Allow cancellation to propagate after cleanup instead of disguising it as success.
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