asyncio TaskGroup gives related asynchronous tasks a shared lifetime within a supported structured-concurrency interface. It retains ownership of created tasks, waits for them when the group exits, and coordinates failure behavior. It does not make external side effects atomic or turn an unlimited number of tasks into a safe workload.
The key design question is which operations belong to one unit of work. This guide explains task ownership, grouped exceptions, cancellation, and capacity so concurrent code remains understandable when one operation fails.
Group work that shares an outcome
Identify tasks whose lifetimes should end with the surrounding operation. Several independent read requests contributing to one response may fit a group. A long-lived background service may instead belong to application startup and shutdown.
Avoid launching tasks and forgetting their references or outcomes. Unobserved failures can surface as warnings long after the relevant request has ended. Structured ownership makes it clear who waits for completion and handles errors.
Check the installed Python version and supported TaskGroup interface. TaskGroup was introduced in Python 3.11, and details evolve across versions. Keep version requirements in the project rather than copying a current example into an unsupported runtime.
Use the group as the ownership scope
A simple pattern creates tasks through the group inside its asynchronous context:
import asyncio
async def gather_records(fetch_a, fetch_b):
async with asyncio.TaskGroup() as group:
task_a = group.create_task(fetch_a())
task_b = group.create_task(fetch_b())
return task_a.result(), task_b.result()This illustrates trusted asynchronous callbacks and successful completion. Results are accessed after the group has finished. Failure policy still needs to be designed for the real operations.
Keep creation inside the group’s active lifetime. Do not assume a task added to an inactive or finished group will run as intended. Read supported lifecycle behavior and avoid retaining the group as an unexplained global task launcher.
Understand the group’s failure behavior
When a task fails with a relevant non-cancellation exception, TaskGroup coordinates cancellation of other tasks and waits for them according to its documented semantics. Multiple failures can be reported through an exception group rather than one ordinary exception.
Decide whether one failure should invalidate the whole unit of work. If partial results are a valid product outcome, represent expected item-level failures deliberately inside task results where appropriate. Do not accidentally suppress unexpected programming errors to simulate success.
TaskGroup is not identical to every gather pattern. Compare the supported behavior when migrating existing code, especially around whether sibling work continues after a failure. A change in concurrency primitive can change the application’s failure semantics.
Handle exception groups intentionally
Python’s exception-group mechanisms allow appropriate handling of grouped failures. Use the language’s supported syntax and distinguish expected dependency errors from unexpected defects. A blanket catch that returns an empty result can hide important failure evidence.
Preserve enough context to identify which operation failed without logging private inputs. Task names or controlled operation identifiers can help. Avoid dumping complete request bodies or credentials because several errors arrived together.
If part of an exception group is handled and another part remains, understand how propagation works. Tests should cover multiple failing tasks, not only the simple case of one timeout and one success.
Keep cancellation cooperative and cleanup reliable
Tasks must respond to cancellation at supported suspension points and clean up resources appropriately. Use try/finally or compatible context managers for owned resources. A task that swallows cancellation can interfere with the structured lifetime the group is intended to provide.
Do not assume cancellation instantly stops synchronous blocking code or reverses work already performed. If a task sent a message or committed a remote action, canceling it does not undo that side effect. Reconciliation and idempotency remain separate requirements.
Review cancellation during cleanup and nested groups using the installed documentation. Keep the design as simple as practical so ownership remains clear when an outer request is canceled while an inner operation is failing.
Bound concurrency and resources
TaskGroup owns tasks; it does not impose a universal concurrency limit. Creating one task for every item in a huge dataset can consume memory and overwhelm dependencies. Use a reviewed semaphore, worker queue, or another supported capacity design.
Choose limits from downstream quotas, connection pools, memory, and latency requirements. More concurrent tasks do not always produce faster useful work. A saturated dependency can turn additional tasks into extra retries and timeouts.
Set overall deadlines and per-operation timeouts where appropriate. The group should fit inside the surrounding request’s budget, including cleanup. Avoid returning a response while supposedly owned work continues unnoticed.
Separate result assembly from side effects
For read-only aggregation, the group can help define when enough evidence is available to assemble a response. Validate each result and handle missing data according to the product’s contract. A completed coroutine does not automatically produce valid application data.
For state changes, define what partial completion means. Several concurrent remote updates are not a distributed transaction merely because they share a TaskGroup. Use a durable workflow or compensating action design where required.
Keep external action authority narrow. If a task fails, recovery code should not broaden permissions or choose a different destination without approval. Failure handling is part of the same authorization boundary as normal work.
Test a realistic failure matrix
Use approved fakes or test services to exercise full success, one failure, multiple failures, caller cancellation, and a task with delayed cleanup. Verify the final exception or result and confirm no unintended task remains running.
Measure concurrency at the dependency boundary. A test can assert the maximum simultaneous calls and demonstrate that capacity controls work. Include malformed outputs separately from transport failures.
A practical example fetches two authorized records for one summary. If either required source fails, sibling work is canceled and the application returns a controlled unavailable result. If optional evidence may be absent, encode that as an explicit item outcome while keeping unexpected defects visible.
Frequently asked questions
Does TaskGroup limit how many tasks run at once?
Not automatically. Add an appropriate capacity control for the workload and verify it.
Does canceling sibling tasks undo completed actions?
No. External side effects need their own idempotency, reconciliation, or transactional workflow.
Where should I check exact failure rules?
Read the Python asyncio task documentation. For cleanup and interrupted operations in more detail, see our asyncio cancellation guide.
