async-pipeline 0.4.0

Lightweight composition of async pipelines where each stage receives the previous stage's output.

async-pipeline

A small library for composing async pipelines: each Stage receives the previous stage’s output, executed in order.

Requirements

• Python 3.14 or newer

Install with uv

In your project:

uv add async-pipeline

To work on this library:

git clone <repo-url>
cd async-pipeline
uv sync

Basic usage

from async_pipeline import Pipeline, Stage

async def add_one(value: int) -> int:
    return value + 1

async def multiply_by_two(value: int) -> int:
    return value * 2

pipeline = Pipeline([
    Stage("add_one", add_one),
    Stage("multiply_by_two", multiply_by_two),
])

result = await pipeline.run(10)
assert result == 22

Synchronous handlers are supported as well (the stage’s run method remains async):

def add_one(value: int) -> int:
    return value + 1

pipeline = Pipeline([
    Stage("add_one", add_one),
])

result = await pipeline.run(1)
assert result == 2

Errors

Failures inside a handler are surfaced as StageExecutionError, including the stage name and the original exception:

from async_pipeline import Pipeline, Stage, StageExecutionError

async def broken(value: int) -> int:
    raise RuntimeError("boom")

pipeline = Pipeline([
    Stage("broken", broken),
])

try:
    await pipeline.run(1)
except StageExecutionError as exc:
    assert exc.stage_name == "broken"
    assert isinstance(exc.original_error, RuntimeError)

A Pipeline with no stages raises ValueError at construction time.

Stage timeout

Per-stage timeouts apply only when the handler returns an awaitable (async handler). Synchronous handlers are unchanged; the timeout argument is ignored for them.

Timeouts use asyncio.timeout (not wait_for). If the awaitable runs longer than the limit, the stage raises StageExecutionError with original_error set to TimeoutError.

import asyncio

from async_pipeline import Pipeline, Stage

async def fetch_data(value: int) -> int:
    await asyncio.sleep(2)
    return value

pipeline = Pipeline([
    Stage("fetch_data", fetch_data, timeout=1.0),
])

Invalid values (timeout <= 0) raise ValueError with message timeout must be greater than 0.

Retry

Configure automatic re-runs when a handler raises an Exception (including TimeoutError from asyncio.timeout). On success, the stage returns immediately. If every attempt fails, the library raises StageExecutionError with the last exception as original_error.

• retries — extra attempts after the first (retries=0 is the default: no retries). Total tries are 1 + retries.
• retry_delay — base seconds to wait after a failed attempt before the next one. If 0, no asyncio.sleep is used between attempts.
• backoff — "fixed" (same delay after each failure) or "exponential" (delay multiplies by 2 ** (failure_number - 1) from the base retry_delay, e.g. 0.5s, 1.0s, 2.0s, …).

CancelledError and KeyboardInterrupt are BaseException, not Exception, so they are not retried and propagate as usual.

from async_pipeline import Stage

stage = Stage(
    "api_call",
    api_call,
    retries=3,
    retry_delay=0.5,
    backoff="exponential",
    timeout=5.0,
)

With timeout: each attempt is wrapped in asyncio.timeout when timeout is set, so a slow await can fail with TimeoutError, trigger a retry (after the backoff sleep), and eventually surface as StageExecutionError if all tries time out or fail.

Invalid retries, retry_delay, or backoff values raise ValueError with a clear message.

Batch processing

Run the same pipeline for many inputs in parallel, with a fixed concurrency limit and stable output order (aligned with the input sequence):

results = await pipeline.map([1, 2, 3], concurrency=5)

Implementation notes:

• Uses asyncio.TaskGroup to run one async worker per item (not gather).
• Uses asyncio.Semaphore so at most concurrency pipelines run at once; workers still start as tasks, but only concurrency of them proceed past the semaphore at a time.
• Each worker calls run() for its item and writes into a pre-sized list by index, so results stay in input order even when tasks finish out of order.

Errors (default): if any item fails, TaskGroup surfaces an ExceptionGroup (and cancels the other workers). StageExecutionError from a stage is propagated like in run() (wrapped inside the group as needed).

Errors (return_exceptions=True): failures are stored in the result list in the matching position as the exception object; the TaskGroup completes without raising, so you get list entries that are either normal outputs or an Exception (often StageExecutionError).

Development commands

uv sync
uv run pytest
uv run ruff check .
uv run mypy src

Roadmap

• Hooks — before/after each stage or for the full pipeline

License

See the LICENSE file.