osiiso 1.0.0

Structured concurrency and parallelism for asyncio, threading, and multiprocessing using queues.

osiiso

Structured task queues for Python across asyncio, threads, and processes.

osiiso gives you one compact queue API for three execution backends:

• AsyncQueue for coroutine-heavy I/O and async integrations.
• ThreadQueue for blocking I/O, synchronous SDKs, filesystem work, and SQLite writes.
• ProcessQueue for CPU-heavy work that benefits from separate subprocesses.

It is dependency-free at runtime, typed with py.typed, and built around a predictable workflow: submit tasks, apply options, run the queue, then inspect handles and a RunSummary.

Contents

• Why osiiso
• Installation
• Choose a queue
• Quick start
• Core concepts
• Task options
• Results and errors
• Examples
• Documentation
• Development
• Community

Why osiiso

• Shared API across async, thread, and process execution.
• Priority scheduling where lower priority numbers run first.
• Retries with optional delay and exponential backoff.
• Per-task timeouts and queue-level run timeouts.
• Built-in rate limiting (rate= / burst=) across all backends.
• Delayed and absolute-time scheduling that never blocks a worker.
• A persistent process pool — subprocesses are reused across tasks, with crash detection and automatic respawn.
• Graceful shutdown that drains outstanding work, with must_complete protection.
• Batch workflows with submit(), map(), and group().
• Awaitable async handles and blocking sync handles, with add_done_callback().
• Completion-order iteration via as_completed() (async) and iter_completed() (sync).
• One-shot helpers: amap(), tmap(), and pmap() return ordered values.
• Structured RunSummary and immutable TaskResult records with metadata passthrough.
• Lifecycle hooks for on_start, on_complete, and on_retry.
• Worker initializer support for threads and subprocesses.
• Optional uvloop integration through osiiso.run().

Installation

pip install osiiso

With optional uvloop support:

pip install "osiiso[uvloop]"

The project targets Python 3.13 and newer.

Choose a queue

Workload	Queue	Good for
Coroutine-based I/O	AsyncQueue	HTTP clients, async databases, websockets, API fan-out
Blocking synchronous work	ThreadQueue	File operations, blocking SDKs, SQLite writes, sync integrations
CPU-heavy functions	ProcessQueue	Ranking, parsing, scoring, transformations, analytics

The queues intentionally look similar, so work can move between execution models with minimal changes.

Quick start

import asyncio
import osiiso


async def fetch(name: str) -> str:
    await asyncio.sleep(0.1)
    return f"fetched {name}"


async def main():
    async with osiiso.AsyncQueue(workers=4) as q:
        q.submit(fetch, "users", priority=0)
        q.submit(fetch, "posts", retries=2, retry_delay=0.25, timeout=5)

        summary = await q.run(strict=True)
        return summary.values


print(osiiso.run(main()))

Core concepts

submit()

Use submit() for one task. It returns a handle immediately.

handle = q.submit(fetch_user, "ada", retries=3, timeout=10, name="fetch-user")

Async handles are awaitable:

result = await handle
value = handle.value()

Thread and process handles are blocking:

result = handle.wait(timeout=5)
value = handle.value()

map()

Use map() for one callable over many inputs.

q.map(download, urls, retries=2, group_id="downloads")
q.map(add, [(1, 2), (3, 4), (5, 6)], name="add")
q.map(request, [{"method": "GET", "url": "https://example.com"}])

Tuple entries are unpacked as positional arguments. Mapping entries are passed as keyword arguments.

group()

Use group() for a named batch, especially when tasks have different callables.

group = q.group(
    [
        (extract, "db"),
        (transform, raw_records),
        (load, destination),
    ],
    group_id="etl-batch-1",
)

summary = q.run()
values = group.values()

For AsyncQueue, use await group.wait() and await group.values(). values() raises ExecutionError if any task failed or was cancelled, so a returned tuple always lines up 1:1 with the inputs. Iterate a group in completion order with group.as_completed().

One-shot helpers

When all you need is "run this function over these inputs", skip the queue ceremony. Each helper builds a queue, runs it, and returns values in input order, raising ExecutionError on any failure:

pages = await osiiso.amap(fetch, urls, workers=8, retries=2)   # AsyncQueue
sizes = osiiso.tmap(stat_file, paths, workers=8)               # ThreadQueue
scores = osiiso.pmap(rank, datasets, workers=4)                # ProcessQueue

Rate limiting

Every queue accepts rate (maximum task attempts per second) and burst (how many attempts may start back-to-back after an idle period):

async with osiiso.AsyncQueue(workers=8, rate=10, burst=3) as q:
    q.map(call_api, payloads, retries=2)
    await q.run()

Bound tasks

Bind a callable to a queue with @q.task().

async with osiiso.AsyncQueue(workers=4) as q:
    @q.task(retries=2, retry_delay=0.25, name="fetch")
    async def fetch(url: str) -> str:
        return await client.get(url)

    fetch("https://example.com")
    fetch.map(["https://example.org", "https://example.net"])

    summary = await q.run(strict=True)

Queue examples

AsyncQueue

import asyncio
import osiiso


async def fetch(name: str) -> str:
    await asyncio.sleep(0.1)
    return f"fetched {name}"


async def main():
    async with osiiso.AsyncQueue(workers=4) as q:
        q.map(fetch, ["users", "posts", "comments"], retries=2, timeout=5)
        summary = await q.run(strict=True)
        print(summary.values)


osiiso.run(main())

ThreadQueue

import time
import osiiso


def resize(path: str) -> str:
    time.sleep(0.1)
    return f"resized {path}"


with osiiso.ThreadQueue(workers=4) as q:
    q.map(resize, ["a.png", "b.png", "c.png"], name="resize")
    summary = q.run(strict=True)

print(summary.values)

ProcessQueue

Each worker owns a persistent subprocess that is reused across tasks, so spawn cost is paid once per worker instead of once per task. Timeouts and cancellation terminate the subprocess (it is respawned for the next task), and a crashed worker is reported as that task's failure while the pool recovers automatically.

Tasks and arguments must be pickleable, and standard spawn rules apply: guard your script's entry point with if __name__ == "__main__":.

import osiiso


def score(n: int) -> int:
    return sum(i * i for i in range(n))


if __name__ == "__main__":
    with osiiso.ProcessQueue(workers=4) as q:
        q.map(score, [10_000, 20_000, 30_000], name="score")
        summary = q.run(strict=True)

    print(summary.values)

Task options

Task behavior can be configured inline or through an immutable TaskOptions object.

from osiiso import TaskOptions


retrying = TaskOptions(retries=3, retry_delay=0.5, backoff=2, timeout=10)
urgent = retrying.replace(priority=0, name="urgent-api-call")

q.submit(fetch, url, opts=urgent)
q.submit(fetch, other_url, retries=3, retry_delay=0.5, backoff=2)

Option	Default	Meaning
priority	3	Lower numbers run first.
must_complete	False	Protects a task during graceful shutdown.
timeout	None	Per-task timeout in seconds.
retries	0	Retry attempts after the first failure.
retry_delay	0.0	Delay before the first retry.
backoff	1.0	Multiplier applied after each retry.
delay	None	Run after this many seconds.
run_at	None	Run at an absolute epoch timestamp.
name	None	Custom result and hook name.
group_id	None	Group label for summaries.
detached	False	Task still runs, but its result is excluded from the RunSummary; observe it via its handle.
metadata	None	Arbitrary user data carried onto the handle and the TaskResult.

TaskOptions validates invalid combinations immediately. For example, delay and run_at are mutually exclusive, negative retries are rejected, and unknown submit options raise TypeError.

Results and errors

Every run() returns a RunSummary.

summary.ok
summary.succeeded
summary.failed
summary.cancelled
summary.timed_out
summary.values
summary.errors
summary.by_task_id()
summary.by_name()
summary.by_group()
summary.raise_for_errors()
summary.display()

Use strict=True when failures should raise ExecutionError after the run finishes:

summary = await q.run(strict=True)

Each task result is stored as a TaskResult with task id, name, status, value, exception, attempts, priority, timing, group id, and cancellation metadata.

Lifecycle and policies

Queues support finite and long-running modes:

q = osiiso.AsyncQueue(mode="finite", fail_policy="continue", on_timeout="complete")

• mode="finite" runs pending work and exits.
• mode="infinite" keeps workers alive until shutdown or timeout.
• fail_policy="continue" records failures and keeps processing.
• fail_policy="fail_first" cancels remaining work after the first failure (must_complete tasks are spared).
• on_timeout="complete" lets must_complete tasks finish when a run times out.
• on_timeout="cancel" cancels everything when a run times out.

Leaving the context manager (or calling shutdown()) drains all outstanding work — including scheduled tasks — before stopping workers. Use shutdown(force=True) (or raise inside the with block) to cancel everything immediately instead.

Bounded queues (size=N) cap outstanding tasks: sync queues block submit() until space frees (natural backpressure), while AsyncQueue.submit() raises QueueFullError.

Hooks give you a simple integration point for logging, metrics, and tracing:

def completed(result: osiiso.TaskResult) -> None:
    print(result.name, result.status, result.duration)


q = osiiso.ThreadQueue(on_complete=completed)

Examples

Run the compact feature gallery:

uv run python examples/feature_gallery.py

Run the complete Hacker News style showcase:

uv run python -m examples.hackernews_showcase --limit 6

Use the live Hacker News API:

uv run python -m examples.hackernews_showcase --limit 20 --online

The showcase uses all three backends:

• AsyncQueue fetches feeds, items, and users.
• ThreadQueue persists records into SQLite.
• ProcessQueue ranks stories and computes keywords.

Documentation

Full documentation is available at ichinga-samuel.github.io/osiiso.

Run the docs locally:

python -m pip install -e ".[docs]"
mkdocs serve

Build the docs strictly:

mkdocs build --strict

Development

Install the development dependencies:

python -m pip install -e ".[dev]"

Run tests:

uv run pytest

Run Ruff:

uv run ruff check .

Build the package:

python -m build

Build the docs with the docs extra:

uv run --extra docs mkdocs build --strict

Community

• Read the contribution guide before opening larger pull requests.
• Check the changelog for release history and upcoming changes.
• Use support guidance for questions, bug reports, and feature requests.
• Report vulnerabilities through the security policy, not public issues.
• Follow the code of conduct when participating in project spaces.

Project layout

.
|-- src/osiiso/                  # Library source
|-- tests/                       # Unit tests for async, thread, process, and options behavior
|-- docs/                        # MkDocs documentation
|-- examples/feature_gallery.py  # Compact API showcase
|-- examples/hackernews_showcase # Complete multi-backend example project
|-- pyproject.toml               # Package metadata and tool configuration
`-- mkdocs.yml                   # Documentation site configuration

Public API

from osiiso import (
    AsyncQueue,
    ThreadQueue,
    ProcessQueue,
    TaskOptions,
    TaskHandle,
    SyncTaskHandle,
    TaskGroup,
    SyncTaskGroup,
    TaskResult,
    RunSummary,
    ExecutionError,
    ClosedError,
    QueueFullError,
    OsiisoError,
    run,
    amap,
    tmap,
    pmap,
    as_completed,
    iter_completed,
)

License

osiiso is released under the MIT License.