---
id: pydocket
version: "0.24.1"
license: # Released under MIT License Copyright (c) 2025 Chris Guidry. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# pydocket — A distributed background task system for Python functions

License: permissive · Maintenance: active · Downloads: 17.2M/mo

## What it is and what it does

Pydocket is a distributed background task queue built on Redis streams for Python async functions. It lets you queue work to run immediately or at a specific future time using the same simple interface: call `add(function, when=optional_datetime)(args)`. A Worker process pulls tasks from the queue and executes them, with full type hints and dependency injection support.

The package targets Python 3.10+ and requires a Redis server (tested with 6.2, 7.4, 8.6, or Valkey 8.1). For local testing, you can use an in-memory backend via burner-redis without a real Redis instance. It depends on redis, cloudpickle, typer, rich, prometheus-client, opentelemetry-api, and several utility libraries for scheduling, logging, and async task management.

Use it for:

- Queue email or notification sends to run asynchronously without blocking the main request.
- Schedule data processing jobs to run at specific times without deploying cron jobs.
- Skip or retry problematic tasks by updating the queue without redeploying code.
- Monitor background work with prometheus-client metrics and opentelemetry-api tracing.
- Test async task logic in isolation using the in-memory backend without Redis.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Docket is a distributed background task system that queues Python async functions for immediate or scheduled execution via Redis streams, with a unified interface for both immediate and future work.

Yes, if you need a lightweight, type-aware background task queue with integrated scheduling. Active maintenance (released 4 days ago), permissive MIT license, low install friction, and zero known vulnerabilities make it a solid choice. Requires Python 3.10+ and Redis 5.0.0+ or in-memory backend for testing. Small dependency footprint and focus on simplicity are strengths if you want to avoid heavier frameworks.

## Install

pip install pydocket
uv add pydocket
poetry add pydocket

## Installing pydocket

Before you install: Low install friction with a pure-Python wheel. Active maintenance—released 4 days ago with a last commit on 2026-08-12. Requires Redis 5.0.0+ or can use an in-memory backend via burner-redis for testing.

License in practice: MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and proprietary projects.

Quickstart:

pip install pydocket

from pydocket import Docket
from datetime import datetime, timedelta, timezone

async def greet(name: str) -> None:
    print(f"Hello, {name}!")

async with Docket() as docket:
    await docket.add(greet)("Jane")
    soon = datetime.now(timezone.utc) + timedelta(seconds=3)
    await docket.add(greet, when=soon)("John")

Requires Python 3.10 or above and a Redis server with Streams support (Redis 5.0.0+, Valkey 8.1+, or in-memory burner-redis for testing).

Verify before relying:

- Whether the package handles task retries, dead-letter queues, or failure callbacks.
- Scalability limits or performance characteristics under high task volume.
- Whether dependency injection support is fully documented with examples.

## Package facts

- License: # Released under MIT License Copyright (c) 2025 Chris Guidry. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 17.2M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

background task queue redis, distributed task scheduler python, async job scheduling, redis streams task worker, future work scheduling, background job system, task queue with scheduling, async-task-queue, redis-streams, scheduling

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