pgqueuer

What it is and what it does

PgQueuer is a Python job queue library that uses your PostgreSQL database as the backing store instead of requiring a separate message broker. You define job handlers as async functions decorated with @pgq.entrypoint, enqueue jobs from your application code, and run workers via the CLI. Jobs live in the same database as your application data, so you can enqueue a job and insert related records in a single transaction—if the transaction rolls back, the job never lands, eliminating dual-write drift.

The library uses PostgreSQL's LISTEN/NOTIFY for instant job dispatch and FOR UPDATE SKIP LOCKED to ensure jobs are never double-processed. It supports cron-style scheduling, deferred execution, per-entrypoint concurrency limits, Prometheus metrics, and a live dashboard. For testing and prototyping, PgQueuer.in_memory() provides a drop-in replacement that runs without a database. Runtime dependencies are anyio, croniter, pydantic, pydantic-settings, tabulate, typer, and uvloop.

Use it for:

• Enqueue background tasks transactionally alongside your business data in a single database transaction.
• Schedule recurring jobs using cron expressions without deploying a separate scheduler service.
• Run integration tests and prototypes using the in-memory queue adapter without a database.
• Monitor queue health and job throughput in real time using the built-in dashboard.
• Process jobs with guaranteed ACID semantics because the queue and your application data share the same database.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

PgQueuer turns PostgreSQL into a background job queue, letting you enqueue, schedule, and process jobs transactionally within your existing database instead of running a separate message broker.

Yes. PgQueuer is actively maintained, permissively licensed (MIT), has no known vulnerabilities, and solves a real problem: if you already run PostgreSQL, you can eliminate a separate message broker and gain transactional consistency. The low install friction and support for modern Python versions (3.10+) make it straightforward to adopt. Start with the in-memory mode for testing, then move to PostgreSQL for production.

Install

pgqueuer on PyPI

pip install pgqueuer

uv add pgqueuer

poetry add pgqueuer

Installing pgqueuer

Before you install

Low friction: pure Python wheel with seven runtime dependencies. Active maintenance—last commit 2026-08-01, released 2026-07-27, 1517 GitHub stars. Targets Python 3.10+.

License in practice

MIT licensed (permissive). No restrictions on commercial or proprietary use; you may modify and distribute freely under the same license.

Quickstart

pip install pgqueuer

from pgqueuer import PgQueuer
from pgqueuer.db import AsyncpgDriver
from pgqueuer.models import Job

async def main():
    pgq = PgQueuer(AsyncpgDriver(connection))
    
    @pgq.entrypoint("my_job")
    async def process(job: Job):
        print(f"Processing: {job}")
    
    await pgq.qm.queries.enqueue("my_job", b"payload")

Requires PostgreSQL 12+ and an existing database connection; jobs are durable only when backed by a real PostgreSQL database (in-memory mode is for testing only).

Verify before relying

• Whether the dashboard (pgq dashboard command) requires additional dependencies or setup beyond the base install.
• Performance characteristics and throughput limits under high concurrency or large job volumes.
• Exact retry and backoff behavior when jobs fail, and whether custom executors are required for production retry strategies.
• Which database drivers (asyncpg, psycopg async/sync) are included or require separate installation.

Package facts

Evidence: pgqueuer-1.3.2-py3-none-any.whl

Tags

More Python Modules packages