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PGQueuer is a minimalist, high-performance job queue library for Python, leveraging PostgreSQL's robustness. Designed with simplicity and efficiency in mind, PGQueuer offers real-time, high-throughput processing for background jobs using PostgreSQL's LISTEN/NOTIFY and FOR UPDATE SKIP LOCKED mechanisms.

• 💡 Simple Integration: Seamlessly integrates with Python applications using PostgreSQL, providing a clean and lightweight interface.
• ⚛️ Efficient Concurrency Handling: Supports FOR UPDATE SKIP LOCKED to ensure reliable concurrency control and smooth job processing without contention.
• 🚧 Real-time Notifications: Uses PostgreSQL's LISTEN and NOTIFY commands for real-time job status updates.
• 👨‍🎓 Batch Processing: Supports large job batches, optimizing enqueueing and dequeuing with minimal overhead.
• ⏳ Graceful Shutdowns: Built-in signal handling ensures safe job processing shutdown without data loss.
• ⌛ Recurring Job Scheduling: Register and manage recurring tasks using cron-like expressions for periodic execution.

Install PGQueuer via pip:

pip install pgqueuer

Below is a minimal example of how to use PGQueuer to process data.

from __future__ import annotations

from datetime import datetime

import asyncpg

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


async def main() -> PgQueuer:
    connection = await asyncpg.connect()
    driver = AsyncpgDriver(connection)
    pgq = PgQueuer(driver)

    # Entrypoint for jobs whos entrypoint is named 'fetch'.
    @pgq.entrypoint("fetch")
    async def process_message(job: Job) -> None:
        print(f"Processed message: {job!r}")

    # Define and register recurring tasks using cron expressions
    # The cron expression "* * * * *" means the task will run every minute
    @pgq.schedule("scheduled_every_minute", "* * * * *")
    async def scheduled_every_minute(schedule: Schedule) -> None:
        print(f"Executed every minute {schedule!r} {datetime.now()!r}")

    return pgq

The above example is located in the examples folder, and can be run by using the pgq cli.

pgq run examples.consumer.main

from __future__ import annotations

import asyncio
import sys

import asyncpg

from pgqueuer.db import AsyncpgDriver
from pgqueuer.queries import Queries


async def main(N: int) -> None:
    connection = await asyncpg.connect()
    driver = AsyncpgDriver(connection)
    queries = Queries(driver)
    await queries.enqueue(
        ["fetch"] * N,
        [f"this is from me: {n}".encode() for n in range(1, N + 1)],
        [0] * N,
    )


if __name__ == "__main__":
    N = 1_000 if len(sys.argv) == 1 else int(sys.argv[1])
    asyncio.run(main(N))

Run the producer:

python3 examples/producer.py 10000

Monitor job processing statistics in real-time using the built-in dashboard:

pgq dashboard --interval 10 --tail 25 --table-format grid

This provides a real-time, refreshing view of job queues and their status.

Example output:

+---------------------------+-------+------------+--------------------------+------------+----------+
|          Created          | Count | Entrypoint | Time in Queue (HH:MM:SS) |   Status   | Priority |
+---------------------------+-------+------------+--------------------------+------------+----------+
| 2024-05-05 16:44:26+00:00 |  49   |    sync    |         0:00:01          | successful |    0     |
...
+---------------------------+-------+------------+--------------------------+------------+----------+

• Built for Scale: Handles thousands of jobs per second, making it ideal for high-throughput applications.
• PostgreSQL Native: Utilizes advanced PostgreSQL features for robust job handling.
• Flexible Concurrency: Offers rate and concurrency limiting to cater to different use-cases, from bursty workloads to critical resource-bound tasks.

PGQueuer is MIT licensed. See LICENSE for more information.

Ready to supercharge your workflows? Install PGQueuer today and take your job management to the next level!