pgqueuer 0.13.0

Pgqueuer is a Python library leveraging PostgreSQL for efficient job queuing.

🚀 PGQueuer - Building Smoother Workflows One Queue at a Time 🚀

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📚 Documentation: Explore the Docs
🔍 Source Code: View on GitHub
💬 Join the Discussion: Discord Community

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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.

Features

• 💡 Simple Integration: PGQueuer seamlessly integrates with any Python application 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 to trigger 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.

Installation

Install PGQueuer via pip:

pip install pgqueuer

Quick Start

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

Step 1: Consumer - Run the Worker

Start a consumer to process incoming jobs as soon as they are enqueued. PGQueuer ensures graceful shutdowns using pre-configured signal handlers.

import asyncpg
from pgqueuer.db import AsyncpgDriver, dsn
from pgqueuer.models import Job
from pgqueuer.qm import QueueManager

async def main() -> QueueManager:
    connection = await asyncpg.connect(dsn())
    driver = AsyncpgDriver(connection)
    qm = QueueManager(driver)

    @qm.entrypoint("fetch")
    async def process_message(job: Job) -> None:
        print(f"Processed message: {job}")

    return qm

Run the consumer:

pgq run examples.consumer.main

Step 2: Producer - Add Jobs to Queue

Now, produce jobs that will be processed by the consumer. Below is a simple script to enqueue 10,000 jobs.

import asyncio
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__":
    asyncio.run(main(10000))

Run the producer:

python3 examples/producer.py 10000

Dashboard

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     |
...
+---------------------------+-------+------------+--------------------------+------------+----------+

Why Choose PGQueuer?

• 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.

Community & Support

Join our Discord Community to discuss PGQueuer, ask questions, and get support from other developers.

License

PGQueuer is MIT licensed. See LICENSE for more information.

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