Install RQ (Redis Queue) in Python Step by Step

RQ (Redis Queue) is a simple Python library for queueing jobs and processing them in the background with workers. It uses Redis as a backend.

This guide will walk you through installing and setting up RQ in Python.

Prerequisites

Before installing RQ, ensure you have the following:

- Python 3.6 or higher installed.

- Redis server installed and running.

- Basic knowledge of Python and Redis.

Step 1: Install Redis

RQ requires Redis to work. Install Redis on your system first.

For Ubuntu/Debian:

sudo apt-get install redis-server

For macOS (using Homebrew):

brew install redis

Start the Redis server:

redis-server

Step 2: Install RQ

Install RQ using pip, Python's package manager.

pip install rq

If you encounter a ModuleNotFoundError, check out our guide on how to solve ModuleNotFoundError.

Step 3: Create a Simple RQ Job

Create a Python script to define a job. Save it as job.py.

 
def add_numbers(a, b):
    return a + b

Step 4: Enqueue the Job

Create another script to enqueue the job. Save it as enqueue.py.

 
from redis import Redis
from rq import Queue
from job import add_numbers

# Connect to Redis
redis_conn = Redis()

# Create a queue
q = Queue(connection=redis_conn)

# Enqueue the job
job = q.enqueue(add_numbers, 5, 7)
print(f"Job ID: {job.id}")

Step 5: Start an RQ Worker

Run an RQ worker to process the job. Open a new terminal and run:

rq worker

The worker will start and process the job.

Step 6: Check the Result

Create a script to check the job result. Save it as result.py.

 
from redis import Redis
from rq import Queue

# Connect to Redis
redis_conn = Redis()

# Create a queue
q = Queue(connection=redis_conn)

# Fetch the job
job = q.fetch_job("YOUR_JOB_ID")  # Replace with your job ID

# Print the result
print(f"Result: {job.result}")

Conclusion

You have successfully installed and set up RQ in Python. RQ is a powerful tool for background job processing.

With Redis as the backend, it ensures reliability and scalability. Try experimenting with more complex jobs.

For more Python tips, explore our other tutorials.