In most real-world applications, some tasks take longer than others. Whether it’s sending a confirmation email, processing a report, or generating thumbnails for uploaded images, certain operations don’t belong in the user’s immediate experience. That’s where background jobs come in.

Python’s ecosystem offers some excellent tools for managing these tasks. Among them, Celery, paired with a message broker like Redis, has emerged as a reliable and battle-tested combination. This post walks through how to get started with Celery and Redis for asynchronous task handling, focusing on practical setup and scaling tips—no overwhelming code dumps, just the core essentials.

Imagine a user clicks "Submit" on a form that triggers an email. If the server pauses to send that email synchronously, the user might be stuck staring at a loading spinner for several seconds. Now scale that up to thousands of users. You're staring at sluggish performance, wasted server resources, and a frustrated user base.

The solution? Push that email task to the background and let the frontend move on.

By offloading heavy or time-consuming processes to background jobs, you:

Celery is a task queue system written in Python. It allows you to define asynchronous tasks and run them in the background with worker processes. Celery is not opinionated about what kind of work you do—it could be emails, image processing, database updates, or even machine learning inference.

Redis is commonly used as the message broker in this setup. Think of Redis as the post office: Celery sends a task to Redis, and worker processes pick up those tasks and execute them.

Here’s what a simple setup typically looks like:

User Request ──▶ Django/Flask View ──▶ Task Sent to Redis

                                         │

                                         ▼

                               Celery Worker Processes

                                         │

                                         ▼

                                  Task Executed

Make sure you have Python, pip, and a running Redis server (you can install Redis locally or use Docker for simplicity).

pip install celery redis

In your Django or Flask project, you’ll usually create a celery.py file near your main application file:

# celery.py

From celery import Celery

app = Celery('my_app', broker='redis://localhost:6379/0')

@app.task

def send_email(recipient):

    print(f"Sending email to {recipient}")

This is your task queue. You define what background tasks look like here.

Once Redis is running, fire up your Celery worker from the command line:

celery -A celery worker --loglevel=info

Now your worker is listening for jobs. When a task is added to the queue, then the worker will retrieves and execute it.

Suppose you're using Flask:

from flask import Flask

from celery import Celery

app = Flask(__name__)

celery_app = Celery('my_app', broker='redis://localhost:6379/0')

@app.route('/send-mail/<email>')

def send_mail(email):

    celery_app.send_task('celery.send_email', args=[email])

    return f"Task queued to send email to {email}"

The user immediately gets a response, while Celery handles the heavy lifting in the background.

This is where most setups fall apart. Writing the task is easy. Running hundreds or thousands of tasks reliably is another story. Here are a few things to consider when scaling.

As load increases, you can spin up more worker processes. Each worker can run multiple concurrent threads (via prefork or eventlet). This is how you go from “a couple of tasks” to “hundreds per second.”

Use a tool like Flower to track running tasks, failures, and worker status. Flower is a lightweight web-based tool for monitoring Celery.

pip install flower

celery -A celery flower

Then visit http://localhost:5555.

What happens if Redis crashes? Or a task gets stuck? Celery has built-in support for retry logic, exponential backoff, and timeouts. These features become crucial as you scale.

@app.task(bind=True, max_retries=3, default_retry_delay=30)

def send_email(self, recipient):

    try:

        # actual logic

        pass

    except SomeError as exc:

        raise self.retry(exc=exc)

When you’re dealing with tasks that don’t need all the same level of attention, then it helps you to arrange or sort them into different queues. If you have something you need like sending push notifications or sending email notifications — those should go into a high-priority queue, so they’re handled right away. Meanwhile, tasks that are not more time-sensitive, like exporting reports or data in bulk, can wait in a low-priority queue and be processed later.

celery -A celery worker -Q high_priority,low_priority

Pros:

Cons:

If your tasks grow in complexity or require persistence, consider switching to RabbitMQ, which is more durable out of the box.

Scaling background jobs with Celery and Redis isn’t about writing clever code—it’s about designing a system that can grow with your app. Once you separate real-time actions from asynchronous jobs, you unlock more reliable, responsive software. Celery’s power lies in its simplicity and extensibility.