Improve Ejaculation with Celery? How to use for result.

Okay, so, “celery ejaculation”… yeah, that’s what I was messing around with today. Sounds weird, I know, but hear me out.

It all started when I was trying to figure out how to make some tasks run in the background. I’ve got this website, see, and sometimes things take a while to process – like sending out a bunch of emails, or crunching some big data. I didn’t want the user to just sit there staring at a spinning wheel, so I needed a way to kick those tasks off and let them run on their own.

Digging Around

I started googling around, and that’s when I stumbled upon this thing called “Celery.” Apparently, it’s a “distributed task queue.” Basically, it’s a system for handing off jobs to worker processes, so your main app doesn’t get bogged down.

First, I had to get it installed. This was pretty straightforward:

pip install celery
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Easy, The installation went smooth.

Setting Things Up

Next, I needed a “message broker.” This is like the middleman that passes messages between my app and the Celery workers. I decided to go with Redis, because it seemed popular and easy to set up. Again, just a simple install:

pip install redis

And I made sure Redis was running on my machine.

The Celery Application

After the Broker, I created a file and defined a Celery application.

from celery import Celery

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

def add(x, y):
return x + y

I imported the Celery, defined the app, and specified that the Redis would be used as a broker.

I also created a simple function to test the Celery application by adding two numbers together.

Kicking Off Workers

Then, I had to fire up some Celery worker processes. This is where the magic happens. These workers sit around waiting for jobs to do. I opened up a new terminal and ran:

celery -A my_tasks worker -l info

• celery is the main command
• -A my_tasks specifies my application
• worker means I’m starting worker processes
• -l info sets the logging level to be more verbose

Testing The Task

Once the worker was set up, I tested my function to make sure it’s working.

from my_tasks import add

result = *(4, 4)
print(*())

I called the task using delay(), got the result object, used get method to get the value of the result, and printed it.

And… boom! It worked! I saw the worker pick up the task and spit out the result. “Celery ejaculation,” as I’ve started calling it, because it’s like my app is… well, you get the idea. It’s sending out these little tasks, and they’re getting done!

Wrapping Up

So, yeah, that’s my adventure with Celery. It’s a pretty powerful tool, and I’m just scratching the surface. It feels good to have these tasks running in the background, freeing up my main app to do other things. I’m definitely going to be using this more in the future.