Async Distributed tasks in python with Celery

Posted 2. May 2022. 2 min read.
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I was working on a project where we need to collect data from domains, since whe have thousands of domains and each domain require a number of tasks in order to collect the needed data, this isn't a job for one machine no matter how powerful it is, it's better to distribute the load between number of machines.

Ray

Ray was the top candidate for this job, but unfortunately we faced so many issues with it and it never worked out as we want.

Celery

A colleague suggested using Celery it was designed to work and integrated with python web frameworks like django and flask, but hey i thing we could use it this way :).

getting started

let's work on scenario where we have a task the take 5 sec to complete

from time import time,sleep

def task():
    sleep(5)


start = time()
task()
end = time()

print(f"time taken: {round(end - start)} seconds")

if we run this of course it will take 5 seconds, but what if we need to run this task on let's say 20 domains, let's see the output:

time taken: 100 seconds

again nothing strange as we know in python unless we don't use threads the event loop will be blocked, if in a case we need to run this like a couple of time we could simply use threads, but we need to run this on a scale.

Ok, let move on to celery, this a basic app with minimal configuration you probably should head to their docs.

from time import sleep
from celery import Celery

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

@app.task
def task():
    sleep(5)

and now we start celery as worker using the command

celery -A index worker --loglevel=info --pool=threads -c 100

we should get something like this 

[tasks]
  . index.task

[2022-05-02 23:09:16,403: INFO/MainProcess] Connected to redis://localhost:6379/0
[2022-05-02 23:09:16,406: INFO/MainProcess] mingle: searching for neighbors
[2022-05-02 23:09:17,417: INFO/MainProcess] mingle: all alone
[2022-05-02 23:09:17,438: INFO/MainProcess] celery@pc ready.

now we have one worker ready to consume tasks, of course we need multiple worker or else we haven't done any thing spacial, i will run it two more times so the total number of workers is 3. to execute task on celery we call task.delay() it return AsyncResult object, we call get() or wait() on it to wait for its execution and get any return if it has. 

from time import time
from index import task

results = []
start = time()
for i in range(20):
    results.append(task.delay())

for result in results:
    result.get()
end = time()

print(f"time taken: {round(end - start)} seconds")

in the above example we run the task 10 times which in the normal case should take 100 seconds, let see the improvement we made. 

time taken: 5 seconds

wow! from 100 to only just 5 seconds, of course this a simple example real life way complicated, but we get the idea. I know this not beginners friendly tutorial the goal is just to show case celery and what it can do.

# python # celery