Introduction to Celery
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Celery is an asynchronous task queue that allows tasks to be handled outside of HTTP requests. For example, a web application could use Celery to poll an API every 10 minutes and store the results in a database without blocking the HTTP response. Celery distributes tasks by passing messages, allowing tasks to run across multiple worker processes. It uses brokers like Redis to manage queues and tasks. Developers define tasks as functions that are executed asynchronously by Celery workers.
![SomeCode…Yay!
from celery import Celery
def make_celery(app):
celery = Celery(app.import_name, broker=app.config['CELERY_BROKER_URL'])
celery.conf.update(app.config)
TaskBase = celery.Task
class ContextTask(TaskBase):
abstract = True
def __call__(self, *args, **kwargs):
with app.app_context():
return TaskBase.__call__(self, *args, **kwargs)
celery.Task = ContextTask
return celery](https://image.slidesharecdn.com/celery-160113100035/85/Introduction-to-Celery-8-320.jpg)
Introduction to Celery
- 2. Whatiscelery? ● Celery is a vegetable ● Also used as a spice
- 3. Well,NotthatCelery Celery what we want is a task queue
- 4. WhattheFishisaTaskQueue? Tasks are handled asynchronously either because they are not initiated by an HTTP request or because they are long- running jobs that would dramatically reduce the performance of an HTTP response.
- 5. For example, a web application could poll the Some API every 10 minutes to collect the names of the top 100 customers in a particular service. A task queue would handle invoking code to call the API, process the results and store them in a persistent database for later use.
- 6. Again,WhatisCelerythen? Celery is an asynchronous task queue based on distributed message passing.
- 7. Distributed?
- 8. SomeCode…Yay! from celery import Celery def make_celery(app): celery = Celery(app.import_name, broker=app.config['CELERY_BROKER_URL']) celery.conf.update(app.config) TaskBase = celery.Task class ContextTask(TaskBase): abstract = True def __call__(self, *args, **kwargs): with app.app_context(): return TaskBase.__call__(self, *args, **kwargs) celery.Task = ContextTask return celery
- 9. Morecode. from flask import Flask app = Flask(__name__) app.config.update( CELERY_BROKER_URL='redis://localhost:6379', CELERY_RESULT_BACKEND='redis://localhost:6379' ) celery = make_celery(app) @celery.task() def add_together(a, b): return a + b
- 10. RunForestRun.. result = add_together.delay(23, 42) result.wait()
- 12. FewThingstoPonder. ● Start Simple (1 Worker, 1 Queue) ● Monitor it. (Celery Flower) ● Consume tasks faster than you produce them (Shamal) ● Tweaking for Concurrency ● Keep your tasks clean ● Manage DB transactions
- 13. AnyQuestions? Get your lazy ass up and Google it my friend!
- 14. ThankYou.