Enrutado manual

How to work with multiple workers in Celery?

Working with multiple workers in Celery is not only efficient, but necessary to handle tasks of different nature or priority. This strategy allows developers to effectively manage different message queues, assigning specific workers for each type of task.

Why use multiple workers?

The use of multiple workers is recommended for:

• Encapsulated tasks: For tasks related to containers or requiring specific processing.
• Different priorities: Allocate greater resources to high priority tasks while equally distributing lower priority tasks.
• Robust system architecture: Follow recommended practices in tools such as RabbitMQ and Celery to maximize performance and efficiency.

How to configure specific workers by task type?

Workers can be configured to listen to specific messages or queues. Here is a basic example to understand how queue configurations work:

from celery import Celery
app = Celery('tasks',  broker='pyamqp://guest@localhost//')
 @app.taskdef add(x, y): return x + y
 @app.taskdef multiply(x, y): return x * y
app.conf.task_routes = { ' tasks.add': {'queue': 'default'}, ' tasks.multiply': {'queue': 'priority'}

How to route tasks manually?

Manual queue management provides additional control over the flow of tasks. Here, the example illustrates how to manually route events between different queues using Celery:

from some_module import process_command
def process_event(event): if event['type'] == 'message' and not event.get('subtype'): process_command.apply_async((event['text'],),  queue='commands')

How to start and run multiple workers?

Once the queues and workers have been configured, you can create specific processes to handle those tasks:

• Start the default worker: celery -A tasks worker --queue=default
• Start the priority worker: celery -A tasks worker --queue=priority

What is the benefit of distributed task execution?

The main benefit of distributed execution lies in the ability to handle multiple tasks concurrently, optimizing resource usage and increasing system resilience. In addition, it allows scalability, both on local servers and on cloud servers.

Real-life example: Large-scale deployments

Imagine that in a cloud application, where data processing is critical, 20 geographically and functionally distributed workers are deployed. This architecture not only improves efficiency, but also minimizes the risk of data or message loss, leveraging the infrastructure to levels previously unattainable with traditional non-distributed architectures.

Be encouraged to continue exploring Celery's capabilities in upcoming lessons, where more advanced routing solutions and other architectural strategies will be covered. 🎉