Introduction
Configuration
Language Support
AMQP 0-9-1 Overview
More Exchange Types
More Consumer Features
Queue Deep-dive
Other Features
Reliable Message Delivery
High Availability and Backup
Monitoring
Management HTTP API
Tutorials
Networking
LavinMQ CLI
LavinMQ with Celery
Setting up Celery with LavinMQ
Setting up LavinMQ with Celery locally involves a few steps, including installing LavinMQ, configuring Celery, defining tasks, and running the Celery worker. Here’s a step-by-step guide:
-
Install LavinMQ: First, you need to install LavinMQ on your local machine. You can download and install it from the official website, or you can set it up in the cloud using CloudAMQP.
-
Install Celery: Open a terminal or command prompt and install Celery using
pip
:
- Create a Celery Configuration:
Create a file named
celeryconfig.py
in your project directory. This file will contain the configuration settings for Celery:
More Broker Settings
The default broker URL. This must be a URL in the form of:
Only the scheme part ´transport://´ is required; the rest is optional and defaults to the specific transports default values. The transport part is the broker implementation, and the default is amqp, (uses librabbitmq if installed or falls back to pyamqp). The scheme can also be a fully qualified path to your transport implementation:
More than one broker URL of the same transport can also be specified. The broker URLs can be passed in as a single string that’s semicolon delimited:
Or as a list:
- Create a Celery Instance:
In your Python code, create an instance of the Celery application using the configuration from
celeryconfig.py
:
- Define Celery Tasks: Define the tasks you want to execute asynchronously using Celery. For example:
- Launch the Celery Worker: Start a Celery worker to process tasks. Open a terminal, navigate to your project directory, and execute:
Replace your_module_name
with the actual name of the Python module containing the Celery instance and task definitions.
- Dispatch Tasks for Execution:
Send tasks for asynchronous execution in your Python code:
- Monitoring with Celery Flower: You can use Celery Flower to monitor and manage your Celery tasks locally. Install Flower using:
Access the Flower web interface by opening your browser and going to http://localhost:5555
.
Setting up LavinMQ with Celery locally enables you to process asynchronous tasks efficiently in your Python application. By following these steps, you can harness the capabilities of Celery and a local LavinMQ server for message queuing.
Ready to take the next steps? Here are some things you should keep in mind:
Managed LavinMQ instance on CloudAMQP
LavinMQ has been built with performance and ease of use in mind - we've benchmarked a throughput of about 1,000,000 messages/sec. You can try LavinMQ without any installation hassle by creating a free instance on CloudAMQP. Signing up is a breeze.
Help and feedback
We welcome your feedback and are eager to address any questions you may have about this piece or using LavinMQ. Join our Slack channel to connect with us directly. You can also find LavinMQ on GitHub.