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ActiveMQ vs Celery: What are the differences?
ActiveMQ vs Celery
ActiveMQ and Celery are both messaging systems used to handle tasks and messages in distributed systems. However, there are several key differences between the two:
Message Broker vs Task Queue: ActiveMQ is primarily a message broker that allows for reliable and scalable messaging between applications. It provides functionalities like message persistence, message filtering, and message routing. On the other hand, Celery is a distributed task queueing system that focuses on task scheduling, execution, and coordination.
Language Support: ActiveMQ is built with Java and supports messaging across multiple language platforms through various protocols like AMQP, MQTT, and STOMP. It has good support for Java-based applications but lacks extensive support for other programming languages. In contrast, Celery is designed to be language-agnostic and provides support for multiple programming languages including Python, Java, and Ruby.
Distribution and Scalability: ActiveMQ can be deployed in a clustered setup, allowing for high scalability and fault tolerance. It enables message distribution across multiple brokers and ensures message delivery even in case of failures. Conversely, Celery relies on a distributed task queue architecture, where tasks are distributed among multiple workers. It supports horizontal scaling by adding more workers and can handle large task volumes efficiently.
Message Durability: ActiveMQ's message persistence feature ensures that messages are not lost in case of system failures. It stores messages in a persistent store (like a database or disk) until they are consumed. Celery, on the other hand, primarily focuses on task execution rather than message persistence. By default, Celery stores the task state in memory and does not guarantee message durability. However, it supports integrating with external result backends like Redis or RabbitMQ for storing task results.
Integration with Existing Systems: ActiveMQ provides connectors and integrations with various enterprise systems like Apache Camel, Spring Framework, and JMS (Java Message Service). It has extensive support for enterprise messaging patterns and is commonly used in Java-based enterprise applications. On the other hand, Celery integrates well with frameworks like Django and Flask in the Python ecosystem. It provides a highly flexible and customizable task execution framework for Python-based applications.
In summary, ActiveMQ is a feature-rich message broker focused on reliable and scalable messaging, while Celery is a flexible and distributed task queueing system for task execution and coordination across a range of programming languages.
I am just a beginner at these two technologies.
Problem statement: I am getting lakh of users from the sequel server for whom I need to create caches in MongoDB by making different REST API requests.
Here these users can be treated as messages. Each REST API request is a task.
I am confused about whether I should go for RabbitMQ alone or Celery.
If I have to go with RabbitMQ, I prefer to use python with Pika module. But the challenge with Pika is, it is not thread-safe. So I am not finding a way to execute a lakh of API requests in parallel using multiple threads using Pika.
If I have to go with Celery, I don't know how I can achieve better scalability in executing these API requests in parallel.
For large amounts of small tasks and caches I have had good luck with Redis and RQ. I have not personally used celery but I am fairly sure it would scale well, and I have not used RabbitMQ for anything besides communication between services. If you prefer python my suggestions should feel comfortable.
Sorry I do not have a more information