ActiveMQ vs Celery

Overview

ActiveMQ

Stacks879

Followers1.3K

Votes77

GitHub Stars2.4K

Forks1.5K

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

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.

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Advice on ActiveMQ, Celery

Shantha

Sep 30, 2020

Needs adviceon

RabbitMQ

Celery

MongoDB

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.

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Comments

Detailed Comparison

ActiveMQ	Celery
Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.	Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment	-
Statistics
GitHub Stars 2.4K	GitHub Stars 27.5K
GitHub Forks 1.5K	GitHub Forks 4.9K
Stacks 879	Stacks 1.7K
Followers 1.3K	Followers 1.6K
Votes 77	Votes 280
Pros & Cons
Pros 18 Easy to use 14 Open source 13 Efficient 10 JMS compliant 6 High Availability Cons 1 Low resilience to exceptions and interruptions 1 ONLY Vertically Scalable 1 Support 1 Difficult to scale	Pros 99 Task queue 63 Python integration 40 Django integration 30 Scheduled Task 19 Publish/subsribe Cons 4 Sometimes loses tasks 1 Depends on broker

What are some alternatives to ActiveMQ, Celery?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

IronMQ

An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

Apache Pulsar

Apache Pulsar is a distributed messaging solution developed and released to open source at Yahoo. Pulsar supports both pub-sub messaging and queuing in a platform designed for performance, scalability, and ease of development and operation.

Related Comparisons

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.

ActiveMQ vs Celery

Overview

ActiveMQ vs Celery: What are the differences?