Amazon MQ vs Celery

Overview

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Amazon MQ

Stacks55

Followers325

Votes12

Amazon MQ vs Celery: What are the differences?

Message Brokering Mechanism: Amazon MQ is a managed message broker service that uses Apache ActiveMQ, an open-source message broker. Celery, on the other hand, is a distributed task queue that uses message brokers such as RabbitMQ, Redis, or even Amazon SQS with additional configuration. Amazon MQ provides a fully managed solution, while Celery allows more flexibility in choosing the underlying message broker.
Scalability and Performance: Amazon MQ can scale horizontally to handle high throughput and can be configured with multiple brokers and message instances for improved performance. Celery offers scalability by distributing tasks across multiple worker nodes and message brokers, allowing for parallel processing of tasks. Amazon MQ may provide a more robust and reliable performance due to being a managed service.
Pricing and Costs: Amazon MQ follows a usage-based pricing model where customers pay based on the number of broker instances and data transfer. Celery is an open-source framework, so there are no direct costs associated with using Celery itself; however, users may have to incur costs for managing and scaling the underlying message brokers like RabbitMQ or Redis.
Programming Language Support: Amazon MQ supports messaging protocols like AMQP, STOMP, MQTT, and OpenWire, making it versatile for different programming languages and application integrations. Celery is designed for Python applications and integrates seamlessly with Django, Flask, and other Python frameworks, limiting its compatibility with other programming languages.
Monitoring and Management: Amazon MQ provides built-in monitoring and management tools through the AWS Management Console, enabling users to monitor message queues, set access policies, and configure alarms for metrics. Celery requires additional monitoring tools and configurations for tracking task states, monitoring worker nodes, and managing the overall task queue system.
Ecosystem and Community Support: Amazon MQ being part of the AWS ecosystem enjoys strong support and integration with other AWS services, making it preferable for users already using AWS infrastructure. Celery, being popular in the Python community, has a vast ecosystem of plugins, extensions, and community support for integrating with various frameworks and technologies beyond just messaging.

In Summary, Amazon MQ and Celery differ in their underlying technologies, scalability options, pricing models, language support, monitoring capabilities, and ecosystem integrations.

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Advice on Celery, Amazon MQ

MITHIRIDI

Software Engineer at LightMetrics

May 8, 2020

Needs adviceon

Amazon SQS

Amazon MQ

I want to schedule a message. Amazon SQS provides a delay of 15 minutes, but I want it in some hours.

Example: Let's say a Message1 is consumed by a consumer A but somehow it failed inside the consumer. I would want to put it in a queue and retry after 4hrs. Can I do this in Amazon MQ? I have seen in some Amazon MQ videos saying scheduling messages can be done. But, I'm not sure how.

303k views303k

Comments

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.

334k views334k

Comments

Detailed Comparison

Celery	Amazon MQ
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.	Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud.
Statistics
GitHub Stars 27.5K	GitHub Stars -
GitHub Forks 4.9K	GitHub Forks -
Stacks 1.7K	Stacks 55
Followers 1.6K	Followers 325
Votes 280	Votes 12
Pros & Cons
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	Pros 7 Supports low IQ developers 3 Supports existing protocols (JMS, NMS, AMQP, STOMP, …) 2 Easy to migrate existing messaging service Cons 4 Slow AF
Integrations
No integrations available	AWS IAM Amazon CloudWatch ActiveMQ

What are some alternatives to Celery, Amazon MQ?

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.

ActiveMQ

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.

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.

Related Comparisons

Amazon MQ vs Celery: What are the differences?

Message Brokering Mechanism: Amazon MQ is a managed message broker service that uses Apache ActiveMQ, an open-source message broker. Celery, on the other hand, is a distributed task queue that uses message brokers such as RabbitMQ, Redis, or even Amazon SQS with additional configuration. Amazon MQ provides a fully managed solution, while Celery allows more flexibility in choosing the underlying message broker.
Scalability and Performance: Amazon MQ can scale horizontally to handle high throughput and can be configured with multiple brokers and message instances for improved performance. Celery offers scalability by distributing tasks across multiple worker nodes and message brokers, allowing for parallel processing of tasks. Amazon MQ may provide a more robust and reliable performance due to being a managed service.
Pricing and Costs: Amazon MQ follows a usage-based pricing model where customers pay based on the number of broker instances and data transfer. Celery is an open-source framework, so there are no direct costs associated with using Celery itself; however, users may have to incur costs for managing and scaling the underlying message brokers like RabbitMQ or Redis.
Programming Language Support: Amazon MQ supports messaging protocols like AMQP, STOMP, MQTT, and OpenWire, making it versatile for different programming languages and application integrations. Celery is designed for Python applications and integrates seamlessly with Django, Flask, and other Python frameworks, limiting its compatibility with other programming languages.
Monitoring and Management: Amazon MQ provides built-in monitoring and management tools through the AWS Management Console, enabling users to monitor message queues, set access policies, and configure alarms for metrics. Celery requires additional monitoring tools and configurations for tracking task states, monitoring worker nodes, and managing the overall task queue system.
Ecosystem and Community Support: Amazon MQ being part of the AWS ecosystem enjoys strong support and integration with other AWS services, making it preferable for users already using AWS infrastructure. Celery, being popular in the Python community, has a vast ecosystem of plugins, extensions, and community support for integrating with various frameworks and technologies beyond just messaging.

In Summary, Amazon MQ and Celery differ in their underlying technologies, scalability options, pricing models, language support, monitoring capabilities, and ecosystem integrations.

Amazon MQ vs Celery

Overview