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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Celery vs Mosquitto

Celery vs Mosquitto

OverviewComparisonAlternatives

Overview

Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K
Mosquitto
Mosquitto
Stacks136
Followers306
Votes14

Celery vs Mosquitto: What are the differences?

Introduction:

When comparing Celery and Mosquitto, two popular technologies used for different purposes in the software development world, several key differences emerge.

  1. Messaging Protocol: One of the fundamental differences between Celery and Mosquitto is their primary use case. Celery is a distributed task queue used for real-time processing of tasks in applications, whereas Mosquitto is a lightweight message broker that implements the MQTT protocol for communication between devices and applications.

  2. Concurrency Model: Another significant difference is in the concurrency model utilized by Celery and Mosquitto. Celery supports parallel task execution by utilizing multiple worker nodes, enabling faster task completion, while Mosquitto focuses on asynchronous messaging using a publish/subscribe methodology, allowing multiple clients to receive messages simultaneously.

  3. Dependency Structure: Celery relies on a combination of a message broker such as RabbitMQ or Redis and a result backend like Redis or a database, creating a more complex dependency structure. On the contrary, Mosquitto operates as a standalone MQTT broker, reducing the number of dependencies required for its implementation.

  4. Ease of Integration: Celery offers seamless integration with popular Python frameworks and libraries, making it a preferred choice for Python developers working on web applications or data processing tasks. In contrast, Mosquitto's simplicity and lightweight nature make it easier to integrate with IoT devices and embedded systems that require efficient communication protocols.

  5. Data Handling Abilities: Celery excels in handling complex data processing tasks with its built-in features for task routing, result handling, and retry mechanisms, making it suitable for applications requiring sophisticated task management. Mosquitto, on the other hand, focuses on efficient message delivery and subscription management, catering to scenarios where lightweight communication is crucial.

  6. Community Support and Documentation: Celery enjoys a larger community of developers contributing to its ecosystem, leading to extensive documentation and a variety of plugins and extensions to enhance its functionality. In comparison, Mosquitto maintains a strong user base within the IoT community, providing specialized support for MQTT protocol-related queries and implementations.

In Summary, when deciding between Celery and Mosquitto, consider the use case requirements, such as real-time task processing for Celery or efficient device communication for Mosquitto, along with factors like integration ease, data handling capabilities, and community support.

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Detailed Comparison

Celery
Celery
Mosquitto
Mosquitto

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.

It is lightweight and is suitable for use on all devices from low power single board computers to full servers.. The MQTT protocol provides a lightweight method of carrying out messaging using a publish/subscribe model. This makes it suitable for Internet of Things messaging such as with low power sensors or mobile devices such as phones, embedded computers or microcontrollers.

Statistics
GitHub Stars
27.5K
GitHub Stars
-
GitHub Forks
4.9K
GitHub Forks
-
Stacks
1.7K
Stacks
136
Followers
1.6K
Followers
306
Votes
280
Votes
14
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
  • 10
    Simple and light
  • 4
    Performance

What are some alternatives to Celery, Mosquitto?

Kafka

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

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

Amazon SQS

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

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

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

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

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

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

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

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.

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