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

Celery vs MQTT

OverviewComparisonAlternatives

Overview

Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K
MQTT
MQTT
Stacks635
Followers577
Votes7

Celery vs MQTT: What are the differences?

Introduction

In this article, we will explore the key differences between Celery and MQTT in terms of their functionalities and use cases.

  1. Architecture: Celery is a distributed task queue system that follows a client-server architecture. It relies on a message broker to exchange messages between the client (producer) and the workers (consumer). On the other hand, MQTT (Message Queuing Telemetry Transport) is a lightweight publish-subscribe messaging protocol that operates on a broker-based model, where clients can publish and subscribe to topics.

  2. Message Delivery: In Celery, messages are delivered reliably using the message broker, ensuring that the tasks are not lost even if the workers are temporarily unavailable. MQTT, on the other hand, sometimes uses a best-effort message delivery approach, which means that messages can be lost during unreliable network conditions.

  3. Message Format: Celery supports multiple serialization formats for messages, including JSON, pickle, and MsgPack. MQTT, on the other hand, uses a binary message format by default, but it can also support JSON payload if required.

  4. Transport Protocols: Celery relies on AMQP (Advanced Message Queuing Protocol), but it can also support other transport protocols like Redis, RabbitMQ, or Kafka. MQTT, on the other hand, operates on top of TCP/IP and can use MQTT-SN (MQTT for Sensor Networks) for constrained devices.

  5. QoS (Quality of Service): Celery supports different levels of QoS, including at-least-once delivery, at-most-once delivery, and exactly-once delivery, depending on the configuration and guarantees desired. MQTT also offers different levels of QoS, including QoS 0 (at-most-once delivery), QoS 1 (at-least-once delivery), and QoS 2 (exactly-once delivery).

  6. Use Cases: Celery is commonly used in distributed computing scenarios where tasks need to be asynchronously executed across multiple workers. It is often used in web applications for background processing, such as sending emails, processing heavy computations, or scheduling periodic tasks. MQTT, on the other hand, is often used in IoT (Internet of Things) applications for real-time data streams and telemetry, where devices publish sensor data and other clients subscribe to receive updates.

In summary, Celery is a distributed task queue system that focuses on asynchronous task execution in distributed computing scenarios, while MQTT is a lightweight publish-subscribe messaging protocol commonly used in IoT applications for real-time data streams and telemetry.

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Detailed Comparison

Celery
Celery
MQTT
MQTT

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 was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium.

Statistics
GitHub Stars
27.5K
GitHub Stars
-
GitHub Forks
4.9K
GitHub Forks
-
Stacks
1.7K
Stacks
635
Followers
1.6K
Followers
577
Votes
280
Votes
7
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
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Very easy to configure and use with open source tools
  • 2
    Lightweight with a relatively small data footprint
Cons
  • 1
    Easy to configure in an unsecure manner

What are some alternatives to Celery, MQTT?

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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