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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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Pros of Celery
Pros of MQTT
  • 99
    Task queue
  • 63
    Python integration
  • 40
    Django integration
  • 30
    Scheduled Task
  • 19
    Publish/subsribe
  • 8
    Various backend broker
  • 6
    Easy to use
  • 5
    Great community
  • 5
    Workflow
  • 4
    Free
  • 1
    Dynamic
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Lightweight with a relatively small data footprint
  • 2
    Very easy to configure and use with open source tools

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Cons of Celery
Cons of MQTT
  • 4
    Sometimes loses tasks
  • 1
    Depends on broker
  • 1
    Easy to configure in an unsecure manner

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What is Celery?

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.

What is MQTT?

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.

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What companies use Celery?
What companies use MQTT?
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What are some alternatives to Celery and MQTT?
RabbitMQ
RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Cucumber
Cucumber is a tool that supports Behaviour-Driven Development (BDD) - a software development process that aims to enhance software quality and reduce maintenance costs.
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.
See all alternatives