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Celery vs Gearman: What are the differences?

Introduction

In this article, we will discuss the key differences between Celery and Gearman in terms of their features and functionality.

  1. Scalability and Job Distribution: Celery is designed to handle large-scale distributed systems and provides efficient task distribution across multiple worker nodes. It supports multiple broker options such as RabbitMQ, Redis, and more, allowing flexibility in job distribution. On the other hand, Gearman is a simple job server that utilizes a centralized architecture. It lacks the extensive scalability features of Celery and requires additional effort for setting up distributed operations.

  2. Language Support: Celery supports multiple programming languages, including Python, JavaScript, Java, and Ruby, making it accessible to a wider range of developers. It provides a consistent API for task creation and management across different languages. In contrast, Gearman primarily focuses on C/C++ and provides limited language support. This limits the versatility and adaptability of Gearman for developers using languages other than C/C++.

  3. Message Passing vs. Persistent Queue: Celery uses a message passing model, where tasks are serialized and transferred through a messaging broker. This enables robust message queues that can handle distributed task processing and provide fault tolerance. Gearman, instead, relies on persistent queues to store tasks in a centralized server. While persistent queues offer reliability, they lack some of the flexibility and fault tolerance of message passing.

  4. Task Routing and Priority: Celery allows fine-grained task routing based on worker availability, task type, or other custom criteria. It supports task priority levels to ensure critical tasks receive immediate processing. Gearman, on the other hand, follows a simple FIFO queue model with limited routing capabilities. It does not provide built-in support for task prioritization, which may limit its suitability for more complex task scheduling scenarios.

  5. Task Result Handling: Celery offers comprehensive support for task result handling. It allows tasks to return results asynchronously, and the results can be retrieved later. Celery also supports result caching and provides a result backend for storing and accessing task results. On the contrary, Gearman lacks built-in support for managing task results. The focus of Gearman is primarily on the execution and distribution of tasks, rather than result handling.

  6. Community and Ecosystem: Celery has a vibrant and active community with extensive documentation, industry adoption, and a wide range of third-party integrations and extensions. It benefits from continuous development and support from a large user base. Gearman, while still actively maintained, has a smaller community and ecosystem. It may be a preferred choice for specific use cases that require lightweight job distribution without the need for extensive features and community support.

In summary, Celery is a scalable, language-agnostic task queue system with extensive features for distributed job processing, task routing, and result handling. Gearman, on the other hand, is a simpler job server that focuses on centralized task distribution with fewer language options and limited features for task management and result handling.

Advice on Celery and Gearman
Needs advice
on
CeleryCelery
and
RabbitMQRabbitMQ

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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Replies (1)
Recommends
on
rqrqRedisRedis

For large amounts of small tasks and caches I have had good luck with Redis and RQ. I have not personally used celery but I am fairly sure it would scale well, and I have not used RabbitMQ for anything besides communication between services. If you prefer python my suggestions should feel comfortable.

Sorry I do not have a more information

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Pros of Celery
Pros of Gearman
  • 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
  • 11
    Ease of use and very simple APIs
  • 11
    Free
  • 6
    Polyglot
  • 5
    No single point of failure
  • 3
    Scalable
  • 3
    High-throughput
  • 2
    Foreground & background processing
  • 2
    Very fast
  • 1
    Different Programming Languages Channel
  • 1
    Many supported programming languages

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Cons of Celery
Cons of Gearman
  • 4
    Sometimes loses tasks
  • 1
    Depends on broker
    Be the first to leave a con

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    - No public GitHub repository available -

    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 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.

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    What tools integrate with Celery?
    What tools integrate with Gearman?

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    What are some alternatives to Celery and Gearman?
    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