Celery vs Gearman

Overview

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Gearman

Stacks77

Followers144

Votes45

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.

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.
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++.
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.
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.
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.
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.

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Advice on Celery, Gearman

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.

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Comments

Detailed Comparison

Celery	Gearman
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.	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.
-	Open Source It’s free! (in both meanings of the word) Gearman has an active open source community that is easy to get involved with if you need help or want to contribute. Worried about licensing? Gearman is BSD;Multi-language - There are interfaces for a number of languages, and this list is growing. You also have the option to write heterogeneous applications with clients submitting work in one language and workers performing that work in another;Flexible - You are not tied to any specific design pattern. You can quickly put together distributed applications using any model you choose, one of those options being Map/Reduce;Fast - Gearman has a simple protocol and interface with an optimized, and threaded, server written in C/C++ to minimize your application overhead;Embeddable - Since Gearman is fast and lightweight, it is great for applications of all sizes. It is also easy to introduce into existing applications with minimal overhead;No single point of failure - Gearman can not only help scale systems, but can do it in a fault tolerant way;No limits on message size - Gearman supports single messages up to 4gig in size. Need to do something bigger? No problem Gearman can chunk messages;Worried about scaling? - Don’t worry about it with Gearman. Craig’s List, Tumblr, Yelp, Etsy,… discover what others have known for years.
Statistics
GitHub Stars 27.5K	GitHub Stars -
GitHub Forks 4.9K	GitHub Forks -
Stacks 1.7K	Stacks 77
Followers 1.6K	Followers 144
Votes 280	Votes 45
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 11 Free 11 Ease of use and very simple APIs 6 Polyglot 5 No single point of failure 3 Scalable

What are some alternatives to Celery, Gearman?

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.

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.

Apache Pulsar

Apache Pulsar is a distributed messaging solution developed and released to open source at Yahoo. Pulsar supports both pub-sub messaging and queuing in a platform designed for performance, scalability, and ease of development and operation.

Related Comparisons

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.

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.
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++.
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.
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.
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.
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.

Celery vs Gearman

Overview

Celery vs Gearman: What are the differences?