Celery vs Kestrel

Overview

Kestrel

Stacks37

Followers58

Votes0

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Celery vs Kestrel: What are the differences?

Introduction:

In this Markdown document, I will provide the key differences between Celery and Kestrel as Markdown code that can be used on a website.

Concurrency Model: Celery is built on a distributed message passing architecture and follows a task-based concurrency model. It allows for the execution of tasks asynchronously and concurrently. On the other hand, Kestrel is an event-driven, lightweight message broker and follows a publish-subscribe pattern, making it suitable for scenarios where high-performance event processing is required.
Language Support: Celery supports multiple programming languages including Python, Java, and .NET. It provides language-specific libraries and integrations, enabling developers to use Celery with their preferred programming language. In contrast, Kestrel is primarily designed for .NET applications and relies on the Microsoft .NET ecosystem.
Message Queuing System: Celery utilizes a message broker (such as RabbitMQ, Redis, or Kafka) to handle the communication between clients and workers. It allows for task routing, prioritization, and advanced message queue features. On the flip side, Kestrel is a lightweight, in-memory message queue that doesn't depend on an external message broker. It is designed for low-latency scenarios and doesn't offer the same level of advanced message queue features as Celery.
Scalability: Celery is highly scalable and supports distributed task processing across multiple workers and machines. It can handle large workloads and provide efficient load balancing. Kestrel, on the other hand, is more suitable for small to medium-scale applications where high throughput and low latency are critical. It may not scale as seamlessly as Celery for extremely high workloads.
Middleware Support: Celery provides a rich set of middleware options that allow for customization and extensibility of its core functionality. Developers can use middleware for tasks such as authentication, logging, error handling, and more. Kestrel, being a lightweight message queue, doesn't have native support for middleware. However, developers can leverage the middleware capabilities of the underlying ASP.NET Core framework when working with Kestrel in a web application context.
Community and Ecosystem: Celery has a large and active community, providing extensive documentation, tutorials, and support. It offers a wide range of third-party integrations and plugins, making it highly versatile. Kestrel, while growing in popularity, has a relatively smaller community and ecosystem compared to Celery. Developers may find more comprehensive resources and community support for Celery.

In Summary, Celery and Kestrel differ in their concurrency models, language support, message queuing systems, scalability, middleware support, and community and ecosystem size.

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Advice on Kestrel, Celery

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

Kestrel	Celery
Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM.	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.
Written by Robey Pointer;Starling clone written in Scala (a port of Starling from Ruby to Scala);Queues are stored in memory, but logged on disk	-
Statistics
GitHub Stars -	GitHub Stars 27.5K
GitHub Forks -	GitHub Forks 4.9K
Stacks 37	Stacks 1.7K
Followers 58	Followers 1.6K
Votes 0	Votes 280
Pros & Cons
No community feedback yet	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

What are some alternatives to Kestrel, Celery?

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.

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

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.

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Celery vs Kestrel: What are the differences?

Introduction:

In this Markdown document, I will provide the key differences between Celery and Kestrel as Markdown code that can be used on a website.

Concurrency Model: Celery is built on a distributed message passing architecture and follows a task-based concurrency model. It allows for the execution of tasks asynchronously and concurrently. On the other hand, Kestrel is an event-driven, lightweight message broker and follows a publish-subscribe pattern, making it suitable for scenarios where high-performance event processing is required.
Language Support: Celery supports multiple programming languages including Python, Java, and .NET. It provides language-specific libraries and integrations, enabling developers to use Celery with their preferred programming language. In contrast, Kestrel is primarily designed for .NET applications and relies on the Microsoft .NET ecosystem.
Message Queuing System: Celery utilizes a message broker (such as RabbitMQ, Redis, or Kafka) to handle the communication between clients and workers. It allows for task routing, prioritization, and advanced message queue features. On the flip side, Kestrel is a lightweight, in-memory message queue that doesn't depend on an external message broker. It is designed for low-latency scenarios and doesn't offer the same level of advanced message queue features as Celery.
Scalability: Celery is highly scalable and supports distributed task processing across multiple workers and machines. It can handle large workloads and provide efficient load balancing. Kestrel, on the other hand, is more suitable for small to medium-scale applications where high throughput and low latency are critical. It may not scale as seamlessly as Celery for extremely high workloads.
Middleware Support: Celery provides a rich set of middleware options that allow for customization and extensibility of its core functionality. Developers can use middleware for tasks such as authentication, logging, error handling, and more. Kestrel, being a lightweight message queue, doesn't have native support for middleware. However, developers can leverage the middleware capabilities of the underlying ASP.NET Core framework when working with Kestrel in a web application context.
Community and Ecosystem: Celery has a large and active community, providing extensive documentation, tutorials, and support. It offers a wide range of third-party integrations and plugins, making it highly versatile. Kestrel, while growing in popularity, has a relatively smaller community and ecosystem compared to Celery. Developers may find more comprehensive resources and community support for Celery.

In Summary, Celery and Kestrel differ in their concurrency models, language support, message queuing systems, scalability, middleware support, and community and ecosystem size.