Celery vs Dramatiq

Overview

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Dramatiq

Stacks6

Followers35

Votes0

Celery vs Dramatiq: What are the differences?

Introduction

In this comparison, we will explore the key differences between Celery and Dramatiq, two popular task queue frameworks used in Python for executing distributed tasks asynchronously.

Scalability: Celery is highly scalable and can handle a large number of simultaneous tasks efficiently. It provides support for distributed task queues using multiple message brokers, such as RabbitMQ and Redis. On the other hand, Dramatiq is designed to be lightweight and focused on simplicity, making it suitable for smaller projects with less demanding scalability needs.
Concurrency Model: Celery uses the pre-fork model for concurrency, where worker processes are forked before receiving tasks. This approach allows Celery to achieve high performance and handle multiple tasks simultaneously. In contrast, Dramatiq utilizes event-driven I/O and non-blocking concurrency, leveraging the async/await syntax from Python's asyncio library. This makes Dramatiq more efficient when dealing with I/O-bound tasks, such as network requests.
Simplicity and Ease of Use: Dramatiq was built with simplicity in mind and provides a straightforward API with fewer configuration options compared to Celery. It aims to minimize the cognitive load for developers and reduce the learning curve. Celery, on the other hand, offers extensive configuration options and a robust feature set, making it more suitable for complex use cases where fine-grained control is required.
Monitoring and Management: Celery provides a built-in monitoring tool called Flower, which offers a web-based interface for monitoring and managing Celery clusters. It allows you to view task progress, set up alerts, and inspect worker statistics. Dramatiq, on the other hand, doesn't have a built-in monitoring tool but can be integrated with third-party libraries or tools for monitoring and management purposes.
Community and Ecosystem: Celery has been around for a longer time and has a larger community and ecosystem compared to Dramatiq. It has a rich set of plugins, extensions, and integrations with other frameworks and tools, making it a versatile solution for various use cases. While Dramatiq is gaining popularity, its community and ecosystem are still relatively smaller.
Compatibility and Integration: Celery is compatible with a wide range of frameworks and libraries, including Django, Flask, and Pyramid, and can be easily integrated into existing projects. Dramatiq, on the other hand, is a standalone library and can be used with any Python application without dependencies on specific frameworks. This flexibility makes Dramatiq a great choice for projects that require lightweight task execution without being tied to a particular framework.

In Summary, Celery and Dramatiq differ in scalability, concurrency model, simplicity, monitoring, community size, and compatibility/integration. Celery offers extensive features and scalability options, while Dramatiq focuses on simplicity and lightweight task execution. Both have their strengths and are suitable for different use cases.

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Advice on Celery, Dramatiq

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.

334k views334k

Comments

Detailed Comparison

Celery	Dramatiq
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.	A distributed task queueing library that is simple and has sane defaults for most SaaS workloads. It draws inspiration from GAE Push Queues and Sidekiq.
-	high reliability; simple and easy to understand core; convention over configuration
Statistics
GitHub Stars 27.5K	GitHub Stars -
GitHub Forks 4.9K	GitHub Forks -
Stacks 1.7K	Stacks 6
Followers 1.6K	Followers 35
Votes 280	Votes 0
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	No community feedback yet

What are some alternatives to Celery, Dramatiq?

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.

Related Comparisons

Celery vs Dramatiq: What are the differences?

Introduction

In this comparison, we will explore the key differences between Celery and Dramatiq, two popular task queue frameworks used in Python for executing distributed tasks asynchronously.

Scalability: Celery is highly scalable and can handle a large number of simultaneous tasks efficiently. It provides support for distributed task queues using multiple message brokers, such as RabbitMQ and Redis. On the other hand, Dramatiq is designed to be lightweight and focused on simplicity, making it suitable for smaller projects with less demanding scalability needs.
Concurrency Model: Celery uses the pre-fork model for concurrency, where worker processes are forked before receiving tasks. This approach allows Celery to achieve high performance and handle multiple tasks simultaneously. In contrast, Dramatiq utilizes event-driven I/O and non-blocking concurrency, leveraging the async/await syntax from Python's asyncio library. This makes Dramatiq more efficient when dealing with I/O-bound tasks, such as network requests.
Simplicity and Ease of Use: Dramatiq was built with simplicity in mind and provides a straightforward API with fewer configuration options compared to Celery. It aims to minimize the cognitive load for developers and reduce the learning curve. Celery, on the other hand, offers extensive configuration options and a robust feature set, making it more suitable for complex use cases where fine-grained control is required.
Monitoring and Management: Celery provides a built-in monitoring tool called Flower, which offers a web-based interface for monitoring and managing Celery clusters. It allows you to view task progress, set up alerts, and inspect worker statistics. Dramatiq, on the other hand, doesn't have a built-in monitoring tool but can be integrated with third-party libraries or tools for monitoring and management purposes.
Community and Ecosystem: Celery has been around for a longer time and has a larger community and ecosystem compared to Dramatiq. It has a rich set of plugins, extensions, and integrations with other frameworks and tools, making it a versatile solution for various use cases. While Dramatiq is gaining popularity, its community and ecosystem are still relatively smaller.
Compatibility and Integration: Celery is compatible with a wide range of frameworks and libraries, including Django, Flask, and Pyramid, and can be easily integrated into existing projects. Dramatiq, on the other hand, is a standalone library and can be used with any Python application without dependencies on specific frameworks. This flexibility makes Dramatiq a great choice for projects that require lightweight task execution without being tied to a particular framework.

Celery vs Dramatiq

Overview

Celery vs Dramatiq: What are the differences?