Celery vs Sparrow

Overview

Sparrow

Stacks6

Followers11

Votes0

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Celery vs Sparrow: What are the differences?

Introduction: Here are the key differences between Celery and Sparrow.

Scalability: Celery is highly scalable and can handle a large number of tasks concurrently, making it suitable for complex applications with heavy workloads. On the other hand, Sparrow is more lightweight and may not be as reliable for handling a large volume of tasks simultaneously.
Ease of Use: Celery offers a more comprehensive set of features and functionalities, making it a preferred choice for developers who require advanced task management capabilities. In contrast, Sparrow is simpler and more straightforward, making it easier for beginners to set up and use without the complexity of Celery.
Community Support: Celery has a larger and more active community of developers and users, providing a wealth of resources, documentation, and support for troubleshooting and learning. Sparrow, being a newer and less widely adopted framework, may have a smaller community and fewer resources available for help and guidance.
Integration: Celery integrates seamlessly with various frameworks, message brokers, and backend storage systems, offering more flexibility and compatibility with different tools and technologies in the development stack. Sparrow, being a lightweight framework, may have limited integration options and may not provide the same level of flexibility as Celery.
Performance: Celery is known for its high performance and efficiency in task execution, thanks to its robust architecture and optimization for handling concurrent operations. Sparrow, while efficient for smaller tasks and simple workflows, may not offer the same level of performance and speed as Celery for more demanding applications.
Maintenance: Celery has been around for a longer time and has undergone multiple updates and improvements, ensuring better stability, security, and long-term maintenance for projects using the framework. Sparrow, being relatively new, may lack the same level of maturity and ongoing support for addressing bugs, security vulnerabilities, and evolving needs over time.

In Summary, when choosing between Celery and Sparrow, consider factors such as scalability, ease of use, community support, integration capabilities, performance, and maintenance requirements to determine the best fit for your project.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Sparrow, 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.

334k views334k

Comments

Detailed Comparison

Sparrow	Celery
Sparrow keeps messages in memory, but persists them to disk, using Sqlite, when the queue is shutdown.	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.
Statistics
GitHub Stars -	GitHub Stars 27.5K
GitHub Forks -	GitHub Forks 4.9K
Stacks 6	Stacks 1.7K
Followers 11	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 Sparrow, 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.

Related Comparisons

Celery vs Sparrow: What are the differences?

Introduction: Here are the key differences between Celery and Sparrow.

Scalability: Celery is highly scalable and can handle a large number of tasks concurrently, making it suitable for complex applications with heavy workloads. On the other hand, Sparrow is more lightweight and may not be as reliable for handling a large volume of tasks simultaneously.
Ease of Use: Celery offers a more comprehensive set of features and functionalities, making it a preferred choice for developers who require advanced task management capabilities. In contrast, Sparrow is simpler and more straightforward, making it easier for beginners to set up and use without the complexity of Celery.
Community Support: Celery has a larger and more active community of developers and users, providing a wealth of resources, documentation, and support for troubleshooting and learning. Sparrow, being a newer and less widely adopted framework, may have a smaller community and fewer resources available for help and guidance.
Integration: Celery integrates seamlessly with various frameworks, message brokers, and backend storage systems, offering more flexibility and compatibility with different tools and technologies in the development stack. Sparrow, being a lightweight framework, may have limited integration options and may not provide the same level of flexibility as Celery.
Performance: Celery is known for its high performance and efficiency in task execution, thanks to its robust architecture and optimization for handling concurrent operations. Sparrow, while efficient for smaller tasks and simple workflows, may not offer the same level of performance and speed as Celery for more demanding applications.
Maintenance: Celery has been around for a longer time and has undergone multiple updates and improvements, ensuring better stability, security, and long-term maintenance for projects using the framework. Sparrow, being relatively new, may lack the same level of maturity and ongoing support for addressing bugs, security vulnerabilities, and evolving needs over time.

Celery vs Sparrow

Overview