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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Celery vs Starling

Celery vs Starling

OverviewDecisionsComparisonAlternatives

Overview

Starling
Starling
Stacks8
Followers11
Votes0
GitHub Stars463
Forks59
Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K

Celery vs Starling: What are the differences?

What is Celery? Distributed task queue. 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 Starling? A light weight server for reliable distributed message passing. Starling is a powerful but simple messaging server that enables reliable distributed queuing with an absolutely minimal overhead. It speaks the MemCache protocol for maximum cross-platform compatibility. Any language that speaks MemCache can take advantage of Starling's queue facilities.

Celery and Starling can be primarily classified as "Message Queue" tools.

Celery and Starling are both open source tools. It seems that Celery with 12.9K GitHub stars and 3.33K forks on GitHub has more adoption than Starling with 468 GitHub stars and 63 GitHub forks.

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Advice on Starling, Celery

Shantha
Shantha

Sep 30, 2020

Needs adviceonRabbitMQRabbitMQCeleryCeleryMongoDBMongoDB

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

Starling
Starling
Celery
Celery

Starling is a powerful but simple messaging server that enables reliable distributed queuing with an absolutely minimal overhead. It speaks the MemCache protocol for maximum cross-platform compatibility. Any language that speaks MemCache can take advantage of Starling's queue facilities.

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 Blaine Cook at Twitter;Starling is a Message Queue Server based on MemCached;Written in Ruby;Stores jobs in memory (message queue)
-
Statistics
GitHub Stars
463
GitHub Stars
27.5K
GitHub Forks
59
GitHub Forks
4.9K
Stacks
8
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 Starling, Celery?

Kafka

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

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Amazon SQS

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

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

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

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

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

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

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

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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