Celery vs Sandglass

Overview

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Sandglass

Stacks1

Followers6

Votes0

Celery vs Sandglass: What are the differences?

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; Sandglass: Distributed, scalable, persistent time-sorted message queue. A distributed, horizontally scalable, persistent, time ordered message queue. Developed to support asynchronous tasks and message scheduling which makes it suitable for usage as a task queue.

Celery and Sandglass can be categorized as "Message Queue" tools.

Celery and Sandglass are both open source tools. It seems that Celery with 12.9K GitHub stars and 3.33K forks on GitHub has more adoption than Sandglass with 1.52K GitHub stars and 40 GitHub forks.

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Advice on Celery, Sandglass

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	Sandglass
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, horizontally scalable, persistent, time ordered message queue. Developed to support asynchronous tasks and message scheduling which makes it suitable for usage as a task queue.
-	Horizontal scalability; Highly available; Persistent storage; Time ordered; Multiple consumers per group for a partition; Produce message to be consumed in the future; Acknowledge/NotAcknowledge each message individually; Automatic redelivery and commit offset tracking; Language agnostic
Statistics
GitHub Stars 27.5K	GitHub Stars -
GitHub Forks 4.9K	GitHub Forks -
Stacks 1.7K	Stacks 1
Followers 1.6K	Followers 6
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
Integrations
No integrations available	Golang Java Python Node.js Ruby

What are some alternatives to Celery, Sandglass?

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.