Celery vs Redis

Overview

Redis

Stacks61.9K

Followers46.5K

Votes3.9K

GitHub Stars42

Forks6

Celery

Stacks1.7K

Followers1.6K

Votes280

GitHub Stars27.5K

Forks4.9K

Celery vs Redis: What are the differences?

Celery is a distributed task queue system while Redis is an in-memory data structure store. Let's explore the key differences between them.

Message Broker vs. Data Store: Celery is essentially a message broker that allows you to manage and distribute tasks across multiple processing units or machines. On the other hand, Redis is primarily a data store that provides fast in-memory storage and retrieval of data.
Task Processing vs. Data Manipulation: Another major difference is the core functionality they provide. Celery is designed to handle the processing of tasks in a distributed manner, where tasks are typically functions or methods that need to be executed asynchronously. Redis, on the other hand, focuses on data manipulation and provides various data structures like strings, lists, sets, and more.
Task Queues vs. Data Structures: Celery utilizes task queues to manage and distribute tasks. It allows you to create queues, prioritize tasks, and distribute them based on availability of workers. In contrast, Redis provides a wide range of data structures like strings, lists, sets, and hashes, which can be used for various purposes including caching, real-time analytics, and more.
Concurrency vs. Persistence: Celery handles task concurrency by allowing multiple workers to process tasks concurrently. It provides a way to scale and distribute tasks across multiple machines. Redis, on the other hand, focuses more on data persistence rather than concurrency. It ensures data durability by allowing you to persist data to disk and provides various mechanisms for replication and fault tolerance.
Task Routing vs. Pub/Sub Messaging: Celery provides flexible task routing mechanisms, allowing you to route tasks to different workers based on predefined rules. It also supports various task result backend options for retrieving the results of completed tasks. Redis, on the other hand, supports publish/subscribe messaging, where you can publish messages to specific channels and subscribers can receive those messages.
Integration with other technologies: Celery integrates well with other technologies and frameworks like Django, Flask, and more. It provides convenient wrappers and utilities for integrating with these frameworks. Redis also has good integration with different programming languages and frameworks, making it easy to use in various application architectures.

In summary, Celery focuses on task processing using task queues and supports task routing and result backends. Redis, on the other hand, is primarily a data store that supports various data structures and provides pub/sub messaging capabilities.

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Advice on Redis, 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

Redis	Celery
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.	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 42	GitHub Stars 27.5K
GitHub Forks 6	GitHub Forks 4.9K
Stacks 61.9K	Stacks 1.7K
Followers 46.5K	Followers 1.6K
Votes 3.9K	Votes 280
Pros & Cons
Pros 888 Performance 542 Super fast 514 Ease of use 444 In-memory cache 324 Advanced key-value cache Cons 15 Cannot query objects directly 3 No secondary indexes for non-numeric data types 1 No WAL	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 Redis, 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.

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

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.

Related Comparisons

Celery vs Redis: What are the differences?

Celery is a distributed task queue system while Redis is an in-memory data structure store. Let's explore the key differences between them.

Message Broker vs. Data Store: Celery is essentially a message broker that allows you to manage and distribute tasks across multiple processing units or machines. On the other hand, Redis is primarily a data store that provides fast in-memory storage and retrieval of data.
Task Processing vs. Data Manipulation: Another major difference is the core functionality they provide. Celery is designed to handle the processing of tasks in a distributed manner, where tasks are typically functions or methods that need to be executed asynchronously. Redis, on the other hand, focuses on data manipulation and provides various data structures like strings, lists, sets, and more.
Task Queues vs. Data Structures: Celery utilizes task queues to manage and distribute tasks. It allows you to create queues, prioritize tasks, and distribute them based on availability of workers. In contrast, Redis provides a wide range of data structures like strings, lists, sets, and hashes, which can be used for various purposes including caching, real-time analytics, and more.
Concurrency vs. Persistence: Celery handles task concurrency by allowing multiple workers to process tasks concurrently. It provides a way to scale and distribute tasks across multiple machines. Redis, on the other hand, focuses more on data persistence rather than concurrency. It ensures data durability by allowing you to persist data to disk and provides various mechanisms for replication and fault tolerance.
Task Routing vs. Pub/Sub Messaging: Celery provides flexible task routing mechanisms, allowing you to route tasks to different workers based on predefined rules. It also supports various task result backend options for retrieving the results of completed tasks. Redis, on the other hand, supports publish/subscribe messaging, where you can publish messages to specific channels and subscribers can receive those messages.
Integration with other technologies: Celery integrates well with other technologies and frameworks like Django, Flask, and more. It provides convenient wrappers and utilities for integrating with these frameworks. Redis also has good integration with different programming languages and frameworks, making it easy to use in various application architectures.

Celery vs Redis

Overview