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Celery

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IronMQ

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Celery vs IronMQ: What are the differences?

Developers describe Celery as "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. On the other hand, IronMQ is detailed as "Message Queue for any deployment". 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.

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

"Task queue" is the top reason why over 84 developers like Celery, while over 10 developers mention "Great Support" as the leading cause for choosing IronMQ.

Celery is an open source tool with 12.9K GitHub stars and 3.33K GitHub forks. Here's a link to Celery's open source repository on GitHub.

Udemy, Robinhood, and Sentry are some of the popular companies that use Celery, whereas IronMQ is used by HotelTonight, Coinbase, and Hubble. Celery has a broader approval, being mentioned in 272 company stacks & 77 developers stacks; compared to IronMQ, which is listed in 9 company stacks and 5 developer stacks.

Advice on Celery and IronMQ
Needs advice
on
CeleryCelery
and
RabbitMQRabbitMQ

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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Replies (1)
Recommends
on
rqrqRedisRedis

For large amounts of small tasks and caches I have had good luck with Redis and RQ. I have not personally used celery but I am fairly sure it would scale well, and I have not used RabbitMQ for anything besides communication between services. If you prefer python my suggestions should feel comfortable.

Sorry I do not have a more information

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Pros of Celery
Pros of IronMQ
  • 99
    Task queue
  • 63
    Python integration
  • 40
    Django integration
  • 30
    Scheduled Task
  • 19
    Publish/subsribe
  • 8
    Various backend broker
  • 6
    Easy to use
  • 5
    Great community
  • 5
    Workflow
  • 4
    Free
  • 1
    Dynamic
  • 12
    Great Support
  • 8
    Heroku Add-on
  • 3
    Push support
  • 3
    Delayed delivery upto 7 days
  • 2
    Super fast
  • 2
    Language agnostic
  • 2
    Good analytics/monitoring
  • 2
    Ease of configuration
  • 2
    GDPR Compliant

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Cons of Celery
Cons of IronMQ
  • 4
    Sometimes loses tasks
  • 1
    Depends on broker
  • 1
    Can't use rabbitmqadmin

Sign up to add or upvote consMake informed product decisions

- No public GitHub repository available -

What is Celery?

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 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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What companies use Celery?
What companies use IronMQ?
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What are some alternatives to Celery and IronMQ?
RabbitMQ
RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Cucumber
Cucumber is a tool that supports Behaviour-Driven Development (BDD) - a software development process that aims to enhance software quality and reduce maintenance costs.
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.
See all alternatives