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Celery vs ZeroMQ: What are the differences?

Introduction

In this article, we will discuss the key differences between Celery and ZeroMQ. Celery is a distributed task queue system that enables efficient job scheduling and task execution in a distributed environment. ZeroMQ, on the other hand, is a high-performance messaging library that allows for lightweight and fast communication between different components of an application. Both Celery and ZeroMQ have their own set of features and advantages, which we will explore in the following paragraphs.

  1. Scalability: Celery is designed to handle large-scale distributed task execution by creating a task queue and allowing workers to process tasks asynchronously. It provides a robust and scalable architecture that can handle a large number of tasks and workers efficiently. ZeroMQ, on the other hand, focuses on lightweight messaging and does not provide built-in task queue management. Instead, it offers a flexible messaging pattern that can be used to build custom task distribution systems.

  2. Transport Protocol: Celery uses various transport protocols such as RabbitMQ, Redis, and in-memory queues for message passing between tasks and workers. It provides a high-level abstraction of these transport protocols, making it easy to switch between different message brokers. ZeroMQ, on the other hand, uses its own lightweight messaging protocol to facilitate communication between different components. It does not rely on external message brokers and provides a low-level socket-based communication system.

  3. Message Persistence: Celery has built-in support for message persistence, which means that if a task fails or a worker crashes, the task message is not lost and can be retried later. This ensures reliable message delivery and fault tolerance. ZeroMQ, on the other hand, does not provide built-in message persistence. If a message fails to reach its destination or a component crashes, the message is lost and cannot be recovered.

  4. Ease of Use: Celery provides a high-level API that abstracts away the complexity of distributed task execution. It offers features like task routing, result tracking, and worker monitoring, making it easy to develop and manage distributed task execution systems. ZeroMQ, on the other hand, is a low-level messaging library that requires more manual configuration and control. It provides flexibility but at the cost of increased complexity and development effort.

  5. Language Support: Celery is primarily designed for Python and provides a Python API for task execution and management. It also has support for other languages such as Java, Ruby, and Node.js through different client libraries. ZeroMQ, on the other hand, provides language bindings for a wide range of programming languages including C/C++, Python, Java, Ruby, and more. It can be easily integrated into applications written in different languages.

  6. Community and Ecosystem: Celery has a large and active community of developers and contributors who contribute to its development and maintenance. It has a wide range of plugins and extensions available, which add additional functionality and integrations with other tools and frameworks. ZeroMQ also has a strong community and ecosystem, but it is not as widely adopted and does not have as many plugins and extensions available compared to Celery.

In summary, Celery and ZeroMQ are both powerful tools for distributed task execution and messaging. Celery provides a higher-level abstraction and ease of use, with built-in support for task queue management and message persistence. ZeroMQ, on the other hand, offers a lightweight and flexible messaging library with language bindings for multiple programming languages. The choice between Celery and ZeroMQ depends on the specific requirements and constraints of the application.

Advice on Celery and ZeroMQ
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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Meili Triantafyllidi
Software engineer at Digital Science · | 6 upvotes · 434.6K views
Needs advice
on
Amazon SQSAmazon SQSRabbitMQRabbitMQ
and
ZeroMQZeroMQ

Hi, we are in a ZMQ set up in a push/pull pattern, and we currently start to have more traffic and cases that the service is unavailable or stuck. We want to: * Not loose messages in services outages * Safely restart service without losing messages (ZeroMQ seems to need to close the socket in the receiver before restart manually)

Do you have experience with this setup with ZeroMQ? Would you suggest RabbitMQ or Amazon SQS (we are in AWS setup) instead? Something else?

Thank you for your time

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Replies (2)
Shishir Pandey
Recommends
on
RabbitMQRabbitMQ

ZeroMQ is fast but you need to build build reliability yourself. There are a number of patterns described in the zeromq guide. I have used RabbitMQ before which gives lot of functionality out of the box, you can probably use the worker queues example from the tutorial, it can also persists messages in the queue.

I haven't used Amazon SQS before. Another tool you could use is Kafka.

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Kevin Deyne
Principal Software Engineer at Accurate Background · | 5 upvotes · 195.1K views
Recommends
on
RabbitMQRabbitMQ

Both would do the trick, but there are some nuances. We work with both.

From the sound of it, your main focus is "not losing messages". In that case, I would go with RabbitMQ with a high availability policy (ha-mode=all) and a main/retry/error queue pattern.

Push messages to an exchange, which sends them to the main queue. If an error occurs, push the errored out message to the retry exchange, which forwards it to the retry queue. Give the retry queue a x-message-ttl and set the main exchange as a dead-letter-exchange. If your message has been retried several times, push it to the error exchange, where the message can remain until someone has time to look at it.

This is a very useful and resilient pattern that allows you to never lose messages. With the high availability policy, you make sure that if one of your rabbitmq nodes dies, another can take over and messages are already mirrored to it.

This is not really possible with SQS, because SQS is a lot more focused on throughput and scaling. Combined with SNS it can do interesting things like deduplication of messages and such. That said, one thing core to its design is that messages have a maximum retention time. The idea is that a message that has stayed in an SQS queue for a while serves no more purpose after a while, so it gets removed - so as to not block up any listener resources for a long time. You can also set up a DLQ here, but these similarly do not hold onto messages forever. Since you seem to depend on messages surviving at all cost, I would suggest that the scaling/throughput benefit of SQS does not outweigh the difference in approach to messages there.

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