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Celery vs NSQ: What are the differences?
Scalability: Celery is a distributed task queue that can handle a large number of tasks and workers. It provides a highly scalable architecture that allows for large-scale task execution and management. On the other hand, NSQ is a real-time distributed messaging platform that focuses on scalability and reliability. NSQ is designed to handle high throughput and message volume, making it a robust solution for demanding workloads.
Delivery Guarantees: Celery does not provide built-in support for delivery guarantees such as at-most-once, at-least-once, or exactly-once semantics. It relies on the message broker for managing task execution and delivery. In contrast, NSQ provides configurable delivery guarantees that allow users to choose the level of reliability they need for their messages. NSQ offers options for at-most-once and at-least-once delivery guarantees, giving users control over message reliability.
Architecture: Celery follows a task queue model where tasks are produced by clients and consumed by worker nodes. It uses a message broker to manage task distribution and worker coordination. NSQ, on the other hand, utilizes a distributed messaging system architecture where messages are published to a topic and consumed by subscribers. NSQ's architecture is designed for real-time message processing and delivery.
Ease of Use: Celery is a feature-rich framework with support for task scheduling, result tracking, and monitoring. It provides a high level of abstraction for managing distributed tasks and workers. NSQ, on the other hand, is focused on simplicity and performance. It offers a lightweight messaging solution with minimal configuration and overhead, making it easy to set up and use for various use cases.
Community Support: Celery has a large and active community of developers and users who contribute to its development and provide support through forums, documentation, and plugins. NSQ also has a growing community of users, but it may not have as comprehensive support resources as Celery. However, NSQ's simplicity and performance make it an attractive choice for users seeking a lightweight messaging solution.
Monitoring and Management: Celery provides built-in tools for monitoring task execution, tracking results, and managing worker nodes. It integrates with popular monitoring systems like Prometheus and Grafana for performance analysis. NSQ offers basic monitoring and management capabilities through its built-in statistics and admin interfaces. Users may need to integrate NSQ with additional tools for more advanced monitoring and management features.
In Summary, Celery and NSQ differ in scalability, delivery guarantees, architecture, ease of use, community support, and monitoring capabilities, catering to specific use cases and preferences for distributed task management and messaging systems.
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.
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
I am looking into IoT World Solution where we have MQTT Broker. This MQTT Broker Sits in one of the Data Center. We are doing a lot of Alert and Alarm related processing on that Data, Currently, we are looking into Solution which can do distributed persistence of log/alert primarily on remote Disk.
Our primary need is to use lightweight where operational complexity and maintenance costs can be significantly reduced. We want to do it on-premise so we are not considering cloud solutions.
We looked into the following alternatives:
Apache Kafka - Great choice but operation and maintenance wise very complex. Rabbit MQ - High availability is the issue, Apache Pulsar - Operational Complexity. NATS - Absence of persistence. Akka Streams - Big learning curve and operational streams.
So we are looking into a lightweight library that can do distributed persistence preferably with publisher and subscriber model. Preferable on JVM stack.
Kafka is best fit here. Below are the advantages with Kafka ACLs (Security), Schema (protobuf), Scale, Consumer driven and No single point of failure.
Operational complexity is manageable with open source monitoring tools.