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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Celery vs NSQ

Celery vs NSQ

OverviewDecisionsComparisonAlternatives

Overview

Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K
NSQ
NSQ
Stacks142
Followers356
Votes148

Celery vs NSQ: What are the differences?

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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Advice on Celery, NSQ

Shantha
Shantha

Sep 30, 2020

Needs adviceonRabbitMQRabbitMQCeleryCeleryMongoDBMongoDB

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.

334k views334k
Comments
Pramod
Pramod

Co Founder at Usability Designs

Mar 2, 2020

Needs advice

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.

572k views572k
Comments

Detailed Comparison

Celery
Celery
NSQ
NSQ

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.

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.

-
support distributed topologies with no SPOF;horizontally scalable (no brokers, seamlessly add more nodes to the cluster);low-latency push based message delivery (performance);combination load-balanced and multicast style message routing;excel at both streaming (high-throughput) and job oriented (low-throughput) workloads;primarily in-memory (beyond a high-water mark messages are transparently kept on disk);runtime discovery service for consumers to find producers (nsqlookupd);transport layer security (TLS);data format agnostic;few dependencies (easy to deploy) and a sane, bounded, default configuration;simple TCP protocol supporting client libraries in any language;HTTP interface for stats, admin actions, and producers (no client library needed to publish);integrates with statsd for realtime instrumentation;robust cluster administration interface (nsqadmin)
Statistics
GitHub Stars
27.5K
GitHub Stars
-
GitHub Forks
4.9K
GitHub Forks
-
Stacks
1.7K
Stacks
142
Followers
1.6K
Followers
356
Votes
280
Votes
148
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
Pros
  • 29
    It's in golang
  • 20
    Lightweight
  • 20
    Distributed
  • 18
    Easy setup
  • 17
    High throughput
Cons
  • 1
    Long term persistence
  • 1
    Get NSQ behavior out of Kafka but not inverse
  • 1
    HA

What are some alternatives to Celery, NSQ?

Kafka

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

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Amazon SQS

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.

ActiveMQ

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

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

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

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

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

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.

Apache Pulsar

Apache Pulsar

Apache Pulsar is a distributed messaging solution developed and released to open source at Yahoo. Pulsar supports both pub-sub messaging and queuing in a platform designed for performance, scalability, and ease of development and operation.

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