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

Celery vs MSMQ

OverviewComparisonAlternatives

Overview

Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K
MSMQ
MSMQ
Stacks33
Followers118
Votes3

Celery vs MSMQ: What are the differences?

# Introduction
In this Markdown code, we will highlight the key differences between Celery and MSMQ for usage in a website.

# 1. **Concurrency Model**:
Celery is designed for distributed task processing and utilizes a distributed system of workers to execute tasks concurrently, while MSMQ is a message queuing system that follows the First In, First Out (FIFO) model, processing messages in the order they were received.

# 2. **Language Support**:
Celery supports multiple programming languages like Python, Java, and Ruby, making it versatile for various development environments. On the other hand, MSMQ is optimized for Windows environments and primarily works with .NET languages, limiting its cross-platform compatibility.

# 3. **Persistence**:
Celery allows for persistent task results, storing them in backend data stores like Redis or databases for later retrieval. In contrast, MSMQ does not natively support persistent storage of messages, requiring additional configurations for durable message queuing.

# 4. **Scalability**:
Celery is highly scalable due to its distributed architecture, enabling easy scaling of worker nodes to handle increased workloads. MSMQ, while capable of supporting scalable applications, may require additional setup and configurations for achieving the same level of scalability as Celery.

# 5. **Ease of Integration**:
Celery provides seamless integration with popular frameworks like Django and Flask, simplifying the implementation of background tasks in web applications. In contrast, integrating MSMQ with web applications may require more manual configurations and custom coding to establish communication between the application and the message queue.

# 6. **Monitoring and Management**:
Celery offers robust monitoring tools and management capabilities through built-in functionalities and third-party extensions, facilitating real-time tracking and optimization of task workflows. MSMQ, on the other hand, may have limited monitoring capabilities out-of-the-box, potentially requiring additional tools or customization for comprehensive monitoring of message queues.

In Summary, Celery and MSMQ differ in their concurrency models, language support, persistence, scalability, ease of integration, and monitoring capabilities, making Celery a preferred choice for distributed task processing in web applications.```

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Detailed Comparison

Celery
Celery
MSMQ
MSMQ

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.

This technology enables applications running at different times to communicate across heterogeneous networks and systems that may be temporarily offline. Applications send messages to queues and read messages from queues.

Statistics
GitHub Stars
27.5K
GitHub Stars
-
GitHub Forks
4.9K
GitHub Forks
-
Stacks
1.7K
Stacks
33
Followers
1.6K
Followers
118
Votes
280
Votes
3
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
  • 2
    Easy to learn
  • 1
    Cloud not needed
Cons
  • 1
    Windows dependency

What are some alternatives to Celery, MSMQ?

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.

NSQ

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

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.

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