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  4. Message Queue
  5. ActiveMQ vs Apache RocketMQ vs RabbitMQ

ActiveMQ vs Apache RocketMQ vs RabbitMQ

OverviewDecisionsComparisonAlternatives

Overview

RabbitMQ
RabbitMQ
Stacks21.8K
Followers18.9K
Votes558
GitHub Stars13.2K
Forks4.0K
ActiveMQ
ActiveMQ
Stacks880
Followers1.3K
Votes77
GitHub Stars2.4K
Forks1.5K
Apache RocketMQ
Apache RocketMQ
Stacks48
Followers200
Votes8

ActiveMQ vs Apache RocketMQ vs RabbitMQ: What are the differences?

Introduction

ActiveMQ, Apache RocketMQ, and RabbitMQ are all messaging systems that are widely used in various software applications. Each of these messaging systems has its own unique features and characteristics. In this analysis, we will highlight the key differences between ActiveMQ, Apache RocketMQ, and RabbitMQ.

  1. Message Broker Pattern: ActiveMQ and Apache RocketMQ follow the message broker pattern, while RabbitMQ follows the publish/subscribe pattern. In the message broker pattern, messages are routed through a central message broker, allowing for more complex routing and message distribution. In contrast, the publish/subscribe pattern allows for the direct exchange of messages between publishers and subscribers.

  2. Protocol Support: ActiveMQ supports multiple protocols such as MQTT, STOMP, and AMQP, while both Apache RocketMQ and RabbitMQ primarily support the AMQP protocol. The support for multiple protocols in ActiveMQ enables easy integration with different types of systems and devices.

  3. Ordering of Messages: Apache RocketMQ focuses on providing strong support for ordered message delivery, ensuring that messages are delivered in the order they were sent. On the other hand, both ActiveMQ and RabbitMQ have weaker guarantees for message ordering, as they prioritize scalability and high throughput.

  4. Distributed Message Queues: Apache RocketMQ is designed with a distributed architecture at its core, providing built-in features for data partitioning and fault tolerance. This makes it well-suited for applications that require high scalability and reliability. ActiveMQ and RabbitMQ also support distributed mode but may require additional configurations for achieving high scalability and fault tolerance.

  5. Plugins and Extensions: RabbitMQ provides a highly extensible platform, allowing developers to leverage a wide range of plugins and extensions to enhance its functionality. ActiveMQ and Apache RocketMQ have a more limited set of plugins and extensions available, although they still offer essential features for various messaging scenarios.

  6. Community and Support: Apache ActiveMQ, Apache RocketMQ, and RabbitMQ all have active and supportive communities. However, RabbitMQ has a larger and more established community with extensive documentation, making it easier for developers to find resources and assistance when needed. ActiveMQ and Apache RocketMQ also have strong community support but may have fewer resources available.

In summary, ActiveMQ and Apache RocketMQ follow the message broker pattern, whereas RabbitMQ follows the publish/subscribe pattern. ActiveMQ supports multiple protocols, while Apache RocketMQ and RabbitMQ primarily support AMQP. Apache RocketMQ provides strong support for ordered message delivery, while ActiveMQ and RabbitMQ prioritize scalability. Apache RocketMQ also has built-in features for distributed architecture, whereas ActiveMQ and RabbitMQ require additional configurations for achieving high scalability. RabbitMQ offers a wide range of plugins and has a larger community and extensive documentation compared to ActiveMQ and Apache RocketMQ.

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Advice on RabbitMQ, ActiveMQ, Apache RocketMQ

viradiya
viradiya

Apr 12, 2020

Needs adviceonAngularJSAngularJSASP.NET CoreASP.NET CoreMSSQLMSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k
Comments
Pulkit
Pulkit

Software Engineer

Oct 30, 2020

Needs adviceonDjangoDjangoAmazon SQSAmazon SQSRabbitMQRabbitMQ

Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.

474k views474k
Comments
Kirill
Kirill

GO/C developer at Duckling Sales

Feb 16, 2021

Decided

Maybe not an obvious comparison with Kafka, since Kafka is pretty different from rabbitmq. But for small service, Rabbit as a pubsub platform is super easy to use and pretty powerful. Kafka as an alternative was the original choice, but its really a kind of overkill for a small-medium service. Especially if you are not planning to use k8s, since pure docker deployment can be a pain because of networking setup. Google PubSub was another alternative, its actually pretty cheap, but I never tested it since Rabbit was matching really good for mailing/notification services.

266k views266k
Comments

Detailed Comparison

RabbitMQ
RabbitMQ
ActiveMQ
ActiveMQ
Apache RocketMQ
Apache RocketMQ

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

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.

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.

Robust messaging for applications;Easy to use;Runs on all major operating systems;Supports a huge number of developer platforms;Open source and commercially supported
Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment
-
Statistics
GitHub Stars
13.2K
GitHub Stars
2.4K
GitHub Stars
-
GitHub Forks
4.0K
GitHub Forks
1.5K
GitHub Forks
-
Stacks
21.8K
Stacks
880
Stacks
48
Followers
18.9K
Followers
1.3K
Followers
200
Votes
558
Votes
77
Votes
8
Pros & Cons
Pros
  • 235
    It's fast and it works with good metrics/monitoring
  • 80
    Ease of configuration
  • 60
    I like the admin interface
  • 52
    Easy to set-up and start with
  • 22
    Durable
Cons
  • 9
    Too complicated cluster/HA config and management
  • 6
    Needs Erlang runtime. Need ops good with Erlang runtime
  • 5
    Configuration must be done first, not by your code
  • 4
    Slow
Pros
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
Cons
  • 1
    ONLY Vertically Scalable
  • 1
    Difficult to scale
  • 1
    Low resilience to exceptions and interruptions
  • 1
    Support
Pros
  • 2
    Support tracing message and transactional message
  • 2
    Million-level message accumulation capacity in a single
  • 1
    BigData Friendly
  • 1
    Low latency
  • 1
    Feature-rich administrative dashboard for configuration
Integrations
No integrations availableNo integrations available
Docker
Docker

What are some alternatives to RabbitMQ, ActiveMQ, Apache RocketMQ?

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

Celery

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.

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.

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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