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  1. Stackups
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  4. Message Queue
  5. ActiveMQ vs Kafka vs ZeroMQ

ActiveMQ vs Kafka vs ZeroMQ

OverviewDecisionsComparisonAlternatives

Overview

ActiveMQ
ActiveMQ
Stacks879
Followers1.3K
Votes77
GitHub Stars2.4K
Forks1.5K
Kafka
Kafka
Stacks24.2K
Followers22.3K
Votes607
GitHub Stars31.2K
Forks14.8K
ZeroMQ
ZeroMQ
Stacks258
Followers586
Votes71
GitHub Stars10.6K
Forks2.5K

ActiveMQ vs Kafka vs ZeroMQ: What are the differences?

Introduction: When it comes to messaging systems, ActiveMQ, Kafka, and ZeroMQ are widely used for their unique capabilities and functionalities. Understanding the key differences between these three tools can help users make informed decisions based on their specific use cases and requirements.

  1. Message Broker vs Message Queue: ActiveMQ: ActiveMQ operates as a traditional message broker, maintaining queues and topics for message distribution. It follows a publish-subscribe model, allowing multiple consumers to receive messages from the same topic.

  2. High Throughput and Low Latency: Kafka: Kafka is designed for high throughput and low latency, making it suitable for scenarios requiring real-time data processing. It uses a distributed commit log architecture that enables efficient handling of large volumes of data with minimal latency.

  3. Protocol Support: ZeroMQ: ZeroMQ supports various messaging patterns such as request-reply, publish-subscribe, and push-pull, providing flexibility in designing communication protocols. It is a lightweight and fast messaging library that facilitates seamless integration into applications.

  4. Persistence and Retention: Kafka: Kafka supports data persistence and retention for extended periods, making it suitable for use cases where data needs to be stored for future analysis or reference. Its log compaction feature ensures that only the latest version of each key-value pair is retained.

  5. Scalability: ActiveMQ and Kafka: ActiveMQ allows horizontal scaling by adding more nodes to the broker cluster to handle increasing message loads. On the other hand, Kafka offers partitioning and replication mechanisms that enable seamless scalability and fault tolerance across multiple nodes.

  6. Ease of Deployment and Configuration: ZeroMQ: ZeroMQ is known for its simplicity in deployment and configuration, requiring minimal overhead to set up messaging patterns. Its lightweight nature makes it a preferred choice for scenarios where a lightweight messaging solution is needed.

In Summary, understanding the key differences between ActiveMQ, Kafka, and ZeroMQ in terms of message queuing mechanisms, throughput, scalability, and deployment options can help users choose the appropriate messaging tool for their specific requirements.

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Advice on ActiveMQ, Kafka, ZeroMQ

Tarun
Tarun

Senior Software Developer at Okta

Dec 4, 2021

Review

We have faced the same question some time ago. Before I begin, DO NOT use Redis as a message broker. It is fast and easy to set up in the beginning but it does not scale. It is not made to be reliable in scale and that is mentioned in the official docs. This analysis of our problems with Redis may help you.

We have used Kafka and RabbitMQ both in scale. We concluded that RabbitMQ is a really good general purpose message broker (for our case) and Kafka is really fast but limited in features. That’s the trade off that we understood from using it. In-fact I blogged about the trade offs between Kafka and RabbitMQ to document it. I hope it helps you in choosing the best pub-sub layer for your use case.

153k views153k
Comments
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
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

ActiveMQ
ActiveMQ
Kafka
Kafka
ZeroMQ
ZeroMQ

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.

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

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.

Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment
Written at LinkedIn in Scala;Used by LinkedIn to offload processing of all page and other views;Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled);Supports both on-line as off-line processing
Connect your code in any language, on any platform.;Carries messages across inproc, IPC, TCP, TPIC, multicast.;Smart patterns like pub-sub, push-pull, and router-dealer.;High-speed asynchronous I/O engines, in a tiny library.;Backed by a large and active open source community.;Supports every modern language and platform.;Build any architecture: centralized, distributed, small, or large.;Free software with full commercial support.
Statistics
GitHub Stars
2.4K
GitHub Stars
31.2K
GitHub Stars
10.6K
GitHub Forks
1.5K
GitHub Forks
14.8K
GitHub Forks
2.5K
Stacks
879
Stacks
24.2K
Stacks
258
Followers
1.3K
Followers
22.3K
Followers
586
Votes
77
Votes
607
Votes
71
Pros & Cons
Pros
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
Cons
  • 1
    ONLY Vertically Scalable
  • 1
    Support
  • 1
    Difficult to scale
  • 1
    Low resilience to exceptions and interruptions
Pros
  • 126
    High-throughput
  • 119
    Distributed
  • 92
    Scalable
  • 86
    High-Performance
  • 66
    Durable
Cons
  • 32
    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging
Pros
  • 23
    Fast
  • 20
    Lightweight
  • 11
    Transport agnostic
  • 7
    No broker required
  • 4
    Low latency
Cons
  • 5
    No message durability
  • 3
    Not a very reliable system - message delivery wise
  • 1
    M x N problem with M producers and N consumers

What are some alternatives to ActiveMQ, Kafka, ZeroMQ?

RabbitMQ

RabbitMQ

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

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.

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.

Confluent

Confluent

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

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