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  5. Kafka vs MQTT

Kafka vs MQTT

OverviewDecisionsComparisonAlternatives

Overview

Kafka
Kafka
Stacks24.2K
Followers22.3K
Votes607
GitHub Stars31.2K
Forks14.8K
MQTT
MQTT
Stacks635
Followers577
Votes7

Kafka vs MQTT: What are the differences?

Apache Kafka and MQTT are two widely used messaging protocols. Let's discuss the key differences between them:

  1. Scalability and Performance: Kafka is designed to handle high-volume, real-time data streams and provides high throughput and low latency. It is horizontally scalable and can handle millions of messages per second. On the other hand, MQTT is a lightweight protocol that is primarily designed for low-bandwidth, constrained devices. It is built for reliable communication over unreliable networks and is suitable for IoT applications with limited resources.

  2. Message Delivery Guarantees: Kafka guarantees both at-least-once and at-most-once message delivery semantics. It achieves this through configurable settings and the use of message offsets. On the contrary, MQTT offers three quality of service (QoS) levels: QoS 0 (at-most-once), QoS 1 (at-least-once), and QoS 2 (exactly-once). This allows MQTT to provide more flexibility in terms of reliability based on the specific requirements of the application.

  3. Message Persistence: Kafka is a distributed commit log that stores all messages for a configurable retention period. This enables data replayability and fault tolerance. MQTT, on the other hand, does not persist messages by default. While some MQTT brokers may support message persistence, it is not a guaranteed feature like in Kafka.

  4. Publish-Subscribe Model: Kafka follows a publish-subscribe (pub-sub) messaging model where messages are published to topics and consumers subscribe to these topics to receive messages. MQTT also supports a similar pub-sub model, but it allows for more granular control through the use of MQTT topics and filters. MQTT topics can be used for specific message routing, filtering, and subscribing.

  5. Client Libraries and Ecosystem: Kafka offers robust client libraries and has a rich ecosystem of connectors and tools, making it well-suited for building data-intensive streaming applications. It has extensive support for different programming languages and provides high-level abstractions for stream processing. MQTT also provides client libraries for various programming languages, but its ecosystem is not as extensive as Kafka's, especially in terms of analytics and stream processing frameworks.

  6. Integration with IoT: MQTT is often preferred for IoT applications due to its lightweight nature and efficient use of network bandwidth. It is widely adopted in IoT platforms and is supported by many embedded devices. Kafka, on the other hand, can also be used in IoT scenarios but is typically favored for high-throughput, real-time processing and analytics use cases where data volume and ingestion rate are critical factors.

In summary, Kafka is a scalable, high-performance messaging system with strong durability guarantees and extensive ecosystem support, while MQTT is a lightweight, reliable protocol designed for IoT applications with limited resources and network constraints.

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Advice on Kafka, MQTT

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
Ishfaq
Ishfaq

Feb 28, 2020

Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

804k views804k
Comments
Roman
Roman

Senior Back-End Developer, Software Architect

Feb 12, 2019

ReviewonKafkaKafka

I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

Downsides of using Kafka are:

  • you have to deal with Zookeeper
  • you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)
10.8k views10.8k
Comments

Detailed Comparison

Kafka
Kafka
MQTT
MQTT

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

It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium.

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
-
Statistics
GitHub Stars
31.2K
GitHub Stars
-
GitHub Forks
14.8K
GitHub Forks
-
Stacks
24.2K
Stacks
635
Followers
22.3K
Followers
577
Votes
607
Votes
7
Pros & Cons
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
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Lightweight with a relatively small data footprint
  • 2
    Very easy to configure and use with open source tools
Cons
  • 1
    Easy to configure in an unsecure manner

What are some alternatives to Kafka, MQTT?

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.

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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