Kafka vs NATS

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

NATS

Stacks393

Followers498

Votes60

Kafka vs NATS: What are the differences?

Kafka and NATS are both popular messaging systems used for building scalable and distributed applications. Let's explore the key differences between Kafka and NATS:

Architecture: Kafka is a distributed streaming platform that follows a publish-subscribe model. It uses a distributed commit log to store messages, allowing multiple consumers to read messages independently and at their own pace. NATS, on the other hand, is a lightweight and high-performance messaging system that follows a simple publish-subscribe model. It focuses on simplicity and minimal overhead, making it suitable for lightweight messaging scenarios.
Scalability: Kafka is known for its scalability and can handle large-scale data streaming and processing. It can handle high message throughput and supports distributed deployment across multiple nodes or clusters. NATS is also designed to be scalable but is more suitable for moderate to high message rates rather than extremely high throughput scenarios. It can scale horizontally by adding more instances to the cluster.
Message Delivery Semantics: Kafka guarantees at-least-once delivery semantics for messages. It ensures that messages are persisted and can be replayed in case of failures. Kafka allows consumers to commit their offset, ensuring that messages are not processed multiple times. NATS, by default, follows an at-most-once delivery semantics, where messages are sent once, and no redelivery is attempted. However, NATS provides optional support for at-least-once delivery through its "acknowledgment" mechanism.
Message Ordering: Kafka guarantees message ordering within a partition. Messages within the same partition are stored in the order they are received. This makes Kafka suitable for applications that require strict message ordering. NATS, on the other hand, does not guarantee strict ordering of messages. It focuses on delivering messages as fast as possible, allowing for higher throughput at the cost of potential out-of-order message delivery.
Protocol and Language Support: Kafka has a rich ecosystem and supports multiple programming languages through its official clients, including Java, Python, Go, and more. It uses a binary protocol for communication. NATS also provides official client libraries for multiple languages, including Go, Python, JavaScript, and Java. NATS uses a lightweight and efficient text-based protocol for communication.
Ecosystem and Features: Kafka has a mature ecosystem. It offers features such as message replay, fault tolerance, and stream processing capabilities through Kafka Streams. Kafka Connect allows easy integration with external systems. NATS, while lightweight, provides features like request-reply messaging, distributed queueing, and support for microservices architecture. It also has NATS Streaming, which adds additional features like message persistence and durable subscriptions.

In summary, Kafka and NATS are messaging systems with distinct characteristics and use cases. Kafka is a distributed streaming platform designed for high-throughput, fault-tolerant message processing, while NATS is a lightweight and efficient messaging system suitable for moderate to high message rates.

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Advice on Kafka, NATS

viradiya

Apr 12, 2020

Needs adviceon

AngularJS

ASP.NET Core

MSSQL

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

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

Senior Back-End Developer, Software Architect

Feb 12, 2019

Reviewon

Kafka

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	NATS
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Unlike traditional enterprise messaging systems, NATS has an always-on dial tone that does whatever it takes to remain available. This forms a great base for building modern, reliable, and scalable cloud and distributed systems.
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 393
Followers 22.3K	Followers 498
Votes 607	Votes 60
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 22 Fastest pub-sub system out there 16 Rock solid 12 Easy to grasp 4 Light-weight 4 Easy, Fast, Secure Cons 2 Persistence with Jetstream supported 1 No Order 1 No Persistence

What are some alternatives to Kafka, NATS?

Firebase

Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds.

Socket.IO

It enables real-time bidirectional event-based communication. It works on every platform, browser or device, focusing equally on reliability and speed.

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 is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

PubNub

PubNub makes it easy for you to add real-time capabilities to your apps, without worrying about the infrastructure. Build apps that allow your users to engage in real-time across mobile, browser, desktop and server.

Pusher

Pusher is the category leader in delightful APIs for app developers building communication and collaboration features.

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 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.

SignalR

SignalR allows bi-directional communication between server and client. Servers can now push content to connected clients instantly as it becomes available. SignalR supports Web Sockets, and falls back to other compatible techniques for older browsers. SignalR includes APIs for connection management (for instance, connect and disconnect events), grouping connections, and authorization.

Ably

Ably offers WebSockets, stream resume, history, presence, and managed third-party integrations to make it simple to build, extend, and deliver digital realtime experiences at scale.

Related Comparisons

Architecture: Kafka is a distributed streaming platform that follows a publish-subscribe model. It uses a distributed commit log to store messages, allowing multiple consumers to read messages independently and at their own pace. NATS, on the other hand, is a lightweight and high-performance messaging system that follows a simple publish-subscribe model. It focuses on simplicity and minimal overhead, making it suitable for lightweight messaging scenarios.
Scalability: Kafka is known for its scalability and can handle large-scale data streaming and processing. It can handle high message throughput and supports distributed deployment across multiple nodes or clusters. NATS is also designed to be scalable but is more suitable for moderate to high message rates rather than extremely high throughput scenarios. It can scale horizontally by adding more instances to the cluster.
Message Delivery Semantics: Kafka guarantees at-least-once delivery semantics for messages. It ensures that messages are persisted and can be replayed in case of failures. Kafka allows consumers to commit their offset, ensuring that messages are not processed multiple times. NATS, by default, follows an at-most-once delivery semantics, where messages are sent once, and no redelivery is attempted. However, NATS provides optional support for at-least-once delivery through its "acknowledgment" mechanism.
Message Ordering: Kafka guarantees message ordering within a partition. Messages within the same partition are stored in the order they are received. This makes Kafka suitable for applications that require strict message ordering. NATS, on the other hand, does not guarantee strict ordering of messages. It focuses on delivering messages as fast as possible, allowing for higher throughput at the cost of potential out-of-order message delivery.
Protocol and Language Support: Kafka has a rich ecosystem and supports multiple programming languages through its official clients, including Java, Python, Go, and more. It uses a binary protocol for communication. NATS also provides official client libraries for multiple languages, including Go, Python, JavaScript, and Java. NATS uses a lightweight and efficient text-based protocol for communication.
Ecosystem and Features: Kafka has a mature ecosystem. It offers features such as message replay, fault tolerance, and stream processing capabilities through Kafka Streams. Kafka Connect allows easy integration with external systems. NATS, while lightweight, provides features like request-reply messaging, distributed queueing, and support for microservices architecture. It also has NATS Streaming, which adds additional features like message persistence and durable subscriptions.

Kafka vs NATS