Kafka vs SignalR

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

SignalR

Stacks656

Followers1.2K

Votes146

GitHub Stars9.3K

Forks2.3K

Kafka vs SignalR: What are the differences?

Introduction

Kafka and SignalR are both popular technologies used for real-time data streaming and messaging. However, they have several key differences that set them apart.

Scalability and Performance: Kafka is known for its high scalability and performance. It is designed to handle a high volume of data and supports distributed message passing, making it suitable for large-scale applications. On the other hand, SignalR is more focused on real-time communication between clients and servers, and it may not be as scalable or performant as Kafka in handling large-scale data streams.
Communication Model: Kafka follows the publish-subscribe model, where messages are published to specific topics and subscribers can consume messages from these topics. It allows multiple consumers to read messages independently and provides a durable storage mechanism for messages. SignalR, on the other hand, uses a hub-based communication model, where clients connect directly to a server-side hub and receive real-time updates from the server. It is more suitable for real-time client-server communication scenarios.
Message Retention: Kafka provides a persistent storage mechanism, which means that messages are stored in a log for a specified period of time or until they are consumed by subscribers. This allows for replaying or reprocessing past messages. SignalR, on the other hand, does not provide built-in message retention capabilities. It is primarily focused on real-time communication and does not store messages for future retrieval.
Support for Languages and Platforms: Kafka has broad language and platform support, with client libraries available for several programming languages such as Java, C#, Python, and more. It can be used in a variety of platforms and ecosystems. SignalR, on the other hand, is primarily focused on .NET and JavaScript platforms. While there are community-supported libraries for other languages, its primary support is for the Microsoft technology stack.
Built-in Streaming vs Real-time Communication: Kafka is designed for building real-time streaming pipelines, where data can be continuously processed and analyzed as it flows through the system. It provides support for stream processing frameworks like Kafka Streams and Apache Flink. SignalR, on the other hand, is focused on real-time communication between clients and servers, allowing for real-time updates and notifications but not providing built-in support for complex stream processing scenarios.
Integration with Ecosystems: Kafka has strong integration capabilities with other data processing and storage systems. It can integrate seamlessly with tools like Apache Hadoop, Apache Spark, and more. It also has built-in connectors to various databases and messaging systems. SignalR, on the other hand, is more tightly integrated with the Microsoft technology stack, particularly with ASP.NET and Azure services. It provides easy integration with ASP.NET applications for real-time updates.

In Summary, Kafka is a high-performance and scalable streaming platform, suitable for large-scale data processing and analysis scenarios, with support for multiple programming languages and integration with various data processing systems. SignalR, on the other hand, is a real-time communication framework focused on client-server interactions, primarily for .NET and JavaScript platforms. It is more lightweight and suited for real-time client-server communication scenarios.

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Advice on Kafka, SignalR

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)

10.9k views10.9k

Comments

Detailed Comparison

Kafka	SignalR
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	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.
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 9.3K
GitHub Forks 14.8K	GitHub Forks 2.3K
Stacks 24.2K	Stacks 656
Followers 22.3K	Followers 1.2K
Votes 607	Votes 146
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 32 Supports .NET server 25 Real-time 18 Free 16 Fallback to SSE, forever frame, long polling 15 WebSockets Cons 2 Requires jQuery 2 Expertise hard to get 1 Big differences between ASP.NET and Core versions 1 Weak iOS and Android support
Integrations
No integrations available	.NET

What are some alternatives to Kafka, SignalR?

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.

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.

Syncano

Syncano is a backend platform to build powerful real-time apps more efficiently. Integrate with any API, minimize boilerplate code and control your data - all from one place.

Related Comparisons

Kafka vs SignalR: What are the differences?

Introduction

Kafka and SignalR are both popular technologies used for real-time data streaming and messaging. However, they have several key differences that set them apart.

Scalability and Performance: Kafka is known for its high scalability and performance. It is designed to handle a high volume of data and supports distributed message passing, making it suitable for large-scale applications. On the other hand, SignalR is more focused on real-time communication between clients and servers, and it may not be as scalable or performant as Kafka in handling large-scale data streams.
Communication Model: Kafka follows the publish-subscribe model, where messages are published to specific topics and subscribers can consume messages from these topics. It allows multiple consumers to read messages independently and provides a durable storage mechanism for messages. SignalR, on the other hand, uses a hub-based communication model, where clients connect directly to a server-side hub and receive real-time updates from the server. It is more suitable for real-time client-server communication scenarios.
Message Retention: Kafka provides a persistent storage mechanism, which means that messages are stored in a log for a specified period of time or until they are consumed by subscribers. This allows for replaying or reprocessing past messages. SignalR, on the other hand, does not provide built-in message retention capabilities. It is primarily focused on real-time communication and does not store messages for future retrieval.
Support for Languages and Platforms: Kafka has broad language and platform support, with client libraries available for several programming languages such as Java, C#, Python, and more. It can be used in a variety of platforms and ecosystems. SignalR, on the other hand, is primarily focused on .NET and JavaScript platforms. While there are community-supported libraries for other languages, its primary support is for the Microsoft technology stack.
Built-in Streaming vs Real-time Communication: Kafka is designed for building real-time streaming pipelines, where data can be continuously processed and analyzed as it flows through the system. It provides support for stream processing frameworks like Kafka Streams and Apache Flink. SignalR, on the other hand, is focused on real-time communication between clients and servers, allowing for real-time updates and notifications but not providing built-in support for complex stream processing scenarios.
Integration with Ecosystems: Kafka has strong integration capabilities with other data processing and storage systems. It can integrate seamlessly with tools like Apache Hadoop, Apache Spark, and more. It also has built-in connectors to various databases and messaging systems. SignalR, on the other hand, is more tightly integrated with the Microsoft technology stack, particularly with ASP.NET and Azure services. It provides easy integration with ASP.NET applications for real-time updates.

Kafka vs SignalR

Overview