Jaydeep Patil

Posted on Feb 4, 2023

Apache Kafka Introduction, Installation, and Implementation Using .NET Core 6

#dotnet #kafka #csharp #dotnetcore

We will go over Apache Kafka basics, installation, and operation, as well as step-by-step implementation using a.NET Core 6 Web Application.

Agenda

Overview of the Event Streaming
Introduction of Apache Kafka
Main concepts and foundation of Kafka
Different Kafka APIs
Use Cases of Apache Kafka
Installation of Kafka on Windows 10
Step-by-step implementation

Prerequisites

Visual Studio 2022
.NET Core 6 SDK
SQL Server
Java JDK 11
Apache Kafka

Overview of the Event Streaming

Events are the things that happen within our application when we navigate something. Ex- We Sign up on any website and order something. So, these are the events.

Event streaming platform records different types of data like transaction, historical, and real-time data.
Also, this platform is used to process events and allow the different consumers to process results immediately and timely manner.
An event-driven platform allows us to monitor our business and real-time data from different types of devices like IoT and many more, after analyzing it provides a good customer experience based on different types of events and needs.

Introduction of Apache Kafka

Kafka is a distributed event store and stream-processing platform.
Kafka is an open source and is written in Java and Scala.
The primary purpose to designed Kafka by Apache foundation is to handle real-time data feeds and provide high throughput and low latency platforms.
Kafka is an event streaming platform that has many capabilities to publish(write) and subscribe to (read) streams of events from a different system.
Also, to store and process events durably as long as we want, by default Kafka store event 7 days of the time period but we can increase that as per need and requirement.

Kafka has distributed system which has servers and clients that can communicate via TCP protocol.
It can be deployed on different virtual machines and containers in on-premise as well as cloud environments as per requirements.
In the Kafka world, a producer sends messages to the Kafka broker. The messages will get stored inside the topics and the consumer subscribes to that topic to consume messages sent by the producer.
Zookeeper is used to manage metadata of Kafka-related things. it tracks which brokers are part of the Kafka cluster and partitions of different topics. Also, manage the status of Kafka nodes and maintain a list of Kafka topics and a list of messages.

Main concepts and foundation of Kafka

1. Event

An event or record is the message that we read and write to the Kafka server; we do this in the form of events in our business world, and it contains key, value, timestamp, and other metadata headers.

Key: “Jaydeep”

Value: “Booked BMW”

Event Timestamp: “Dec. 11, 2022, at 12:00 p.m.”

2. Producer

The producer is a client application that sends messages to the Kafka Node/Broker.

3. Consumer

The consumer is an application that receives data from Kafka.

4. Kafka Cluster

The Kafka cluster is the set of computers that share the workload with each other and serve some purpose.

5. Broker

The broker is a Kafka server that acts as an agent between both the producer and consumer, who communicate via the broker.

6. Topic

The events are stored inside the Topic it’s similar to our folder in which we store multiple files.

Each topic has one or more producers and consumer which write and reads data from the topic.
Events in topic we can read as often as needed it persists events and it’s not like another messaging system that removes messages after consuming that.

7. Partitions

Topics are partitions, meaning the topic is spread over multiple partitions that we created inside the topic. When the producer sends some event to the topic, it will store it inside the particular partitions, and then the consumer can read the event from the corresponding topic partition in sequence.

8. Offset

Kafka assigns one unique ID to the message stored inside the topic partition when the message arrives from the producer.

9. Consumer Groups

In the Kafka world, the consumer group acts as a single logical unit.

10. Replica

In Kafka, to make data fault-tolerant and highly available, we can replicate topics in different regions and brokers. So, in case something wrong happens with data in one topic we can easily get that from another where replicate the same.

Different Kafka APIs

Kafka has five core APIs which serve different purposes

Admin API
This API manages different topics, brokers, and Kafka objects

Producer API
This API is used to write/publish events to different Kafka topics

Consumer API
This API is used to receive the different messages corresponding to the topics which are subscribed by the consumer

Kafka Stream API
This API is used to perform different types of operations like windowing, joins, aggregation, and many others. Basically, it uses to transform objects.

Kafka Connect API
This API works as a connector to Kafka, which helps different systems connect with Kafka easily. It has different types of ready-to-use connectors related to Kafka.

Use Cases of Apache Kafka

Messaging
User Activity Tracking
Log Aggregation
Stream Processing
Realtime Data Analytics

Installation of Kafka on Windows 10

Step 1

Download and install the Java SDK of version 8 or more. (Note: I have Java 11 that’s why I put the same path in all commands which I used over here)

https://www.oracle.com/java/technologies/downloads/#java8

Step 2

Open and install exe

Step 3

Set the environment variable for Java using the command prompt as admin.

setx -m JAVA_HOME “C:\Program Files\Java\jdk-11.0.16.1”
setx -m PATH “%JAVA_HOME%\bin;%PATH%”

Step 4

After that, download and install Kafka

https://kafka.apache.org/downloads

Step 5

Extract the downloaded Kafka file at rename it as Kafka.

Step 6

Open D:\Kafka\config\ and create a zookeeper-data and kafka-logs folder inside that.

Step 7

Next, open D:\Kafka\config\zookeeper.properties file and add the folder path inside that.

D:\Kafka\config\zookeeper.properties

dataDir=D:/Kafka/zookeeper-data

Step 8

After that, open D:\Kafka\config\server.properties file and change the log path over there

D:\Kafka\config\server.properties

log.dirs=D:/Kafka/kafka-logs

Step 9

Saves and closes both files.

Step 10

Run zookeeper

D:\Kafka> .\bin\windows\zookeeper-server-start.bat .\config\zookeeper.properties

Step 11

Start Kafka

D:\Kafka> .\bin\windows\kafka-server-start.bat .\config\server.properties

Step 12

Create Kafka Topic

D:\Kafka\bin\windows>kafka-topics.bat — create — bootstrap-server localhost:9092 — replication-factor 1 — partitions 1 — topic testdata

Step 13

Create a producer and send some messages after you've started both a producer and a consumer.

D:\Kafka\bin\windows>kafka-console-producer.bat — broker-list localhost:9092 — topic testdata

Step 14

Next, Create a Consumer and you can see a message when the producer sent.

D:\Kafka\bin\windows>kafka-console-consumer.bat — bootstrap-server localhost:9092 — topic testdata

Step-by-step implementation

Let’s start with practical implementation

Step 1

Create a new .NET Core Producer Web API

Step 2

Configure your application

Step 3

Provide additional details

Step 4

Install the following two NuGet packages

Step 5

Add configuration details inside the appsettings.json file

{
  "Logging": {
    "LogLevel": {
      "Default": "Information",
      "Microsoft.AspNetCore": "Warning"
    }
  },
  "AllowedHosts": "*",
  "producerconfiguration": {
    "bootstrapservers": "localhost:9092"
  },
  "TopicName": "testdata"
}

Step 6

using Confluent.Kafka;

var builder = WebApplication.CreateBuilder(args);

// Add services to the container.
var producerConfiguration = new ProducerConfig();
builder.Configuration.Bind("producerconfiguration", producerConfiguration);

builder.Services.AddSingleton<ProducerConfig>(producerConfiguration);

builder.Services.AddControllers();
// Learn more about configuring Swagger/OpenAPI at https://aka.ms/aspnetcore/swashbuckle
builder.Services.AddEndpointsApiExplorer();
builder.Services.AddSwaggerGen();

var app = builder.Build();

// Configure the HTTP request pipeline.
if (app.Environment.IsDevelopment())
{
    app.UseSwagger();
    app.UseSwaggerUI();
}

app.UseHttpsRedirection();

app.UseAuthorization();

app.MapControllers();

app.Run();

Step 7

Next, Create CarDetails model class

using Microsoft.AspNetCore.Authentication;

namespace ProducerApplication.Models
{
    public class CarDetails
    {
        public int CarId { get; set; }
        public string CarName { get; set; }
        public string BookingStatus { get; set; }
    }
}

Now, create CarsController

using Confluent.Kafka;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Extensions.Configuration;
using Newtonsoft.Json;
using ProducerApplication.Models;

namespace ProducerApplication.Controllers
{
    [Route("api/[controller]")]
    [ApiController]
    public class CarsController : ControllerBase
    {
        private ProducerConfig _configuration;
        private readonly IConfiguration _config;
        public CarsController(ProducerConfig configuration, IConfiguration config)
        {
            _configuration = configuration;
            _config = config;
        }
        [HttpPost("sendBookingDetails")]
        public async Task<ActionResult> Get([FromBody] CarDetails employee)
        {
            string serializedData = JsonConvert.SerializeObject(employee);

            var topic = _config.GetSection("TopicName").Value;

            using (var producer = new ProducerBuilder<Null, string>(_configuration).Build())
            {
                await producer.ProduceAsync(topic, new Message<Null, string> { Value = serializedData });
                producer.Flush(TimeSpan.FromSeconds(10));
                return Ok(true);
            }
        }
    }
}

Step 9

Finally, run the application and send a message

Step 10

Now, create a Consumer application

For that, Create a new .NET Core Console Application

Step 11

Configure your application

Step 12

Provide additional information

Step 13

Install this NuGet

Step 14

Add the following code which consumes messages sent by the consumer.

using Confluent.Kafka;

var config = new ConsumerConfig
{
    GroupId = "gid-consumers",
    BootstrapServers = "localhost:9092"
};

using (var consumer = new ConsumerBuilder<Null, string>(config).Build())
{
    consumer.Subscribe("testdata");
    while (true)
    {
        var bookingDetails = consumer.Consume();
        Console.WriteLine(bookingDetails.Message.Value);
    }
}

Step 15

Finally, run both producer and consumer, sent a message using the producer app and you can see the message immediately inside the consumer console sent by the producer

GitHub URL

https://github.com/Jaydeep-007/Kafka-Demo

Conclusion

Here we discussed Apache Kafka introduction, working, benefits, and step-by-step implementation using .NET Core 6.

Happy Coding!

Oldest comments (1)

Otávio Larrosa • Feb 5 '23

Hello Jaydeep, have you ever tried the StreamNet project to work with Kafka? It provides you a new way to work with Kafka, you don't need to implement a DLQ, and you implement less code =)

stream-net.github.io/docs/

It's good :-)

[ConsumerGroup("consumerGroupId")] //Required
[TopicName("your.topic.name")] //Optional
public class MessageSampleEventConsumer : Consumer<MessageSampleEvent>
{
    private readonly IUseCaseTestImplementation _useCase;

    public MessageSampleEventConsumer(IUseCaseTestImplementation useCase, ILogger<TestConsumer> logger) : base(logger)
    {
        _useCase = useCase;
    }

    protected override async Task HandleAsync()
    {
        await _useCase.ExecuteAsync(Message);
        return;
    }
}

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