How to Implement Kafka in .NET Applications

You can change your .NET apps by adding Kafka. Kafka is a strong event streaming tool. Kafka helps you make apps that can grow and work in real time. You can use it for tracking actions, sending messages, and putting logs together. Many developers use Kafka with .NET apps to handle real-time data. It also helps build event-driven systems and supports microservices with ASP.NET 6 Core. The table below shows how Kafka helps .NET and ASP.NET projects in different ways.

Key Takeaways

• Kafka lets .NET apps work with live data easily. It helps make event-driven systems and supports microservices well.

• Learning Kafka basics like records, offsets, partitions, and replicas is important. This helps your data work well and grow as needed.

• Docker makes setting up Kafka easier. You can use it to run a local Kafka cluster for testing and building.

• Keep your .NET projects neat by having separate producer and consumer services. Use the Confluent Kafka client to connect them smoothly.

• Use good habits like turning on idempotence and watching offsets closely. Handle errors with dead-letter queues. Scale your microservices to make strong and steady Kafka apps.

Kafka Concepts for .NET Developers

Records and Offsets

You use Apache Kafka by sending and getting events. Each event is called a record. A record has a key, value, time, and extra info. When you send an event, the Kafka broker saves it in a topic. Each record in a partition gets its own offset. The offset is like a bookmark. It helps you know which events your app has read. Offsets are important for making sure messages are delivered right. You can save offsets by yourself or let Kafka do it. If your .NET app stops working, you can start again from the last saved offset. This helps you not lose or repeat data.

Tip: Good offset management helps your system recover fast after problems.

Here is a simple look at how offsets work in Kafka:

Partitions and Replicas

Kafka splits each topic into smaller parts called partitions. This helps your app handle more data at once. Each partition keeps events in order. This makes it easy to process data. Kafka brokers spread partitions over many computers. This shares the work and makes things faster. Replicas are copies of partitions. They help keep your data safe. If one broker stops, another replica can give you the data. This keeps your app running.

• Partitions let you handle many events at the same time.

• Replicas keep your data safe and your system strong.

• One broker is the leader for each partition. It handles all reads and writes.

• Other brokers are followers. They copy data from the leader to stay up to date.

Note: Watching partition sizes and replica health helps your Kafka system work better and stay safe.

SerDes and Schema Registry

SerDes means turning events into bytes and back. This is needed for sending data in Kafka. In .NET Kafka apps, you often use a Schema Registry. The producer gets or adds a schema ID from the registry. It puts this ID in the event. The consumer uses the ID to get the schema and read the event right. This makes sure both sides use the same data format and stops mistakes.

The Schema Registry works with Avro, Protobuf, and JSON Schema. It checks that new schemas work with old ones. This lets you change schemas without breaking things. By saving only the schema ID in each event, messages are smaller and faster. .NET Kafka clients use special NuGet packages to work with the Schema Registry. This makes handling schemas easy.

Using SerDes and Schema Registry together keeps your data correct and your Kafka system working well.

Apache Kafka Setup with Docker

Setting up apache kafka with Docker makes things easier. You can build a strong kafka cluster for .NET work. This lets you run kafka on your own computer. You do not need to do hard steps by hand. This part will show you how to install kafka. It also helps you check if your kafka cluster is ready for .NET apps.

Installing Kafka

You can use Docker to install apache kafka fast. Here are the steps to make a simple kafka cluster:

1. Make a folder for your kafka data. This keeps your data safe if you restart.

2. Make a docker-compose.yml file. This file sets up your apache kafka cluster. It includes brokers and a UI tool. Here is an easy example:

version: '3'
services:
  zookeeper:
    image: bitnami/zookeeper:latest
    ports:
      - "2181:2181"
    environment:
      - ALLOW_ANONYMOUS_LOGIN=yes

  kafka:
    image: bitnami/kafka:latest
    ports:
      - "9092:9092"
    environment:
      - KAFKA_BROKER_ID=1
      - KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181
      - KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://localhost:9092
      - ALLOW_PLAINTEXT_LISTENER=yes

1. Start your apache kafka cluster by typing:

docker compose up -d

1. Check if your containers are running:

docker ps

If you see DNS or connection errors, look at your KAFKA_ADVERTISED_LISTENERS setting. Use the container name or localhost if needed. Make sure your Docker network lets containers talk to each other.

Verifying the Cluster

After you install kafka, you need to check if your apache kafka cluster works. Do these steps:

1. List your running containers:

docker ps

1. Make a test topic in your kafka container:

docker exec kafka kafka-topics --create --topic test-topic --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1

1. Send a message to your topic:

docker exec -ti kafka kafka-console-producer.sh --bootstrap-server localhost:9092 --topic test-topic

1. Type a message and press Enter.

2. Read the message from your topic:

docker exec -ti kafka kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test-topic --from-beginning

1. You should see your message show up.

• If you cannot connect, check your port mapping and listener settings. Make sure your .NET app uses the right address for the kafka cluster.

Many people get errors if the advertised listeners or Docker network are not set up right. Always check these settings if you have trouble connecting to your apache kafka cluster.

By following these steps, you finish installing kafka. You also make sure your apache kafka cluster is ready for .NET work.

.NET and ASP.NET 6 Core Project Setup

Creating the Project

First, get your computer ready to use Kafka with .NET. You need Visual Studio 2022, .NET 7.0, and ASP.NET 7.0 Runtime. You also need Kafka and Java Runtime Environment (JRE). Set up JAVA_HOME and PATH so Kafka can work with your system.

Here are the steps to make your project:

1. Open Visual Studio 2022.

2. Make a new blank solution called InventoryManagement.

3. Add two ASP.NET Core Web API projects. Name them InventoryProducer and InventoryConsumer.

4. In each project, make folders named Models and Services. This helps you keep your code neat.

5. You can run Kafka and Zookeeper on your computer with Docker. This makes setup easier.

Tip: Having a producer and a consumer project helps you split up the work. It also makes your asp.net core web app easier to grow.

Adding Confluent Kafka Client

You must add Kafka client tools to your asp.net projects. Use the Confluent Kafka .NET client. You can get it from NuGet Package Manager. This package gives you everything you need to talk to Kafka.

To install it, type this in the Package Manager Console:

Install-Package Confluent.Kafka

When it is done, put Kafka settings in your appsettings.json file. Write the Kafka broker address, like "localhost:9092", and the topic name. In Program.cs, register Kafka producer services. Bind the ProducerConfig from your settings and add it as a singleton. Now your asp.net core web app can send and get messages with Kafka.

Note: The Confluent client lets you send messages without waiting. Use the ProduceAsync method. This works well with new asp.net apps.

Project Structure

A good project structure makes your Kafka asp.net apps easier to fix and grow. Try this setup:

1. Keep the producer and consumer in different projects.

2. Put data models, like InventoryUpdateRequest, in the Models folder.

3. Write the producer and consumer code in the Services folder.

4. Register Kafka producers as singleton services. This saves resources.

5. Add Kafka consumers as hosted services. Do this by using IHostedService in your asp.net core web app.

6. If you use Avro, Protobuf, or JSON, add the Confluent Schema Registry and SerDes packages.

7. If both producer and consumer need the same models or tools, put them in a shared project.

This setup helps you make strong asp.net 6 core apps. Your apps can use Kafka for real-time messages and event-driven work.

Kafka Producer and Consumer in .NET

Producer Implementation

You can make a strong kafka producer in your net app by following some good steps. First, set up your producer to deal with problems and try again if needed. Pick how many times to retry and how long to wait between tries. This helps your producer keep working if something goes wrong. Turn on idempotence so you do not send the same message twice. If you want each message sent only once, use transaction APIs. Change batch size and linger time to get the right speed and delay. Use compression to make messages smaller and save network space. Pick a way to split messages so they go to different partitions.

Here is a simple kafka producer example in net using the Confluent.Kafka library. This code sends messages to a kafka topic and shows if they were sent:

using Confluent.Kafka;

var config = new ProducerConfig
{
    BootstrapServers = "localhost:9092",
    EnableIdempotence = true,
    Acks = Acks.All,
    MessageSendMaxRetries = 5,
    RetryBackoffMs = 100,
    BatchSize = 32000,
    LingerMs = 5,
    CompressionType = CompressionType.Snappy
};

using var producer = new ProducerBuilder<string, string>(config).Build();

for (char c = 'A'; c <= 'Z'; c++)
{
    var key = c.ToString();
    var value = $"Letter {c}";
    var result = await producer.ProduceAsync("alphabet", new Message<string, string> { Key = key, Value = value });
    Console.WriteLine($"Delivered '{result.Value}' to '{result.TopicPartitionOffset}'");
}

Tip: Watch how much memory and buffer your producer uses. Change these settings if you send lots of events. This keeps your kafka producer working well.

When you make an event producer, do these things:

1. Set up retries and backoff to fix problems.

2. Turn on idempotence for safe tries.

3. Make batch size and compression better.

4. Pick a way to split the load.

5. Watch and change resource settings.

These steps help you build a strong kafka producer for your asp.net or net microservices.

Consumer Implementation

A kafka consumer in net listens to a kafka topic and works with the events. You can run your kafka consumer as a Windows service or a net worker service. This lets your event consumer run in the background and handle events as they come. Many people use a worker service to handle events and save results in a database.

Here is a basic kafka consumer example in net using Confluent.Kafka:

using Confluent.Kafka;

var config = new ConsumerConfig
{
    BootstrapServers = "localhost:9092",
    GroupId = "inventory-consumer-group",
    AutoOffsetReset = AutoOffsetReset.Earliest,
    EnableAutoCommit = false
};

using var consumer = new ConsumerBuilder<string, string>(config).Build();
consumer.Subscribe("alphabet");

try
{
    while (true)
    {
        var consumeResult = consumer.Consume();
        Console.WriteLine($"Received: {consumeResult.Message.Value}");
        // Process the event here
        consumer.Commit(consumeResult);
    }
}
catch (OperationCanceledException)
{
    consumer.Close();
}

Note: Turn off auto-commit and only save offsets after you finish each event. This stops you from losing or repeating data.

You should also set "auto.offset.reset" to "earliest" so you do not miss events if your consumer restarts. Change session timeout and heartbeat times to find problems fast but not rebalance too much. Give each kafka consumer a special group instance ID to stop extra rebalances.

Some ways to make a kafka consumer in net are:

• Run the consumer as a net worker service or Windows service.

• Use Kafka Connect to link straight to a database if you can.

• Use MSK Connect if you use apache kafka on AWS.

These ideas help you make a strong event consumer for your asp.net apps or microservices.

Configuration and Serialization

Kafka uses byte arrays to send and get events. You must change your net objects into bytes before sending them to a kafka topic. The Confluent.Kafka client has built-in serializers for simple things like strings and numbers. For harder types, you can use JSON, Avro, or Protocol Buffers. Each one has its own good points:

You can also make your own serializers by using the ISerializer or IAsyncSerializer interfaces. Add your custom serializer to the ProducerBuilder with SetValueSerializer or SetKeySerializer. For getting data back, use the IDeserializer interface. This lets you change your objects to and from kafka's format.

Here is how you set up a producer with a custom serializer in net:

var producer = new ProducerBuilder<string, MyCustomType>(config)
    .SetValueSerializer(new MyCustomTypeSerializer())
    .Build();

Tip: Pick JSON if you want it easy, Avro if you need to change schemas, and Protocol Buffers for fast systems.

When you set up your kafka producer and kafka consumer, always make ProducerConfig and ConsumerConfig objects. Add your serializers and deserializers to handle events right. This setup helps your asp.net apps work well with apache kafka.

Event-Driven Architecture Best Practices

Reliability and Idempotency

You want your Kafka .NET apps to handle each event only once. To do this, turn on idempotence in your Kafka producer by setting EnableIdempotence = true. Also, set Acks = All so Kafka waits for all replicas to say they got the message. Turn off auto offset commits in your Kafka consumer. Only save offsets by hand after you finish each event. This stops you from losing data or doing the same event twice.

Make your consumer logic idempotent. Keep track of message IDs you have seen using MemoryCache with a time limit. This helps you skip events you already did. If your app gets a message it cannot handle, catch the error and send it to a dead-letter queue (DLQ). This keeps your data pipeline working well. Always close your consumers and producers the right way. This stops you from losing offsets or wasting resources.

Tip: Good offset management and idempotent logic make your event-driven system reliable.

Error Handling

You need a clear plan to handle errors in Kafka streaming. Use a dead-letter queue to hold messages that fail after trying again. This keeps your main event flow clean. Here are steps for strong error handling:

1. Make a DLQ for failed messages.

2. Set up a way to handle bad events and alert your team.

3. Use retry rules with longer waits each time for short-term errors.

4. Add error info and times to DLQ messages to help fix problems.

5. Watch your DLQ often and use dashboards to see issues.

6. Decide if you will try DLQ messages again, drop them, or study them.

7. Catch and log errors for each event to stop poison messages.

8. Use type tags if you handle different message types.

Note: DLQs can change message order and offline work, so think before using them.

Scaling Microservices

Kafka helps you build microservices that can grow and handle real-time data. Use async messages to keep your services apart. Put your .NET microservices in Docker containers for the same setup everywhere. Use Kubernetes to manage, scale, and check your services. Ocelot works as an API gateway to route requests and make things stronger.

Put all needed data in Kafka messages to cut down on links between services. This makes your system stronger and helps real-time data work for microservices. For big messages, use outside APIs instead of sending huge data. Watch your services with tracing tools like OpenTelemetry and use one place for logs. Make your databases faster with caching and sharding.

Kafka lets you grow your microservices on their own and build strong, event-driven systems.

---

You now know how to use Kafka in your .NET and ASP.NET 6 Core apps. The main steps are learning about Kafka topics, producers, consumers, and partitions. Kafka helps you work with real-time data and build event-driven systems for microservices.

• Look into more Kafka tools like stream processing and Kafka Connect.

• Try making real-time analytics or use Kafka in a real project.

• If you want to learn more, see the "Apache Kafka for .NET Developers" course and Microsoft Learn docs.

FAQ

What is the best way to connect .NET apps to Kafka?

You should use the Confluent.Kafka NuGet package. This library helps you send and get messages. It gives you easy tools for producers and consumers. You can write simple code to use Kafka in your app.

How do you handle schema changes in Kafka messages?

You can use the Confluent Schema Registry. This tool keeps track of your message schemas. It checks if new schemas work with old ones. It helps you change schemas without breaking things.

Tip: Always try out new schemas before using them for real work.

Can you run Kafka locally for development?

Yes, you can run Kafka on your own computer with Docker. This lets you test your .NET apps at home. You do not need a cloud service to try things out.

docker compose up -d

How do you monitor Kafka in .NET applications?

You can add logs and metrics to your .NET services. Use tools like Prometheus and Grafana to watch how Kafka is doing. These tools help you see if something is wrong.

Monitoring helps you spot and fix problems fast.