.Net Core Microservice Communication Using Kafka.

In today’s data-driven world, real-time data processing is critical for many applications. To meet this demand, Apache Kafka has emerged as a leading open-source platform for managing and processing data streams at scale. Known for its robustness and efficiency, Kafka has become the go-to solution for stream processing.

In this blog, we’ll explore the core concepts of Apache Kafka, its advantages, and how microservices communicate using Kafka. By the end of this journey, you’ll be well-equipped to use Apache Kafka to its full potential, from understanding the theory to configuring it within your .net application.

So, let’s start with basic information about Kafka and its benefits.

What is Apache Kafka?

• Apache Kafka is an open-source distributed streaming platform.
• Kafka helps you to decouple applications through Producers and Consumers.
• Producers and Consumers communicate events through topics.
• Topics are created in the broker, Kafka can have 1 to 100s of brokers.
• It's highly scalable and available through its replication of topics

Components of Kafka architecture

1. Producer: The producer in Kafka is responsible for sending data or messages to Kafka topics.

2. Broker: Kafka brokers are the core servers that manage the storage, retrieval, and distribution of messages. They handle tasks like message persistence, partitioning of data, and replication across nodes to ensure fault tolerance and reliability.

3. Topic: Topics act as message categories in Kafka, where producers publish records. Each topic represents a stream of related data, providing an organizational structure that simplifies data management and consumption.

4. Partition: Kafka topics are divided into partitions, allowing for parallel data processing. Partitions enable horizontal scalability by distributing the data load across multiple brokers, enhancing both performance and fault tolerance.

5. Consumer: Consumers retrieve and process messages from Kafka topics. They enable downstream applications to react to real-time data, making them fundamental for building data-driven applications, analytics, and more.

6. Offset: Offsets are unique identifiers for messages within a partition. Consumers use offsets to keep track of their progress in reading messages, allowing them to resume from where they left off even after restarts.

Possible Values for auto.offset.reset:

earliest:
This setting makes the consumer start reading from the earliest message available in the topic if no offset exists (e.g., on the first run).
If the consumer’s current offset is out of range (e.g., messages have been deleted), it will start reading from the earliest available offset.

latest:
With this setting, the consumer starts reading from the latest message (i.e., the next message to be produced).
This is useful when you only care about new messages and don’t need to process older messages that the consumer missed.

none:
This value tells Kafka to throw an error if no offset is found or the offset is out of range.
This is useful if you want to ensure that your application always processes data from a known valid offset.

anything else:
If the value of auto.offset.reset is set to anything other than the above options, it will be considered invalid, and Kafka will throw an error.

Now, let’s begin building microservice communication with Kafka, step by step.

1. Install Docker desktop.
2. Run Command "docker-compose -f kafka.yml up"
   • This command will download Zookeeper and Kafka images and run at the docker desktop application

1. Create an (MicroserviceWithKafka)Empty solution using Visual Studio and add two .net core web API(Producer and Consumer)

1. Install Confluent.Kafka nuget package to both APIs.

5.Producer API

• Create KafkaProducer.cs class which will be responsible for getting data from the controller post end Point and producing data to a topic

6.Consumer API

• Create a hosted service that will consume data from the topic.

Conclusion:

In this article, we have shared practical demo code to understand Apache Kafka and how it’s used in communication between microservices. Using Confluent’s .NET Producer and Consumer APIs is simple and straightforward, which makes it easy to adopt them for real microservices/streaming apps.

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Download the Source code: Github Link