Kafka Streams API

The Kafka Streams API is a powerful tool for building real-time applications and microservices that transform and process data stored in Apache Kafka. It enables developers to perform complex operations on streams of data in a distributed and fault-tolerant manner.

2.1 Streams and Tables

In Kafka Streams, data is represented as either a stream or a table:

• Stream: A continuous flow of records, such as transactions or events.
• Table: A changelog of key-value pairs representing the latest state of the data.

2.2 Processing Topology

The processing topology defines the flow of data through the application, including sources, processors, and sinks.

2.3 State Stores

State stores allow for the storage of intermediate results, enabling complex aggregations and joins.

2.4 Windowing

Windowing allows for time-based aggregations, grouping events that occur within a specified time frame.

3.1 Prerequisites

• Apache Kafka installed and running
• Java Development Kit (JDK) 8 or higher
• Maven or Gradle for dependency management

3.2 Dependencies

Include the following dependencies in your pom.xml for Maven:

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <version>3.1.0</version>
</dependency>
                

4.1 Simple Kafka Streams Application

Here’s a simple example of a Kafka Streams application that counts the occurrences of words from a stream of text:

import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;

import java.util.Properties;

public class WordCount {
    public static void main(String[] args) {
        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "word-count-app");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

        StreamsBuilder builder = new StreamsBuilder();
        KStream textLines = builder.stream("input-topic");
        textLines
            .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
            .groupBy(value -> value)
            .count()
            .toStream()
            .to("output-topic");

        KafkaStreams streams = new KafkaStreams(builder.build(), props);
        streams.start();
    }
}
                

This application reads from an input-topic, processes the text, and writes the word counts to an output-topic.

• Ensure proper error handling to avoid application crashes.
• Use state stores for stateful processing to avoid data loss.
• Optimize the number of partitions to balance load and increase parallelism.
• Monitor performance and resource utilization regularly.