Build stream processing applications using Spring Boot and Apache Kafka Streams

With the advent of the big data era, more and more enterprises are beginning to pay attention to stream processing technology to meet the needs of real-time data processing and analysis. Apache Kafka is a high-throughput, scalable distributed message queue system that has become the de facto standard in the field of stream processing. Spring Boot is a tool for quickly developing Spring applications, which can help us build stream processing applications faster and easier. This article will introduce how to build a stream processing application using Spring Boot and Apache Kafka Streams, and discuss the advantages and disadvantages of these two tools and how to optimize application performance.

1. Create a Kafka topic

Before we start building the application, we need to first create a Kafka topic. In this article, we will create a topic called "user-clicks" that will store user click events on the website.

Execute the following command in the command line:

bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic user-clicks

This will create a topic named "user-clicks" on the Kafka server, which has only one partition and a local copy .

2. Create a Spring Boot application

Next, we will create a basic application using Spring Boot. In Spring Boot, we can use Spring Initializr to quickly create a basic application. When creating the application, make sure to select the following dependencies:

• Spring Kafka
• Spring Web

After creating the application, we will Add the following dependencies:

<dependency>
   <groupId>org.apache.kafka</groupId>
   <artifactId>kafka-streams</artifactId>
   <version>2.6.0</version>
</dependency>

This will provide us with the Kafka stream processing API.

3. Implementing Kafka stream processing

Now we can start writing Kafka stream processing code. When creating the application, we defined a controller class called "UserController". Now we will add a POST request handler named "clicks" in the controller class. This handler will get the user's click events from the POST request and send them to a Kafka topic named "user-clicks". The code is as follows:

@RestController
public class UserController {

   private final KafkaTemplate<String, String> kafkaTemplate;

   @Autowired
   public UserController(KafkaTemplate<String, String> kafkaTemplate) {
       this.kafkaTemplate = kafkaTemplate;
   }

   @PostMapping("/clicks")
   public void clicks(@RequestBody String click) {
       kafkaTemplate.send("user-clicks", click);
   }
}

In the above code, we use Spring's dependency injection function to inject a KafkaTemplate object named "kafkaTemplate". This object can be used to send messages to Kafka topics.

4. Create a Kafka stream processing topology

Next, we will create a Kafka stream processing topology for handling click events received from the "user-clicks" topic . In our example, we will use the Kafka Streams API to implement the stream processing topology.

In the Spring Boot application, we will create a class called "UserClicksStream" that will use the Kafka Streams API to handle click events. The code is as follows:

@Configuration
@EnableKafkaStreams
public class UserClicksStream {

   @Value("${spring.kafka.bootstrap-servers}")
   private String bootstrapServers;

   @Bean
   public KStream<String, String> kStream(StreamsBuilder builder) {

       KStream<String, String> stream = builder.stream("user-clicks");

       stream.foreach((key, value) -> {
           System.out.println("Received: " + value);
       });

       return stream;
   }

   @Bean
   public KafkaStreams kafkaStreams(StreamsBuilder builder) {
       Properties props = new Properties();
       props.put(StreamsConfig.APPLICATION_ID_CONFIG, "user-clicks-stream");
       props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
       return new KafkaStreams(builder.build(), props);
   }
}

In the above code, we use Spring's dependency injection function to inject a StreamsBuilder object named "StreamsBuilder". This object is used to create a Kafka stream processing topology.

In the kStream method, we create a KStream object from the "user-clicks" topic and print the received events using the foreach method. froeach is a terminal operation that we will use in later steps.

In the kafkaStreams method, we create an application named "user-clicks-stream" and specify the address of the Kafka server. This application will automatically perform the stream processing operations we defined in the previous topology.

5. Run the application

Now we have written all the code for the application. Before running the application, we need to start the Kafka server.

Execute the following command in the command line:

bin/kafka-server-start.sh config/server.properties

This will start the Kafka server. Now we can start our application.

Execute the following command in the command line:

mvn spring-boot:run

This will launch our application. Now we can send POST request to the application using any HTTP client like cURL or Postman. Each request will generate a click event and print it out in the console.

If we want to perform more operations in the topology (such as aggregation, window calculation, etc.), we can use other operations provided by the Kafka Streams API to build the topology.

6. Summary

Building stream processing applications using Spring Boot and Apache Kafka Streams is a fast and convenient way to help us process real-time data more easily. However, we need to pay attention to some optimization performance issues, such as topology design, buffer size, stream processing time, etc. By understanding these issues, we can better build efficient stream processing applications.

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