在 Spring Boot 中使用 Apache Flink，你需要先添加 Flink 的依賴項到你的項目中。以下是一個簡單的例子，展示了如何在 Spring Boot 應用程序中配置 Flink 數據源（Source）和數據接收器（Sink）。

1. 首先，在你的 pom.xml 文件中添加 Flink 的依賴項：

    <!-- Spring Boot dependencies -->
    ...

    <!-- Flink dependencies -->
   <dependency>
       <groupId>org.apache.flink</groupId>
       <artifactId>flink-java</artifactId>
       <version>${flink.version}</version>
    </dependency>
   <dependency>
       <groupId>org.apache.flink</groupId>
       <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
       <version>${flink.version}</version>
    </dependency>
   <dependency>
       <groupId>org.apache.flink</groupId>
       <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
       <version>${flink.version}</version>
    </dependency>
</dependencies>

2. 創建一個 Flink 配置類，用于定義數據源和數據接收器：

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.util.Properties;

@Configuration
public class FlinkConfiguration {

    @Value("${kafka.bootstrap.servers}")
    private String kafkaBootstrapServers;

    @Value("${kafka.input.topic}")
    private String inputTopic;

    @Value("${kafka.output.topic}")
    private String outputTopic;

    @Bean
    public StreamExecutionEnvironment streamExecutionEnvironment() {
        return StreamExecutionEnvironment.getExecutionEnvironment();
    }

    @Bean
    public FlinkKafkaConsumer<String> kafkaConsumer() {
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", kafkaBootstrapServers);
        properties.setProperty("group.id", "flink-spring-boot");
        return new FlinkKafkaConsumer<>(inputTopic, new SimpleStringSchema(), properties);
    }

    @Bean
    public FlinkKafkaProducer<String> kafkaProducer() {
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", kafkaBootstrapServers);
        return new FlinkKafkaProducer<>(outputTopic, new SimpleStringSchema(), properties);
    }

    @Bean
    public DataStream<String> dataStream(StreamExecutionEnvironment env, FlinkKafkaConsumer<String> consumer) {
        return env.addSource(consumer);
    }
}

3. 在你的 application.properties 文件中配置 Kafka 相關參數：

kafka.bootstrap.servers=localhost:9092
kafka.input.topic=input-topic
kafka.output.topic=output-topic

4. 最后，在你的 Spring Boot 應用程序中使用 Flink 數據源和數據接收器：

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.CommandLineRunner;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class FlinkSpringBootApplication implements CommandLineRunner {

    @Autowired
    private StreamExecutionEnvironment env;

    @Autowired
    private DataStream<String> dataStream;

    @Autowired
    private FlinkKafkaProducer<String> kafkaProducer;

    public static void main(String[] args) {
        SpringApplication.run(FlinkSpringBootApplication.class, args);
    }

    @Override
    public void run(String... args) throws Exception {
        // Process the data stream as needed
        dataStream.map(value -> value.toUpperCase()).addSink(kafkaProducer);

        // Execute the Flink job
        env.execute("Flink Spring Boot Example");
    }
}

這個例子展示了如何在 Spring Boot 應用程序中配置 Flink 數據源（從 Kafka 讀取數據）和數據接收器（將處理后的數據寫入 Kafka）。你可以根據自己的需求修改數據處理邏輯。