Flink在Spring Boot中的数据源和数据接收器配置

在 Spring Boot 中使用 Apache Flink，你需要先添加 Flink 的依赖项到你的项目中。以下是一个简单的例子，展示了如何在 Spring Boot 应用程序中配置 Flink 数据源（Source）和数据接收器（Sink）。

1. 首先，在你的 pom.xml 文件中添加 Flink 的依赖项：

    
    ...

    
   
       org.apache.flink
       flink-java
       ${flink.version}
    
   
       org.apache.flink
       flink-streaming-java_${scala.binary.version}
       ${flink.version}
    
   
       org.apache.flink
       flink-connector-kafka_${scala.binary.version}
       ${flink.version}
    

2. 创建一个 Flink 配置类，用于定义数据源和数据接收器：

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

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

3. 在你的 application.properties 文件中配置 Kafka 相关参数：

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

4. 最后，在你的 Spring Boot 应用程序中使用 Flink 数据源和数据接收器：

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 dataStream;

    @Autowired
    private FlinkKafkaProducer 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");
    }
}

这个例子展示了如何在 Spring Boot 应用程序中配置 Flink 数据源（从 Kafka 读取数据）和数据接收器（将处理后的数据写入 Kafka）。你可以根据自己的需求修改数据处理逻辑。