在 Spring Boot 中配置 Flink 的资源管理，需要遵循以下步骤：
添加 Flink 依赖项在你的 pom.xml 文件中，添加 Flink 和 Flink-connector-kafka 的依赖项。这里以 Flink 1.14 版本为例：
<!-- Flink dependencies --> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>1.14.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_${scala.binary.version}</artifactId> <version>1.14.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId> <version>1.14.0</version> </dependency></dependencies>创建一个名为 FlinkConfiguration 的配置类，用于定义 Flink 的相关配置。
import org.apache.flink.configuration.Configuration;import org.springframework.context.annotation.Bean;import org.springframework.context.annotation.Configuration;@Configurationpublic class FlinkConfiguration { @Bean public Configuration getFlinkConfiguration() { Configuration configuration = new Configuration(); // 设置 Flink 的相关配置，例如： configuration.setString("rest.port", "8081"); configuration.setString("taskmanager.numberOfTaskSlots", "4"); return configuration; }}创建一个名为 FlinkJobManager 的类，用于管理 Flink 作业的生命周期。
import org.apache.flink.api.common.JobExecutionResult;import org.apache.flink.configuration.Configuration;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.stereotype.Component;@Componentpublic class FlinkJobManager { @Autowired private Configuration flinkConfiguration; public JobExecutionResult execute(FlinkJob job) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(flinkConfiguration); // 配置 StreamExecutionEnvironment，例如设置 Checkpoint 等 job.execute(env); return env.execute(job.getJobName()); }}创建一个名为 FlinkJob 的接口，用于定义 Flink 作业的基本方法。
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;public interface FlinkJob { String getJobName(); void execute(StreamExecutionEnvironment env);}创建一个实现了 FlinkJob 接口的类，用于定义具体的 Flink 作业逻辑。
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 java.util.Properties;public class MyFlinkJob implements FlinkJob { @Override public String getJobName() { return "My Flink Job"; } @Override public void execute(StreamExecutionEnvironment env) { Properties kafkaProperties = new Properties(); kafkaProperties.setProperty("bootstrap.servers", "localhost:9092"); kafkaProperties.setProperty("group.id", "my-flink-job"); FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("my-topic", new SimpleStringSchema(), kafkaProperties); DataStream<String> stream = env.addSource(kafkaConsumer); // 实现 Flink 作业逻辑 // ... }}在你的 Spring Boot 应用中，使用 FlinkJobManager 运行 Flink 作业。
import org.springframework.beans.factory.annotation.Autowired;import org.springframework.boot.CommandLineRunner;import org.springframework.boot.SpringApplication;import org.springframework.boot.autoconfigure.SpringBootApplication;@SpringBootApplicationpublic class MyApplication implements CommandLineRunner { @Autowired private FlinkJobManager flinkJobManager; @Autowired private MyFlinkJob myFlinkJob; public static void main(String[] args) { SpringApplication.run(MyApplication.class, args); } @Override public void run(String... args) throws Exception { flinkJobManager.execute(myFlinkJob); }}通过以上步骤，你可以在 Spring Boot 中配置和运行 Flink 作业。注意，这里只是一个简单的示例，你可能需要根据实际需求调整代码。
