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Spring Boot 整合 Apache Flink 教程

一、背景与目标

Apache Flink 是一个高性能的分布式流处理框架，而Spring Boot提供了快速构建企业级应用的能力。整合二者可实现：

1. 利用Spring Boot的依赖注入、配置管理等功能简化Flink作业开发
2. 构建完整的微服务架构，将流处理嵌入Spring生态
3. 实现动态作业提交与管理

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二、环境准备

• JDK 17+
• Maven 3.8+
• Spring Boot 3.1.5
• Flink 1.17.2

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三、创建项目 & 添加依赖

1. 创建Spring Boot项目

使用Spring Initializr生成基础项目，选择：

• Maven
• Spring Web（可选，用于创建REST接口）

2. 添加Flink依赖

<!-- pom.xml -->
<dependencies><!-- Spring Boot Starter --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter</artifactId></dependency><!-- Flink核心依赖 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>1.17.2</version><scope>provided</scope></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java</artifactId><version>1.17.2</version><scope>provided</scope></dependency><!-- 本地执行时需添加 --><dependency><groupId>org.apache.flink</groupId><artifactId>flink-runtime</artifactId><version>1.17.2</version><scope>test</scope></dependency>
</dependencies>

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四、基础整合示例

1. 编写Flink流处理作业

// src/main/java/com/example/demo/flink/WordCountJob.javaimport org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;public class WordCountJob {public static void execute() throws Exception {final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<String> text = env.fromElements("Spring Boot整合Flink","Flink实时流处理","Spring生态集成");DataStream<WordCount> counts = text.flatMap(new FlatMapFunction<String, WordCount>() {@Overridepublic void flatMap(String value, Collector<WordCount> out) {for (String word : value.split("\\s")) {out.collect(new WordCount(word, 1L));}}}).keyBy(value -> value.word).sum("count");counts.print();env.execute("Spring Boot Flink Job");}public static class WordCount {public String word;public long count;public WordCount() {}public WordCount(String word, long count) {this.word = word;this.count = count;}@Overridepublic String toString() {return word + " : " + count;}}
}

2. 在Spring Boot中启动作业

// src/main/java/com/example/demo/DemoApplication.java@SpringBootApplication
public class DemoApplication implements CommandLineRunner {public static void main(String[] args) {SpringApplication.run(DemoApplication.class, args);}@Overridepublic void run(String... args) throws Exception {WordCountJob.execute(); // 启动Flink作业}
}

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五、进阶整合 - 通过REST API动态提交作业

1. 创建Job提交服务

// src/main/java/com/example/demo/service/FlinkJobService.java@Service
public class FlinkJobService {public String submitWordCountJob(List<String> inputLines) {try {final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<String> text = env.fromCollection(inputLines);// ...（同上WordCount逻辑）JobExecutionResult result = env.execute();return "JobID: " + result.getJobID();} catch (Exception e) {return "Job Failed: " + e.getMessage();}}
}

2. 创建REST控制器

// src/main/java/com/example/demo/controller/JobController.java@RestController
@RequestMapping("/jobs")
public class JobController {@Autowiredprivate FlinkJobService flinkJobService;@PostMapping("/wordcount")public String submitWordCount(@RequestBody List<String> inputs) {return flinkJobService.submitWordCountJob(inputs);}
}

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六、关键配置说明

1. application.properties

# 设置Flink本地执行环境
spring.flink.local.enabled=true
spring.flink.job.name=SpringBootFlinkJob# 调整并行度（根据CPU核心数）
spring.flink.parallelism=4

2. 解决依赖冲突

在pom.xml中排除冲突依赖：

<dependency><groupId>org.apache.flink</groupId><artifactId>flink-core</artifactId><version>1.17.2</version><exclusions><exclusion><groupId>log4j</groupId><artifactId>log4j</artifactId></exclusion></exclusions>
</dependency>

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七、运行与验证

1. 启动Spring Boot应用：

mvn spring-boot:run

2. 调用API提交作业：

curl -X POST -H "Content-Type: application/json" \
-d '["Hello Flink", "Spring Boot Integration"]' \
http://localhost:8080/jobs/wordcount

3. 查看控制台输出：

Flink> Spring : 1
Flink> Boot : 1
Flink> Integration : 1
...

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八、生产环境注意事项

1. 集群部署：将打包后的jar提交到Flink集群

   flink run -c com.example.demo.DemoApplication your-application.jar
   

2. 状态管理：集成Flink State Backend（如RocksDB）

3. 监控集成：通过Micrometer接入Spring Boot Actuator

4. 资源隔离：使用Yarn或Kubernetes部署模式

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九、完整项目结构

src/
├── main/
│   ├── java/
│   │   ├── com/example/demo/
│   │   │   ├── DemoApplication.java
│   │   │   ├── flink/
│   │   │   │   └── WordCountJob.java
│   │   │   ├── controller/
│   │   │   ├── service/
│   ├── resources/
│   │   └── application.properties
pom.xml

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通过以上步骤，即可实现Spring Boot与Apache Flink的深度整合。这种架构特别适合需要将实时流处理能力嵌入微服务体系的场景，如实时风控系统、IoT数据处理平台等。后续可扩展集成Kafka、HBase等大数据组件。