在Spring Boot中實現Flink作業的動態擴容需要以下幾個步驟：

1. 引入依賴

在你的Spring Boot項目的pom.xml文件中，添加以下依賴：

   <groupId>org.apache.flink</groupId>
   <artifactId>flink-connector-kafka_2.11</artifactId>
   <version>${flink.version}</version>
</dependency><dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-stream-kafka</artifactId>
</dependency>

2. 配置Flink作業

在application.yml或application.properties文件中，添加以下配置：

spring:
  cloud:
    stream:
      bindings:
        input:
          destination: your-input-topic
          group: your-consumer-group
          contentType: application/json
        output:
          destination: your-output-topic
          contentType: application/json
      kafka:
        binder:
          brokers: your-kafka-broker
          autoCreateTopics: false
          minPartitionCount: 1
          replicationFactor: 1
        bindings:
          input:
            consumer:
              autoCommitOffset: true
              autoCommitOnError: true
              startOffset: earliest
              configuration:
                fetch.min.bytes: 1048576
                fetch.max.wait.ms: 500
          output:
            producer:
              sync: true
              configuration:
                retries: 3

3. 創建Flink作業

創建一個Flink作業類，繼承StreamExecutionEnvironment，并實現你的業務邏輯。例如：

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.api.common.serialization.SimpleStringSchema;

@Configuration
public class FlinkJob {

    @Autowired
    private StreamExecutionEnvironment env;

    @Value("${spring.cloud.stream.bindings.input.destination}")
    private String inputTopic;

    @Value("${spring.cloud.stream.bindings.output.destination}")
    private String outputTopic;

    @Value("${spring.cloud.stream.kafka.binder.brokers}")
    private String kafkaBrokers;

    @PostConstruct
    public void execute() throws Exception {
        // 創建Kafka消費者
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>(
                inputTopic,
                new SimpleStringSchema(),
                PropertiesUtil.getKafkaProperties(kafkaBrokers)
        );

        // 創建Kafka生產者
        FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>(
                outputTopic,
                new SimpleStringSchema(),
                PropertiesUtil.getKafkaProperties(kafkaBrokers)
        );

        // 從Kafka讀取數據
        DataStream<String> inputStream = env.addSource(kafkaConsumer);

        // 實現你的業務邏輯
        DataStream<String> processedStream = inputStream.map(new YourBusinessLogic());

        // 將處理后的數據寫入Kafka
        processedStream.addSink(kafkaProducer);

        // 執行Flink作業
        env.execute("Flink Job");
    }
}

4. 實現動態擴容

要實現Flink作業的動態擴容，你需要監控你的應用程序的性能指標，例如CPU使用率、內存使用率等。當這些指標超過預設的閾值時，你可以通過調整Flink作業的并行度來實現動態擴容。你可以使用Flink的REST API來實現這一功能。以下是一個示例：

import org.apache.flink.client.program.ClusterClient;
import org.apache.flink.client.program.rest.RestClusterClient;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.runtime.jobgraph.JobGraph;
import org.apache.flink.runtime.jobgraph.JobVertex;
import org.apache.flink.runtime.jobgraph.JobVertexID;

public void scaleJob(JobID jobId, int newParallelism) throws Exception {
    Configuration config = new Configuration();
    config.setString("jobmanager.rpc.address", "localhost");
    config.setInteger("jobmanager.rpc.port", 6123);

    ClusterClient<StandaloneClusterId> client = new RestClusterClient<>(config, StandaloneClusterId.getInstance());

    JobGraph jobGraph = client.getJobGraph(jobId).get();
    JobVertex jobVertex = jobGraph.getJobVertex(new JobVertexID());
    jobVertex.setParallelism(newParallelism);

    client.rescaleJob(jobId, newParallelism);
}

請注意，這個示例僅用于說明如何使用Flink的REST API實現動態擴容。在實際應用中，你需要根據你的需求和環境進行相應的調整。