在 Spring Boot 中配置 Flink 的資源管理,需要遵循以下步驟:
在你的 pom.xml
文件中,添加 Flink 和 Flink-connector-kafka 的依賴(lài)項(xiàng)。這里以 Flink 1.14 版本為例:
<!-- 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>
創(chuàng)建一個(gè)名為 FlinkConfiguration
的配置類(lèi),用于定義 Flink 的相關(guān)配置。
import org.apache.flink.configuration.Configuration;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class FlinkConfiguration {
@Bean
public Configuration getFlinkConfiguration() {
Configuration configuration = new Configuration();
// 設(shè)置 Flink 的相關(guān)配置,例如:
configuration.setString("rest.port", "8081");
configuration.setString("taskmanager.numberOfTaskSlots", "4");
return configuration;
}
}
創(chuàng)建一個(gè)名為 FlinkJobManager
的類(lèi),用于管理 Flink 作業(yè)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;
@Component
public class FlinkJobManager {
@Autowired
private Configuration flinkConfiguration;
public JobExecutionResult execute(FlinkJob job) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(flinkConfiguration);
// 配置 StreamExecutionEnvironment,例如設(shè)置 Checkpoint 等
job.execute(env);
return env.execute(job.getJobName());
}
}
創(chuàng)建一個(gè)名為 FlinkJob
的接口,用于定義 Flink 作業(yè)的基本方法。
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
public interface FlinkJob {
String getJobName();
void execute(StreamExecutionEnvironment env);
}
創(chuàng)建一個(gè)實(shí)現(xiàn)了 FlinkJob
接口的類(lèi),用于定義具體的 Flink 作業(yè)邏輯。
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);
// 實(shí)現(xiàn) Flink 作業(yè)邏輯
// ...
}
}
在你的 Spring Boot 應(yīng)用中,使用 FlinkJobManager
運(yùn)行 Flink 作業(yè)。
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 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);
}
}
通過(guò)以上步驟,你可以在 Spring Boot 中配置和運(yùn)行 Flink 作業(yè)。注意,這里只是一個(gè)簡(jiǎn)單的示例,你可能需要根據(jù)實(shí)際需求調(diào)整代碼。