ChatGPT入门实战课 AI时代更具竞争力的开发者(完结)

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-streaming-java_2.11</artifactId>
    <version>1.13.3</version>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka_2.11</artifactId>
    <version>1.13.3</version>
</dependency>

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 RealTimeRiskControl {
    public static void main(String[] args) throws Exception {
        // 创立执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 从 Kafka 中读取实时数据
        Properties properties = new Properties();
    // Kafka 消费者配置
        FlinkKafkaConsumer consumer = new FlinkKafkaConsumer<>("topic-name", new SimpleStringSchema(), properties);
        DataStream stream = env.addSource(consumer);
        // 停止实时风控处置
        DataStream riskEvents = stream.map(new RiskEventParser());
        // 输出处置结果
        riskEvents.print();
        // 启动执行环境
        env.execute("Real Time Risk Control");
    }
}

import org.apache.flink.api.common.functions.MapFunction;
public class RiskEventParser implements MapFunction<String, RiskEvent> {
    @Override
    public RiskEvent map(String value) throws Exception {
        // TODO: 解析 Kafka 中的数据并生成 RiskEvent 对象
        return null;
    }
}