java框架中集成数据流处理中间件的指南

// 使用 Spring Kafka 集成 Kafka
@SpringBootApplication
public class DataAnalyticsApplication {

    public static void main(String[] args) {
        SpringApplication.run(DataAnalyticsApplication.class, args);
    }

    @KafkaListener(topics = "transactions")
    public void processTransactions(ConsumerRecord<String, String> record) {
        // 处理收到的交易数据
    }
}

// 使用 Apache Flink 集成 Flink
public class WindowedSumApplication extends PipelineJob {

    public static void main(String[] args) {
        PipelineJob pipelineJob = new WindowedSumApplication();
        pipelineJob.run(args);
    }

    @Override
    public void run(String[] args) {

        try {
            // 创建流式执行环境
            ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

            // 创建数据源
            DataStream<Transaction> input = env
                .fromSource(new SocketTextStreamFunction(), Serdes.TRANSACTION_SERIALIZER, "socket-input");

            // 按每个交易金额分时间窗口进行计算
            SingleOutputStreamOperator<Transaction> result = input
                .keyBy(Transaction::getAmount)
                .timeWindow(Time.milliseconds(5000), Time.milliseconds(2000))
                .sum("amount");

            // 输出结果
            result.addSink(new PrintSinkFunction());

            // 执行管道
            env.execute();
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}