scala2.12编写Flink1.14.4消费kafka中的数据，统计商城每分钟的GMV

以下是使用Scala 2.12编写的Flink 1.14.4消费Kafka中数据并统计商城每分钟GMV的代码：

import java.util.Properties

import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer

object GMVCalculator {

  def main(args: Array[String]): Unit = {

    // set up the execution environment
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    // set up Kafka consumer properties
    val kafkaProps = new Properties()
    kafkaProps.setProperty("bootstrap.servers", "localhost:9092")
    kafkaProps.setProperty("group.id", "flink-gmv-calculator")

    // create a Kafka consumer
    val kafkaConsumer = new FlinkKafkaConsumer[String]("transactions", new SimpleStringSchema(), kafkaProps)

    // add Kafka consumer as a data source
    val dataStream = env.addSource(kafkaConsumer)

    // parse each transaction record into a case class
    case class Transaction(id: String, timestamp: Long, amount: Double)
    val transactionStream = dataStream.map(record => {
      val fields = record.split(",")
      Transaction(fields(0), fields(1).toLong, fields(2).toDouble)
    })

    // group transactions by minute and sum the amounts
    val gmvStream = transactionStream
      .keyBy(transaction => transaction.timestamp / (60 * 1000)) // group by minute
      .sum("amount") // sum the amounts

    // print the GMV for each minute
    gmvStream.print()

    // execute the Flink job
    env.execute("GMV Calculator")
  }
}

在上述代码中，我们首先设置了执行环境，然后创建了一个Kafka消费者，并将其添加为数据源。接着，我们解析每个交易记录并将其转换为一个case class。随后，我们按照每分钟分组交易记录，并对每分钟的交易金额求和，得到商城每分钟的GMV。最后，我们打印每分钟的GMV，并执行Flink作业。