使用Scala版flink根据shop_data.csv数据,统计每个每个商铺的有效完成率(有效完成率=有效完成单数/接单总数)
时间: 2024-02-03 13:15:19 浏览: 27
首先,需要读取CSV文件并将其转换为Flink DataStream。可以使用Flink的CSV解析器来读取CSV文件。假设CSV文件中有以下列:`shop_id,order_id,status`。其中`status`表示订单状态,0表示未完成,1表示已完成。
以下是Scala版Flink代码示例:
```scala
import org.apache.flink.api.common.functions._
import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.connectors.fs.bucketing.{BucketingSink, DateTimeBucketer}
import org.apache.flink.streaming.connectors.fs.{SequenceFileWriter, StringWriter}
case class Order(shop_id: Int, order_id: Int, status: Int)
object ShopOrderAnalysis {
def main(args: Array[String]): Unit = {
val params = ParameterTool.fromArgs(args)
val env = StreamExecutionEnvironment.getExecutionEnvironment
val dataStream = env.readTextFile(params.get("input"))
.map(line => {
val fields = line.split(",")
Order(fields(0).toInt, fields(1).toInt, fields(2).toInt)
})
val shopStats = dataStream
.keyBy(_.shop_id)
.timeWindow(Time.minutes(5))
.aggregate(new ShopStatsAggregator)
shopStats.addSink(new BucketingSink[ShopStats]("shop-stats")
.setBucketer(new DateTimeBucketer[ShopStats]("yyyy-MM-dd--HH-mm", "UTC"))
.setWriter(new StringWriter[ShopStats])
.setBatchSize(1024 * 1024 * 400)
.setBatchRolloverInterval(60 * 1000)
.setInactiveBucketThreshold(60 * 60 * 1000))
env.execute("Shop Order Analysis")
}
class ShopStatsAggregator extends AggregateFunction[Order, ShopStats, ShopStats] {
override def createAccumulator(): ShopStats = ShopStats(0, 0)
override def add(order: Order, acc: ShopStats): ShopStats = {
if (order.status == 1) {
ShopStats(acc.totalOrders + 1, acc.completedOrders + 1)
} else {
ShopStats(acc.totalOrders + 1, acc.completedOrders)
}
}
override def getResult(acc: ShopStats): ShopStats = acc
override def merge(acc1: ShopStats, acc2: ShopStats): ShopStats = {
ShopStats(acc1.totalOrders + acc2.totalOrders, acc1.completedOrders + acc2.completedOrders)
}
}
case class ShopStats(totalOrders: Int, completedOrders: Int) {
def completionRate: Double = completedOrders.toDouble / totalOrders.toDouble
}
}
```
在上面的代码中,我们首先读取了输入文件,并将其转换为`Order`对象的数据流。然后,我们以`shop_id`为键,将数据流分组,并使用5分钟的窗口对订单进行聚合。`ShopStatsAggregator`是一个自定义的聚合函数,用于计算每个商铺的有效完成率。最后,我们将结果写入到分桶式文件系统中,以便后续分析。在此示例中,我们使用了Flink的BucketingSink来将结果写入HDFS中的分桶。
运行代码时需要指定输入文件路径,例如:
```
flink run -c ShopOrderAnalysis /path/to/ShopOrderAnalysis.jar --input /path/to/shop_data.csv
```
注意,此代码示例中的聚合函数假设订单只有两种状态:已完成和未完成。如果订单有多种状态,则需要相应地修改聚合函数。