scala> result.product
时间: 2023-10-23 12:10:51 浏览: 74
这是一个合法的语句。在 Scala 中,List 类型提供了一个名为 product 的方法,用于返回 List 包含的所有元素的乘积。因此,如果执行 result.product 语句,将返回整数 6,即 1*2*3 的结果。需要注意的是,如果 List 中包含的元素不是数值类型,或者 List 为空,则 product 方法将抛出异常。
相关问题
import org.apache.spark.SparkContext import org.apache.spark.SparkConf import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.functions._ class Sale { } object Sale { case class Sale(cardId:String,productId:Int,quantity:Int,money:Double) def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("Pharmacy Sales Analysis").setMaster("local") val sc = new SparkContext(conf) sc.setLogLevel("ERROR") val sqlContext = new SQLContext(sc) // load data val data = sc.textFile(args(0)) // parse data val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0), arr(1).toInt, arr(2).toInt, arr(3).toDouble)) import sqlContext.implicits._ val df = salesRDD.toDF() val nameMoneyDF = df.select("productId","money","quantity") nameMoneyDF.show(5) val totalSales = nameMoneyDF.groupBy("productId").sum("quantity") //totalSales.show(5) val topSales = totalSales.select(totalSales("productId"),totalSales("sum(quantity)").as("Totalquantity")) .orderBy(desc("Totalquantity")) topSales.show(5) val totalbuy = df.groupBy("cardId").sum("quantity") //totalbuy.show(5) val topbuy = totalbuy.select(totalbuy("cardId"),totalbuy("sum(quantity)").as("Totalquantity")) .orderBy(desc("Totalquantity")) topbuy.show(5) val Oneproduct:Dataset[Row]= topSales.select("productId","Totalquantity").filter(row=>row.getAs[String]("productId") == 230010) Oneproduct.show() sc.stop() }}帮我写个注释
```scala
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
class Sale { }
object Sale {
case class Sale(cardId:String,productId:Int,quantity:Int,money:Double)
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("Pharmacy Sales Analysis").setMaster("local")
val sc = new SparkContext(conf)
sc.setLogLevel("ERROR")
val sqlContext = new SQLContext(sc)
// load data
val data = sc.textFile(args(0))
// parse data
val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0), arr(1).toInt, arr(2).toInt, arr(3).toDouble))
import sqlContext.implicits._
val df = salesRDD.toDF()
// select columns and show top 5 rows
val nameMoneyDF = df.select("productId","money","quantity")
nameMoneyDF.show(5)
// group by productId and sum the quantity, then show top 5 products with highest sales
val totalSales = nameMoneyDF.groupBy("productId").sum("quantity")
val topSales = totalSales.select(totalSales("productId"),totalSales("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topSales.show(5)
// group by cardId and sum the quantity, then show top 5 customers with highest purchase quantity
val totalbuy = df.groupBy("cardId").sum("quantity")
val topbuy = totalbuy.select(totalbuy("cardId"),totalbuy("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topbuy.show(5)
// select rows for one specific product (productId = 230010) and show the result
val Oneproduct:Dataset[Row]= topSales.select("productId","Totalquantity").filter(row=>row.getAs[String]("productId") == 230010)
Oneproduct.show()
sc.stop()
}
}
```
注释如下:
```scala
// 导入Spark相关的库
import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
// 定义Sale类
class Sale { }
// Sale伴生对象
object Sale {
// 定义Sale类的属性
case class Sale(cardId:String,productId:Int,quantity:Int,money:Double)
def main(args: Array[String]): Unit = {
// 设置Spark的配置信息,并创建SparkContext
val conf = new SparkConf().setAppName("Pharmacy Sales Analysis").setMaster("local")
val sc = new SparkContext(conf)
sc.setLogLevel("ERROR")
val sqlContext = new SQLContext(sc)
// 加载数据
val data = sc.textFile(args(0))
// 解析数据并创建RDD
val salesRDD: RDD[Sale] = data.map(line => line.split(" ")).map(arr => Sale(arr(0), arr(1).toInt, arr(2).toInt, arr(3).toDouble))
// 将RDD转换为DataFrame
import sqlContext.implicits._
val df = salesRDD.toDF()
// 选择需要的列并展示前5行
val nameMoneyDF = df.select("productId","money","quantity")
nameMoneyDF.show(5)
// 按productId分组并计算销售量,然后展示销售量最高的5个商品
val totalSales = nameMoneyDF.groupBy("productId").sum("quantity")
val topSales = totalSales.select(totalSales("productId"),totalSales("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topSales.show(5)
// 按cardId分组并计算购买量,然后展示购买量最高的5个客户
val totalbuy = df.groupBy("cardId").sum("quantity")
val topbuy = totalbuy.select(totalbuy("cardId"),totalbuy("sum(quantity)").as("Totalquantity")).orderBy(desc("Totalquantity"))
topbuy.show(5)
// 选择productId为230010的行并展示结果
val Oneproduct:Dataset[Row]= topSales.select("productId","Totalquantity").filter(row=>row.getAs[String]("productId") == 230010)
Oneproduct.show()
// 停止SparkContext
sc.stop()
}
}
```
spark(24) -- spark sql -- spark sql查询代码实现(scala版)
spark(24)是什么意思?如果是指Spark版本2.4,那么以下是Spark SQL查询代码实现(Scala版)的示例:
```scala
import org.apache.spark.sql.SparkSession
object SparkSQLExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.appName("SparkSQLExample")
.master("local[*]")
.getOrCreate()
// 读取数据
val df = spark.read
.option("header", "true")
.option("inferSchema", "true")
.csv("path/to/csv/file")
// 创建临时表
df.createOrReplaceTempView("sales")
// 执行SQL查询
val result = spark.sql("SELECT product, SUM(amount) as total_sales FROM sales GROUP BY product")
// 显示结果
result.show()
// 停止SparkSession
spark.stop()
}
}
```
以上代码读取一个CSV文件,将其转换为DataFrame,并创建一个名为“sales”的临时表。然后,它执行一个SQL查询,计算每个产品的总销售额,并将结果显示在控制台上。最后,它停止SparkSession。