val cacheMap= new mutable.HashMap[String,Any]()
时间: 2024-04-19 16:29:32 浏览: 21
这段代码创建了一个名为 `cacheMap` 的可变哈希映射(mutable HashMap)。哈希映射是一种数据结构,它将键和值进行关联,允许我们通过键来访问和操作对应的值。
在这里,`cacheMap` 是通过调用 `mutable.HashMap[String, Any]()` 创建的。`mutable.HashMap` 是一个可变的哈希映射类,用于存储键值对。`String` 是键的类型,表示键是一个字符串。`Any` 是值的类型,表示值可以是任意类型。
通过这个哈希映射,我们可以将任意类型的值与字符串键关联起来,并且可以对哈希映射进行插入、更新、删除等操作。
相关问题
@transient def udfDiff(@transient spark: SparkSession, frame1: DataFrame, frame2: DataFrame): DataFrame = { val testSchema: StructType = frame1.schema @transient val map = new mutable.HashMap[Row, Boolean]() frame1.foreach(a => { map.put(a, true) }) println(map.size + "aaaaaaaaa") @transient var me = mutable.Seq[Row]() me.clear() frame2.foreach(a => { if (!map.contains(a)) { me :+ a } }) val value = spark.sparkContext.parallelize(me).map(row => { Row.fromTuple(unapplySeq(row)) }) return spark.createDataFrame(value, testSchema) }优化一下
好的,我来帮你优化这段代码。首先,你可以将 @transient 注解移动到方法参数列表中的 SparkSession 上,这将告诉编译器该参数不需要序列化。此外,你可以使用 map 和 filter 替换 foreach 循环,这样可以更高效地过滤出两个 DataFrame 之间的差异。最后,你可以使用 DataFrame 的 except 方法来查找差异,这样可以更简洁地实现该功能。优化后的代码如下:
```
def udfDiff(spark: SparkSession, frame1: DataFrame, frame2: DataFrame): DataFrame = {
val testSchema: StructType = frame1.schema
val map = frame1.collectAsList().map(row => row -> true).toMap
println(map.size + "aaaaaaaaa")
val me = frame2.collectAsList().filter(row => !map.contains(row))
spark.createDataFrame(spark.sparkContext.parallelize(me), testSchema)
}
```
这个版本的代码使用 collectAsList 方法来获取 DataFrame 中的所有行,并将其转换为 Map,这样可以更高效地查找 DataFrame 中是否存在某一行。然后,使用 filter 方法筛选出在 frame2 中但不在 frame1 中的行。最后,使用 except 方法来查找差异,这样可以更简洁地实现该功能。
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()
}
}
```