Question

我有以下情况：

我有2个数据帧，只包含1列Lets say

DF1=(1,2,3,4,5)
DF2=(3,6,7,8,9,10)

基本上这些值是键，如果DF1中的键不在DF2中，我正在创建DF1的镶木地板文件（在当前示例中，它应该返回false）。我目前实现我的要求的方法是：

val df1count= DF1.count
val df2count=DF2.count
val diffDF=DF2.except(DF1)
val diffCount=diffDF.count
if(diffCount==(df2count-df1count)) true
else false

这种方法的问题是我将动作元素调用了4次，这肯定不是最好的方法。有人可以建议我实现这个目标的最有效方法吗？

Answer 1

您可以使用以下功能：

import org.apache.spark.sql.functions._

def diff(key: String, df1: DataFrame, df2: DataFrame): DataFrame = {
  val fields = df1.schema.fields.map(_.name)
  val diffColumnName = "Diff"

  df1
    .join(df2, df1(key) === df2(key), "full_outer")
    .withColumn(
      diffColumnName,
      when(df1(key).isNull, "New row in DataFrame 2")
        .otherwise(
          when(df2(key).isNull, "New row in DataFrame 1")
            .otherwise(
              concat_ws("",
                fields.map(f => when(df1(f) =!= df2(f), s"$f ").otherwise("")):_*
              )
            )
        )
    )
    .filter(col(diffColumnName) =!= "")
    .select(
      fields.map(f =>
        when(df1(key).isNotNull, df1(f)).otherwise(df2(f)).alias(f)
      ) :+ col(diffColumnName):_*
    )
}

在你的情况下运行：

diff("emp_id", df1, df2)

示例

import org.apache.spark.sql.{DataFrame, SparkSession} import org.apache.spark.sql.functions._ object DiffDataFrames extends App { val session = SparkSession.builder().master("local").getOrCreate() import session.implicits._ val df1 = session.createDataset(Seq((1,"a",11),(2,"b",2),(3,"c",33),(5,"e",5))).toDF("n", "s", "i") val df2 = session.createDataset(Seq((1,"a",11),(2,"bb",2),(3,"cc",34),(4,"d",4))).toDF("n", "s", "i") def diff(key: String, df1: DataFrame, df2: DataFrame): DataFrame = /* above definition */ diff("n", df1, df2).show(false) }

Answer 2

这是一种在两个数据帧之间获取不常见行的方法：

val d1 = Seq((3, "Chennai", "rahman", "9848022330", 45000, "SanRamon"), (1, "Hyderabad", "ram", "9848022338", 50000, "SF"), (2, "Hyderabad", "robin", "9848022339", 40000, "LA"), (4, "sanjose", "romin", "9848022331", 45123, "SanRamon"))
val d2 = Seq((3, "Chennai", "rahman", "9848022330", 45000, "SanRamon"), (1, "Hyderabad", "ram", "9848022338", 50000, "SF"), (2, "Hyderabad", "robin", "9848022339", 40000, "LA"), (4, "sanjose", "romin", "9848022331", 45123, "SanRamon"), (4, "sanjose", "romino", "9848022331", 45123, "SanRamon"), (5, "LA", "Test", "1234567890", 12345, "Testuser"))

val df1 = d1.toDF("emp_id" ,"emp_city" ,"emp_name" ,"emp_phone" ,"emp_sal" ,"emp_site")
val df2 = d2.toDF("emp_id" ,"emp_city" ,"emp_name" ,"emp_phone" ,"emp_sal" ,"emp_site")

spark.sql("((select * from df1) union (select * from df2)) minus ((select * from df1) intersect (select * from df2))").show //spark is SparkSession

两个数据帧的相等性

2 个答案: