仅当条件存在于数据框中的列时才应用条件

问题描述

我在Java8中使用spark-sql-2.4.1v.如果给定的数据框列列表中存在列，我有一种情况需要执行某些操作

I am using spark-sql-2.4.1v with java8. I have a scenario where I need to perform certain operation if columns presents in the given dataframe column list

我有以下示例数据框，根据在数据库表上执行的外部查询，数据框的列会有所不同.

I have Sample data frame as below, the columns of dataframe would differ based on external query executed on the database table.

val data = List(
  ("20", "score", "school", "2018-03-31", 14 , 12 , 20),
  ("21", "score", "school", "2018-03-31", 13 , 13 , 21),
  ("22", "rate", "school", "2018-03-31", 11 , 14, 22),
  ("21", "rate", "school", "2018-03-31", 13 , 12, 23)
 )

val df = data.toDF("id", "code", "entity", "date", "column1", "column2" ,"column3"..."columnN")

如上面的数据帧"data"所示.列不是固定的，并且会有所不同，并且将具有"column1"，"column2". ，"column3" ..."columnN" ...

as show above dataframe "data" columns are not fixed and would vary and would have "column1", "column2" ,"column3"..."columnN" ...

因此，取决于列的可用性，我需要执行一些操作对于相同的原因，我试图使用"when-clause" ，当存在一列时，我必须在指定的列上执行某些操作，否则继续进行下一个操作.

So depend on the column availability i need to perform some operationsfor the same i am trying to use "when-clause" , when a column present then i have to perform certain operation on the specified column else move on to the next operation..

我正在尝试以下两种使用"when-cluase"的方法

I am trying below two ways using "when-cluase"

 Dataset<Row> resultDs =  df.withColumn("column1_avg",
                     when( df.schema().fieldNames().contains(col("column1")) , avg(col("column1"))))
                     )

  Dataset<Row> resultDs =  df.withColumn("column2_sum",
                     when( df.columns().contains(col("column2")) , sum(col("column1"))))
                     )

错误:

无法在数组类型String []上调用contains(Column)

Cannot invoke contains(Column) on the array type String[]

那么如何使用java8代码处理这种情况?

推荐答案

您可以创建包含所有列名称的列.那么您可以检查该列是否存在，并处理该列是否可用-

You can create a column having all the column names. then you can check if the column is present or not and process if it is available-

 df.withColumn("columns_available", array(df.columns.map(lit): _*))
      .withColumn("column1_org",
      when( array_contains(col("columns_available"),"column1") , col("column1")))
      .withColumn("x",
        when( array_contains(col("columns_available"),"column4") , col("column1")))
      .withColumn("column2_new",
        when( array_contains(col("columns_available"),"column2") , sqrt("column2")))
      .show(false)

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