Scala spark：如何使用数据集来创建具有snake_case架构的案例类？

Question

我有以下案例类：

case class User(userId: String)

和以下架构：

+--------------------+------------------+
|            col_name|         data_type|
+--------------------+------------------+
|             user_id|            string|
+--------------------+------------------+

当我尝试使用DataFrame将Dataset[User]转换为类型化的spark.read.table("MyTable").as[User]时，我收到一个错误，即字段名称不匹配：

Exception in thread "main" org.apache.spark.sql.AnalysisException:
    cannot resolve ''`user_id`' given input columns: [userId];;

有没有简单的方法来解决这个问题，而不打破scala成语和命名我的字段user_id？当然，我的真实表有很多字段，而且我有更多的case类/表，所以为每个case类手动定义一个Encoder是不可行的（而且我不太了解宏，所以这是一个问题;虽然我很乐意使用一个，如果存在的话！）。

我觉得我错过了一个非常明显的“将snake_case转换为camelCase = true”选项，因为实际上我已经使用过任何ORM。

Answer 1

scala> val df = Seq(("Eric" ,"Theodore", "Cartman"), ("Butters", "Leopold", "Stotch")).toDF.select(concat($"_1", lit(" "), ($"_2")) as "first_and_middle_name", $"_3" as "last_name")
df: org.apache.spark.sql.DataFrame = [first_and_middle_name: string, last_name: string]

scala> df.show
+---------------------+---------+
|first_and_middle_name|last_name|
+---------------------+---------+
|        Eric Theodore|  Cartman|
|      Butters Leopold|   Stotch|
+---------------------+---------+


scala> val ccnames = df.columns.map(sc => {val ccn = sc.split("_")
    | (ccn.head +: ccn.tail.map(_.capitalize)).mkString
    | })
ccnames: Array[String] = Array(firstAndMiddleName, lastName)

scala> df.toDF(ccnames: _*).show
+------------------+--------+
|firstAndMiddleName|lastName|
+------------------+--------+
|     Eric Theodore| Cartman|
|   Butters Leopold|  Stotch|
+------------------+--------+

编辑：这会有帮助吗？定义一个带有loader的函数：String => DataFrame和path：String。

scala> val parquetloader = spark.read.parquet _
parquetloader: String => org.apache.spark.sql.DataFrame = <function1>

scala> val tableloader = spark.read.table _
tableloader: String => org.apache.spark.sql.DataFrame = <function1>

scala> val textloader = spark.read.text _
textloader: String => org.apache.spark.sql.DataFrame = <function1>

// csv loader and others

def snakeCaseToCamelCaseDataFrameColumns(path: String, loader: String => DataFrame): DataFrame = {
  val ccnames = loader(path).columns.map(sc => {val ccn = sc.split("_")
    (ccn.head +: ccn.tail.map(_.capitalize)).mkString
    })
  df.toDF(ccnames: _*)
}

scala> :paste
// Entering paste mode (ctrl-D to finish)

def snakeCaseToCamelCaseDataFrameColumns(path: String, loader: String => DataFrame): DataFrame = {
      val ccnames = loader(path).columns.map(sc => {val ccn = sc.split("_")
        (ccn.head +: ccn.tail.map(_.capitalize)).mkString
        })
      df.toDF(ccnames: _*)
    }

// Exiting paste mode, now interpreting.

snakeCaseToCamelCaseDataFrameColumns: (path: String, loader: String => org.apache.spark.sql.DataFrame)org.apache.spark.sql.DataFrame

val oneDF = snakeCaseToCamelCaseDataFrameColumns(tableloader("/path/to/table"))
val twoDF = snakeCaseToCamelCaseDataFrameColumns(parquetloader("/path/to/parquet/file"))