向 Spark DataFrame 添加嵌套列

Question

如何向任何嵌套级别的结构添加或替换字段？

这个输入：

val rdd = sc.parallelize(Seq(
  """{"a": {"xX": 1,"XX": 2},"b": {"z": 0}}""",
  """{"a": {"xX": 3},"b": {"z": 0}}""",
  """{"a": {"XX": 3},"b": {"z": 0}}""",
  """{"a": {"xx": 4},"b": {"z": 0}}"""))
var df = sqlContext.read.json(rdd)

产生以下架构：

root
 |-- a: struct (nullable = true)
 |    |-- XX: long (nullable = true)
 |    |-- xX: long (nullable = true)
 |    |-- xx: long (nullable = true)
 |-- b: struct (nullable = true)
 |    |-- z: long (nullable = true)

那么我可以这样做：

import org.apache.spark.sql.functions._
val overlappingNames = Seq(col("a.xx"), col("a.xX"), col("a.XX"))
df = df
  .withColumn("a_xx",
    coalesce(overlappingNames:_*))
  .dropNestedColumn("a.xX")
  .dropNestedColumn("a.XX")
  .dropNestedColumn("a.xx")

(dropNestedColumn 是从这个答案借来的： https://stackoverflow.com/a/39943812/1068385。我基本上是在寻找它的逆运算。）

架构变成：

root
 |-- a: struct (nullable = false)
 |-- b: struct (nullable = true)
 |    |-- z: long (nullable = true)
 |-- a_xx: long (nullable = true)

显然它不会替换（或添加）a.xx，而是在根级别添加新字段a_xx。

我希望能够这样做：

val overlappingNames = Seq(col("a.xx"), col("a.xX"), col("a.XX"))
df = df
  .withNestedColumn("a.xx",
    coalesce(overlappingNames:_*))
  .dropNestedColumn("a.xX")
  .dropNestedColumn("a.XX")

这样就会产生这个架构：

root
 |-- a: struct (nullable = false)
 |    |-- xx: long (nullable = true)
 |-- b: struct (nullable = true)
 |    |-- z: long (nullable = true)

我怎样才能做到这一点？

这里的实际目标是对输入 JSON 中的列名不区分大小写。最后一步很简单：收集所有重叠的列名并在每个列上应用合并。

Answer 1

它可能没有它应有的优雅或高效，但这是我想出的：

object DataFrameUtils {
  private def nullableCol(parentCol: Column, c: Column): Column = {
    when(parentCol.isNotNull, c)
  }

  private def nullableCol(c: Column): Column = {
    nullableCol(c, c)
  }

  private def createNestedStructs(splitted: Seq[String], newCol: Column): Column = {
    splitted
      .foldRight(newCol) {
        case (colName, nestedStruct) => nullableCol(struct(nestedStruct as colName))
      }
  }

  private def recursiveAddNestedColumn(splitted: Seq[String], col: Column, colType: DataType, nullable: Boolean, newCol: Column): Column = {
    colType match {
      case colType: StructType if splitted.nonEmpty => {
        var modifiedFields: Seq[(String, Column)] = colType.fields
          .map(f => {
            var curCol = col.getField(f.name)
            if (f.name == splitted.head) {
              curCol = recursiveAddNestedColumn(splitted.tail, curCol, f.dataType, f.nullable, newCol)
            }
            (f.name, curCol as f.name)
          })

        if (!modifiedFields.exists(_._1 == splitted.head)) {
          modifiedFields :+= (splitted.head, nullableCol(col, createNestedStructs(splitted.tail, newCol)) as splitted.head)
        }

        var modifiedStruct: Column = struct(modifiedFields.map(_._2): _*)
        if (nullable) {
          modifiedStruct = nullableCol(col, modifiedStruct)
        }
        modifiedStruct
      }
      case _  => createNestedStructs(splitted, newCol)
    }
  }

  private def addNestedColumn(df: DataFrame, newColName: String, newCol: Column): DataFrame = {
    if (newColName.contains('.')) {
      var splitted = newColName.split('.')

      val modifiedOrAdded: (String, Column) = df.schema.fields
        .find(_.name == splitted.head)
        .map(f => (f.name, recursiveAddNestedColumn(splitted.tail, col(f.name), f.dataType, f.nullable, newCol)))
        .getOrElse {
          (splitted.head, createNestedStructs(splitted.tail, newCol) as splitted.head)
        }

      df.withColumn(modifiedOrAdded._1, modifiedOrAdded._2)

    } else {
      // Top level addition, use spark method as-is
      df.withColumn(newColName, newCol)
    }
  }

  implicit class ExtendedDataFrame(df: DataFrame) extends Serializable {
    /**
      * Add nested field to DataFrame
      *
      * @param newColName Dot-separated nested field name
      * @param newCol New column value
      */
    def withNestedColumn(newColName: String, newCol: Column): DataFrame = {
      DataFrameUtils.addNestedColumn(df, newColName, newCol)
    }
  }
}

请随时改进。

val data = spark.sparkContext.parallelize(List("""{ "a1": 1, "a3": { "b1": 3, "b2": { "c1": 5, "c2": 6 } } }"""))
val df: DataFrame = spark.read.json(data)

val df2 = df.withNestedColumn("a3.b2.c3.d1", $"a3.b2")

应该产生：

assertResult("struct<a1:bigint,a3:struct<b1:bigint,b2:struct<c1:bigint,c2:bigint,c3:struct<d1:struct<c1:bigint,c2:bigint>>>>>")(df2.shema.simpleString)