scala - 如何使用 Spark/Scala 将这种嵌套的多行 json 文件读入数据帧

Question

我有以下json：

{
    "value":[
            {"C1":"val1","C2":"val2"},
            {"C1":"val1","C2":"val2"},
            {"C1":"val1","C2":"val2"}
        ]
}

我正在尝试这样阅读：

spark.read
  .option("multiLine", true).option("mode", "PERMISSIVE")
  .json("/Projects.json")
  .show(10)

但它无法在数据框中正确显示我的记录，我如何绕过那个“值”嵌套以正确地将我的行放在数据框中？

当前结果：

我试图得到的结果是：

    C1   |   C2
-------------------
    VAL1 |   VAL2
    VAL1 |   VAL2
    ...etc

Answer 1

查看 spark.read 返回的 Dataframe ( jsonDf) 的架构：

jsonDf.printSchema()
root
 |-- value: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- C1: string (nullable = true)
 |    |    |-- C2: string (nullable = true)

您可以使用 sql 函数explode，然后选择这两个元素C1，C2如下所示：

  val df = jsonDf
    .withColumn("parsedJson", explode(col("value")))
    .withColumn("C1", col("parsedJson.C1"))
    .withColumn("C2", col("parsedJson.C2"))
    .select(col("C1"), col("C2"))
    .show(false)

这导致了所需的结果：

+----+----+
|C1  |C2  |
+----+----+
|val1|val2|
|val1|val2|
|val1|val2|
+----+----+

Answer 2

我终于设法使用以下功能找到了解决问题的方法：

  def flattenDataframe(df: DataFrame): DataFrame = {

    val fields = df.schema.fields
    val fieldNames = fields.map(x => x.name)
    val length = fields.length
    
    for(i <- 0 to fields.length-1){
      val field = fields(i)
      val fieldtype = field.dataType
      val fieldName = field.name
      fieldtype match {
        case arrayType: ArrayType =>
          val fieldNamesExcludingArray = fieldNames.filter(_!=fieldName)
          val fieldNamesAndExplode = fieldNamesExcludingArray ++ Array(s"explode_outer($fieldName) as $fieldName")
         // val fieldNamesToSelect = (fieldNamesExcludingArray ++ Array(s"$fieldName.*"))
          val explodedDf = df.selectExpr(fieldNamesAndExplode:_*)
          return flattenDataframe(explodedDf)
        case structType: StructType =>
          val childFieldnames = structType.fieldNames.map(childname => fieldName +"."+childname)
          val newfieldNames = fieldNames.filter(_!= fieldName) ++ childFieldnames
          val renamedcols = newfieldNames.map(x => (col(x.toString()).as(x.toString().replace(".", "_"))))
         val explodedf = df.select(renamedcols:_*)
          return flattenDataframe(explodedf)
        case _ =>
      }
    }
    df
  }

来源https://medium.com/@saikrishna_55717/flattening-nested-data-json-xml-using-apache-spark-75fa4c8ea2a7

Answer 3

使用inline将完成这项工作：

val df = spark.read
  .option("multiLine", true).option("mode", "PERMISSIVE")
  .json("/Projects.json")

val df2 = df.selectExpr("inline(value)")
df2.show
+----+----+
|  C1|  C2|
+----+----+
|val1|val2|
|val1|val2|
|val1|val2|
+----+----+