嵌套的json扁平化火花数据框

Posted 2023-04-18

【中文标题】嵌套的json扁平化火花数据框

【英文标题】：nested json flattening spark dataframe

【发布时间】：2020-10-07 04:30:50

【问题描述】：

我正在尝试从嵌套的 jsonString 创建一个数据帧并拆分为多个数据帧，即外部元素数据将转到一个数据帧，而嵌套的子数据将转到另一个数据帧。可能有多个嵌套元素。我查看了其他帖子，它们都没有为以下场景提供工作示例。下面是一个状态数是动态的示例，我想将国家信息和状态信息存储在 2 个单独的 hdfs 文件夹中。所以父数据框保持如下一行。

val jsonStr=""""country":"US","ISD":"001","states":["state1":"NJ","state2":"NY","state3 ":"PA"]"""

val countryDf = spark.read.json(Seq(jsonStr).toDS)

countryDf.show(false)

+---+-------+--------------+
|ISD|country|states        |
+---+-------+--------------+
|001|US     |[[NJ, NY, PA]]|
+---+-------+--------------+

countryDf.withColumn("states",explode($"states")).show(false)



val statesDf = countryDf.select(explode(countryDf("states").as("states")))
statesDf.show(false)
+------------+
|col         |
+------------+
|[NJ, NY, PA]|
+------------+

Expected out put  
2 Dataframes 

countryDf
+---+-------+
|ISD|country|
+---+-------+
|001|US     |
+---+-------+

statesDf 

+------+-------+-------+-------+
country| state1| state2|  state3
+------+---------------+-------+
US     |  NJ      NY      PA
+------+-------+-------+-------+

我查看了堆栈溢出中有关嵌套 json flattening 的其他问题。没有人有相同的工作解决方案。

【问题讨论】：

将 statesDf 扁平化为列而不是行不是更好吗？总是有固定数量的状态吗？

感谢 Shailesh 没有状态是动态的

【参考方案1】：

这里有一些代码可以完成这项工作。您应该考虑性能以及列数是否非常大。我已经收集了所有地图字段并将它们添加到数据框中。

val jsonStr=""""country":"US","ISD":"001","states":["state1":"NJ","state2":"NY","state3":"PA"]"""
import spark.implicits._

val countryDf = spark.read.json(Seq(jsonStr).toDS)

countryDf.show(false)
val statesDf = countryDf.select($"country", explode($"states").as("states"))

val index = statesDf.schema.fieldIndex("states")
val stateSchema = statesDf.schema(index).dataType.asInstanceOf[StructType]
var columns = mutable.LinkedHashSet[Column]()
stateSchema.fields.foreach(field =>
  columns.add(lit(field.name))
  columns.add(col( "state." + field.name))
)


val s2 = statesDf
  .withColumn("statesMap", map(columns.toSeq: _*))

val allMapKeys = s2.select(explode($"statesMap")).select($"key").distinct.collect().map(_.get(0).toString)

val s3 = allMapKeys.foldLeft(s2)((a, b) => a.withColumn(b, a("statesMap")(b)))
  .drop("statesMap")
s3.show(false)

【参考方案2】：

当您读取嵌套 JSON 并将其转换为数据集时，嵌套部分将存储为结构类型。因此，您必须考虑在数据框中展平结构类型。

val jsonStr=""""country":"US","ISD":"001","states":["state1":"NJ","state2":"NY","state3":"PA"]"""
val countryDf = spark.read.json(Seq(jsonStr).toDS)

countryDf.show(false)
+---+-------+--------------+
|ISD|country|states        |
+---+-------+--------------+
|001|US     |[[NJ, NY, PA]]|
+---+-------+--------------+

val countryDfExploded = countryDf.withColumn("states",explode($"states"))
countryDfExploded.show(false)
+---+-------+------------+
|ISD|country|states      |
+---+-------+------------+
|001|US     |[NJ, NY, PA]|
+---+-------+------------+

val countrySelectDf = countryDfExploded.select($"ISD", $"country")
countrySelectDf.show(false)
+---+-------+
|ISD|country|
+---+-------+
|001|US     |
+---+-------+

val statesDf = countryDfExploded.select( $"country",$"states.*")
statesDf.show(false)
+-------+------+------+------+
|country|state1|state2|state3|
+-------+------+------+------+
|US     |NJ    |NY    |PA    |
+-------+------+------+------+