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[英]Flatten any nested json string and convert to dataframe using spark scala
[英]Flatten only deepest level in scala spark dataframe
我有一個 Spark 作業,它有一個具有以下值的 DataFrame:
{
"id": "abchchd",
"test_id": "ndsbsb",
"props": {
"type": {
"isMale": true,
"id": "dd",
"mcc": 1234,
"name": "Adam"
}
}
}
{
"id": "abc",
"test_id": "asf",
"props": {
"type2": {
"isMale": true,
"id": "dd",
"mcc": 12134,
"name": "Perth"
}
}
}
我想優雅地將它展平(因為沒有未知的鍵和類型等),這樣道具仍然是一個struct
但它里面的所有東西都被展平了(不管嵌套的級別如何)
所需的輸出是:
{
"id": "abchchd",
"test_id": "ndsbsb",
"props": {
"type.isMale": true,
"type.id": "dd",
"type.mcc": 1234,
"type.name": "Adam"
}
}
{
"id": "abc",
"test_id": "asf",
"props": {
"type2.isMale": true,
"type2.id": "dd",
"type2.mcc": 12134,
"type2.name": "Perth"
}
}
我使用了Automatically and Elegantly flatten DataFrame in Spark SQL 中提到的解決方案
但是,我無法保持道具場完好無損。 它也會被壓扁。 有人可以幫助我擴展此解決方案嗎?
最終的架構應該是這樣的:
root
|-- id: string (nullable = true)
|-- props: struct (nullable = true)
| |-- type.id: string (nullable = true)
| |-- type.isMale: boolean (nullable = true)
| |-- type.mcc: long (nullable = true)
| |-- type.name: string (nullable = true)
|-- type2.id: string (nullable = true)
| |-- type2.isMale: boolean (nullable = true)
| |-- type2.mcc: long (nullable = true)
| |-- type2.name: string (nullable = true)
|-- test_id: string (nullable = true)
我已經能夠使用 RDD API 實現這一點:
val jsonRDD = df.rdd.map{row =>
def unnest(r: Row): Map[String, Any] = {
r.schema.fields.zipWithIndex.flatMap{case (f, i) =>
(f.name, f.dataType) match {
case ("props", _:StructType) =>
val propsObject = r.getAs[Row](f.name)
Map(f.name -> propsObject.schema.fields.flatMap{propsAttr =>
val subObject = propsObject.getAs[Row](propsAttr.name)
subObject.schema.fields.map{subField =>
s"${propsAttr.name}.${subField.name}" -> subObject.get(subObject.fieldIndex(subField.name))
}
}.toMap)
case (fname, _: StructType) => Map(fname -> unnest(r.getAs[Row](fname)))
case (fname, ArrayType(_: StructType,_)) => Map(fname -> r.getAs[Seq[Row]](fname).map(unnest))
case _ => Map(f.name -> r.get(i))
}
}
}.toMap
val asMap = unnest(row)
new ObjectMapper().registerModule(DefaultScalaModule).writeValueAsString(asMap)
}
val finalDF = spark.read.json(jsonRDD.toDS).cache
由於遞歸,解決方案應該接受深度嵌套的輸入。
有了你的數據,我們得到了:
finalDF.printSchema()
finalDF.show(false)
finalDF.select("props.*").show()
輸出:
root
|-- id: string (nullable = true)
|-- props: struct (nullable = true)
| |-- type.id: string (nullable = true)
| |-- type.isMale: boolean (nullable = true)
| |-- type.mcc: long (nullable = true)
| |-- type.name: string (nullable = true)
|-- test_id: string (nullable = true)
+-------+----------------------+-------+
|id |props |test_id|
+-------+----------------------+-------+
|abchchd|[dd, true, 1234, Adam]|ndsbsb |
+-------+----------------------+-------+
+-------+-----------+--------+---------+
|type.id|type.isMale|type.mcc|type.name|
+-------+-----------+--------+---------+
| dd| true| 1234| Adam|
+-------+-----------+--------+---------+
但是我們也可以傳遞更多嵌套/復雜的結構,例如:
val str2 = """{"newroot":[{"mystruct":{"id":"abchchd","test_id":"ndsbsb","props":{"type":{"isMale":true,"id":"dd","mcc":1234,"name":"Adam"}}}}]}"""
...
finalDF.printSchema()
finalDF.show(false)
給出以下輸出:
root
|-- newroot: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- mystruct: struct (nullable = true)
| | | |-- id: string (nullable = true)
| | | |-- props: struct (nullable = true)
| | | | |-- type.id: string (nullable = true)
| | | | |-- type.isMale: boolean (nullable = true)
| | | | |-- type.mcc: long (nullable = true)
| | | | |-- type.name: string (nullable = true)
| | | |-- test_id: string (nullable = true)
+---------------------------------------------+
|root |
+---------------------------------------------+
|[[[abchchd, [dd, true, 1234, Adam], ndsbsb]]]|
+---------------------------------------------+
編輯:正如您所提到的,如果您有不同結構的記錄,您需要將上述subObject
值包裝在一個選項中。
這是固定的unnest
函數:
def unnest(r: Row): Map[String, Any] = {
r.schema.fields.zipWithIndex.flatMap{case (f, i) =>
(f.name, f.dataType) match {
case ("props", _:StructType) =>
val propsObject = r.getAs[Row](f.name)
Map(f.name -> propsObject.schema.fields.flatMap{propsAttr =>
val subObjectOpt = Option(propsObject.getAs[Row](propsAttr.name))
subObjectOpt.toSeq.flatMap{subObject => subObject.schema.fields.map{subField =>
s"${propsAttr.name}.${subField.name}" -> subObject.get(subObject.fieldIndex(subField.name))
}}
}.toMap)
case (fname, _: StructType) => Map(fname -> unnest(r.getAs[Row](fname)))
case (fname, ArrayType(_: StructType,_)) => Map(fname -> r.getAs[Seq[Row]](fname).map(unnest))
case _ => Map(f.name -> r.get(i))
}
}
}.toMap
新的printSchema
給出:
root
|-- id: string (nullable = true)
|-- props: struct (nullable = true)
| |-- type.id: string (nullable = true)
| |-- type.isMale: boolean (nullable = true)
| |-- type.mcc: long (nullable = true)
| |-- type.name: string (nullable = true)
| |-- type2.id: string (nullable = true)
| |-- type2.isMale: boolean (nullable = true)
| |-- type2.mcc: long (nullable = true)
| |-- type2.name: string (nullable = true)
|-- test_id: string (nullable = true)
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