Flatten only deepest level in scala spark dataframe

问题

I have a Spark job, which has a DataFrame with the following value :

{
  "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"
    }
  }
}

and I want to flatten it out elegantly (as no of keys is unknown and type etc) in such a way that props remains as a struct but everything inside it is flattened off (irrespective of the level of nesting)

The output desired is :

{
  "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"
  }
}

I used the solution mentioned in Automatically and Elegantly flatten DataFrame in Spark SQL

however, I'm unable to keep the props field intact. It also gets flattened off. Can somebody help me with extending this solution?

The final schema should be something like :

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)

回答1:

I've been able to achieve this with the RDD API :

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

The solution should accept deeply nested inputs, thanks to recursion.

With your data, here's what we get :

finalDF.printSchema()
finalDF.show(false)
finalDF.select("props.*").show()

Outputs :

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|
+-------+-----------+--------+---------+

But we can also pass more nested/complexe structures like for instance :

val str2 = """{"newroot":[{"mystruct":{"id":"abchchd","test_id":"ndsbsb","props":{"type":{"isMale":true,"id":"dd","mcc":1234,"name":"Adam"}}}}]}"""

...

finalDF.printSchema()
finalDF.show(false)

Gives the following output :

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]]]|
+---------------------------------------------+

EDIT: As you mentioned, if you have records with different structure you need to wrap the above subObject value in an option.
Here's the fixed unnest function :

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

New printSchema gives :

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)

来源：https://stackoverflow.com/questions/59662227/flatten-only-deepest-level-in-scala-spark-dataframe