DataFrame partitionBy on nested columns

Question

I am trying to call partitionBy on a nested field like below:

val rawJson = sqlContext.read.json(filename)
rawJson.write.partitionBy("data.dataDetails.name").parquet(filenameParquet)

I get the below error when I run it. I do see the 'name' listed as the field in the below schema. Is there a different format to specify the column name which is nested?

java.lang.RuntimeException: Partition column data.dataDetails.name not found in schema StructType(StructField(name,StringType,true), StructField(time,StringType,true), StructField(data,StructType(StructField(dataDetails,StructType(StructField(name,StringType,true), StructField(id,StringType,true),true)),true))

This is my json file:

{  
  "name": "AssetName",
  "time": "2016-06-20T11:57:19.4941368-04:00",
  "data": {
    "type": "EventData",
    "dataDetails": {
      "name": "EventName"
      "id": "1234"

    }
  }
} 

Answer 1

This appears to be a known issue listed here: https://issues.apache.org/jira/browse/SPARK-18084

I had this issue as well and to work around it I was able to un-nest the columns on my dataset. My dataset was a little different than your dataset, but here is the strategy...

Original Json:

{  
  "name": "AssetName",
  "time": "2016-06-20T11:57:19.4941368-04:00",
  "data": {
    "type": "EventData",
    "dataDetails": {
      "name": "EventName"
      "id": "1234"

    }
  }
} 

Modified Json:

{  
  "name": "AssetName",
  "time": "2016-06-20T11:57:19.4941368-04:00",
  "data_type": "EventData",
  "data_dataDetails_name" : "EventName",
  "data_dataDetails_id": "1234"
  }
} 

Code to get to Modified Json:

def main(args: Array[String]) {
  ...

  val data = df.select(children("data", df) ++ $"name" ++ $"time"): _*)

  data.printSchema

  data.write.partitionBy("data_dataDetails_name").format("csv").save(...)
}

def children(colname: String, df: DataFrame) = {
  val parent = df.schema.fields.filter(_.name == colname).head
  val fields = parent.dataType match {
    case x: StructType => x.fields
    case _ => Array.empty[StructField]
  }
  fields.map(x => col(s"$colname.${x.name}").alias(s"$colname" + s"_" + s"${x.name}"))
}

Answer 2

Since the feature is un-available as of Spark 2.3.1, here's a workaround. Make sure to handle name conflicts between the nested fields and the fields at the root level.

{"date":"20180808","value":{"group":"xxx","team":"yyy"}}

df.select("date","value.group","value.team")
      .write
      .partitionBy("date","group","team")
      .parquet(filenameParquet)

The partitions end up like

date=20180808/group=xxx/team=yyy/part-xxx.parquet