Spark 2.0（而非2.1）Dataset [Row]或Dataframe-选择几列以JSON

Question

I have a Spark Dataframe with 10 columns and I need to store this in Postgres/ RDBMS. The table has 7 columns and 7th column takes in text (of JSON format) for further processing.

How do I select 6 columns and convert the remaining 4 columns in the DF to JSON format?

If the whole DF is to be stored as JSON, then we could use DF.write.format("json"), but only the last 4 columns are required to be in JSON format.

I tried creating a UDF (with either Jackson or Lift lib), but not successful in sending the 4 columns to the UDF.

for JSON, the DF column name is the key, DF column's value is the value.

eg:

dataset name: ds_base
root
 |-- bill_id: string (nullable = true)
 |-- trans_id: integer (nullable = true)
 |-- billing_id: decimal(3,-10) (nullable = true)
 |-- asset_id: string (nullable = true)
 |-- row_id: string (nullable = true)
 |-- created: string (nullable = true)
 |-- end_dt: string (nullable = true)
 |-- start_dt: string (nullable = true)
 |-- status_cd: string (nullable = true)
 |-- update_start_dt: string (nullable = true)

I want to do,
ds_base
 .select ( $"bill_id",
    $"trans_id",
    $"billing_id",
    $"asset_id",
    $"row_id",
    $"created",
    ?? <JSON format of 4 remaining columns>
    )

Answer 1

You can use struct and to_json :

import org.apache.spark.sql.functions.{to_json, struct}

to_json(struct($"end_dt", $"start_dt", $"status_cd", $"update_start_dt"))

As a workaround for legacy Spark versions you could convert whole object to JSON and extracting required:

import org.apache.spark.sql.functions.get_json_object

// List of column names to be kept as-is
val scalarColumns: Seq[String] = Seq("bill_id", "trans_id", ...)
// List of column names to be put in JSON
val jsonColumns: Seq[String] = Seq(
  "end_dt", "start_dt", "status_cd", "update_start_dt"
)

// Convert all records to JSON, keeping selected fields as a nested document
val json = df.select(
  scalarColumns.map(col _) :+ 
  struct(jsonColumns map col: _*).alias("json"): _*
).toJSON

json.select(
  // Extract selected columns from JSON field and cast to required types
  scalarColumns.map(c => 
    get_json_object($"value", s"$$.$c").alias(c).cast(df.schema(c).dataType)) :+ 
  // Extract JSON struct
  get_json_object($"value", "$.json").alias("json"): _*
)

This will work only as long as you have atomic types. Alternatively you could use standard JSON reader and specify schema for the JSON field.

import org.apache.spark.sql.types._

val combined = df.select(
  scalarColumns.map(col _) :+ 
  struct(jsonColumns map col: _*).alias("json"): _*
)

val newSchema = StructType(combined.schema.fields map {
   case StructField("json", _, _, _) => StructField("json", StringType)
   case s => s
})

spark.read.schema(newSchema).json(combined.toJSON.rdd)