For starters, your example data has an extraneous comma after "y\":200 which will prevent it from being parsed as it is not valid JSON.
From there, you can use from_json to parse the field, assuming you know the schema. In this example, I'm parsing the field separately to first get the schema:
scala> val json = spark.read.json(Seq("""{"test":{"id":1,"name":"name","problem_field": "{\"x\":100,\"y\":200}"}}""").toDS)
json: org.apache.spark.sql.DataFrame = [test: struct<id: bigint, name: string ... 1 more field>]
scala> json.printSchema
root
|-- test: struct (nullable = true)
| |-- id: long (nullable = true)
| |-- name: string (nullable = true)
| |-- problem_field: string (nullable = true)
scala> val problem_field = spark.read.json(json.select($"test.problem_field").map{
case org.apache.spark.sql.Row(x : String) => x
})
problem_field: org.apache.spark.sql.DataFrame = [x: bigint, y: bigint]
scala> problem_field.printSchema
root
|-- x: long (nullable = true)
|-- y: long (nullable = true)
scala> val fixed = json.withColumn("test", struct($"test.id", $"test.name", from_json($"test.problem_field", problem_field.schema).as("problem_field")))
fixed: org.apache.spark.sql.DataFrame = [test: struct<id: bigint, name: string ... 1 more field>]
scala> fixed.printSchema
root
|-- test: struct (nullable = false)
| |-- id: long (nullable = true)
| |-- name: string (nullable = true)
| |-- problem_field: struct (nullable = true)
| | |-- x: long (nullable = true)
| | |-- y: long (nullable = true)
If the schema of problem_fields contents is inconsistent between rows, this solution will still work but may not be an optimal way of handling things, as it will produce a sparse Dataframe where each row contains every field encountered in problem_field. For example:
scala> val json = spark.read.json(Seq("""{"test":{"id":1,"name":"name","problem_field": "{\"x\":100,\"y\":200}"}}""", """{"test":{"id":1,"name":"name","problem_field": "{\"a\":10,\"b\":20}"}}""").toDS)
json: org.apache.spark.sql.DataFrame = [test: struct<id: bigint, name: string ... 1 more field>]
scala> val problem_field = spark.read.json(json.select($"test.problem_field").map{case org.apache.spark.sql.Row(x : String) => x})
problem_field: org.apache.spark.sql.DataFrame = [a: bigint, b: bigint ... 2 more fields]
scala> problem_field.printSchema
root
|-- a: long (nullable = true)
|-- b: long (nullable = true)
|-- x: long (nullable = true)
|-- y: long (nullable = true)
scala> val fixed = json.withColumn("test", struct($"test.id", $"test.name", from_json($"test.problem_field", problem_field.schema).as("problem_field")))
fixed: org.apache.spark.sql.DataFrame = [test: struct<id: bigint, name: string ... 1 more field>]
scala> fixed.printSchema
root
|-- test: struct (nullable = false)
| |-- id: long (nullable = true)
| |-- name: string (nullable = true)
| |-- problem_field: struct (nullable = true)
| | |-- a: long (nullable = true)
| | |-- b: long (nullable = true)
| | |-- x: long (nullable = true)
| | |-- y: long (nullable = true)
scala> fixed.select($"test.problem_field.*").show
+----+----+----+----+
| a| b| x| y|
+----+----+----+----+
|null|null| 100| 200|
| 10| 20|null|null|
+----+----+----+----+
Over the course of hundreds, thousands, or millions of rows, you can see how this would present a problem.
