Spark 3.4.1 - working with 'java.lang.ClassCastException' While Using UDF on Nested JSON Data
I'm working with Apache Spark 3.4.1 and working with a scenario when applying a User Defined Function (UDF) to a DataFrame that contains nested JSON structures. My DataFrame schema is as follows: ```scala root |-- id: integer (nullable = true) |-- details: struct (nullable = true) | |-- name: string (nullable = true) | |-- info: struct (nullable = true) | | |-- age: integer (nullable = true) | | |-- address: string (nullable = true) ``` I defined a UDF to extract the age from the nested structure: ```scala import org.apache.spark.sql.functions.udf val extractAge = udf((info: Row) => info.getAs[Int]("age")) ``` When I apply this UDF to my DataFrame, I get the following behavior: ``` java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema want to be cast to org.apache.spark.sql.catalyst.expressions.Row ``` I attempted to extract the age using `dataFrame.withColumn("age", extractAge(col("details.info")))`, but it seems like the type mismatch is causing the scenario. I've also tried using `dataFrame.select(extractAge(col("details.info")))` but end up with the same behavior. I've verified that the structure of the column I am passing to the UDF indeed contains the nested JSON and is not empty. To troubleshoot, I printed the schema of the DataFrame right before the UDF application, and it looks correct. Here’s the snippet for checking the schema: ```scala dataFrame.printSchema() ``` Do I need to modify how I'm accessing the nested fields, or is there a different approach I should take to apply a UDF to a struct field? Any help would be greatly appreciated!