[GitHub] [spark] swapnilushinde edited a comment on issue #24724: User friendly dataset, dataframe generation for csv datasources without explicit StructType definitions.

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swapnilushinde edited a comment on issue #24724: User friendly dataset, dataframe generation for csv datasources without explicit StructType definitions.
URL: https://github.com/apache/spark/pull/24724#issuecomment-496367606
 
 
   Hi, @dongjoon-hyun Thanks for reply. Yes, I use this API sometimes as well. Passing schema as DDL string is one-liner but would require to define case class for Dataset creation anyways. So, creating dataset would require to define schema as both DDL string and case class. for instance, 
   ```
   case class A(id: Int, name: String, subject: String, marks: Int, result: Boolean)
   val df = spark.read.schema("id int, name string, subject string, marks int, result boolean").load("/tmp/csv")
   val ds = df.as[A]
   ```
   Above change would need to define schema just once with Product class and dataset/dataframes can be created easily.
   Furthermore, this API is in line with all other similar APIs of creating dataset/dataframe. 
   

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