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

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dongjoon-hyun 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-496373474
 
 
   First of all, the followings are the most frequent use cases.
   1. HEADER and INFERSCHEMA
   ```scala
   scala> spark.read.option("header", true).option("inferSchema", true).csv("/tmp/csv").as[Person]
   res0: org.apache.spark.sql.Dataset[Person] = [name: string, age: int]
   ```
   
   2. USER-DEFINED SCHEMA or Hive MetaStore
   ```scala
   scala> case class Person(name: String, age: Long)
   scala> spark.read.schema("name string, age long").csv("/tmp/csv").as[Person]
   res0: org.apache.spark.sql.Dataset[Person] = [name: string, age: bigint]
   ```
   
   I believe the above two are more natural.
   
   Anyway, cc @HyukjinKwon and @MaxGekk 

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