[jira] [Resolved] (SPARK-19214) Inconsistencies between DataFrame and Dataset APIs

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-19214.
-------------------------------
    Resolution: Won't Fix

> Inconsistencies between DataFrame and Dataset APIs
> --------------------------------------------------
>
>                 Key: SPARK-19214
>                 URL: https://issues.apache.org/jira/browse/SPARK-19214
>             Project: Spark
>          Issue Type: Improvement
>    Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0
>            Reporter: Alexander Alexandrov
>            Priority: Trivial
>
> I am not sure whether this has been reported already, but there are some confusing & annoying inconsistencies when programming the same expression in the Dataset and the DataFrame APIs.
> Consider the following minimal example executed in a Spark Shell:
> {code}
> case class Point(x: Int, y: Int, z: Int)
> val ps = spark.createDataset(for {
>   x <- 1 to 10
>   y <- 1 to 10
>   z <- 1 to 10
> } yield Point(x, y, z))
> // Problem 1:
> // count produces different fields in the Dataset / DataFrame variants
> // count() on grouped DataFrame: field name is `count`
> ps.groupBy($"x").count().printSchema
> // root
> //  |-- x: integer (nullable = false)
> //  |-- count: long (nullable = false)
> // count() on grouped Dataset: field name is `count(1)`
> ps.groupByKey(_.x).count().printSchema
> // root
> //  |-- value: integer (nullable = true)
> //  |-- count(1): long (nullable = false)
> // Problem 2:
> // groupByKey produces different `key` field name depending
> // on the result type
> // this is especially confusing in the first case below (simple key types)
> // where the key field is actually named `value`
> // simple key types
> ps.groupByKey(p => p.x).count().printSchema
> // root
> //  |-- value: integer (nullable = true)
> //  |-- count(1): long (nullable = false)
> // complex key types
> ps.groupByKey(p => (p.x, p.y)).count().printSchema
> // root
> //  |-- key: struct (nullable = false)
> //  |    |-- _1: integer (nullable = true)
> //  |    |-- _2: integer (nullable = true)
> //  |-- count(1): long (nullable = false)
> {code}



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