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https://issues.apache.org/jira/browse/SPARK-19214?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15833447#comment-15833447
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Sean Owen commented on SPARK-19214:
-----------------------------------
I'm pretty sure we would not change the naming here, even if it were a bit
inconsistent, because it's a breaking change. I am not yet clear it's not on
purpose though.
> 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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