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Posted to issues@spark.apache.org by "Alexander Alexandrov (JIRA)" <ji...@apache.org> on 2017/01/23 11:19:26 UTC
[jira] [Updated] (SPARK-19214) Inconsistencies between DataFrame
and Dataset APIs
[ https://issues.apache.org/jira/browse/SPARK-19214?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Alexander Alexandrov updated SPARK-19214:
-----------------------------------------
Description:
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($"x").count().printSchema
// root
// |-- x: integer (nullable = false)
// |-- count: long (nullable = false)
// count() on grouped Dataset: field name is `count(1)`
ps.groupByKey(_.x).count().printSchema
// root
// |-- value: integer (nullable = true)
// |-- count(1): long (nullable = false)
// Problem 2:
// groupByKey produces different `key` field name depending
// on the result type
// this is especially confusing in the first case below (simple key types)
// where the key field is actually named `value`
// simple key types
ps.groupByKey(p => p.x).count().printSchema
// root
// |-- value: integer (nullable = true)
// |-- count(1): long (nullable = false)
// complex key types
ps.groupByKey(p => (p.x, p.y)).count().printSchema
// root
// |-- key: struct (nullable = false)
// | |-- _1: integer (nullable = true)
// | |-- _2: integer (nullable = true)
// |-- count(1): long (nullable = false)
{code}
was:
I am not sure whether this has been reported already, but there are some confusing & annoying inconsistencies when programming the same expression in the Dataset and the DataFrame APIs.
Consider the following minimal example executed in a Spark Shell:
{code}
case class Point(x: Int, y: Int, z: Int)
val ps = spark.createDataset(for {
x <- 1 to 10;
y <- 1 to 10;
z <- 1 to 10
} yield Point(x, y, z))
// Problem 1:
// count produces different fields in the Dataset / DataFrame variants
// count() on grouped DataFrame: field name is `count`
ps.groupBy($"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}
> 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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