[
https://issues.apache.org/jira/browse/SPARK-7322?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Reynold Xin updated SPARK-7322:
-------------------------------
Description:
Here's a proposal for supporting window functions in the DataFrame DSL:
1. Add an over function to Column:
{code}
class Column {
...
def over(window: Window): Column
...
}
{code}
2. Window:
{code}
object Window {
def partitionBy(...): Window
def orderBy(...): Window
object Frame {
def unbounded: Frame
def preceding(n: Long): Frame
def following(n: Long): Frame
}
class Frame
}
class Window {
def orderBy(...): Window
def rowsBetween(Frame, Frame): Window
def rangeBetween(Frame, Frame): Window // maybe add this later
}
{code}
Here's an example to use it:
{code}
df.select(
avg(“age”).over(Window.partitionBy(“..”, “..”).orderBy(“..”, “..”)
.rowsBetween(Frame.unbounded, Frame.currentRow))
)
df.select(
avg(“age”).over(Window.partitionBy(“..”, “..”).orderBy(“..”, “..”)
.rowsBetween(Frame.preceding(50), Frame.following(10)))
)
{code}
was:
Here's a proposal for supporting window functions in the DataFrame DSL:
1. Add an over function to Column:
{code}
class Column {
...
def over(): WindowFunctionSpec
...
}
{code}
2. WindowFunctionSpec:
{code}
// By default frame = full partition
class WindowFunctionSpec {
def partitionBy(cols: Column*): WindowFunctionSpec
def orderBy(cols: Column*): WindowFunctionSpec
// restrict frame beginning from current row - n position
def rowsPreceding(n: Int): WindowFunctionSpec
// restrict frame ending from current row - n position
def rowsFollowing(n: Int): WindowFunctionSpec
def rangePreceding(n: Int): WindowFunctionSpec
def rowsFollowing(n: Int): WindowFunctionSpec
}
{code}
Here's an example to use it:
{code}
df.select(
df.store,
df.date,
df.sales,
avg(df.sales).over.partitionBy(df.store)
.orderBy(df.store)
.rowsFollowing(0) // this means from unbounded preceding
to current row
)
{code}
> Add DataFrame DSL for window function support
> ---------------------------------------------
>
> Key: SPARK-7322
> URL: https://issues.apache.org/jira/browse/SPARK-7322
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Reynold Xin
> Assignee: Cheng Hao
> Labels: DataFrame
>
> Here's a proposal for supporting window functions in the DataFrame DSL:
> 1. Add an over function to Column:
> {code}
> class Column {
> ...
> def over(window: Window): Column
> ...
> }
> {code}
> 2. Window:
> {code}
> object Window {
> def partitionBy(...): Window
> def orderBy(...): Window
> object Frame {
> def unbounded: Frame
> def preceding(n: Long): Frame
> def following(n: Long): Frame
> }
> class Frame
> }
> class Window {
> def orderBy(...): Window
> def rowsBetween(Frame, Frame): Window
> def rangeBetween(Frame, Frame): Window // maybe add this later
> }
> {code}
> Here's an example to use it:
> {code}
> df.select(
> avg(“age”).over(Window.partitionBy(“..”, “..”).orderBy(“..”, “..”)
> .rowsBetween(Frame.unbounded, Frame.currentRow))
> )
> df.select(
> avg(“age”).over(Window.partitionBy(“..”, “..”).orderBy(“..”, “..”)
> .rowsBetween(Frame.preceding(50), Frame.following(10)))
> )
> {code}
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