[
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(): 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}
was:
class Column {
...
def over(): WindowFunctionSpec
...
}
// 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
}
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
)
> 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
>
> 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}
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