MartijnVisser commented on a change in pull request #17670:
URL: https://github.com/apache/flink/pull/17670#discussion_r742781618
##########
File path: docs/content/docs/dev/table/sql/queries/window-agg.md
##########
@@ -40,7 +40,9 @@ Unlike other aggregations on continuous tables, window
aggregation do not emit i
### Windowing TVFs
-Flink supports `TUMBLE`, `HOP` and `CUMULATE` types of window aggregations,
which can be defined on either [event or processing time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}). See [Windowing TVF]({{< ref
"docs/dev/table/sql/queries/window-tvf" >}}) for more windowing functions
information.
+Flink supports `TUMBLE`, `HOP` and `CUMULATE` types of window aggregations.
+For SQL queries on streaming tables, the time attribute field of window table
function must be on either [event or processing time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}). See [Windowing TVF]({{< ref
"docs/dev/table/sql/queries/window-tvf" >}}) for more windowing functions
information.
Review comment:
```suggestion
For SQL queries on streaming tables, the time attribute field of a window
table-valued function must be on either [event or processing time
attributes]({{< ref "docs/dev/table/concepts/time_attributes" >}}). See
[Windowing TVF]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) for more
windowing functions information.
```
##########
File path: docs/content.zh/docs/dev/table/sql/queries/window-join.md
##########
@@ -23,7 +23,7 @@ under the License.
-->
# Window Join
-{{< label Streaming >}}
+{{< label Batch >}} {{< label Streaming >}}
A window join adds the dimension of time into the join criteria themselves. In
doing so, the window join joins the elements of two streams that share a common
key and lie in the same window. The semantic of window join is same to the
[DataStream window join]({{< ref "docs/dev/datastream/operators/joining"
>}}#window-join)
Review comment:
```suggestion
A window join adds the dimension of time into the join criteria themselves.
In doing so, the window join joins the elements of two streams that share a
common key and are in the same window. The semantic of window join is same to
the [DataStream window join]({{< ref "docs/dev/datastream/operators/joining"
>}}#window-join)
```
##########
File path: docs/content/docs/dev/table/sql/queries/window-tvf.md
##########
@@ -48,15 +48,21 @@ See more how to apply further computations based on
windowing TVF:
## Window Functions
-Apache Flink provides 3 built-in windowing TVFs: `TUMBLE`, `HOP` and
`CUMULATE`. The return value of windowing TVF is a new relation that includes
all columns of original relation as well as additional 3 columns named
"window_start", "window_end", "window_time" to indicate the assigned window.
The "window_time" field is a [time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) of the window after windowing
TVF which can be used in subsequent time-based operations, e.g. another
windowing TVF, or <a href="{{< ref "docs/dev/table/sql/queries/joins"
>}}#interval-joins">interval joins</a>, <a href="{{< ref
"docs/dev/table/sql/queries/over-agg" >}}">over aggregations</a>. The value of
`window_time` always equal to `window_end - 1ms`.
+Apache Flink provides 3 built-in windowing TVFs: `TUMBLE`, `HOP` and
`CUMULATE`. The return value of windowing TVF is a new relation that includes
all columns of original relation as well as additional 3 columns named
"window_start", "window_end", "window_time" to indicate the assigned window.
+For SQL queries on streaming tables, the "window_time" field is a [time
attributes]({{< ref "docs/dev/table/concepts/time_attributes" >}}) of the
window.
+For SQL queries on streaming tables, the "window_time" field is an attribute
of type `TIMESTAMP` or `TIMESTAMP_LTZ` based on input time field type.
Review comment:
Do you mean `SQL queries on batch tables`? Since the line above is also
about streaming tables?
##########
File path: docs/content.zh/docs/dev/table/sql/queries/window-tvf.md
##########
@@ -48,15 +48,21 @@ See more how to apply further computations based on
windowing TVF:
## Window Functions
-Apache Flink provides 3 built-in windowing TVFs: `TUMBLE`, `HOP` and
`CUMULATE`. The return value of windowing TVF is a new relation that includes
all columns of original relation as well as additional 3 columns named
"window_start", "window_end", "window_time" to indicate the assigned window.
The "window_time" field is a [time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) of the window after windowing
TVF which can be used in subsequent time-based operations, e.g. another
windowing TVF, or <a href="{{< ref "docs/dev/table/sql/queries/joins"
>}}#interval-joins">interval joins</a>, <a href="{{< ref
"docs/dev/table/sql/queries/over-agg" >}}">over aggregations</a>. The value of
`window_time` always equal to `window_end - 1ms`.
+Apache Flink provides 3 built-in windowing TVFs: `TUMBLE`, `HOP` and
`CUMULATE`. The return value of windowing TVF is a new relation that includes
all columns of original relation as well as additional 3 columns named
"window_start", "window_end", "window_time" to indicate the assigned window.
+For SQL queries on streaming tables, the "window_time" field is a [time
attributes]({{< ref "docs/dev/table/concepts/time_attributes" >}}) of the
window.
+For SQL queries on streaming tables, the "window_time" field is an attribute
of type `TIMESTAMP` or `TIMESTAMP_LTZ` based on input time field type.
Review comment:
Do you mean `SQL queries on batch tables`? Since the line above is also
about streaming tables?
##########
File path: docs/content/docs/dev/table/sql/queries/window-join.md
##########
@@ -23,7 +23,7 @@ under the License.
-->
# Window Join
-{{< label Streaming >}}
+{{< label Batch >}} {{< label Streaming >}}
A window join adds the dimension of time into the join criteria themselves. In
doing so, the window join joins the elements of two streams that share a common
key and lie in the same window. The semantic of window join is same to the
[DataStream window join]({{< ref "docs/dev/datastream/operators/joining"
>}}#window-join)
Review comment:
```suggestion
A window join adds the dimension of time into the join criteria themselves.
In doing so, the window join joins the elements of two streams that share a
common key and are in the same window. The semantic of window join is same to
the [DataStream window join]({{< ref "docs/dev/datastream/operators/joining"
>}}#window-join)
```
##########
File path: docs/content.zh/docs/dev/table/sql/queries/window-agg.md
##########
@@ -40,7 +40,9 @@ Unlike other aggregations on continuous tables, window
aggregation do not emit i
### Windowing TVFs
-Flink supports `TUMBLE`, `HOP` and `CUMULATE` types of window aggregations,
which can be defined on either [event or processing time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}). See [Windowing TVF]({{< ref
"docs/dev/table/sql/queries/window-tvf" >}}) for more windowing functions
information.
+Flink supports `TUMBLE`, `HOP` and `CUMULATE` types of window aggregations.
+For SQL queries on streaming tables, the time attribute field of window table
function must be on either [event or processing time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}). See [Windowing TVF]({{< ref
"docs/dev/table/sql/queries/window-tvf" >}}) for more windowing functions
information.
Review comment:
```suggestion
For SQL queries on streaming tables, the time attribute field of a window
table-valued function must be on either [event or processing time
attributes]({{< ref "docs/dev/table/concepts/time_attributes" >}}). See
[Windowing TVF]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) for more
windowing functions information.
```
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