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new 6b6d0f4 [hotfix][docs] Corrected tvf description and format
6b6d0f4 is described below
commit 6b6d0f4f08c507b7116dc50485bb4283453b489c
Author: mans2singh <[email protected]>
AuthorDate: Mon Jun 28 21:28:02 2021 -0400
[hotfix][docs] Corrected tvf description and format
This closes #16230
---
docs/content.zh/docs/dev/table/sql/queries/window-tvf.md | 8 ++++----
docs/content/docs/dev/table/sql/queries/window-tvf.md | 8 ++++----
2 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/docs/content.zh/docs/dev/table/sql/queries/window-tvf.md
b/docs/content.zh/docs/dev/table/sql/queries/window-tvf.md
index 3738e58..2110757 100644
--- a/docs/content.zh/docs/dev/table/sql/queries/window-tvf.md
+++ b/docs/content.zh/docs/dev/table/sql/queries/window-tvf.md
@@ -48,7 +48,7 @@ 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. ano [...]
+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. a [...]
### TUMBLE
@@ -142,7 +142,7 @@ For example, you could have windows of size 10 minutes that
slides by 5 minutes.
The `HOP` function assigns windows that cover rows within the interval of size
and shifting every slide based on a [time attribute]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) column. The return value of
`HOP` 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 original time attribute "timecol" will be a
regular timestamp column after windowing TVF.
-`HOP` takes three required parameters.
+`HOP` takes four required parameters.
```sql
HOP(TABLE data, DESCRIPTOR(timecol), slide, size [, offset ])
@@ -214,7 +214,7 @@ For example, you could have a cumulating window for 1 hour
step and 1 day max si
The `CUMULATE` functions assigns windows based on a [time attribute]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) column. The return value of
`CUMULATE` 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 original time attribute "timecol" will be
a regular timestamp column after window TVF.
-`CUMULATE` takes three required parameters.
+`CUMULATE` takes four required parameters.
```sql
CUMULATE(TABLE data, DESCRIPTOR(timecol), step, size)
@@ -223,7 +223,7 @@ CUMULATE(TABLE data, DESCRIPTOR(timecol), step, size)
- `data`: is a table parameter that can be any relation with an time attribute
column.
- `timecol`: is a column descriptor indicating which [time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) column of data should be mapped
to tumbling windows.
- `step`: is a duration specifying the increased window size between the end
of sequential cumulating windows.
-- `size`: is a duration specifying the max width of the cumulating windows.
size must be an integral multiple of step .
+- `size`: is a duration specifying the max width of the cumulating windows.
`size` must be an integral multiple of `step`.
Here is an example invocation on the Bid table:
diff --git a/docs/content/docs/dev/table/sql/queries/window-tvf.md
b/docs/content/docs/dev/table/sql/queries/window-tvf.md
index 58159c2..e41922f 100644
--- a/docs/content/docs/dev/table/sql/queries/window-tvf.md
+++ b/docs/content/docs/dev/table/sql/queries/window-tvf.md
@@ -48,7 +48,7 @@ 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. ano [...]
+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. a [...]
### TUMBLE
@@ -142,7 +142,7 @@ For example, you could have windows of size 10 minutes that
slides by 5 minutes.
The `HOP` function assigns windows that cover rows within the interval of size
and shifting every slide based on a [time attribute]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) column. The return value of
`HOP` 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 original time attribute "timecol" will be a
regular timestamp column after windowing TVF.
-`HOP` takes three required parameters.
+`HOP` takes four required parameters.
```sql
HOP(TABLE data, DESCRIPTOR(timecol), slide, size [, offset ])
@@ -214,7 +214,7 @@ For example, you could have a cumulating window for 1 hour
step and 1 day max si
The `CUMULATE` functions assigns windows based on a [time attribute]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) column. The return value of
`CUMULATE` 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 original time attribute "timecol" will be
a regular timestamp column after window TVF.
-`CUMULATE` takes three required parameters.
+`CUMULATE` takes four required parameters.
```sql
CUMULATE(TABLE data, DESCRIPTOR(timecol), step, size)
@@ -223,7 +223,7 @@ CUMULATE(TABLE data, DESCRIPTOR(timecol), step, size)
- `data`: is a table parameter that can be any relation with an time attribute
column.
- `timecol`: is a column descriptor indicating which [time attributes]({{< ref
"docs/dev/table/concepts/time_attributes" >}}) column of data should be mapped
to tumbling windows.
- `step`: is a duration specifying the increased window size between the end
of sequential cumulating windows.
-- `size`: is a duration specifying the max width of the cumulating windows.
size must be an integral multiple of step .
+- `size`: is a duration specifying the max width of the cumulating windows.
`size` must be an integral multiple of `step`.
Here is an example invocation on the Bid table:
