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commit 369e3b9feac318802eff5d9655157297d963a7c7 Author: HaiYang Chen <[email protected]> AuthorDate: Wed Feb 1 23:19:39 2023 +0800 [FLINK-30744][docs-zh] Translate "Window Top-N" page of "Queries" This closes #21824 --- .../docs/dev/table/sql/queries/window-topn.md | 34 +++++++++++----------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/content.zh/docs/dev/table/sql/queries/window-topn.md b/docs/content.zh/docs/dev/table/sql/queries/window-topn.md index b938621936a..828b4e40803 100644 --- a/docs/content.zh/docs/dev/table/sql/queries/window-topn.md +++ b/docs/content.zh/docs/dev/table/sql/queries/window-topn.md @@ -22,20 +22,20 @@ specific language governing permissions and limitations under the License. --> -# Window Top-N +# 窗口 Top-N {{< label Batch >}} {{< label Streaming >}} -Window Top-N is a special [Top-N]({{< ref "docs/dev/table/sql/queries/topn" >}}) which returns the N smallest or largest values for each window and other partitioned keys. +窗口 Top-N 是特殊的 [Top-N]({{< ref "docs/dev/table/sql/queries/topn" >}})，它返回每个窗口和其他分区键的N个最小或最大值。 -For streaming queries, unlike regular Top-N on continuous tables, window Top-N does not emit intermediate results but only a final result, the total top N records at the end of the window. Moreover, window Top-N purges all intermediate state when no longer needed. -Therefore, window Top-N queries have better performance if users don't need results updated per record. Usually, Window Top-N is used with [Windowing TVF]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) directly. Besides, Window Top-N could be used with other operations based on [Windowing TVF]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}), such as [Window Aggregation]({{< ref "docs/dev/table/sql/queries/window-agg" >}}), [Window TopN]({{< ref "docs/dev/table/sql/queries/wind [...] +对于流式查询，与持续查询的普通Top-N不同，它只在窗口最后返回汇总的Top-N数据，不会产生中间结果。窗口 Top-N 会清除不需要的中间状态。 +因此，窗口 Top-N 查询在用户不需要更新结果时，性能较好。通常，窗口 Top-N 直接用于 [窗口表值函数]({{< ref "docs/dev/table/sql/queries/window-tvf" >}})上，另外，窗口 Top-N 可以用于基于 [窗口表值函数]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) 的操作。比如 [窗口聚合]({{< ref "docs/dev/table/sql/queries/window-agg" >}})，[窗口 Top-N]({{< ref "docs/dev/table/sql/queries/window-topn">}}) 和 [窗口关联]({{< ref "docs/dev/table/sql/queries/window-join">}})。 -Window Top-N can be defined in the same syntax as regular Top-N, see [Top-N documentation]({{< ref "docs/dev/table/sql/queries/topn" >}}) for more information. -Besides that, Window Top-N requires the `PARTITION BY` clause contains `window_start` and `window_end` columns of the relation applied [Windowing TVF]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) or [Window Aggregation]({{< ref "docs/dev/table/sql/queries/window-agg" >}}). -Otherwise, the optimizer won’t be able to translate the query. +窗口 Top-N 的语法和普通的 Top-N 相同，更多信息参见：[Top-N 文档]({{< ref "docs/dev/table/sql/queries/topn" >}})。 +除此之外，窗口 Top-N 需要 `PARTITION BY` 子句包含 [窗口表值函数]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) 或 [窗口聚合]({{< ref "docs/dev/table/sql/queries/window-agg" >}}) 产生的 `window_start` 和 `window_end`。 +否则优化器无法翻译。 -The following shows the syntax of the Window Top-N statement: +下面展示了窗口 Top-N 的语法： ```sql SELECT [column_list] @@ -47,11 +47,11 @@ FROM ( WHERE rownum <= N [AND conditions] ``` -## Example +## 示例 -### Window Top-N follows after Window Aggregation +### 在窗口聚合后进行窗口 Top-N -The following example shows how to calculate Top 3 suppliers who have the highest sales for every tumbling 10 minutes window. +下面的示例展示了在10分钟的滚动窗口上计算销售额位列前三的供应商。 ```sql -- tables must have time attribute, e.g. `bidtime` in this table @@ -102,11 +102,11 @@ Flink SQL> SELECT * +------------------+------------------+-------------+-------+-----+--------+ ``` -*Note: in order to better understand the behavior of windowing, we simplify the displaying of timestamp values to not show the trailing zeros, e.g. `2020-04-15 08:05` should be displayed as `2020-04-15 08:05:00.000` in Flink SQL Client if the type is `TIMESTAMP(3)`.* +*注意： 为了更好地理解窗口行为，这里把 timestamp 值后面的0去掉了。例如：在 Flink SQL Client 中，如果类型是 `TIMESTAMP(3)` ，`2020-04-15 08:05` 应该显示成 `2020-04-15 08:05:00.000` 。* -### Window Top-N follows after Windowing TVF +### 在窗口表值函数后进行窗口 Top-N -The following example shows how to calculate Top 3 items which have the highest price for every tumbling 10 minutes window. +下面的示例展示了在10分钟的滚动窗口上计算价格位列前三的数据。 ```sql Flink SQL> SELECT * @@ -127,11 +127,11 @@ Flink SQL> SELECT * +------------------+-------+------+-------------+------------------+------------------+--------+ ``` -*Note: in order to better understand the behavior of windowing, we simplify the displaying of timestamp values to not show the trailing zeros, e.g. `2020-04-15 08:05` should be displayed as `2020-04-15 08:05:00.000` in Flink SQL Client if the type is `TIMESTAMP(3)`.* +*注意： 为了更好地理解窗口行为，这里把 timestamp 值后面的0去掉了。例如：在 Flink SQL Client 中，如果类型是 `TIMESTAMP(3)` ，`2020-04-15 08:05` 应该显示成 `2020-04-15 08:05:00.000` 。* -## Limitation +## 限制 -Currently, Flink only supports Window Top-N follows after [Windowing TVF]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) with Tumble Windows, Hop Windows and Cumulate Windows. Window Top-N follows after [Windowing TVF]({{< ref "docs/dev/table/sql/queries/window-tvf" >}}) with Session windows will be supported in the near future. +目前，Flink只支持在滚动，滑动和累计 [窗口表值函数]({{< ref "docs/dev/table/sql/queries/window-tvf" >}})后进行窗口 Top-N。会话窗口不久之后就会支持。 {{< top >}}
