shuiqiangchen commented on code in PR #21823:
URL: https://github.com/apache/flink/pull/21823#discussion_r1101043029
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docs/content.zh/docs/dev/table/sql/queries/window-tvf.md:
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@@ -22,61 +22,61 @@ specific language governing permissions and limitations
under the License.
-->
-# Windowing table-valued functions (Windowing TVFs)
+# 窗口表值函数（Windowing TVFs）
{{< label Batch >}} {{< label Streaming >}}
-Windows are at the heart of processing infinite streams. Windows split the
stream into “buckets” of finite size, over which we can apply computations.
This document focuses on how windowing is performed in Flink SQL and how the
programmer can benefit to the maximum from its offered functionality.
+窗口是处理无限流的核心。窗口把流分割为有限大小的 “桶”，这样就可以在其之上进行计算。本文档聚焦于窗口在 Flink SQL
中是如何工作的，编程人员如何最大化地利用好它。
-Apache Flink provides several window table-valued functions (TVF) to divide
the elements of your table into windows, including:
+Apache Flink 提供了如下 `窗口表值函数`（TVF）把表的数据划分到窗口中：
-- [Tumble Windows](#tumble)
-- [Hop Windows](#hop)
-- [Cumulate Windows](#cumulate)
-- Session Windows (will be supported soon)
+- [滚动窗口](#tumble)
+- [滑动窗口](#hop)
+- [累计窗口](#cumulate)
+- 会话窗口 (很快就能支持了)
-Note that each element can logically belong to more than one window, depending
on the windowing table-valued function you use. For example, HOP windowing
creates overlapping windows wherein a single element can be assigned to
multiple windows.
+注意：每一个元素可以被逻辑上应用于超过一个窗口。这取决于使用的 `窗口表值函数`。例如：滑动窗口可以把单个元素分配给多个窗口。
Review Comment:
逻辑上，每个元素可以应用于一个或多个窗口，这取决于所使用的`窗口表值函数`。
```suggestion
注意：每一个元素可以被逻辑上应用于超过一个窗口。这取决于使用的 `窗口表值函数`。例如：滑动窗口可以把单个元素分配给多个窗口。
```
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