yljee commented on a change in pull request #16316:
URL: https://github.com/apache/flink/pull/16316#discussion_r671037817
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File path: docs/content.zh/docs/dev/table/tableApi.md
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@@ -28,25 +28,24 @@ under the License.
# Table API
-The Table API is a unified, relational API for stream and batch processing.
Table API queries can be run on batch or streaming input without modifications.
The Table API is a super set of the SQL language and is specially designed for
working with Apache Flink. The Table API is a language-integrated API for
Scala, Java and Python. Instead of specifying queries as String values as
common with SQL, Table API queries are defined in a language-embedded style in
Java, Scala or Python with IDE support like autocompletion and syntax
validation.
+Table API 是批处理和流处理的统一的关系 API。Table API 的查询不需要修改代码就可以采用批输入或流输入来运行。Table API 是
SQL 语言的超集，并且是针对 Apache Flink 专门设计的。Table API 集成了 Scala，Java 和 Python 语言的
API。Table API 的查询是使用 Java，Scala 或 Python 语言嵌入的风格定义的，有诸如自动补全和语法校验的 IDE
支持，而不是像普通 SQL 一样使用字符串类型的值来指定查询。
-The Table API shares many concepts and parts of its API with Flink's SQL
integration. Have a look at the [Common Concepts & API]({{< ref
"docs/dev/table/common" >}}) to learn how to register tables or to create a
`Table` object. The [Streaming Concepts]({{< ref
"docs/dev/table/concepts/overview" >}}) pages discuss streaming specific
concepts such as dynamic tables and time attributes.
+Table API 和 Flink SQL 共享许多概念以及部分集成的 API。通过查看 [公共概念 & API]({{< ref
"docs/dev/table/common" >}}) 来学习如何注册表或如何创建一个 `表` 对象。 [流概念]({{< ref
"docs/dev/table/concepts/overview" >}})页面讨论了诸如动态表和时间属性等流特有的概念。
-The following examples assume a registered table called `Orders` with
attributes `(a, b, c, rowtime)`. The `rowtime` field is either a logical [time
attribute]({{< ref "docs/dev/table/concepts/time_attributes" >}}) in streaming
or a regular timestamp field in batch.
+下面的例子中假定有一张叫 `Orders` 的表，表中有属性 `(a, b, c, rowtime)` 。 `rowtime`
字段是流任务中的逻辑[时间属性]({{< ref "docs/dev/table/concepts/time_attributes"
>}})或是批任务中的普通时间戳字段。
-
-Overview & Examples
+概述 & 例子
-----------------------------
-The Table API is available for Scala, Java and Python. The Scala Table API
leverages on Scala expressions, the Java Table API supports both Expression DSL
and strings which are parsed and converted into equivalent expressions, the
Python Table API currently only supports strings which are parsed and converted
into equivalent expressions.
+Table API 支持 Scala, Java 和 Python 语言。Scala 语言的 Table API 利用了 Scala 表达式，Java
语言的 Table API 支持 DSL 表达式和解析并转换为等价表达式的字符串，Python 语言的 Table API
仅支持解析并转换为等价表达式的字符串。
-The following example shows the differences between the Scala, Java and Python
Table API. The table program is executed in a batch environment. It scans the
`Orders` table, groups by field `a`, and counts the resulting rows per group.
+下面的例子展示了 Scala、Java 和 Python 语言的 Table API 的不同之处。表程序是在批环境下执行的。程序扫描了 `Orders`
表，通过字段 `a` 进行分组，并计算了每组结果的行数。
{{< tabs "8ffbc88b-54d7-4936-9a53-b63cb22c4a56" >}}
{{< tab "Java" >}}
-The Java Table API is enabled by importing
`org.apache.flink.table.api.java.*`. The following example shows how a Java
Table API program is constructed and how expressions are specified as strings.
-For the Expression DSL it is also necessary to import static
`org.apache.flink.table.api.Expressions.*`
+ Java 的 Table API 通过引入 `org.apache.flink.table.api.java.*` 来使用。下面的例子展示了如何创建一个
Java 的 Table API 程序，以及表达式是如何指定为字符串的。
+使用DSL表达式时也需要引入静态的 `org.apache.flink.table.api.Expressions.*`。
```java
import org.apache.flink.table.api.*;
Review comment:
I think this is a very long page. Maybe we can translate all the
comments in later version ？
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