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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2020/07/06 05:40:11 UTC
[GitHub] [flink] klion26 commented on a change in pull request #12798: [FLINK-16087][docs-zh] Translate "Detecting Patterns" page of "Streaming Concepts" into Chinese
klion26 commented on a change in pull request #12798:
URL: https://github.com/apache/flink/pull/12798#discussion_r449984080
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -24,28 +24,17 @@ specific language governing permissions and limitations
under the License.
-->
-It is a common use case to search for a set of event patterns, especially in case of data streams.
-Flink comes with a [complex event processing (CEP) library]({{ site.baseurl }}/dev/libs/cep.html)
-which allows for pattern detection in event streams. Furthermore, Flink's SQL API provides a
-relational way of expressing queries with a large set of built-in functions and rule-based
-optimizations that can be used out of the box.
-
-In December 2016, the International Organization for Standardization (ISO) released a new version
-of the SQL standard which includes _Row Pattern Recognition in SQL_
-([ISO/IEC TR 19075-5:2016](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)).
-It allows Flink to consolidate CEP and SQL API using the `MATCH_RECOGNIZE` clause for complex event
-processing in SQL.
-
-A `MATCH_RECOGNIZE` clause enables the following tasks:
-* Logically partition and order the data that is used with the `PARTITION BY` and `ORDER BY`
- clauses.
-* Define patterns of rows to seek using the `PATTERN` clause. These patterns use a syntax similar to
- that of regular expressions.
-* The logical components of the row pattern variables are specified in the `DEFINE` clause.
-* Define measures, which are expressions usable in other parts of the SQL query, in the `MEASURES`
- clause.
-
-The following example illustrates the syntax for basic pattern recognition:
+搜索一组事件模式（event pattern）是一种常见的用例，尤其是在数据流的情况下。Flink 提供 [复杂事件处理（CEP）库]({% link dev/libs/cep.zh.md %})，该库允许在事件流中进行模式检测。此外，Flink 的 SQL API 提供了一种关系式的查询表达方式，其中包含一大组内置函数和基于规则的优化，可以开箱即用。
Review comment:
`一大组内置` -> `大量内置` 会更好一些吗
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -24,28 +24,17 @@ specific language governing permissions and limitations
under the License.
-->
-It is a common use case to search for a set of event patterns, especially in case of data streams.
-Flink comes with a [complex event processing (CEP) library]({{ site.baseurl }}/dev/libs/cep.html)
-which allows for pattern detection in event streams. Furthermore, Flink's SQL API provides a
-relational way of expressing queries with a large set of built-in functions and rule-based
-optimizations that can be used out of the box.
-
-In December 2016, the International Organization for Standardization (ISO) released a new version
-of the SQL standard which includes _Row Pattern Recognition in SQL_
-([ISO/IEC TR 19075-5:2016](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)).
-It allows Flink to consolidate CEP and SQL API using the `MATCH_RECOGNIZE` clause for complex event
-processing in SQL.
-
-A `MATCH_RECOGNIZE` clause enables the following tasks:
-* Logically partition and order the data that is used with the `PARTITION BY` and `ORDER BY`
- clauses.
-* Define patterns of rows to seek using the `PATTERN` clause. These patterns use a syntax similar to
- that of regular expressions.
-* The logical components of the row pattern variables are specified in the `DEFINE` clause.
-* Define measures, which are expressions usable in other parts of the SQL query, in the `MEASURES`
- clause.
-
-The following example illustrates the syntax for basic pattern recognition:
+搜索一组事件模式（event pattern）是一种常见的用例，尤其是在数据流的情况下。Flink 提供 [复杂事件处理（CEP）库]({% link dev/libs/cep.zh.md %})，该库允许在事件流中进行模式检测。此外，Flink 的 SQL API 提供了一种关系式的查询表达方式，其中包含一大组内置函数和基于规则的优化，可以开箱即用。
+
+2016年12月，国际标准化组织（ISO）发布了新版本的 SQL 标准，其中包括在 _SQL 中的行模式识别（Row Pattern Recognition in SQL）_([ISO/IEC TR 19075-5:2016](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip))。它允许 Flink 使用 `MATCH_RECOGNIZE` 子句融合 CEP 和 SQL API，以便在 SQL 中进行复杂事件处理。
Review comment:
```suggestion
2016 年 12 月，国际标准化组织（ISO）发布了新版本的 SQL 标准，其中包括在 _SQL 中的行模式识别（Row Pattern Recognition in SQL）_([ISO/IEC TR 19075-5:2016](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip))。它允许 Flink 使用 `MATCH_RECOGNIZE` 子句融合 CEP 和 SQL API，以便在 SQL 中进行复杂事件处理。
```
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -65,24 +54,19 @@ FROM MyTable
) AS T
{% endhighlight %}
-This page will explain each keyword in more detail and will illustrate more complex examples.
+本页将更详细地解释每个关键字，并演示说明更复杂的示例。
-<span class="label label-danger">Attention</span> Flink's implementation of the `MATCH_RECOGNIZE`
-clause is a subset of the full standard. Only those features documented in the following sections
-are supported. Since the development is still in an early phase, please also take a look at the
-[known limitations](#known-limitations).
+<span class="label label-danger">注意</span> Flink 的 `MATCH_RECOGNIZE` 子句实现是完整标准的一个子集。仅支持以下部分中记录的功能。由于开发仍处于初期阶段，请查看[已知的局限](#已知的局限)。
Review comment:
标题的链接一般建议增加 `<a>` 标签，具体可以参考 [cwiki](https://cwiki.apache.org/confluence/display/FLINK/Flink+Translation+Specifications) 的第 4 点
本文其他地方也一样
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -24,28 +24,17 @@ specific language governing permissions and limitations
under the License.
-->
-It is a common use case to search for a set of event patterns, especially in case of data streams.
-Flink comes with a [complex event processing (CEP) library]({{ site.baseurl }}/dev/libs/cep.html)
-which allows for pattern detection in event streams. Furthermore, Flink's SQL API provides a
-relational way of expressing queries with a large set of built-in functions and rule-based
-optimizations that can be used out of the box.
-
-In December 2016, the International Organization for Standardization (ISO) released a new version
-of the SQL standard which includes _Row Pattern Recognition in SQL_
-([ISO/IEC TR 19075-5:2016](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip)).
-It allows Flink to consolidate CEP and SQL API using the `MATCH_RECOGNIZE` clause for complex event
-processing in SQL.
-
-A `MATCH_RECOGNIZE` clause enables the following tasks:
-* Logically partition and order the data that is used with the `PARTITION BY` and `ORDER BY`
- clauses.
-* Define patterns of rows to seek using the `PATTERN` clause. These patterns use a syntax similar to
- that of regular expressions.
-* The logical components of the row pattern variables are specified in the `DEFINE` clause.
-* Define measures, which are expressions usable in other parts of the SQL query, in the `MEASURES`
- clause.
-
-The following example illustrates the syntax for basic pattern recognition:
+搜索一组事件模式（event pattern）是一种常见的用例，尤其是在数据流的情况下。Flink 提供 [复杂事件处理（CEP）库]({% link dev/libs/cep.zh.md %})，该库允许在事件流中进行模式检测。此外，Flink 的 SQL API 提供了一种关系式的查询表达方式，其中包含一大组内置函数和基于规则的优化，可以开箱即用。
+
+2016年12月，国际标准化组织（ISO）发布了新版本的 SQL 标准，其中包括在 _SQL 中的行模式识别（Row Pattern Recognition in SQL）_([ISO/IEC TR 19075-5:2016](https://standards.iso.org/ittf/PubliclyAvailableStandards/c065143_ISO_IEC_TR_19075-5_2016.zip))。它允许 Flink 使用 `MATCH_RECOGNIZE` 子句融合 CEP 和 SQL API，以便在 SQL 中进行复杂事件处理。
+
+`MATCH_RECOGNIZE` 子句启用以下任务：
+* 对与 `PARTITION BY` 和 `ORDER BY` 子句一起使用的数据进行逻辑分区和排序。
Review comment:
”对与 `PARTITION BY` 和 `ORDER BY` 子句“ -> "对 `PARTITION BY` 和 `ORDER BY` 子句" 会更好一些吗
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -65,24 +54,19 @@ FROM MyTable
) AS T
{% endhighlight %}
-This page will explain each keyword in more detail and will illustrate more complex examples.
+本页将更详细地解释每个关键字，并演示说明更复杂的示例。
-<span class="label label-danger">Attention</span> Flink's implementation of the `MATCH_RECOGNIZE`
-clause is a subset of the full standard. Only those features documented in the following sections
-are supported. Since the development is still in an early phase, please also take a look at the
-[known limitations](#known-limitations).
+<span class="label label-danger">注意</span> Flink 的 `MATCH_RECOGNIZE` 子句实现是完整标准的一个子集。仅支持以下部分中记录的功能。由于开发仍处于初期阶段，请查看[已知的局限](#已知的局限)。
* This will be replaced by the TOC
{:toc}
-Introduction and Examples
+介绍和示例
-------------------------
-### Installation Guide
+### 安装指南
-The pattern recognition feature uses the Apache Flink's CEP library internally. In order to be able
-to use the `MATCH_RECOGNIZE` clause, the library needs to be added as a dependency to your Maven
-project.
+模式识别特性在使用 Apache Flink 内部的 CEP 库。为了能够使用 `MATCH_RECOGNIZE` 子句，需要将库作为依赖项添加到 Maven 项目中。
Review comment:
```suggestion
模式识别特性使用 Apache Flink 内部的 CEP 库。为了能够使用 `MATCH_RECOGNIZE` 子句，需要将库作为依赖项添加到 Maven 项目中。
```
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -92,52 +76,39 @@ project.
</dependency>
{% endhighlight %}
-Alternatively, you can also add the dependency to the cluster classpath (see the
-[dependency section]({{ site.baseurl}}/dev/project-configuration.html) for more information).
+或者，也可以将依赖项添加到集群类路径（查看[依赖部分]({% link dev/project-configuration.zh.md %})获取更多信息）。
-If you want to use the `MATCH_RECOGNIZE` clause in the
-[SQL Client]({{ site.baseurl}}/dev/table/sqlClient.html), you don't have to do anything as all the
-dependencies are included by default.
+如果你想在 [SQL Client]({% link dev/table/sqlClient.zh.md %}) 中使用 `MATCH_RECOGNIZE` 子句，你无需执行任何操作，因为默认情况下包含所有依赖项。
-### SQL Semantics
+### SQL 语义
-Every `MATCH_RECOGNIZE` query consists of the following clauses:
+每个 `MATCH_RECOGNIZE` 查询都包含以下子句：
-* [PARTITION BY](#partitioning) - defines the logical partitioning of the table; similar to a
- `GROUP BY` operation.
-* [ORDER BY](#order-of-events) - specifies how the incoming rows should be ordered; this is
- essential as patterns depend on an order.
-* [MEASURES](#define--measures) - defines output of the clause; similar to a `SELECT` clause.
-* [ONE ROW PER MATCH](#output-mode) - output mode which defines how many rows per match should be
- produced.
-* [AFTER MATCH SKIP](#after-match-strategy) - specifies where the next match should start; this is
- also a way to control how many distinct matches a single event can belong to.
-* [PATTERN](#defining-a-pattern) - allows constructing patterns that will be searched for using a
- _regular expression_-like syntax.
-* [DEFINE](#define--measures) - this section defines the conditions that the pattern variables must
- satisfy.
+* [PARTITION BY](#分区) - 定义表的逻辑分区；类似于 `GROUP BY` 操作。
Review comment:
这里业内的跳转建议使用 `<a> `标签
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -92,52 +76,39 @@ project.
</dependency>
{% endhighlight %}
-Alternatively, you can also add the dependency to the cluster classpath (see the
-[dependency section]({{ site.baseurl}}/dev/project-configuration.html) for more information).
+或者，也可以将依赖项添加到集群类路径（查看[依赖部分]({% link dev/project-configuration.zh.md %})获取更多信息）。
Review comment:
这个地方 classpath 不翻译的话是否可以呢？classpath 更像是一个专有名词。
`依赖部分` 这里能否换成一个更具体的名词呢？目标页面有很多个 依赖的 section
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -311,10 +247,8 @@ symbol rowtime price tax
'ACME' '01-Apr-11 10:00:11' 30 1
{% endhighlight %}
-The query will accumulate events as part of the pattern variable `A` as long as the average price
-of them does not exceed `15`. For example, such a limit exceeding happens at `01-Apr-11 10:00:04`.
-The following period exceeds the average price of `15` again at `01-Apr-11 10:00:11`. Thus the
-results for said query will be:
+只要事件的平均价格不超过 `15`，查询就会将事件作为模式变量 `A` 的一部分进行累积。
+例如，这种限制发生在`01-Apr-11 10：00：04`。接下来的时间段在 `01-Apr-11 10:00:11` 再次超过平均价格 `15`。因此，所述查询的结果将是：
Review comment:
```suggestion
例如，这种限制发生在 `01-Apr-11 10：00：04`。接下来的时间段在 `01-Apr-11 10:00:11` 再次超过平均价格 `15`。因此，所述查询的结果将是：
```
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -323,52 +257,43 @@ ACME 01-APR-11 10:00:00 01-APR-11 10:00:03 14.5
ACME 01-APR-11 10:00:05 01-APR-11 10:00:10 13.5
{% endhighlight %}
-<span class="label label-info">Note</span> Aggregations can be applied to expressions, but only if
-they reference a single pattern variable. Thus `SUM(A.price * A.tax)` is a valid one, but
-`AVG(A.price * B.tax)` is not.
+<span class="label label-info">注意</span> 聚合可以应用于表达式，但前提是它们引用单个模式变量。因此，`SUM(A.price * A.tax)` 是有效的，而 `AVG(A.price * B.tax)` 则是无效的。
-<span class="label label-danger">Attention</span> `DISTINCT` aggregations are not supported.
+<span class="label label-danger">注意</span> 不支持 `DISTINCT` 聚合。
-Defining a Pattern
+定义模式
------------------
-The `MATCH_RECOGNIZE` clause allows users to search for patterns in event streams using a powerful
-and expressive syntax that is somewhat similar to the widespread regular expression syntax.
+`MATCH_RECOGNIZE` 子句允许用户在事件流中使用功能强大、表达力强的语法搜索模式，这种语法与广泛使用的正则表达式语法有些相似。
-Every pattern is constructed from basic building blocks, called _pattern variables_, to which
-operators (quantifiers and other modifiers) can be applied. The whole pattern must be enclosed in
-brackets.
+每个模式都是由基本的构建块构造的，称为 _pattern variables_，可以应用算子（量词和其他修饰符）到这些模块中。整个模式必须用括号括起来。
-An example pattern could look like:
+示例模式如下所示：
{% highlight sql %}
PATTERN (A B+ C* D)
{% endhighlight %}
-One may use the following operators:
+可以使用以下算子：
+
+* _Concatenation_ - 像 `(A B)` 这样的模式意味着 `A` 和 `B` 之间的连接是严格的。因此，在它们之间不能存在没有映射到 `A` 或 `B` 的行。
+* _Quantifiers_ - 修改可以映射到模式变量的行数。
+ * `*` — _0_ 或者多行
+ * `+` — _1_ 或者多行
+ * `?` — _0_ 或者 _1_ 行
+ * `{ n }` — 严格 _n_ 行（_n > 0_）
+ * `{ n, }` — _n_ 或者更多行（_n ≥ 0_）
+ * `{ n, m }` — 在 _n_ 到 _m_（包含）行之间（_0 ≤ n ≤ m，0 < m_）
+ * `{ , m }` — 在 _0_ 到 _m_（包含）行之间（_m > 0_）
-* _Concatenation_ - a pattern like `(A B)` means that the contiguity is strict between `A` and `B`.
- Therefore, there can be no rows that were not mapped to `A` or `B` in between.
-* _Quantifiers_ - modify the number of rows that can be mapped to the pattern variable.
- * `*` — _0_ or more rows
- * `+` — _1_ or more rows
- * `?` — _0_ or _1_ rows
- * `{ n }` — exactly _n_ rows (_n > 0_)
- * `{ n, }` — _n_ or more rows (_n ≥ 0_)
- * `{ n, m }` — between _n_ and _m_ (inclusive) rows (_0 ≤ n ≤ m, 0 < m_)
- * `{ , m }` — between _0_ and _m_ (inclusive) rows (_m > 0_)
-<span class="label label-danger">Attention</span> Patterns that can potentially produce an empty
-match are not supported. Examples of such patterns are `PATTERN (A*)`, `PATTERN (A? B*)`,
-`PATTERN (A{0,} B{0,} C*)`, etc.
+<span class="label label-danger">注意</span> 不支持可能产生空匹配的模式。此类模式的示例如 `PATTERN (A*)`，`PATTERN (A? B*)`，`PATTERN (A{0,} B{0,} C*)` 等。
-### Greedy & Reluctant Quantifiers
+### 贪婪和勉强量词
Review comment:
个人感觉：这两个翻译可以再优化下
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -180,99 +150,65 @@ FROM Ticker
) MR;
{% endhighlight %}
-The query partitions the `Ticker` table by the `symbol` column and orders it by the `rowtime`
-time attribute.
+此查询将 `Ticker` 表按照 `symbol` 列进行分区并按照 `rowtime` 属性进行排序。
-The `PATTERN` clause specifies that we are interested in a pattern with a starting event `START_ROW`
-that is followed by one or more `PRICE_DOWN` events and concluded with a `PRICE_UP` event. If such
-a pattern can be found, the next pattern match will be seeked at the last `PRICE_UP` event as
-indicated by the `AFTER MATCH SKIP TO LAST` clause.
+`PATTERN` 子句指定我们对以下模式感兴趣：该模式具有开始事件 `START_ROW`，然后是一个或多个 `PRICE_DOWN` 事件，并以 `PRICE_UP` 事件结束。如果可以找到这样的模式，如 `AFTER MATCH SKIP TO LAST` 子句所示，则从最后一个 `PRICE_UP` 事件开始寻找下一个模式匹配。
-The `DEFINE` clause specifies the conditions that need to be met for a `PRICE_DOWN` and `PRICE_UP`
-event. Although the `START_ROW` pattern variable is not present it has an implicit condition that
-is evaluated always as `TRUE`.
+`DEFINE` 子句指定 `PRICE_DOWN` 和 `PRICE_UP` 事件需要满足的条件。尽管不存在 `START_ROW` 模式变量，但它具有一个始终被评估为 `TRUE` 隐式条件。
-A pattern variable `PRICE_DOWN` is defined as a row with a price that is smaller than the price of
-the last row that met the `PRICE_DOWN` condition. For the initial case or when there is no last row
-that met the `PRICE_DOWN` condition, the price of the row should be smaller than the price of the
-preceding row in the pattern (referenced by `START_ROW`).
+模式变量 `PRICE_DOWN` 定义为价格小于满足 `PRICE_DOWN` 条件的最后一行价格的行。对于初始情况或没有满足 `PRICE_DOWN` 条件的最后一行时，该行的价格应小于该模式中前一行（由 `START_ROW` 引用）的价格。
-A pattern variable `PRICE_UP` is defined as a row with a price that is larger than the price of the
-last row that met the `PRICE_DOWN` condition.
+模变变量 `PRICE_UP` 定义为价格大于满足 `PRICE_DOWN` 条件的最后一行价格的行。
-This query produces a summary row for each period in which the price of a stock was continuously
-decreasing.
+此查询为股票价格持续下跌的每个期间生成摘要行。
-The exact representation of the output rows is defined in the `MEASURES` part of the query. The
-number of output rows is defined by the `ONE ROW PER MATCH` output mode.
+输出行的确切表示在查询的 `MEASURES` 部分中定义。输出行数由 `ONE ROW PER MATCH` 输出方式定义。
{% highlight text %}
symbol start_tstamp bottom_tstamp end_tstamp
========= ================== ================== ==================
ACME 01-APR-11 10:00:04 01-APR-11 10:00:07 01-APR-11 10:00:08
{% endhighlight %}
-The resulting row describes a period of falling prices that started at `01-APR-11 10:00:04` and
-achieved the lowest price at `01-APR-11 10:00:07` that increased again at `01-APR-11 10:00:08`.
+该行结果描述了从 `01-APR-11 10:00:04` 开始的价格下跌期，在 `01-APR-11 10:00:07` 达到最低价格，到 `01-APR-11 10:00:08` 再次上涨。
-Partitioning
+
+分区
------------
-It is possible to look for patterns in partitioned data, e.g., trends for a single ticker or a
-particular user. This can be expressed using the `PARTITION BY` clause. The clause is similar to
-using `GROUP BY` for aggregations.
+可以在分区数据中寻找模式，例如单个股票行情或特定用户的趋势。这可以用 `PARTITION BY` 子句来表示。该子句类似于对聚合使用 `GROUP BY`。
-<span class="label label-danger">Attention</span> It is highly advised to partition the incoming
-data because otherwise the `MATCH_RECOGNIZE` clause will be translated into a non-parallel operator
-to ensure global ordering.
+<span class="label label-danger">注意</span> 强烈建议对传入的数据进行分区，否则 `MATCH_RECOGNIZE` 子句将被转换为非并行算子，以确保全局排序。
-Order of Events
+事件顺序
---------------
-Apache Flink allows for searching for patterns based on time; either
-[processing time or event time](time_attributes.html).
+Apache Flink 可以根据时间（[processing time or event time]({% link dev/table/streaming/time_attributes.zh.md %})）进行模式搜索。
-In case of event time, the events are sorted before they are passed to the internal pattern state
-machine. As a consequence, the produced output will be correct regardless of the order in which
-rows are appended to the table. Instead, the pattern is evaluated in the order specified by the
-time contained in each row.
+如果是 event time，则在将事件传递到内部模式状态机之前对其进行排序。所以，无论行添加到表的顺序如何，生成的输出都是正确的。相反，模式是按照每行中包含的时间指定的顺序计算的。
-The `MATCH_RECOGNIZE` clause assumes a [time attribute](time_attributes.html) with ascending
-ordering as the first argument to `ORDER BY` clause.
+`MATCH_RECOGNIZE` 子句假定升序的 [time attribute]({% link dev/table/streaming/time_attributes.zh.md %}) 是 `ORDER BY` 子句的第一个参数。
-For the example `Ticker` table, a definition like `ORDER BY rowtime ASC, price DESC` is valid but
-`ORDER BY price, rowtime` or `ORDER BY rowtime DESC, price ASC` is not.
+对于示例 `Ticker` 表，诸如 `ORDER BY rowtime ASC, price DESC` 的定义是有效的，但 `ORDER BY price, rowtime` 或者 `ORDER BY rowtime DESC, price ASC` 是无效的。
Define & Measures
-----------------
-The `DEFINE` and `MEASURES` keywords have similar meanings to the `WHERE` and `SELECT` clauses in a
-simple SQL query.
+`DEFINE` 和 `MEASURES` 关键字与简单 SQL 查询中的 `WHERE` 和 `SELECT` 子句具有相近的含义。
-The `MEASURES` clause defines what will be included in the output of a matching pattern. It can
-project columns and define expressions for evaluation. The number of produced rows depends on the
-[output mode](#output-mode) setting.
+`MEASURES` 子句定义匹配模式的输出中要包含哪些内容。它可以投影列并定义表达式进行计算。产生的行数取决于 [output mode](#输出方式) 设置。
-The `DEFINE` clause specifies conditions that rows have to fulfill in order to be classified to a
-corresponding [pattern variable](#defining-a-pattern). If a condition is not defined for a pattern
-variable, a default condition will be used which evaluates to `true` for every row.
+`DEFINE` 子句指定行必须满足的条件才能被分类到相应的 [pattern variable](#定义模式)。如果没有为模式变量定义条件，则将使用对每一行的计算结果为 `true` 的默认条件。
-For a more detailed explanation about expressions that can be used in those clauses, please have a
-look at the [event stream navigation](#pattern-navigation) section.
+有关在这些子句中可使用的表达式的更详细的说明，请查看 [event stream navigation](#模式导航) 部分。
### Aggregations
-Aggregations can be used in `DEFINE` and `MEASURES` clauses. Both
-[built-in]({{ site.baseurl }}/dev/table/functions/systemFunctions.html) and custom
-[user defined]({{ site.baseurl }}/dev/table/functions/udfs.html) functions are supported.
+Aggregations 可以在 `DEFINE` 和 `MEASURES` 子句中使用。支持 [built-in]({% link dev/table/functions/systemFunctions.zh.md %}) 和用户 [user defined]({% link dev/table/functions/udfs.zh.md %}) 函数。
Review comment:
这里是否翻译成 `内置函数` 和 `用户自定义函数` 会更好呢？
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -720,8 +610,7 @@ LAST(variable.field, n)
{% endhighlight %}
</td>
<td>
- <p>Returns the value of the field from the event that was mapped to the <i>n</i>-th
- <i>last</i> element of the variable. The counting starts at the last element mapped.</p>
+ <p>返回映射到变量最后n个元素的事件中的字段值。计数从映射的最后一个元素开始。</p>
Review comment:
```suggestion
<p>返回映射到变量最后 n 个元素的事件中的字段值。计数从映射的最后一个元素开始。</p>
```
英文/数字 与 中文间需要有空格，本文其他地方也可以检查下
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -180,99 +150,65 @@ FROM Ticker
) MR;
{% endhighlight %}
-The query partitions the `Ticker` table by the `symbol` column and orders it by the `rowtime`
-time attribute.
+此查询将 `Ticker` 表按照 `symbol` 列进行分区并按照 `rowtime` 属性进行排序。
-The `PATTERN` clause specifies that we are interested in a pattern with a starting event `START_ROW`
-that is followed by one or more `PRICE_DOWN` events and concluded with a `PRICE_UP` event. If such
-a pattern can be found, the next pattern match will be seeked at the last `PRICE_UP` event as
-indicated by the `AFTER MATCH SKIP TO LAST` clause.
+`PATTERN` 子句指定我们对以下模式感兴趣：该模式具有开始事件 `START_ROW`，然后是一个或多个 `PRICE_DOWN` 事件，并以 `PRICE_UP` 事件结束。如果可以找到这样的模式，如 `AFTER MATCH SKIP TO LAST` 子句所示，则从最后一个 `PRICE_UP` 事件开始寻找下一个模式匹配。
-The `DEFINE` clause specifies the conditions that need to be met for a `PRICE_DOWN` and `PRICE_UP`
-event. Although the `START_ROW` pattern variable is not present it has an implicit condition that
-is evaluated always as `TRUE`.
+`DEFINE` 子句指定 `PRICE_DOWN` 和 `PRICE_UP` 事件需要满足的条件。尽管不存在 `START_ROW` 模式变量，但它具有一个始终被评估为 `TRUE` 隐式条件。
-A pattern variable `PRICE_DOWN` is defined as a row with a price that is smaller than the price of
-the last row that met the `PRICE_DOWN` condition. For the initial case or when there is no last row
-that met the `PRICE_DOWN` condition, the price of the row should be smaller than the price of the
-preceding row in the pattern (referenced by `START_ROW`).
+模式变量 `PRICE_DOWN` 定义为价格小于满足 `PRICE_DOWN` 条件的最后一行价格的行。对于初始情况或没有满足 `PRICE_DOWN` 条件的最后一行时，该行的价格应小于该模式中前一行（由 `START_ROW` 引用）的价格。
-A pattern variable `PRICE_UP` is defined as a row with a price that is larger than the price of the
-last row that met the `PRICE_DOWN` condition.
+模变变量 `PRICE_UP` 定义为价格大于满足 `PRICE_DOWN` 条件的最后一行价格的行。
-This query produces a summary row for each period in which the price of a stock was continuously
-decreasing.
+此查询为股票价格持续下跌的每个期间生成摘要行。
-The exact representation of the output rows is defined in the `MEASURES` part of the query. The
-number of output rows is defined by the `ONE ROW PER MATCH` output mode.
+输出行的确切表示在查询的 `MEASURES` 部分中定义。输出行数由 `ONE ROW PER MATCH` 输出方式定义。
{% highlight text %}
symbol start_tstamp bottom_tstamp end_tstamp
========= ================== ================== ==================
ACME 01-APR-11 10:00:04 01-APR-11 10:00:07 01-APR-11 10:00:08
{% endhighlight %}
-The resulting row describes a period of falling prices that started at `01-APR-11 10:00:04` and
-achieved the lowest price at `01-APR-11 10:00:07` that increased again at `01-APR-11 10:00:08`.
+该行结果描述了从 `01-APR-11 10:00:04` 开始的价格下跌期，在 `01-APR-11 10:00:07` 达到最低价格，到 `01-APR-11 10:00:08` 再次上涨。
-Partitioning
+
+分区
------------
-It is possible to look for patterns in partitioned data, e.g., trends for a single ticker or a
-particular user. This can be expressed using the `PARTITION BY` clause. The clause is similar to
-using `GROUP BY` for aggregations.
+可以在分区数据中寻找模式，例如单个股票行情或特定用户的趋势。这可以用 `PARTITION BY` 子句来表示。该子句类似于对聚合使用 `GROUP BY`。
-<span class="label label-danger">Attention</span> It is highly advised to partition the incoming
-data because otherwise the `MATCH_RECOGNIZE` clause will be translated into a non-parallel operator
-to ensure global ordering.
+<span class="label label-danger">注意</span> 强烈建议对传入的数据进行分区，否则 `MATCH_RECOGNIZE` 子句将被转换为非并行算子，以确保全局排序。
-Order of Events
+事件顺序
---------------
-Apache Flink allows for searching for patterns based on time; either
-[processing time or event time](time_attributes.html).
+Apache Flink 可以根据时间（[processing time or event time]({% link dev/table/streaming/time_attributes.zh.md %})）进行模式搜索。
Review comment:
`processing time or event time` 这里是否可以翻译下呢？
文章中其他地方也一样。在目标页面中这两个名词已经翻译成中文了
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -1035,34 +899,28 @@ use [time attributes](time_attributes.html). To select those there are available
<td>
<code>MATCH_ROWTIME()</code><br/>
</td>
- <td><p>Returns the timestamp of the last row that was mapped to the given pattern.</p>
- <p>The resulting attribute is a <a href="time_attributes.html">rowtime attribute</a>
- that can be used in subsequent time-based operations such as
- <a href="#joins">interval joins</a> and <a href="#aggregations">group window or over
- window aggregations</a>.</p></td>
+ <td>
+ <p>返回映射到给定模式的最后一行的时间戳。</p>
+ <p>结果属性是 <a href="{% link dev/table/streaming/time_attributes.zh.md %}">rowtime attribute</a>，可用于后续基于时间的操作，例如 <a href="{% link dev/table/streaming/joins.zh.md %}#interval-joins">interval joins</a> 和 <a href="#aggregations">group window or over window aggregations</a>。</p>
+ </td>
</tr>
<tr>
<td>
<code>MATCH_PROCTIME()</code><br/>
</td>
- <td><p>Returns a <a href="time_attributes.html#processing-time">proctime attribute</a>
- that can be used in subsequent time-based operations such as
- <a href="#joins">interval joins</a> and <a href="#aggregations">group window or over
- window aggregations</a>.</p></td>
+ <td>
+ <p>返回 <a href="{% link dev/table/streaming/time_attributes.zh.md %}#处理时间">proctime attribute</a>，该属性可用于随后的基于时间的操作，例如 <a href="{% link dev/table/streaming/joins.zh.md %}#interval-joins">interval joins</a> 和 <a href="#aggregations">group window or over window aggregations</a>。</p>
Review comment:
时间属性相关的英文建议翻译一下，目标文档已经翻译成中文了
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -498,36 +409,30 @@ symbol rowtime price tax
'ACME' '01-Apr-11 13:20:00' 19 1
{% endhighlight %}
-The query will produce the following results:
+查询将生成以下结果：
{% highlight text %}
symbol dropTime dropDiff
====== ==================== =============
'ACME' '01-Apr-11 13:00:00' 14
{% endhighlight %}
-The resulting row represents a price drop from `15` (at `01-Apr-11 12:00:00`) to `1` (at
-`01-Apr-11 13:00:00`). The `dropDiff` column contains the price difference.
+结果行代表价格从`15`（在`01-Apr-11 12:00:00`）下降到`1`（在`01-Apr-11 13:00:00`）。`dropDiff` 列包含价格差异。
-Notice that even though prices also drop by higher values, for example, by `11` (between
-`01-Apr-11 10:00:00` and `01-Apr-11 11:40:00`), the time difference between those two events is
-larger than 1 hour. Thus, they don't produce a match.
+请注意，即使价格也下降了较高的值，例如，下降了`11`（在`01-Apr-11 10:00:00`和`01-Apr-11 11:40:00`之间），这两个事件之间的时间差大于1小时。因此，它们不会产生匹配。
-Output Mode
+输出方式
-----------
-The _output mode_ describes how many rows should be emitted for every found match. The SQL standard
-describes two modes:
+_output mode_ 描述每个找到的匹配项应该输出多少行。SQL 标准描述了两种方式：
- `ALL ROWS PER MATCH`
-- `ONE ROW PER MATCH`.
+- `ONE ROW PER MATCH`。
Review comment:
这里渲染之后是列表，“。”是否可以去掉呢？
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -558,43 +463,36 @@ For the following input rows:
XYZ 2 11 2018-09-17 10:00:05
{% endhighlight %}
-The query will produce the following output:
+该查询将生成以下输出：
{% highlight text %}
symbol startPrice topPrice lastPrice
======== ============ ========== ===========
XYZ 10 13 11
{% endhighlight %}
-The pattern recognition is partitioned by the `symbol` column. Even though not explicitly mentioned
-in the `MEASURES` clause, the partitioned column is added at the beginning of the result.
+该模式识别由 `symbol` 列分区。即使在 `MEASURES` 子句中未明确提及，分区列仍会添加到结果的开头。
-Pattern Navigation
+模式导航
------------------
-The `DEFINE` and `MEASURES` clauses allow for navigating within the list of rows that (potentially)
-match a pattern.
+`DEFINE` 和 `MEASURES` 子句允许在（可能）匹配模式的行列表中进行导航。
-This section discusses this navigation for declaring conditions or producing output results.
+本节讨论用于声明条件或产生输出结果的导航。
-### Pattern Variable Referencing
+### 引用模式变量
-A _pattern variable reference_ allows a set of rows mapped to a particular pattern variable in the
-`DEFINE` or `MEASURES` clauses to be referenced.
+_pattern variable reference_ 允许引用一组映射到 `DEFINE` 或 `MEASURES` 子句中特定模式变量的行。
Review comment:
上面对这个名词做了翻译，这里为什么又用原文呢？
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -419,13 +343,9 @@ produce:
XYZ 16
{% endhighlight %}
-The pattern variable `B` matches only to the row with price `12` instead of swallowing the rows
-with prices `12`, `13`, and `14`.
+模式变量 `B` 只匹配价格为`12`的行，而不是包含价格为`12`、`13`和`14`的行。
Review comment:
```suggestion
模式变量 `B` 只匹配价格为 `12` 的行，而不是包含价格为 `12`、`13` 和 `14` 的行。
```
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -558,43 +463,36 @@ For the following input rows:
XYZ 2 11 2018-09-17 10:00:05
{% endhighlight %}
-The query will produce the following output:
+该查询将生成以下输出：
{% highlight text %}
symbol startPrice topPrice lastPrice
======== ============ ========== ===========
XYZ 10 13 11
{% endhighlight %}
-The pattern recognition is partitioned by the `symbol` column. Even though not explicitly mentioned
-in the `MEASURES` clause, the partitioned column is added at the beginning of the result.
+该模式识别由 `symbol` 列分区。即使在 `MEASURES` 子句中未明确提及，分区列仍会添加到结果的开头。
-Pattern Navigation
+模式导航
------------------
-The `DEFINE` and `MEASURES` clauses allow for navigating within the list of rows that (potentially)
-match a pattern.
+`DEFINE` 和 `MEASURES` 子句允许在（可能）匹配模式的行列表中进行导航。
-This section discusses this navigation for declaring conditions or producing output results.
+本节讨论用于声明条件或产生输出结果的导航。
-### Pattern Variable Referencing
+### 引用模式变量
-A _pattern variable reference_ allows a set of rows mapped to a particular pattern variable in the
-`DEFINE` or `MEASURES` clauses to be referenced.
+_pattern variable reference_ 允许引用一组映射到 `DEFINE` 或 `MEASURES` 子句中特定模式变量的行。
-For example, the expression `A.price` describes a set of rows mapped so far to `A` plus the current
-row if we try to match the current row to `A`. If an expression in the `DEFINE`/`MEASURES` clause
-requires a single row (e.g. `A.price` or `A.price > 10`), it selects the last value belonging to
-the corresponding set.
+如果 `DEFINE`/`MEASURES` 子句中的表达式需要一行（例如`a.price`或`a.price>10`），它将选择属于相应集合的最后一个值。
Review comment:
```suggestion
如果 `DEFINE`/`MEASURES` 子句中的表达式需要一行（例如 `a.price` 或 `a.price>10`），它将选择属于相应集合的最后一个值。
```
##########
File path: docs/dev/table/streaming/match_recognize.zh.md
##########
@@ -731,17 +620,15 @@ FIRST(variable.field, n)
{% endhighlight %}
</td>
<td>
- <p>Returns the value of the field from the event that was mapped to the <i>n</i>-th element
- of the variable. The counting starts at the first element mapped.</p>
+ <p>返回映射到变量的第<i>n</i>个元素的事件中的字段值。计数从映射的第一个元素开始。</p>
Review comment:
```suggestion
<p>返回映射到变量的第 <i>n</i> 个元素的事件中的字段值。计数从映射的第一个元素开始。</p>
```
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