src/zh/UserGuide/latest-Table/SQL-Manual/Row-Pattern-Recognition_timecho.md

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# 模式查询

## 1. 语法定义

```SQL
MATCH_RECOGNIZE (
  [ PARTITION BY column [, ...] ]
  [ ORDER BY column [, ...] ]
  [ MEASURES measure_definition [, ...] ]
  [ ROWS PER MATCH ]
  [ AFTER MATCH skip_to ]
  PATTERN ( row_pattern )
  [ SUBSET subset_definition [, ...] ]
  DEFINE variable_definition [, ...]
)
```

**说明：**

* PARTITION BY ： 可选，用于对输入表进行分组，每个分组能独立进行模式匹配。如果未声明该子句，则整个输入表将作为一个整体进行处理。
* ORDER BY ：可选，用于确保输入数据按某种顺序进行匹配处理。
* MEASURES ：可选，用于指定从匹配到的一段数据中提取哪些信息。
* ROWS PER MATCH ：可选，用于指定模式匹配成功后结果集的输出方式。
* AFTER MATCH SKIP ：可选，用于指定在识别到一个非空匹配后，下一次模式匹配应从哪一行继续进行。
* PATTERN ：用于定义需要匹配的行模式。
* SUBSET ：可选，用于将多个基本模式变量所匹配的行合并为一个逻辑集合。
* DEFINE ：用于定义行模式的基本模式变量。

更多详细功能介绍请参考：[模式查询](../User-Manual/Pattern-Query_timecho.md)

## 2. 使用示例

以[示例数据](../Reference/Sample-Data.md)为源数据

1. 时间分段查询

将 table1 中的数据按照时间间隔小于等于 24 小时分段，查询每段中的数据总条数，以及开始、结束时间。

查询SQL

```SQL
SELECT start_time, end_time, cnt 
FROM table1 
MATCH_RECOGNIZE (
    ORDER BY time 
    MEASURES 
        RPR_FIRST(A.time) AS start_time, 
        RPR_LAST(time) AS end_time, 
        COUNT() AS cnt 
    PATTERN (A B*)  
    DEFINE  B AS (cast(B.time as INT64) - cast(PREV(B.time) as INT64)) <= 86400000
) AS m
```

查询结果

```SQL
+-----------------------------+-----------------------------+---+
|                   start_time|                     end_time|cnt|
+-----------------------------+-----------------------------+---+
|2024-11-26T13:37:00.000+08:00|2024-11-26T13:38:00.000+08:00|  2|
|2024-11-27T16:38:00.000+08:00|2024-11-30T14:30:00.000+08:00| 16|
+-----------------------------+-----------------------------+---+
Total line number = 2
```

2. 差值分段查询

将 table2 中的数据按照 humidity 湿度值差值小于 0.1 分段，查询每段中的数据总条数，以及开始、结束时间。

* 查询sql

```SQL
SELECT start_time, end_time, cnt 
FROM table2 
MATCH_RECOGNIZE (
    ORDER BY time 
    MEASURES 
        RPR_FIRST(A.time) AS start_time, 
        RPR_LAST(time) AS end_time, 
        COUNT() AS cnt 
    PATTERN (A B*)  
    DEFINE  B AS (B.humidity - PREV(B.humidity )) <=0.1
) AS m;
```

* 查询结果

```SQL
+-----------------------------+-----------------------------+---+
|                   start_time|                     end_time|cnt|
+-----------------------------+-----------------------------+---+
|2024-11-26T13:37:00.000+08:00|2024-11-27T00:00:00.000+08:00|  2|
|2024-11-28T08:00:00.000+08:00|2024-11-29T00:00:00.000+08:00|  2|
|2024-11-29T11:00:00.000+08:00|2024-11-30T00:00:00.000+08:00|  2|
+-----------------------------+-----------------------------+---+
Total line number = 3
```

3. 事件统计查询

将 table1 中数据按照设备号分组，统计上海地区湿度大于 35 的开始、结束时间及最大湿度值。

* 查询sql

```SQL
SELECT m.device_id, m.match, m.event_start, m.event_end, m.max_humidity 
FROM table1
MATCH_RECOGNIZE (
  PARTITION BY device_id
  ORDER BY time 
  MEASURES
    MATCH_NUMBER() AS match,
    RPR_FIRST(A.time) AS event_start,
    RPR_LAST(A.time) AS event_end,
    MAX(A.humidity) AS max_humidity 
  ONE ROW PER MATCH
  PATTERN (A+)
  DEFINE
    A AS A.region= '上海' AND A.humidity> 35
) AS m
```

* 查询结果

```SQL
+---------+-----+-----------------------------+-----------------------------+------------+
|device_id|match|                  event_start|                    event_end|max_humidity|
+---------+-----+-----------------------------+-----------------------------+------------+
|      100|    1|2024-11-28T09:00:00.000+08:00|2024-11-29T18:30:00.000+08:00|        45.1|
|      101|    1|2024-11-30T09:30:00.000+08:00|2024-11-30T09:30:00.000+08:00|        35.2|
+---------+-----+-----------------------------+-----------------------------+------------+
Total line number = 2
```