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doc: update query result

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Shengliang Guan 2024-10-31 12:11:44 +08:00
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docs/zh/05-basic/03-query.md
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@ -20,21 +20,22 @@ taosBenchmark --start-timestamp=1600000000000 --tables=100 --records=10000000 --
```sql
SELECT * FROM meters
WHERE voltage > 10
WHERE voltage > 230
ORDER BY ts DESC
LIMIT 5
LIMIT 5;
```
上面的 SQL，从超级表 `meters` 中查询出电压 `voltage` 大于 10 的记录，按时间降序排列，且仅输出前 5 行。查询结果如下：
上面的 SQL，从超级表 `meters` 中查询出电压 `voltage` 大于 230 的记录，按时间降序排列，且仅输出前 5 行。查询结果如下：
```text
ts | current | voltage | phase | groupid | location |
==========================================================================================================
2023-11-14 22:13:10.000 | 1.1294620 | 18 | 0.3531540 | 8 | California.MountainView |
2023-11-14 22:13:10.000 | 1.0294620 | 12 | 0.3631540 | 2 | California.Campbell |
2023-11-14 22:13:10.000 | 1.0294620 | 16 | 0.3531540 | 1 | California.Campbell |
2023-11-14 22:13:10.000 | 1.1294620 | 18 | 0.3531540 | 2 | California.Campbell |
2023-11-14 22:13:10.000 | 1.1294620 | 16 | 0.3431540 | 7 | California.PaloAlto |
ts | current | voltage | phase | groupid | location |
============================================================================================================
2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale |
2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 1 | California.LosAngles |
2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 10 | California.Sunnyvale |
2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 5 | California.Cupertino |
2023-11-15 06:13:10.000 | 14.0601978 | 232 | 146.5000000 | 4 | California.SanFrancisco |
Query OK, 5 row(s) in set (0.145403s)
```
## 聚合查询
@ -48,28 +49,28 @@ TDengine 支持通过 GROUP BY 子句，对数据进行聚合查询。SQL 语句
group by 子句用于对数据进行分组，并为每个分组返回一行汇总信息。在 group by 子句中，可以使用表或视图中的任何列作为分组依据，这些列不需要出现在 select 列表中。此外，用户可以直接在超级表上执行聚合查询，无须预先创建子表。以智能电表的数据模型为例，使用 group by 子句的 SQL 如下：
```sql
SELECT groupid,avg(voltage)
SELECT groupid, avg(voltage)
FROM meters
WHERE ts >= "2022-01-01T00:00:00+08:00"
AND ts < "2023-01-01T00:00:00+08:00"
GROUP BY groupid
GROUP BY groupid;
```
上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2023-01-01T00:00:00+08:00` 的数据，按照 `groupid` 进行分组，求每组的平均电压。查询结果如下：
```text
groupid | avg(voltage) |
==========================================
8 | 9.104040404040404 |
5 | 9.078333333333333 |
1 | 9.087037037037037 |
7 | 8.991414141414142 |
9 | 8.789814814814815 |
6 | 9.051010101010101 |
4 | 9.135353535353536 |
10 | 9.213131313131314 |
2 | 9.008888888888889 |
3 | 8.783888888888889 |
groupid | avg(voltage) |
======================================
8 | 243.961981544901079 |
5 | 243.961981544901079 |
1 | 243.961981544901079 |
7 | 243.961981544901079 |
9 | 243.961981544901079 |
6 | 243.961981544901079 |
4 | 243.961981544901079 |
10 | 243.961981544901079 |
2 | 243.961981544901079 |
3 | 243.961981544901079 |
Query OK, 10 row(s) in set (0.042446s)
```
@ -110,24 +111,24 @@ TDengine 按如下方式处理数据切分子句。
```sql
SELECT location, avg(voltage)
FROM meters
PARTITION BY location
PARTITION BY location;
```
上面的示例 SQL 查询超级表 `meters`，将数据按标签 `location` 进行分组，每个分组计算电压的平均值。查询结果如下：
```text
location | avg(voltage) |
=========================================================
California.SantaClara | 8.793334320000000 |
California.SanFrancisco | 9.017645882352941 |
California.SanJose | 9.156112940000000 |
California.LosAngles | 9.036753507692307 |
California.SanDiego | 8.967037053333334 |
California.Sunnyvale | 8.978572085714285 |
California.PaloAlto | 8.936665800000000 |
California.Cupertino | 8.987654066666666 |
California.MountainView | 9.046297266666667 |
California.Campbell | 9.149999028571429 |
location | avg(voltage) |
======================================================
California.SantaClara | 243.962050000000005 |
California.SanFrancisco | 243.962050000000005 |
California.SanJose | 243.962050000000005 |
California.LosAngles | 243.962050000000005 |
California.SanDiego | 243.962050000000005 |
California.Sunnyvale | 243.962050000000005 |
California.PaloAlto | 243.962050000000005 |
California.Cupertino | 243.962050000000005 |
California.MountainView | 243.962050000000005 |
California.Campbell | 243.962050000000005 |
Query OK, 10 row(s) in set (2.415961s)
```
@ -200,20 +201,20 @@ SLIMIT 2;
上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据；数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，且每个时间窗口向后偏移 5 秒；最后，仅取前 2 个分片的数据作为结果。查询结果如下：
```text
tbname | _wstart | _wend | avg(voltage) |
==========================================================================================
d40 | 2021-12-31 15:59:05.000 | 2021-12-31 16:00:05.000 | 4.000000000000000 |
d40 | 2021-12-31 16:00:05.000 | 2021-12-31 16:01:05.000 | 5.000000000000000 |
d40 | 2021-12-31 16:01:05.000 | 2021-12-31 16:02:05.000 | 8.000000000000000 |
d40 | 2021-12-31 16:02:05.000 | 2021-12-31 16:03:05.000 | 7.666666666666667 |
d40 | 2021-12-31 16:03:05.000 | 2021-12-31 16:04:05.000 | 9.666666666666666 |
d40 | 2021-12-31 16:04:05.000 | 2021-12-31 16:05:05.000 | 15.199999999999999 |
d41 | 2021-12-31 15:59:05.000 | 2021-12-31 16:00:05.000 | 4.000000000000000 |
d41 | 2021-12-31 16:00:05.000 | 2021-12-31 16:01:05.000 | 7.000000000000000 |
d41 | 2021-12-31 16:01:05.000 | 2021-12-31 16:02:05.000 | 9.000000000000000 |
d41 | 2021-12-31 16:02:05.000 | 2021-12-31 16:03:05.000 | 10.666666666666666 |
d41 | 2021-12-31 16:03:05.000 | 2021-12-31 16:04:05.000 | 8.333333333333334 |
d41 | 2021-12-31 16:04:05.000 | 2021-12-31 16:05:05.000 | 9.600000000000000 |
tbname | _wstart | _wend | avg(voltage) |
======================================================================================
d2 | 2021-12-31 23:59:05.000 | 2022-01-01 00:00:05.000 | 253.000000000000000 |
d2 | 2022-01-01 00:00:05.000 | 2022-01-01 00:01:05.000 | 244.166666666666657 |
d2 | 2022-01-01 00:01:05.000 | 2022-01-01 00:02:05.000 | 241.833333333333343 |
d2 | 2022-01-01 00:02:05.000 | 2022-01-01 00:03:05.000 | 243.166666666666657 |
d2 | 2022-01-01 00:03:05.000 | 2022-01-01 00:04:05.000 | 240.833333333333343 |
d2 | 2022-01-01 00:04:05.000 | 2022-01-01 00:05:05.000 | 244.800000000000011 |
d26 | 2021-12-31 23:59:05.000 | 2022-01-01 00:00:05.000 | 253.000000000000000 |
d26 | 2022-01-01 00:00:05.000 | 2022-01-01 00:01:05.000 | 244.166666666666657 |
d26 | 2022-01-01 00:01:05.000 | 2022-01-01 00:02:05.000 | 241.833333333333343 |
d26 | 2022-01-01 00:02:05.000 | 2022-01-01 00:03:05.000 | 243.166666666666657 |
d26 | 2022-01-01 00:03:05.000 | 2022-01-01 00:04:05.000 | 240.833333333333343 |
d26 | 2022-01-01 00:04:05.000 | 2022-01-01 00:05:05.000 | 244.800000000000011 |
Query OK, 12 row(s) in set (0.021265s)
```
@ -255,19 +256,19 @@ SLIMIT 1;
上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据，数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，且时间窗口按照 30 秒进行滑动；最后，仅取前 1 个分片的数据作为结果。查询结果如下：
```text
tbname | _wstart | avg(voltage) |
================================================================
d40 | 2021-12-31 15:59:30.000 | 4.000000000000000 |
d40 | 2021-12-31 16:00:00.000 | 5.666666666666667 |
d40 | 2021-12-31 16:00:30.000 | 4.333333333333333 |
d40 | 2021-12-31 16:01:00.000 | 5.000000000000000 |
d40 | 2021-12-31 16:01:30.000 | 9.333333333333334 |
d40 | 2021-12-31 16:02:00.000 | 9.666666666666666 |
d40 | 2021-12-31 16:02:30.000 | 10.000000000000000 |
d40 | 2021-12-31 16:03:00.000 | 10.333333333333334 |
d40 | 2021-12-31 16:03:30.000 | 10.333333333333334 |
d40 | 2021-12-31 16:04:00.000 | 13.000000000000000 |
d40 | 2021-12-31 16:04:30.000 | 15.333333333333334 |
tbname | _wstart | avg(voltage) |
=============================================================
d2 | 2021-12-31 23:59:30.000 | 248.333333333333343 |
d2 | 2022-01-01 00:00:00.000 | 246.000000000000000 |
d2 | 2022-01-01 00:00:30.000 | 244.666666666666657 |
d2 | 2022-01-01 00:01:00.000 | 240.833333333333343 |
d2 | 2022-01-01 00:01:30.000 | 239.500000000000000 |
d2 | 2022-01-01 00:02:00.000 | 243.833333333333343 |
d2 | 2022-01-01 00:02:30.000 | 243.833333333333343 |
d2 | 2022-01-01 00:03:00.000 | 241.333333333333343 |
d2 | 2022-01-01 00:03:30.000 | 241.666666666666657 |
d2 | 2022-01-01 00:04:00.000 | 244.166666666666657 |
d2 | 2022-01-01 00:04:30.000 | 244.666666666666657 |
Query OK, 11 row(s) in set (0.013153s)
```
@ -290,13 +291,13 @@ SLIMIT 1;
上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据，数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，且时间窗口按照 1 分钟进行切分；最后，仅取前 1 个分片的数据作为结果。查询结果如下：
```text
tbname | _wstart | _wend | avg(voltage) |
=================================================================================================================
d28 | 2021-12-31 16:00:00.000 | 2021-12-31 16:01:00.000 | 7.333333333333333 |
d28 | 2021-12-31 16:01:00.000 | 2021-12-31 16:02:00.000 | 8.000000000000000 |
d28 | 2021-12-31 16:02:00.000 | 2021-12-31 16:03:00.000 | 11.000000000000000 |
d28 | 2021-12-31 16:03:00.000 | 2021-12-31 16:04:00.000 | 6.666666666666667 |
d28 | 2021-12-31 16:04:00.000 | 2021-12-31 16:05:00.000 | 10.000000000000000 |
tbname | _wstart | _wend | avg(voltage) |
======================================================================================
d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:00.000 | 246.000000000000000 |
d2 | 2022-01-01 00:01:00.000 | 2022-01-01 00:02:00.000 | 240.833333333333343 |
d2 | 2022-01-01 00:02:00.000 | 2022-01-01 00:03:00.000 | 243.833333333333343 |
d2 | 2022-01-01 00:03:00.000 | 2022-01-01 00:04:00.000 | 241.333333333333343 |
d2 | 2022-01-01 00:04:00.000 | 2022-01-01 00:05:00.000 | 244.166666666666657 |
Query OK, 5 row(s) in set (0.016812s)
```
@ -342,53 +343,65 @@ SLIMIT 2;
上面的 SQL，查询超级表 `meters` 中，时间戳大于等于 `2022-01-01T00:00:00+08:00`，且时间戳小于 `2022-01-01T00:05:00+08:00` 的数据；数据首先按照子表名 `tbname` 进行数据切分，再按照每 1 分钟的时间窗口进行切分，如果窗口内的数据出现缺失，则使用使用前一个非 NULL 值填充数据；最后，仅取前 2 个分片的数据作为结果。查询结果如下：
```text
tbname | _wstart | _wend | avg(voltage) |
=================================================================================================================
d40 | 2021-12-31 16:00:00.000 | 2021-12-31 16:01:00.000 | 5.666666666666667 |
d40 | 2021-12-31 16:01:00.000 | 2021-12-31 16:02:00.000 | 5.000000000000000 |
d40 | 2021-12-31 16:02:00.000 | 2021-12-31 16:03:00.000 | 9.666666666666666 |
d40 | 2021-12-31 16:03:00.000 | 2021-12-31 16:04:00.000 | 10.333333333333334 |
d40 | 2021-12-31 16:04:00.000 | 2021-12-31 16:05:00.000 | 13.000000000000000 |
d41 | 2021-12-31 16:00:00.000 | 2021-12-31 16:01:00.000 | 5.666666666666667 |
d41 | 2021-12-31 16:01:00.000 | 2021-12-31 16:02:00.000 | 9.333333333333334 |
d41 | 2021-12-31 16:02:00.000 | 2021-12-31 16:03:00.000 | 11.000000000000000 |
d41 | 2021-12-31 16:03:00.000 | 2021-12-31 16:04:00.000 | 7.666666666666667 |
d41 | 2021-12-31 16:04:00.000 | 2021-12-31 16:05:00.000 | 10.000000000000000 |
tbname | _wstart | _wend | avg(voltage) |
=======================================================================================
d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:00.000 | 246.000000000000000 |
d2 | 2022-01-01 00:01:00.000 | 2022-01-01 00:02:00.000 | 240.833333333333343 |
d2 | 2022-01-01 00:02:00.000 | 2022-01-01 00:03:00.000 | 243.833333333333343 |
d2 | 2022-01-01 00:03:00.000 | 2022-01-01 00:04:00.000 | 241.333333333333343 |
d2 | 2022-01-01 00:04:00.000 | 2022-01-01 00:05:00.000 | 244.166666666666657 |
d26 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:00.000 | 246.000000000000000 |
d26 | 2022-01-01 00:01:00.000 | 2022-01-01 00:02:00.000 | 240.833333333333343 |
d26 | 2022-01-01 00:02:00.000 | 2022-01-01 00:03:00.000 | 243.833333333333343 |
d26 | 2022-01-01 00:03:00.000 | 2022-01-01 00:04:00.000 | 241.333333333333343 |
d26 | 2022-01-01 00:04:00.000 | 2022-01-01 00:05:00.000 | 244.166666666666657 |
Query OK, 10 row(s) in set (0.022866s)
```
### 状态窗口
使用整数（布尔值）或字符串来标识产生记录时候设备的状态量。产生的记录如果具有相同的状态量数值则归属于同一个状态窗口，数值改变后该窗口关闭。TDengine 还支持将 CASE 表达式用在状态量，可以表达某个状态的开始是由满足某个条件而触发，这个状态的结束是由另外一个条件满足而触发的语义。以智能电表为例，电压正常范围是 205V 到 235V，那么可以通过监控电压来判断电路是否正常。
使用整数（布尔值）或字符串来标识产生记录时候设备的状态量。产生的记录如果具有相同的状态量数值则归属于同一个状态窗口，数值改变后该窗口关闭。TDengine 还支持将 CASE 表达式用在状态量，可以表达某个状态的开始是由满足某个条件而触发，这个状态的结束是由另外一个条件满足而触发的语义。以智能电表为例，电压正常范围是 225V 到 235V，那么可以通过监控电压来判断电路是否正常。
```sql
SELECT tbname, _wstart, _wend,_wduration, CASE WHEN voltage >= 205 and voltage <= 235 THEN 1 ELSE 0 END status
SELECT tbname, _wstart, _wend,_wduration, CASE WHEN voltage >= 225 and voltage <= 235 THEN 1 ELSE 0 END status
FROM meters
WHERE ts >= "2022-01-01T00:00:00+08:00"
AND ts < "2022-01-01T00:05:00+08:00"
PARTITION BY tbname
STATE_WINDOW(
CASE WHEN voltage >= 205 and voltage <= 235 THEN 1 ELSE 0 END
CASE WHEN voltage >= 225 and voltage <= 235 THEN 1 ELSE 0 END
)
SLIMIT 10;
SLIMIT 2;
```
以上 SQL，查询超级表 meters 中，时间戳大于等于 2022-01-01T00:00:00+08:00，且时间戳小于 2022-01-01T00:05:00+08:00的数据；数据首先按照子表名 tbname 进行数据切分；根据电压是否在正常范围内进行状态窗口的划分；最后，取前 10 个分片的数据作为结果。查询结果如下：
以上 SQL，查询超级表 meters 中，时间戳大于等于 2022-01-01T00:00:00+08:00，且时间戳小于 2022-01-01T00:05:00+08:00的数据；数据首先按照子表名 tbname 进行数据切分；根据电压是否在正常范围内进行状态窗口的划分；最后，取前 2 个分片的数据作为结果。查询结果如下：（由于数据是随机生成，结果集包含的数据条数会有不同）
```text
tbname | _wstart | _wend | _wduration | status |
=====================================================================================================================================
d76 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d47 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d37 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d87 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d64 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d35 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d83 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d51 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d63 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
d0 | 2021-12-31 16:00:00.000 | 2021-12-31 16:04:50.000 | 290000 | 0 |
Query OK, 10 row(s) in set (0.040495s)
tbname | _wstart | _wend | _wduration | status |
===============================================================================================
d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:20.000 | 80000 | 0 |
d2 | 2022-01-01 00:01:30.000 | 2022-01-01 00:01:30.000 | 0 | 1 |
d2 | 2022-01-01 00:01:40.000 | 2022-01-01 00:01:40.000 | 0 | 0 |
d2 | 2022-01-01 00:01:50.000 | 2022-01-01 00:01:50.000 | 0 | 1 |
d2 | 2022-01-01 00:02:00.000 | 2022-01-01 00:02:20.000 | 20000 | 0 |
d2 | 2022-01-01 00:02:30.000 | 2022-01-01 00:02:30.000 | 0 | 1 |
d2 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:00.000 | 20000 | 0 |
d2 | 2022-01-01 00:03:10.000 | 2022-01-01 00:03:10.000 | 0 | 1 |
d2 | 2022-01-01 00:03:20.000 | 2022-01-01 00:03:40.000 | 20000 | 0 |
d2 | 2022-01-01 00:03:50.000 | 2022-01-01 00:03:50.000 | 0 | 1 |
d2 | 2022-01-01 00:04:00.000 | 2022-01-01 00:04:50.000 | 50000 | 0 |
d26 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:20.000 | 80000 | 0 |
d26 | 2022-01-01 00:01:30.000 | 2022-01-01 00:01:30.000 | 0 | 1 |
d26 | 2022-01-01 00:01:40.000 | 2022-01-01 00:01:40.000 | 0 | 0 |
d26 | 2022-01-01 00:01:50.000 | 2022-01-01 00:01:50.000 | 0 | 1 |
d26 | 2022-01-01 00:02:00.000 | 2022-01-01 00:02:20.000 | 20000 | 0 |
d26 | 2022-01-01 00:02:30.000 | 2022-01-01 00:02:30.000 | 0 | 1 |
d26 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:00.000 | 20000 | 0 |
d26 | 2022-01-01 00:03:10.000 | 2022-01-01 00:03:10.000 | 0 | 1 |
d26 | 2022-01-01 00:03:20.000 | 2022-01-01 00:03:40.000 | 20000 | 0 |
d26 | 2022-01-01 00:03:50.000 | 2022-01-01 00:03:50.000 | 0 | 1 |
d26 | 2022-01-01 00:04:00.000 | 2022-01-01 00:04:50.000 | 50000 | 0 |
Query OK, 22 row(s) in set (0.153403s)
```
### 会话窗口
@ -417,18 +430,18 @@ SLIMIT 10;
上面的 SQL，查询超级表 meters 中，时间戳大于等于 2022-01-01T00:00:00+08:00，且时间戳小于 2022-01-01T00:10:00+08:00的数据；数据先按照子表名 tbname 进行数据切分，再根据 10 分钟的会话窗口进行切分；最后，取前 10 个分片的数据作为结果，返回子表名、窗口开始时间、窗口结束时间、窗口宽度、窗口内数据条数。查询结果如下：
```text
tbname | _wstart | _wend | _wduration | count(*) |
=====================================================================================================================================
d76 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d47 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d37 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d87 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d64 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d35 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d83 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d51 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d63 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
d0 | 2021-12-31 16:00:00.000 | 2021-12-31 16:09:50.000 | 590000 | 60 |
tbname | _wstart | _wend | _wduration | count(*) |
===============================================================================================
d2 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d26 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d52 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d64 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d76 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d28 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d4 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d88 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d77 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
d54 | 2022-01-01 00:00:00.000 | 2022-01-01 00:09:50.000 | 590000 | 60 |
Query OK, 10 row(s) in set (0.043489s)
```
@ -458,26 +471,26 @@ FROM meters
WHERE ts >= "2022-01-01T00:00:00+08:00"
AND ts < "2022-01-01T00:10:00+08:00"
PARTITION BY tbname
EVENT_WINDOW START WITH voltage >= 10 END WITH voltage < 20
LIMIT 10;
EVENT_WINDOW START WITH voltage >= 225 END WITH voltage < 235
LIMIT 5;
```
上面的 SQL，查询超级表meters中，时间戳大于等于2022-01-01T00:00:00+08:00，且时间戳小于2022-01-01T00:10:00+08:00的数据；数据先按照子表名tbname进行数据切分，再根据事件窗口条件：电压大于等于 10V，且小于 20V 进行切分；最后，取前 10 行的数据作为结果，返回子表名、窗口开始时间、窗口结束时间、窗口宽度、窗口内数据条数。查询结果如下：
上面的 SQL，查询超级表meters中，时间戳大于等于2022-01-01T00:00:00+08:00，且时间戳小于2022-01-01T00:10:00+08:00的数据；数据先按照子表名tbname进行数据切分，再根据事件窗口条件：电压大于等于 225V，且小于 235V 进行切分；最后，取每个分片的前 5 行的数据作为结果，返回子表名、窗口开始时间、窗口结束时间、窗口宽度、窗口内数据条数。查询结果如下：
```text
tbname | _wstart | _wend | _wduration | count(*) |
=====================================================================================================================================
d0 | 2021-12-31 16:00:00.000 | 2021-12-31 16:00:00.000 | 0 | 1 |
d0 | 2021-12-31 16:00:30.000 | 2021-12-31 16:00:30.000 | 0 | 1 |
d0 | 2021-12-31 16:00:40.000 | 2021-12-31 16:00:40.000 | 0 | 1 |
d0 | 2021-12-31 16:01:20.000 | 2021-12-31 16:01:20.000 | 0 | 1 |
d0 | 2021-12-31 16:02:20.000 | 2021-12-31 16:02:20.000 | 0 | 1 |
d0 | 2021-12-31 16:02:30.000 | 2021-12-31 16:02:30.000 | 0 | 1 |
d0 | 2021-12-31 16:03:10.000 | 2021-12-31 16:03:10.000 | 0 | 1 |
d0 | 2021-12-31 16:03:30.000 | 2021-12-31 16:03:30.000 | 0 | 1 |
d0 | 2021-12-31 16:03:40.000 | 2021-12-31 16:03:40.000 | 0 | 1 |
d0 | 2021-12-31 16:03:50.000 | 2021-12-31 16:03:50.000 | 0 | 1 |
Query OK, 10 row(s) in set (0.034127s)
tbname | _wstart | _wend | _wduration | count(*) |
==============================================================================================
d0 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:30.000 | 90000 | 10 |
d0 | 2022-01-01 00:01:40.000 | 2022-01-01 00:02:30.000 | 50000 | 6 |
d0 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:10.000 | 30000 | 4 |
d0 | 2022-01-01 00:03:20.000 | 2022-01-01 00:07:10.000 | 230000 | 24 |
d0 | 2022-01-01 00:07:20.000 | 2022-01-01 00:07:50.000 | 30000 | 4 |
d1 | 2022-01-01 00:00:00.000 | 2022-01-01 00:01:30.000 | 90000 | 10 |
d1 | 2022-01-01 00:01:40.000 | 2022-01-01 00:02:30.000 | 50000 | 6 |
d1 | 2022-01-01 00:02:40.000 | 2022-01-01 00:03:10.000 | 30000 | 4 |
d1 | 2022-01-01 00:03:20.000 | 2022-01-01 00:07:10.000 | 230000 | 24 |
……
Query OK, 500 row(s) in set (0.328557s)
```
### 计数窗口
@ -492,17 +505,25 @@ sliding_val 是一个常量，表示窗口滑动的数量，类似于 interval
select _wstart, _wend, count(*)
from meters
where ts >= "2022-01-01T00:00:00+08:00" and ts < "2022-01-01T00:30:00+08:00"
count_window(10);
count_window(1000);
```
上面的 SQL 查询超级表 meters 中时间戳大于等于 2022-01-01T00:00:00+08:00 且时间戳小于 2022-01-01T00:10:00+08:00 的数据。以每 10 条数据为一组，返回每组的开始时间、结束时间和分组条数。查询结果如下。
上面的 SQL 查询超级表 meters 中时间戳大于等于 2022-01-01T00:00:00+08:00 且时间戳小于 2022-01-01T00:10:00+08:00 的数据。以每 1000 条数据为一组，返回每组的开始时间、结束时间和分组条数。查询结果如下：
```text
_wstart | _wend |count(*)|
===========================================================
2021-12-31 16:00:00.000 | 2021-12-31 16:10:00.000 | 10 |
2021-12-31 16:10:00.000 | 2021-12-31 16:20:00.000 | 10 |
2021-12-31 16:20:00.000 | 2021-12-31 16:30:00.000 | 10 |
_wstart | _wend | count(*) |
=====================================================================
2022-01-01 00:00:00.000 | 2022-01-01 00:01:30.000 | 1000 |
2022-01-01 00:01:40.000 | 2022-01-01 00:03:10.000 | 1000 |
2022-01-01 00:03:20.000 | 2022-01-01 00:04:50.000 | 1000 |
2022-01-01 00:05:00.000 | 2022-01-01 00:06:30.000 | 1000 |
2022-01-01 00:06:40.000 | 2022-01-01 00:08:10.000 | 1000 |
2022-01-01 00:08:20.000 | 2022-01-01 00:09:50.000 | 1000 |
2022-01-01 00:10:00.000 | 2022-01-01 00:11:30.000 | 1000 |
2022-01-01 00:11:40.000 | 2022-01-01 00:13:10.000 | 1000 |
2022-01-01 00:13:20.000 | 2022-01-01 00:14:50.000 | 1000 |
2022-01-01 00:15:00.000 | 2022-01-01 00:16:30.000 | 1000 |
Query OK, 10 row(s) in set (0.062794s)
```
## 时序数据特有函数
@ -563,14 +584,14 @@ UNION ALL
上面的 SQL，分别查询：子表 d1 的 1 条数据，子表 d11 的 2 条数据，子表 d21 的 3 条数据，并将结果合并。返回的结果如下：
```text
tbname | ts | current | voltage | phase |
=================================================================================================
d11 | 2020-09-13 12:26:40.000 | 1.0260611 | 6 | 0.3620200 |
d11 | 2020-09-13 12:26:50.000 | 2.9544230 | 8 | 1.0048079 |
d21 | 2020-09-13 12:26:40.000 | 1.0260611 | 2 | 0.3520200 |
d21 | 2020-09-13 12:26:50.000 | 2.9544230 | 2 | 0.9948080 |
d21 | 2020-09-13 12:27:00.000 | -0.0000430 | 12 | 0.0099860 |
d1 | 2020-09-13 12:26:40.000 | 1.0260611 | 10 | 0.3520200 |
tbname | ts | current | voltage | phase |
====================================================================================
d11 | 2020-09-13 20:26:40.000 | 11.5680809 | 247 | 146.5000000 |
d11 | 2020-09-13 20:26:50.000 | 14.2392311 | 234 | 148.0000000 |
d1 | 2020-09-13 20:26:40.000 | 11.5680809 | 247 | 146.5000000 |
d21 | 2020-09-13 20:26:40.000 | 11.5680809 | 247 | 146.5000000 |
d21 | 2020-09-13 20:26:50.000 | 14.2392311 | 234 | 148.0000000 |
d21 | 2020-09-13 20:27:00.000 | 10.0999422 | 251 | 146.0000000 |
Query OK, 6 row(s) in set (0.006438s)
```