Multi device downsampling alignment query

src/UserGuide/Master/Table/Basic-Concept/Query-Data.md

@@ -140,6 +140,33 @@ Total line number = 7
 It costs 0.106s
 ```
 
+**Example 3:Multi device time aligned query**
+
+```SQL
+IoTDB> SELECT date_bin_gapfill(1d, TIME) AS a_time,
+              device_id,
+              AVG(temperature) AS avg_temp
+       FROM table1
+       WHERE TIME >= 2024-11-26 13:00:00
+         AND TIME <= 2024-11-27 17:00:00
+       GROUP BY 1, device_id FILL METHOD PREVIOUS; 
+```
+
+**Result**:
+
+```SQL
++-----------------------------+---------+--------+
+|                       a_time|device_id|avg_temp|
++-----------------------------+---------+--------+
+|2024-11-26T08:00:00.000+08:00|      100|    90.0|
+|2024-11-27T08:00:00.000+08:00|      100|    90.0|
+|2024-11-26T08:00:00.000+08:00|      101|    90.0|
+|2024-11-27T08:00:00.000+08:00|      101|    85.0|
++-----------------------------+---------+--------+
+Total line number = 4
+It costs 0.048s
+```
+
 ### 3.3 Aggregation Query
 
 **Example**: Calculate the average, maximum, and minimum temperature for each `device_id` within a specific time range.
@@ -213,7 +240,7 @@ IoTDB> SELECT device_id,date_bin(1d ,time) as day_time, AVG(temperature) as avg_
 Total line number = 5
 It costs 0.066s
 ```
-###  3.6 Multi sequence downsampling query with misaligned timestamps
+###  3.6 Multi device downsampling alignment query
 
 #### 3.6.1 Sampling Frequency is the Same, but Time is Different
 

src/UserGuide/Master/Table/SQL-Manual/Nested-Queries.md

@@ -878,4 +878,4 @@ Note:
 
 **Example:**
 
-* Downsampling queries for multiple sequences with misaligned timestamps. For detailed examples, see: [Example](../Basic-Concept/Query-Data.md#36-multi-sequence-downsampling-query-with-misaligned-timestamps)
+* * Multi device downsampling alignment query. For detailed examples, see: [Example](../Basic-Concept/Query-Data.md#36-multi-device-downsampling-alignment-query)

src/UserGuide/latest-Table/Basic-Concept/Query-Data.md

@@ -139,6 +139,32 @@ IoTDB> SELECT time, temperature, humidity
 Total line number = 7
 It costs 0.106s
 ```
+**Example 3:Multi device time aligned query**
+
+```SQL
+IoTDB> SELECT date_bin_gapfill(1d, TIME) AS a_time,
+              device_id,
+              AVG(temperature) AS avg_temp
+       FROM table1
+       WHERE TIME >= 2024-11-26 13:00:00
+         AND TIME <= 2024-11-27 17:00:00
+       GROUP BY 1, device_id FILL METHOD PREVIOUS; 
+```
+
+**Result**:
+
+```SQL
++-----------------------------+---------+--------+
+|                       a_time|device_id|avg_temp|
++-----------------------------+---------+--------+
+|2024-11-26T08:00:00.000+08:00|      100|    90.0|
+|2024-11-27T08:00:00.000+08:00|      100|    90.0|
+|2024-11-26T08:00:00.000+08:00|      101|    90.0|
+|2024-11-27T08:00:00.000+08:00|      101|    85.0|
++-----------------------------+---------+--------+
+Total line number = 4
+It costs 0.048s
+```
 
 ### 3.3 Aggregation Query
 
@@ -213,7 +239,7 @@ IoTDB> SELECT device_id,date_bin(1d ,time) as day_time, AVG(temperature) as avg_
 Total line number = 5
 It costs 0.066s
 ```
-###  3.6 Multi sequence downsampling query with misaligned timestamps
+###  3.6 Multi device downsampling alignment query
 
 #### 3.6.1 Sampling Frequency is the Same, but Time is Different
 

src/UserGuide/latest-Table/SQL-Manual/Nested-Queries.md

@@ -878,4 +878,4 @@ Note:
 
 **Example:**
 
-* Downsampling queries for multiple sequences with misaligned timestamps. For detailed examples, see: [Example](../Basic-Concept/Query-Data.md#36-multi-sequence-downsampling-query-with-misaligned-timestamps)
+* Multi device downsampling alignment query. For detailed examples, see: [Example](../Basic-Concept/Query-Data.md#36-multi-device-downsampling-alignment-query)

src/zh/UserGuide/Master/Table/Basic-Concept/Query-Data.md

@@ -141,6 +141,33 @@ Total line number = 7
 It costs 0.106s
 ```
 
+**示例4：多设备按时间对齐查询**
+
+```SQL
+IoTDB> SELECT date_bin_gapfill(1d, TIME) AS a_time,
+              device_id,
+              AVG(temperature) AS avg_temp
+       FROM table1
+       WHERE TIME >= 2024-11-26 13:00:00
+         AND TIME <= 2024-11-27 17:00:00
+       GROUP BY 1, device_id FILL METHOD PREVIOUS; 
+```
+
+执行结果如下：
+
+```SQL
++-----------------------------+---------+--------+
+|                       a_time|device_id|avg_temp|
++-----------------------------+---------+--------+
+|2024-11-26T08:00:00.000+08:00|      100|    90.0|
+|2024-11-27T08:00:00.000+08:00|      100|    90.0|
+|2024-11-26T08:00:00.000+08:00|      101|    90.0|
+|2024-11-27T08:00:00.000+08:00|      101|    85.0|
++-----------------------------+---------+--------+
+Total line number = 4
+It costs 0.048s
+```
+
 ### 3.3 聚合查询
 
 **示例：查询计算了在指定时间范围内，每个`device_id`的平均温度、最高温度和最低温度。**
@@ -215,7 +242,7 @@ Total line number = 5
 It costs 0.066s
 ```
 
-### 3.6 时间戳不对齐的多序列降采样查询
+### 3.6 多设备降采样对齐查询
 
 #### 3.6.1 采样频率相同，时间不同
 

src/zh/UserGuide/Master/Table/SQL-Manual/Nested-Queries.md

@@ -877,4 +877,4 @@ IoTDB> SELECT s1 <=
 
 **示例：**
 
-* 时间戳不对齐的多序列降采样查询，详细示例可见：[示例](../Basic-Concept/Query-Data.md#36-时间戳不对齐的多序列降采样查询)
+* 多设备降采样对齐查询，详细示例可见：[示例](../Basic-Concept/Query-Data.md#36-多设备降采样对齐查询)

src/zh/UserGuide/latest-Table/Basic-Concept/Query-Data.md

@@ -141,6 +141,33 @@ Total line number = 7
 It costs 0.106s
 ```
 
+**示例4：多设备按时间对齐查询**
+
+```SQL
+IoTDB> SELECT date_bin_gapfill(1d, TIME) AS a_time,
+              device_id,
+              AVG(temperature) AS avg_temp
+       FROM table1
+       WHERE TIME >= 2024-11-26 13:00:00
+         AND TIME <= 2024-11-27 17:00:00
+       GROUP BY 1, device_id FILL METHOD PREVIOUS; 
+```
+
+执行结果如下：
+
+```SQL
++-----------------------------+---------+--------+
+|                       a_time|device_id|avg_temp|
++-----------------------------+---------+--------+
+|2024-11-26T08:00:00.000+08:00|      100|    90.0|
+|2024-11-27T08:00:00.000+08:00|      100|    90.0|
+|2024-11-26T08:00:00.000+08:00|      101|    90.0|
+|2024-11-27T08:00:00.000+08:00|      101|    85.0|
++-----------------------------+---------+--------+
+Total line number = 4
+It costs 0.048s
+```
+
 ### 3.3 聚合查询
 
 **示例：查询计算了在指定时间范围内，每个`device_id`的平均温度、最高温度和最低温度。**
@@ -215,7 +242,7 @@ Total line number = 5
 It costs 0.066s
 ```
 
-### 3.6 时间戳不对齐的多序列降采样查询
+### 3.6 多设备降采样对齐查询
 
 #### 3.6.1 采样频率相同，时间不同
 

src/zh/UserGuide/latest-Table/SQL-Manual/Nested-Queries.md

@@ -877,4 +877,4 @@ IoTDB> SELECT s1 <=
 
 **示例：**
 
-* 时间戳不对齐的多序列降采样查询，详细示例可见：[示例](../Basic-Concept/Query-Data.md#36-时间戳不对齐的多序列降采样查询)
+* 多设备降采样对齐查询，详细示例可见：[示例](../Basic-Concept/Query-Data.md#36-多设备降采样对齐查询)