diff --git a/src/UserGuide/Master/User-Manual/Database-Programming.md b/src/UserGuide/Master/User-Manual/Database-Programming.md
index f501e2955..64cfa8734 100644
--- a/src/UserGuide/Master/User-Manual/Database-Programming.md
+++ b/src/UserGuide/Master/User-Manual/Database-Programming.md
@@ -23,7 +23,7 @@
 
 ## TRIGGER
 
-### 1. Instructions
+### Instructions
 
 The trigger provides a mechanism for listening to changes in time series data. With user-defined logic, tasks such as alerting and data forwarding can be conducted.
 
@@ -49,7 +49,7 @@ There are currently two trigger events for the trigger, and other trigger events
 - BEFORE INSERT: Fires before the data is persisted. **Please note that currently the trigger does not support data cleaning and will not change the data to be persisted itself.**
 - AFTER INSERT: Fires after the data is persisted.
 
-### 2. How to Implement a Trigger
+### How to Implement a Trigger
 
 You need to implement the trigger by writing a Java class, where the dependency shown below is required. If you use [Maven](http://search.maven.org/), you can search for them directly from the [Maven repository](http://search.maven.org/).
 
@@ -320,7 +320,7 @@ public class ClusterAlertingExample implements Trigger {
 }
 ```
 
-### 3. Trigger Management
+### Trigger Management
 
 You can create and drop a trigger through an SQL statement, and you can also query all registered triggers through an SQL statement.
 
@@ -450,7 +450,7 @@ During the process of creating and dropping triggers in the cluster, we maintain
 | DROPPING     | Intermediate state of executing `DROP TRIGGER`, the cluster is in the process of dropping the trigger. | NO                                |
 | TRANSFERRING | The cluster is migrating the location of this trigger instance. | NO                                |
 
-### 4. Notes
+### Notes
 
 - The trigger takes effect from the time of registration, and does not process the existing historical data. **That is, only insertion requests that occur after the trigger is successfully registered will be listened to by the trigger. **
 - The fire process of trigger is synchronous currently, so you need to ensure the efficiency of the trigger, otherwise the writing performance may be greatly affected. **You need to guarantee concurrency safety of triggers yourself**.
@@ -460,7 +460,7 @@ During the process of creating and dropping triggers in the cluster, we maintain
 - The trigger JAR package has a size limit, which must be less than min(`config_node_ratis_log_appender_buffer_size_max`, 2G), where `config_node_ratis_log_appender_buffer_size_max` is a configuration item. For the specific meaning, please refer to the IOTDB configuration item description.
 - **It is better not to have classes with the same full class name but different function implementations in different JAR packages.** For example, trigger1 and trigger2 correspond to resources trigger1.jar and trigger2.jar respectively. If two JAR packages contain a `org.apache.iotdb.trigger.example.AlertListener` class, when `CREATE TRIGGER` uses this class, the system will randomly load the class in one of the JAR packages, which will eventually leads the inconsistent behavior of trigger and other issues.
 
-### 5. Configuration Parameters
+### Configuration Parameters
 
 | Parameter                                         | Meaning                                                      |
 | ------------------------------------------------- | ------------------------------------------------------------ |
@@ -469,13 +469,13 @@ During the process of creating and dropping triggers in the cluster, we maintain
 
 ## CONTINUOUS QUERY (CQ)
 
-### 1. Introduction
+### Introduction
 
 Continuous queries(CQ) are queries that run automatically and periodically on realtime data and store query results in other specified time series.
 
 Users can implement sliding window streaming computing through continuous query, such as calculating the hourly average temperature of a sequence and writing it into a new sequence. Users can customize the `RESAMPLE` clause to create different sliding windows, which can achieve a certain degree of tolerance for out-of-order data.
 
-### 2. Syntax
+### Syntax
 
 ```sql
 CREATE (CONTINUOUS QUERY | CQ) <cq_id> 
@@ -540,7 +540,7 @@ END
 
 ##### `<every_interval>`  is not zero
 
-![4](https://alioss.timecho.com/docs/img/UserGuide/Process-Data/Continuous-Query/pic4.png?raw=true)
+![](https://alioss.timecho.com/docs/img/UserGuide/Process-Data/Continuous-Query/pic4.png?raw=true)
 
 
 - `TIMEOUT POLICY` specify how we deal with the cq task whose previous time interval execution is not finished while the next execution time has reached. The default value is `BLOCKED`.
@@ -548,7 +548,7 @@ END
     - `DISCARD` means that we just discard the current cq execution task and wait for the next execution time and do the next time interval cq task. If using `DISCARD` policy, some time intervals won't be executed when the execution time of one cq task is longer than the `<every_interval>`. However, once a cq task is executed, it will use the latest time interval, so it can catch up at the sacrifice of some time intervals being discarded.
 
 
-### 3. Examples of CQ
+### Examples of CQ
 
 The examples below use the following sample data. It's a real time data stream and we can assume that the data arrives on time.
 
@@ -931,7 +931,7 @@ At **2021-05-11T22:19:00.000+08:00**, `cq5` executes a query within the time ran
 +-----------------------------+-------------------------------+-----------+
 ````
 
-### 4. CQ Management
+### CQ Management
 
 #### Listing continuous queries
 
@@ -979,7 +979,7 @@ DROP CONTINUOUS QUERY s1_count_cq;
 CQs can't be altered once they're created. To change a CQ, you must `DROP` and re`CREATE` it with the updated settings.
 
 
-### 5. CQ Use Cases
+### CQ Use Cases
 
 #### Downsampling and Data Retention
 
@@ -1005,7 +1005,7 @@ SELECT avg(count_s1) from (select count(s1) as count_s1 from root.sg.d group by(
 
 To get the same results:
 
-**1. Create a CQ**
+**Create a CQ**
 
 This step performs the nested sub query in from clause of the query above. The following CQ automatically calculates the number of non-null values of `s1` at 30 minute intervals and writes those counts into the new `root.sg_count.d.count_s1` time series.
 
@@ -1019,7 +1019,7 @@ BEGIN
 END
 ```
 
-**2. Query the CQ results**
+**Query the CQ results**
 
 Next step performs the avg([...]) part of the outer query above.
 
@@ -1030,7 +1030,7 @@ SELECT avg(count_s1) from root.sg_count.d;
 ```
 
 
-### 6. System Parameter Configuration
+### System Parameter Configuration
 
 | Name                                        | Description                                                  | Data Type | Default Value |
 | :------------------------------------------ | ------------------------------------------------------------ | --------- | ------------- |
@@ -1662,8 +1662,6 @@ This method is called by the framework. For a UDF instance, `beforeDestroy` will
 
 
 
-
-
 ### Maven Project Example
 
 If you use Maven, you can build your own UDF project referring to our **udf-example** module. You can find the project [here](https://github.com/apache/iotdb/tree/master/example/udf).
diff --git a/src/UserGuide/latest/User-Manual/Database-Programming.md b/src/UserGuide/latest/User-Manual/Database-Programming.md
index c2f640734..64cfa8734 100644
--- a/src/UserGuide/latest/User-Manual/Database-Programming.md
+++ b/src/UserGuide/latest/User-Manual/Database-Programming.md
@@ -23,7 +23,7 @@
 
 ## TRIGGER
 
-### 1. Instructions
+### Instructions
 
 The trigger provides a mechanism for listening to changes in time series data. With user-defined logic, tasks such as alerting and data forwarding can be conducted.
 
@@ -49,7 +49,7 @@ There are currently two trigger events for the trigger, and other trigger events
 - BEFORE INSERT: Fires before the data is persisted. **Please note that currently the trigger does not support data cleaning and will not change the data to be persisted itself.**
 - AFTER INSERT: Fires after the data is persisted.
 
-### 2. How to Implement a Trigger
+### How to Implement a Trigger
 
 You need to implement the trigger by writing a Java class, where the dependency shown below is required. If you use [Maven](http://search.maven.org/), you can search for them directly from the [Maven repository](http://search.maven.org/).
 
@@ -320,7 +320,7 @@ public class ClusterAlertingExample implements Trigger {
 }
 ```
 
-### 3. Trigger Management
+### Trigger Management
 
 You can create and drop a trigger through an SQL statement, and you can also query all registered triggers through an SQL statement.
 
@@ -450,7 +450,7 @@ During the process of creating and dropping triggers in the cluster, we maintain
 | DROPPING     | Intermediate state of executing `DROP TRIGGER`, the cluster is in the process of dropping the trigger. | NO                                |
 | TRANSFERRING | The cluster is migrating the location of this trigger instance. | NO                                |
 
-### 4. Notes
+### Notes
 
 - The trigger takes effect from the time of registration, and does not process the existing historical data. **That is, only insertion requests that occur after the trigger is successfully registered will be listened to by the trigger. **
 - The fire process of trigger is synchronous currently, so you need to ensure the efficiency of the trigger, otherwise the writing performance may be greatly affected. **You need to guarantee concurrency safety of triggers yourself**.
@@ -460,7 +460,7 @@ During the process of creating and dropping triggers in the cluster, we maintain
 - The trigger JAR package has a size limit, which must be less than min(`config_node_ratis_log_appender_buffer_size_max`, 2G), where `config_node_ratis_log_appender_buffer_size_max` is a configuration item. For the specific meaning, please refer to the IOTDB configuration item description.
 - **It is better not to have classes with the same full class name but different function implementations in different JAR packages.** For example, trigger1 and trigger2 correspond to resources trigger1.jar and trigger2.jar respectively. If two JAR packages contain a `org.apache.iotdb.trigger.example.AlertListener` class, when `CREATE TRIGGER` uses this class, the system will randomly load the class in one of the JAR packages, which will eventually leads the inconsistent behavior of trigger and other issues.
 
-### 5. Configuration Parameters
+### Configuration Parameters
 
 | Parameter                                         | Meaning                                                      |
 | ------------------------------------------------- | ------------------------------------------------------------ |
@@ -469,13 +469,13 @@ During the process of creating and dropping triggers in the cluster, we maintain
 
 ## CONTINUOUS QUERY (CQ)
 
-### 1. Introduction
+### Introduction
 
 Continuous queries(CQ) are queries that run automatically and periodically on realtime data and store query results in other specified time series.
 
 Users can implement sliding window streaming computing through continuous query, such as calculating the hourly average temperature of a sequence and writing it into a new sequence. Users can customize the `RESAMPLE` clause to create different sliding windows, which can achieve a certain degree of tolerance for out-of-order data.
 
-### 2. Syntax
+### Syntax
 
 ```sql
 CREATE (CONTINUOUS QUERY | CQ) <cq_id> 
@@ -540,7 +540,7 @@ END
 
 ##### `<every_interval>`  is not zero
 
-![4](https://alioss.timecho.com/docs/img/UserGuide/Process-Data/Continuous-Query/pic4.png?raw=true)
+![](https://alioss.timecho.com/docs/img/UserGuide/Process-Data/Continuous-Query/pic4.png?raw=true)
 
 
 - `TIMEOUT POLICY` specify how we deal with the cq task whose previous time interval execution is not finished while the next execution time has reached. The default value is `BLOCKED`.
@@ -548,7 +548,7 @@ END
     - `DISCARD` means that we just discard the current cq execution task and wait for the next execution time and do the next time interval cq task. If using `DISCARD` policy, some time intervals won't be executed when the execution time of one cq task is longer than the `<every_interval>`. However, once a cq task is executed, it will use the latest time interval, so it can catch up at the sacrifice of some time intervals being discarded.
 
 
-### 3. Examples of CQ
+### Examples of CQ
 
 The examples below use the following sample data. It's a real time data stream and we can assume that the data arrives on time.
 
@@ -931,7 +931,7 @@ At **2021-05-11T22:19:00.000+08:00**, `cq5` executes a query within the time ran
 +-----------------------------+-------------------------------+-----------+
 ````
 
-### 4. CQ Management
+### CQ Management
 
 #### Listing continuous queries
 
@@ -979,7 +979,7 @@ DROP CONTINUOUS QUERY s1_count_cq;
 CQs can't be altered once they're created. To change a CQ, you must `DROP` and re`CREATE` it with the updated settings.
 
 
-### 5. CQ Use Cases
+### CQ Use Cases
 
 #### Downsampling and Data Retention
 
@@ -1005,7 +1005,7 @@ SELECT avg(count_s1) from (select count(s1) as count_s1 from root.sg.d group by(
 
 To get the same results:
 
-**1. Create a CQ**
+**Create a CQ**
 
 This step performs the nested sub query in from clause of the query above. The following CQ automatically calculates the number of non-null values of `s1` at 30 minute intervals and writes those counts into the new `root.sg_count.d.count_s1` time series.
 
@@ -1019,7 +1019,7 @@ BEGIN
 END
 ```
 
-**2. Query the CQ results**
+**Query the CQ results**
 
 Next step performs the avg([...]) part of the outer query above.
 
@@ -1030,7 +1030,7 @@ SELECT avg(count_s1) from root.sg_count.d;
 ```
 
 
-### 6. System Parameter Configuration
+### System Parameter Configuration
 
 | Name                                        | Description                                                  | Data Type | Default Value |
 | :------------------------------------------ | ------------------------------------------------------------ | --------- | ------------- |
diff --git a/src/zh/UserGuide/Master/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/Master/SQL-Manual/SQL-Manual.md
index bcdd9a8ee..435daad2f 100644
--- a/src/zh/UserGuide/Master/SQL-Manual/SQL-Manual.md
+++ b/src/zh/UserGuide/Master/SQL-Manual/SQL-Manual.md
@@ -2,7 +2,7 @@
 
 ## 元数据操作
 
-### 1、数据库管理
+### 数据库管理
 
 #### 创建数据库
 
@@ -35,7 +35,7 @@ count databases root.sgcc.*
 count databases root.sgcc
 ```
 
-### 2、时间序列管理
+### 时间序列管理
 
 #### 创建时间序列
 
@@ -86,11 +86,12 @@ drop timeseries root.ln.wf02.*
 ```sql
 SHOW TIMESERIES
 SHOW TIMESERIES <Path>
-show timeseries root.**
-show timeseries root.ln.**
-show timeseries root.ln.** limit 10 offset 10
-show timeseries root.ln.** where timeseries contains 'wf01.wt'
-show timeseries root.ln.** where dataType=FLOAT
+SHOW TIMESERIES root.**
+SHOW TIMESERIES root.ln.**
+SHOW TIMESERIES root.ln.** limit 10 offset 10
+SHOW TIMESERIES root.ln.** where timeseries contains 'wf01.wt'
+SHOW TIMESERIES root.ln.** where dataType=FLOAT
+SHOW TIMESERIES root.ln.** where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:00;
 SHOW LATEST TIMESERIES
 ```
 
@@ -106,6 +107,7 @@ COUNT TIMESERIES root.** WHERE DATATYPE = INT64
 COUNT TIMESERIES root.** WHERE TAGS(unit) contains 'c' 
 COUNT TIMESERIES root.** WHERE TAGS(unit) = 'c' 
 COUNT TIMESERIES root.** WHERE TIMESERIES contains 'sgcc' group by level = 1
+COUNT TIMESERIES root.** WHERE time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:00;
 COUNT TIMESERIES root.** GROUP BY LEVEL=1
 COUNT TIMESERIES root.ln.** GROUP BY LEVEL=2
 COUNT TIMESERIES root.ln.wf01.* GROUP BY LEVEL=2
@@ -195,7 +197,7 @@ create aligned timeseries root.sg1.d1(s1 INT32 tags(tag1=v1, tag2=v2) attributes
 show timeseries where TAGS(tag1)='v1'
 ```
 
-### 3、时间序列路径管理
+### 时间序列路径管理
 
 #### 查看路径的所有子路径
 
@@ -218,6 +220,8 @@ SHOW CHILD NODES pathPattern
 IoTDB> show devices
 
 IoTDB> show devices root.ln.**
+
+IoTDB> show devices where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:00;
 ```
 ##### 查看设备及其 database 信息
 
@@ -240,19 +244,21 @@ IoTDB > COUNT NODES root.**.temperature LEVEL=3
 #### 统计设备数量
 
 ```sql
-IoTDB> show devices
 
 IoTDB> count devices
 
 IoTDB> count devices root.ln.**
+
+IoTDB> count devices where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:00;
 ```
-### 4、设备模板管理
+### 设备模板管理
 
-![img](https://alioss.timecho.com/docs/img/%E6%A8%A1%E6%9D%BF.png)
 
+![img](https://alioss.timecho.com/docs/img/%E6%A8%A1%E6%9D%BF.png)
 ![img](https://alioss.timecho.com/docs/img/template.jpg)
 
 
+
 #### 创建设备模板
 
 ```Go
@@ -321,7 +327,7 @@ IoTDB> unset device template t1 from root.sg1.d1
 ```sql
 IoTDB> drop device template t1
 ```
-### 5、数据存活时间管理
+### 数据存活时间管理
 
 #### 设置 TTL
 ```sql
@@ -353,25 +359,25 @@ IoTDB> SHOW TTL ON StorageGroupNames
 ```
 ## 写入数据
 
-### 1、写入单列数据
+### 写入单列数据
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp,status) values(1,true)
 ```
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp,hardware) values(1, 'v1'),(2, 'v1')
 ```
-### 2、写入多列数据
+### 写入多列数据
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp, status, hardware) values (2, false, 'v2')
 ```
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp, status, hardware) VALUES (3, false, 'v3'),(4, true, 'v4')
 ```
-### 3、使用服务器时间戳
+### 使用服务器时间戳
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(status, hardware) values (false, 'v2')
 ```
-### 4、写入对齐时间序列数据
+### 写入对齐时间序列数据
 ```sql
 IoTDB > create aligned timeseries root.sg1.d1(s1 INT32, s2 DOUBLE)
 ```
@@ -384,7 +390,7 @@ IoTDB > insert into root.sg1.d1(timestamp, s1, s2) aligned values(2, 2, 2), (3,
 ```sql
 IoTDB > select * from root.sg1.d1
 ```
-### 5、加载 TsFile 文件数据
+### 加载 TsFile 文件数据
 
 load '<path/dir>' [sglevel=int][verify=true/false][onSuccess=delete/none]
 
@@ -410,7 +416,7 @@ load '<path/dir>' [sglevel=int][verify=true/false][onSuccess=delete/none]
 
 ## 删除数据
 
-### 1、删除单列数据
+### 删除单列数据
 ```sql
 delete from root.ln.wf02.wt02.status where time<=2017-11-01T16:26:00;
 ```
@@ -451,7 +457,7 @@ expressions like : time > XXX, time <= XXX, or two atomic expressions connected
 ```sql
 delete from root.ln.wf02.wt02.status
 ```
-### 2、删除多列数据
+### 删除多列数据
 ```sql
 delete from root.ln.wf02.wt02.* where time <= 2017-11-01T16:26:00;
 ```
@@ -463,7 +469,7 @@ Msg: The statement is executed successfully.
 ```
 ## 数据查询
 
-### 1、基础查询
+### 基础查询
 
 #### 时间过滤查询
 ```sql
@@ -485,7 +491,7 @@ select wf01.wt01.status, wf02.wt02.hardware from root.ln where (time > 2017-11-0
 ```sql
 select * from root.ln.** where time > 1 order by time desc limit 10;
 ```
-### 2、选择表达式
+### 选择表达式
 
 #### 使用别名
 ```sql
@@ -591,7 +597,7 @@ IoTDB> select last status, temperature from root.ln.wf01.wt01 where time >= 2017
 ```sql
 IoTDB> select last * from root.ln.wf01.wt01 order by timeseries desc;
 ```
-### 3、查询过滤条件
+### 查询过滤条件
 
 #### 时间过滤条件
 
@@ -660,7 +666,7 @@ IoTDB> select * from root.sg.d1 where value regexp '^[A-Za-z]+$'
 IoTDB> select * from root.sg.d1 where value regexp '^[a-z]+$' and time > 100
 ```
 
-### 4、分段分组聚合
+### 分段分组聚合
 
 #### 未指定滑动步长的时间区间分组聚合查询
 ```sql
@@ -782,7 +788,7 @@ select count(charging_stauts), first_value(soc) from root.sg group by count(char
 ```sql
 select count(charging_stauts), first_value(soc) from root.sg group by count(charging_status,5,ignoreNull=false) 
 ```
-### 5、聚合结果过滤
+### 聚合结果过滤
 
 不正确的：
 ```sql
@@ -800,7 +806,7 @@ SQL 示例：
 
  select count(s1), count(s2) from root.** group by ([1,11),2ms) having count(s2) > 1 align by device;
 ```
-### 6、结果集补空值
+### 结果集补空值
 ```sql
 FILL '(' PREVIOUS | LINEAR | constant (, interval=DURATION_LITERAL)? ')'
 ```
@@ -824,7 +830,7 @@ select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:
 ```sql
 select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:37:00.000 and time <= 2017-11-01T16:40:00.000 fill(true);
 ```
-### 7、查询结果分页
+### 查询结果分页
 
 #### 按行分页
 
@@ -862,7 +868,7 @@ select max_value(*) from root.ln.wf01.wt01 group by ([2017-11-01T00:00:00, 2017-
 ```sql
 select * from root.ln.wf01.wt01 limit 10 offset 100 slimit 2 soffset 0
 ```
-### 8、排序
+### 排序
 
 时间对齐模式下的排序
 ```sql
@@ -884,13 +890,13 @@ select * from root.ln.** where time <= 2017-11-01T00:01:00 align by device;
 ```sql
 select count(*) from root.ln.** group by ((2017-11-01T00:00:00.000+08:00,2017-11-01T00:03:00.000+08:00],1m) order by device asc,time asc align by device
 ```
-### 9、查询对齐模式
+### 查询对齐模式
 
 #### 按设备对齐
 ```sql
 select * from root.ln.** where time <= 2017-11-01T00:01:00 align by device;
 ```
-### 10、查询写回（SELECT INTO）
+### 查询写回（SELECT INTO）
 
 #### 整体描述
 ```sql
@@ -944,7 +950,7 @@ IoTDB> select s1 + s2 into root.expr.add(d1s1_d1s2), root.expr.add(d2s1_d2s2) fr
 
 ##### 按时间对齐（默认）
 
-###### （1）目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
 ```
 
 select s1, s2
@@ -964,7 +970,7 @@ into root.sg_copy.d1(s1), root.sg_copy.d2(s1), root.sg_copy.d1(s2), root.sg_copy
 from root.sg.d1, root.sg.d2;
 ```
 
-###### （2）目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
 
 ```
 select d1.s1, d1.s2, d2.s3, d3.s4 
@@ -974,7 +980,7 @@ into ::(s1_1, s2_2), root.sg.d2_2(s3_3), root.${2}_copy.::(s4)
 from root.sg;
 ```
 
-###### （3）目标设备使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表使用变量占位符
 
 ```
 select * into root.sg_bk.::(::) from root.sg.**;
@@ -982,7 +988,7 @@ select * into root.sg_bk.::(::) from root.sg.**;
 
 ##### 按设备对齐（使用 `ALIGN BY DEVICE`）
 
-###### （1）目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
 ```
 
 select s1, s2, s3, s4
@@ -994,7 +1000,7 @@ from root.sg.d1, root.sg.d2, root.sg.d3
 align by device;
 ```
 
-###### （2）目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
 ```
 
 select avg(s1), sum(s2) + sum(s3), count(s4)
@@ -1006,7 +1012,7 @@ from root.**
 align by device;
 ```
 
-###### （3）目标设备使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表使用变量占位符
 ```
 
 select * into ::(backup_${4}) from root.sg.** align by device;
@@ -1026,7 +1032,6 @@ explain select s1,s2 from root.sg.d1
 ```
 explain analyze select s1,s2 from root.sg.d1 order by s1
 ```
-
 ## 运算符
 
 更多见文档[Operator-and-Expression](../Reference/Function-and-Expression.md#算数运算符和函数)
@@ -1189,8 +1194,7 @@ SELECT DIFF(s1, 'ignoreNull'='false'), DIFF(s2, 'ignoreNull'='false') from root.
 更多见文档[Sample Functions](../Reference/Function-and-Expression.md#采样函数)。
 ### 时间序列处理函数
 
-| 函数名        | 输入序列类型                   | 参数 | 输出序列类型             | 功能描述                   |
-| ---)
+更多见文档[Sample Functions](../Reference/Function-and-Expression.md#时间序列处理函数)。
 
 ```sql
 select equal_size_bucket_random_sample(temperature,'proportion'='0.1') as random_sample from root.ln.wf01.wt01;
diff --git a/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md b/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md
index c904b6630..435daad2f 100644
--- a/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md
+++ b/src/zh/UserGuide/latest/SQL-Manual/SQL-Manual.md
@@ -2,7 +2,7 @@
 
 ## 元数据操作
 
-### 1、数据库管理
+### 数据库管理
 
 #### 创建数据库
 
@@ -35,7 +35,7 @@ count databases root.sgcc.*
 count databases root.sgcc
 ```
 
-### 2、时间序列管理
+### 时间序列管理
 
 #### 创建时间序列
 
@@ -197,7 +197,7 @@ create aligned timeseries root.sg1.d1(s1 INT32 tags(tag1=v1, tag2=v2) attributes
 show timeseries where TAGS(tag1)='v1'
 ```
 
-### 3、时间序列路径管理
+### 时间序列路径管理
 
 #### 查看路径的所有子路径
 
@@ -251,7 +251,7 @@ IoTDB> count devices root.ln.**
 
 IoTDB> count devices where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:00;
 ```
-### 4、设备模板管理
+### 设备模板管理
 
 
 ![img](https://alioss.timecho.com/docs/img/%E6%A8%A1%E6%9D%BF.png)
@@ -327,7 +327,7 @@ IoTDB> unset device template t1 from root.sg1.d1
 ```sql
 IoTDB> drop device template t1
 ```
-### 5、数据存活时间管理
+### 数据存活时间管理
 
 #### 设置 TTL
 ```sql
@@ -359,25 +359,25 @@ IoTDB> SHOW TTL ON StorageGroupNames
 ```
 ## 写入数据
 
-### 1、写入单列数据
+### 写入单列数据
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp,status) values(1,true)
 ```
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp,hardware) values(1, 'v1'),(2, 'v1')
 ```
-### 2、写入多列数据
+### 写入多列数据
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp, status, hardware) values (2, false, 'v2')
 ```
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(timestamp, status, hardware) VALUES (3, false, 'v3'),(4, true, 'v4')
 ```
-### 3、使用服务器时间戳
+### 使用服务器时间戳
 ```sql
 IoTDB > insert into root.ln.wf02.wt02(status, hardware) values (false, 'v2')
 ```
-### 4、写入对齐时间序列数据
+### 写入对齐时间序列数据
 ```sql
 IoTDB > create aligned timeseries root.sg1.d1(s1 INT32, s2 DOUBLE)
 ```
@@ -390,7 +390,7 @@ IoTDB > insert into root.sg1.d1(timestamp, s1, s2) aligned values(2, 2, 2), (3,
 ```sql
 IoTDB > select * from root.sg1.d1
 ```
-### 5、加载 TsFile 文件数据
+### 加载 TsFile 文件数据
 
 load '<path/dir>' [sglevel=int][verify=true/false][onSuccess=delete/none]
 
@@ -416,7 +416,7 @@ load '<path/dir>' [sglevel=int][verify=true/false][onSuccess=delete/none]
 
 ## 删除数据
 
-### 1、删除单列数据
+### 删除单列数据
 ```sql
 delete from root.ln.wf02.wt02.status where time<=2017-11-01T16:26:00;
 ```
@@ -457,7 +457,7 @@ expressions like : time > XXX, time <= XXX, or two atomic expressions connected
 ```sql
 delete from root.ln.wf02.wt02.status
 ```
-### 2、删除多列数据
+### 删除多列数据
 ```sql
 delete from root.ln.wf02.wt02.* where time <= 2017-11-01T16:26:00;
 ```
@@ -469,7 +469,7 @@ Msg: The statement is executed successfully.
 ```
 ## 数据查询
 
-### 1、基础查询
+### 基础查询
 
 #### 时间过滤查询
 ```sql
@@ -491,7 +491,7 @@ select wf01.wt01.status, wf02.wt02.hardware from root.ln where (time > 2017-11-0
 ```sql
 select * from root.ln.** where time > 1 order by time desc limit 10;
 ```
-### 2、选择表达式
+### 选择表达式
 
 #### 使用别名
 ```sql
@@ -597,7 +597,7 @@ IoTDB> select last status, temperature from root.ln.wf01.wt01 where time >= 2017
 ```sql
 IoTDB> select last * from root.ln.wf01.wt01 order by timeseries desc;
 ```
-### 3、查询过滤条件
+### 查询过滤条件
 
 #### 时间过滤条件
 
@@ -666,7 +666,7 @@ IoTDB> select * from root.sg.d1 where value regexp '^[A-Za-z]+$'
 IoTDB> select * from root.sg.d1 where value regexp '^[a-z]+$' and time > 100
 ```
 
-### 4、分段分组聚合
+### 分段分组聚合
 
 #### 未指定滑动步长的时间区间分组聚合查询
 ```sql
@@ -788,7 +788,7 @@ select count(charging_stauts), first_value(soc) from root.sg group by count(char
 ```sql
 select count(charging_stauts), first_value(soc) from root.sg group by count(charging_status,5,ignoreNull=false) 
 ```
-### 5、聚合结果过滤
+### 聚合结果过滤
 
 不正确的：
 ```sql
@@ -806,7 +806,7 @@ SQL 示例：
 
  select count(s1), count(s2) from root.** group by ([1,11),2ms) having count(s2) > 1 align by device;
 ```
-### 6、结果集补空值
+### 结果集补空值
 ```sql
 FILL '(' PREVIOUS | LINEAR | constant (, interval=DURATION_LITERAL)? ')'
 ```
@@ -830,7 +830,7 @@ select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:
 ```sql
 select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:37:00.000 and time <= 2017-11-01T16:40:00.000 fill(true);
 ```
-### 7、查询结果分页
+### 查询结果分页
 
 #### 按行分页
 
@@ -868,7 +868,7 @@ select max_value(*) from root.ln.wf01.wt01 group by ([2017-11-01T00:00:00, 2017-
 ```sql
 select * from root.ln.wf01.wt01 limit 10 offset 100 slimit 2 soffset 0
 ```
-### 8、排序
+### 排序
 
 时间对齐模式下的排序
 ```sql
@@ -890,13 +890,13 @@ select * from root.ln.** where time <= 2017-11-01T00:01:00 align by device;
 ```sql
 select count(*) from root.ln.** group by ((2017-11-01T00:00:00.000+08:00,2017-11-01T00:03:00.000+08:00],1m) order by device asc,time asc align by device
 ```
-### 9、查询对齐模式
+### 查询对齐模式
 
 #### 按设备对齐
 ```sql
 select * from root.ln.** where time <= 2017-11-01T00:01:00 align by device;
 ```
-### 10、查询写回（SELECT INTO）
+### 查询写回（SELECT INTO）
 
 #### 整体描述
 ```sql
@@ -950,7 +950,7 @@ IoTDB> select s1 + s2 into root.expr.add(d1s1_d1s2), root.expr.add(d2s1_d2s2) fr
 
 ##### 按时间对齐（默认）
 
-###### （1）目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
 ```
 
 select s1, s2
@@ -970,7 +970,7 @@ into root.sg_copy.d1(s1), root.sg_copy.d2(s1), root.sg_copy.d1(s2), root.sg_copy
 from root.sg.d1, root.sg.d2;
 ```
 
-###### （2）目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
 
 ```
 select d1.s1, d1.s2, d2.s3, d3.s4 
@@ -980,7 +980,7 @@ into ::(s1_1, s2_2), root.sg.d2_2(s3_3), root.${2}_copy.::(s4)
 from root.sg;
 ```
 
-###### （3）目标设备使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表使用变量占位符
 
 ```
 select * into root.sg_bk.::(::) from root.sg.**;
@@ -988,7 +988,7 @@ select * into root.sg_bk.::(::) from root.sg.**;
 
 ##### 按设备对齐（使用 `ALIGN BY DEVICE`）
 
-###### （1）目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备不使用变量占位符 & 目标物理量列表使用变量占位符
 ```
 
 select s1, s2, s3, s4
@@ -1000,7 +1000,7 @@ from root.sg.d1, root.sg.d2, root.sg.d3
 align by device;
 ```
 
-###### （2）目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表不使用变量占位符
 ```
 
 select avg(s1), sum(s2) + sum(s3), count(s4)
@@ -1012,7 +1012,7 @@ from root.**
 align by device;
 ```
 
-###### （3）目标设备使用变量占位符 & 目标物理量列表使用变量占位符
+###### 目标设备使用变量占位符 & 目标物理量列表使用变量占位符
 ```
 
 select * into ::(backup_${4}) from root.sg.** align by device;