diff --git a/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md b/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md
index 575551b3b..6c0ecd937 100644
--- a/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md
+++ b/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md
@@ -156,30 +156,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 Supported Aggregate Functions
-| Function Name | Description | Allowed Input Types | Output Type |
-|:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
-| COUNT | Counts the number of data points. | All types | INT64 |
-| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
-| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
-| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
-| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | Finds the maximum value. | All types | Same as input type |
-| MIN | Finds the minimum value. | All types | Same as input type |
-| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
-| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
-| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
-| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
-| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
-| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
-| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
-| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| Function Name | Description | Allowed Input Types | Output Type |
+|:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
+| COUNT | Counts the number of data points. | All types | INT64 |
+| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
+| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
+| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
+| APPROX_PERCENTILE | The APPROX_PERCENTILE function calculates the value at a specified percentile in a dataset, helping quickly understand data distribution (e.g., median, quartiles). It supports weighted percentile calculation. If the percentile does not point to an exact position, it returns a linear interpolation of adjacent values at that position.Memory usage depends on the number of centroids, and the maximum number of centroids can be limited using the compression parameter. Error can be estimated using empirical formulas.Note: This function is supported since V2.0.9-beta. | Unweighted Version: APPROX_PERCENTILE(x, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
percentage: Target percentile, DOUBLE type.
Weighted Version: APPROX_PERCENTILE(x, w, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
w: Weight column, integer type (must align with the length of x; NULL or 0 means the row is ignored).
percentage: Target percentile, DOUBLE type. | Same as the input column x. |
+| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | Finds the maximum value. | All types | Same as input type |
+| MIN | Finds the minimum value. | All types | Same as input type |
+| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
+| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
+| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
+| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
+| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
+| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
+| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
### 2.3 Examples
@@ -272,8 +273,27 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+Calculate the 90th percentile of the `temperature` column and the 50th percentile (median) of the `humidity` column from `table1` respectively, and return these two approximate percentile values.
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+**Execution Result:**
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+#### 2.3.7 First
Finds the values with the smallest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -293,7 +313,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
Finds the values with the largest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -313,7 +333,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
Finds the `time` value of the row with the smallest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the smallest timestamp that is not NULL in the `temperature` column.
@@ -333,7 +353,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
Queries the `time` value of the row with the largest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the largest timestamp that is not NULL in the `temperature` column.
@@ -353,7 +373,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
Queries the `time` value of the row where the `temperature` column is at its maximum, and the `humidity` value of the row where the `temperature` column is at its maximum.
@@ -373,7 +393,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
Queries the `time` value of the row where the `temperature` column is at its minimum, and the `humidity` value of the row where the `temperature` column is at its minimum.
diff --git a/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md b/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md
index 7f354dc89..4a007216e 100644
--- a/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md
+++ b/src/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md
@@ -157,30 +157,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 Supported Aggregate Functions
-| Function Name | Description | Allowed Input Types | Output Type |
-|:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
-| COUNT | Counts the number of data points. | All types | INT64 |
-| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
-| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
-| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
-| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | Finds the maximum value. | All types | Same as input type |
-| MIN | Finds the minimum value. | All types | Same as input type |
-| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
-| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
-| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
-| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
-| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
-| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
-| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
-| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| Function Name | Description | Allowed Input Types | Output Type |
+|:-----------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
+| COUNT | Counts the number of data points. | All types | INT64 |
+| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
+| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
+| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
+| APPROX_PERCENTILE | The APPROX_PERCENTILE function calculates the value at a specified percentile in a dataset, helping quickly understand data distribution (e.g., median, quartiles). It supports weighted percentile calculation. If the percentile does not point to an exact position, it returns a linear interpolation of adjacent values at that position.Memory usage depends on the number of centroids, and the maximum number of centroids can be limited using the compression parameter. Error can be estimated using empirical formulas.Note: This function is supported since V2.0.9.1. | Unweighted Version: APPROX_PERCENTILE(x, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
percentage: Target percentile, DOUBLE type.
Weighted Version: APPROX_PERCENTILE(x, w, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
w: Weight column, integer type (must align with the length of x; NULL or 0 means the row is ignored).
percentage: Target percentile, DOUBLE type. | Same as the input column x. |
+| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | Finds the maximum value. | All types | Same as input type |
+| MIN | Finds the minimum value. | All types | Same as input type |
+| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
+| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
+| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
+| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
+| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
+| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
+| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
### 2.3 Examples
@@ -273,8 +274,27 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+Calculate the 90th percentile of the `temperature` column and the 50th percentile (median) of the `humidity` column from `table1` respectively, and return these two approximate percentile values.
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+**Execution Result:**
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+#### 2.3.7 First
Finds the values with the smallest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -294,7 +314,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
Finds the values with the largest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -314,7 +334,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
Finds the `time` value of the row with the smallest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the smallest timestamp that is not NULL in the `temperature` column.
@@ -334,7 +354,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
Queries the `time` value of the row with the largest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the largest timestamp that is not NULL in the `temperature` column.
@@ -354,7 +374,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
Queries the `time` value of the row where the `temperature` column is at its maximum, and the `humidity` value of the row where the `temperature` column is at its maximum.
@@ -374,7 +394,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
Queries the `time` value of the row where the `temperature` column is at its minimum, and the `humidity` value of the row where the `temperature` column is at its minimum.
diff --git a/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md b/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md
index 575551b3b..6c0ecd937 100644
--- a/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md
+++ b/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md
@@ -156,30 +156,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 Supported Aggregate Functions
-| Function Name | Description | Allowed Input Types | Output Type |
-|:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
-| COUNT | Counts the number of data points. | All types | INT64 |
-| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
-| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
-| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
-| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | Finds the maximum value. | All types | Same as input type |
-| MIN | Finds the minimum value. | All types | Same as input type |
-| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
-| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
-| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
-| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
-| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
-| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
-| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
-| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| Function Name | Description | Allowed Input Types | Output Type |
+|:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
+| COUNT | Counts the number of data points. | All types | INT64 |
+| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
+| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
+| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
+| APPROX_PERCENTILE | The APPROX_PERCENTILE function calculates the value at a specified percentile in a dataset, helping quickly understand data distribution (e.g., median, quartiles). It supports weighted percentile calculation. If the percentile does not point to an exact position, it returns a linear interpolation of adjacent values at that position.Memory usage depends on the number of centroids, and the maximum number of centroids can be limited using the compression parameter. Error can be estimated using empirical formulas.Note: This function is supported since V2.0.9-beta. | Unweighted Version: APPROX_PERCENTILE(x, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
percentage: Target percentile, DOUBLE type.
Weighted Version: APPROX_PERCENTILE(x, w, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
w: Weight column, integer type (must align with the length of x; NULL or 0 means the row is ignored).
percentage: Target percentile, DOUBLE type. | Same as the input column x. |
+| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | Finds the maximum value. | All types | Same as input type |
+| MIN | Finds the minimum value. | All types | Same as input type |
+| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
+| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
+| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
+| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
+| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
+| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
+| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
### 2.3 Examples
@@ -272,8 +273,27 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+Calculate the 90th percentile of the `temperature` column and the 50th percentile (median) of the `humidity` column from `table1` respectively, and return these two approximate percentile values.
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+**Execution Result:**
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+#### 2.3.7 First
Finds the values with the smallest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -293,7 +313,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
Finds the values with the largest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -313,7 +333,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
Finds the `time` value of the row with the smallest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the smallest timestamp that is not NULL in the `temperature` column.
@@ -333,7 +353,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
Queries the `time` value of the row with the largest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the largest timestamp that is not NULL in the `temperature` column.
@@ -353,7 +373,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
Queries the `time` value of the row where the `temperature` column is at its maximum, and the `humidity` value of the row where the `temperature` column is at its maximum.
@@ -373,7 +393,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
Queries the `time` value of the row where the `temperature` column is at its minimum, and the `humidity` value of the row where the `temperature` column is at its minimum.
diff --git a/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md b/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md
index 7f354dc89..4a007216e 100644
--- a/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md
+++ b/src/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md
@@ -157,30 +157,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 Supported Aggregate Functions
-| Function Name | Description | Allowed Input Types | Output Type |
-|:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
-| COUNT | Counts the number of data points. | All types | INT64 |
-| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
-| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
-| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
-| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | Finds the maximum value. | All types | Same as input type |
-| MIN | Finds the minimum value. | All types | Same as input type |
-| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
-| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
-| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
-| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
-| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
-| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
-| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
-| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| Function Name | Description | Allowed Input Types | Output Type |
+|:-----------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|
+| COUNT | Counts the number of data points. | All types | INT64 |
+| COUNT_IF | COUNT_IF(exp) counts the number of rows that satisfy a specified boolean expression. | `exp` must be a boolean expression,(e.g. `count_if(temperature>20)`) | INT64 |
+| APPROX_COUNT_DISTINCT | The APPROX_COUNT_DISTINCT(x[, maxStandardError]) function provides an approximation of COUNT(DISTINCT x), returning the estimated number of distinct input values. | `x`: The target column to be calculated, supports all data types.
`maxStandardError` (optional): Specifies the maximum standard error allowed for the function's result. Valid range is [0.0040625, 0.26]. Defaults to 0.023 if not specified. | INT64 |
+| APPROX_MOST_FREQUENT | The APPROX_MOST_FREQUENT(x, k, capacity) function is used to approximately calculate the top k most frequent elements in a dataset. It returns a JSON-formatted string where the keys are the element values and the values are their corresponding approximate frequencies. （Available since V2.0.5.1) | `x` : The column to be calculated, supporting all existing data types in IoTDB;
`k`: The number of top-k most frequent values to return;
`capacity`: The number of buckets used for computation, which relates to memory usage—a larger value reduces error but consumes more memory, while a smaller value increases error but uses less memory. | STRING |
+| APPROX_PERCENTILE | The APPROX_PERCENTILE function calculates the value at a specified percentile in a dataset, helping quickly understand data distribution (e.g., median, quartiles). It supports weighted percentile calculation. If the percentile does not point to an exact position, it returns a linear interpolation of adjacent values at that position.Memory usage depends on the number of centroids, and the maximum number of centroids can be limited using the compression parameter. Error can be estimated using empirical formulas.Note: This function is supported since V2.0.9.1. | Unweighted Version: APPROX_PERCENTILE(x, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
percentage: Target percentile, DOUBLE type.
Weighted Version: APPROX_PERCENTILE(x, w, percentage)
x: Column to compute. Supports all numeric types: INT32, INT64, FLOAT, DOUBLE, TIMESTAMP.
w: Weight column, integer type (must align with the length of x; NULL or 0 means the row is ignored).
percentage: Target percentile, DOUBLE type. | Same as the input column x. |
+| SUM | Calculates the sum. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | Calculates the average. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | Finds the maximum value. | All types | Same as input type |
+| MIN | Finds the minimum value. | All types | Same as input type |
+| FIRST | Finds the value with the smallest timestamp that is not NULL. | All types | Same as input type |
+| LAST | Finds the value with the largest timestamp that is not NULL. | All types | Same as input type |
+| STDDEV | Alias for STDDEV_SAMP, calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | Calculates the population standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | Calculates the sample standard deviation. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | Alias for VAR_SAMP, calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | Calculates the population variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | Calculates the sample variance. | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | Finds the value with the largest absolute value. If the largest absolute values of positive and negative values are equal, returns the positive value. | INT32 INT64 FLOAT DOUBLE | Same as input type |
+| MODE | Finds the mode. Note: 1. There is a risk of memory exception when the number of distinct values in the input sequence is too large; 2. If all elements have the same frequency, i.e., there is no mode, a random element is returned; 3. If there are multiple modes, a random mode is returned; 4. NULL values are also counted in frequency, so even if not all values in the input sequence are NULL, the final result may still be NULL. | All types | Same as input type |
+| MAX_BY | MAX_BY(x, y) finds the value of x corresponding to the maximum y in the binary input x and y. MAX_BY(time, x) returns the timestamp when x is at its maximum. | x and y can be of any type | Same as the data type of the first input x |
+| MIN_BY | MIN_BY(x, y) finds the value of x corresponding to the minimum y in the binary input x and y. MIN_BY(time, x) returns the timestamp when x is at its minimum. | x and y can be of any type | Same as the data type of the first input x |
+| FIRST_BY | FIRST_BY(x, y) finds the value of x in the same row when y is the first non-null value. | x and y can be of any type | Same as the data type of the first input x |
+| LAST_BY | LAST_BY(x, y) finds the value of x in the same row when y is the last non-null value. | x and y can be of any type | Same as the data type of the first input x |
### 2.3 Examples
@@ -273,8 +274,27 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+Calculate the 90th percentile of the `temperature` column and the 50th percentile (median) of the `humidity` column from `table1` respectively, and return these two approximate percentile values.
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+**Execution Result:**
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+#### 2.3.7 First
Finds the values with the smallest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -294,7 +314,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
Finds the values with the largest timestamp that are not NULL in the `temperature` and `humidity` columns.
@@ -314,7 +334,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
Finds the `time` value of the row with the smallest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the smallest timestamp that is not NULL in the `temperature` column.
@@ -334,7 +354,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
Queries the `time` value of the row with the largest timestamp that is not NULL in the `temperature` column, and the `humidity` value of the row with the largest timestamp that is not NULL in the `temperature` column.
@@ -354,7 +374,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
Queries the `time` value of the row where the `temperature` column is at its maximum, and the `humidity` value of the row where the `temperature` column is at its maximum.
@@ -374,7 +394,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
Queries the `time` value of the row where the `temperature` column is at its minimum, and the `humidity` value of the row where the `temperature` column is at its minimum.
diff --git a/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md b/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md
index 78febd1b0..2cb7130bc 100644
--- a/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md
+++ b/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_apache.md
@@ -155,30 +155,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 支持的聚合函数
-| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
-|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|------------------|
-| COUNT | 计算数据点数。 | 所有类型 | INT64 |
-| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
-| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
-| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
-| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
-| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
-| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
-| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
-| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
+|-----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|------------------|
+| COUNT | 计算数据点数。 | 所有类型 | INT64 |
+| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
+| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
+| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
+| APPROX_PERCENTILE | APPROX_PERCENTILE 函数用于计算数据集中指定百分位数的值，帮助快速了解数据分布情况（如中位数、四分位数等），支持基于权重的百分位数计算；若百分位数不指向精确位置，返回相邻数值在该位置的线性插值。内存占用与质心数量相关，可通过 compression 参数限定最大质心数量，误差可通过经验公式预估。注意：该函数自 V2.0.9-beta 起支持 | 单权重版本：APPROX_PERCENTILE (x, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
percentage：目标分位数，DOUBLE 类型。
带权重版本：APPROX_PERCENTILE (x, w, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
w：权重列，整型（与待计算列长度对齐，Null 或 0 表示该行忽略）；
percentage：目标分位数，DOUBLE 类型。 | 与待计算列 x 的类型相同 |
+| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
+| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
+| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
+| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
+| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
### 2.3 示例
@@ -271,8 +272,28 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+从`table1` 中，分别计算列 temperature 的90 分位数和列 humidity 的50 分位数（中位数），返回这两个近似百分位数值。
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+执行结果如下：
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+
+#### 2.3.7 First
查询`temperature`列、`humidity`列时间戳最小且不为 NULL 的值。
@@ -292,7 +313,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
查询`temperature`列、`humidity`列时间戳最大且不为 NULL 的值。
@@ -312,7 +333,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
查询 `temperature` 列中非 NULL 且时间戳最小的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最小的行的 `humidity` 值。
@@ -332,7 +353,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
查询`temperature` 列中非 NULL 且时间戳最大的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最大的行的 `humidity` 值。
@@ -352,7 +373,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
查询`temperature` 列中最大值所在行的 `time` 值，以及`temperature` 列中最大值所在行的 `humidity` 值。
@@ -372,7 +393,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
查询`temperature` 列中最小值所在行的 `time` 值，以及`temperature` 列中最小值所在行的 `humidity` 值。
@@ -1272,7 +1293,7 @@ coalesce(value1, value2[, ...])
#### 8.1.1 cast(value AS type) → type
1. 显式地将一个值转换为指定类型。
-2. 可以用于将字符串（varchar）转换为数值类型，或数值转换为字符串类型。
+2. 可以用于将字符串（varchar）转换为数值类型，或数值转换为字符串类型
3. 如果转换失败，将抛出运行时错误。
示例：
diff --git a/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md b/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md
index 210eb2613..5ddef7e5c 100644
--- a/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md
+++ b/src/zh/UserGuide/Master/Table/SQL-Manual/Basis-Function_timecho.md
@@ -155,30 +155,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 支持的聚合函数
-| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
-|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|------------------|
-| COUNT | 计算数据点数。 | 所有类型 | INT64 |
-| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
-| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
-| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
-| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
-| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
-| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
-| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
-| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
+|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|
+| COUNT | 计算数据点数。 | 所有类型 | INT64 |
+| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
+| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
+| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
+| APPROX_PERCENTILE | APPROX_PERCENTILE 函数用于计算数据集中指定百分位数的值，帮助快速了解数据分布情况（如中位数、四分位数等），支持基于权重的百分位数计算；若百分位数不指向精确位置，返回相邻数值在该位置的线性插值。内存占用与质心数量相关，可通过 compression 参数限定最大质心数量，误差可通过经验公式预估。注意：该函数自 V2.0.9.1 起支持 | 单权重版本：APPROX_PERCENTILE (x, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
percentage：目标分位数，DOUBLE 类型。
带权重版本：APPROX_PERCENTILE (x, w, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
w：权重列，整型（与待计算列长度对齐，Null 或 0 表示该行忽略）；
percentage：目标分位数，DOUBLE 类型。 | 与待计算列 x 的类型相同 |
+| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
+| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
+| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
+| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
+| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
### 2.3 示例
@@ -271,8 +272,28 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+从`table1` 中，分别计算列 temperature 的90 分位数和列 humidity 的50 分位数（中位数），返回这两个近似百分位数值。
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+执行结果如下：
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+
+#### 2.3.7 First
查询`temperature`列、`humidity`列时间戳最小且不为 NULL 的值。
@@ -292,7 +313,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
查询`temperature`列、`humidity`列时间戳最大且不为 NULL 的值。
@@ -312,7 +333,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
查询 `temperature` 列中非 NULL 且时间戳最小的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最小的行的 `humidity` 值。
@@ -332,7 +353,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
查询`temperature` 列中非 NULL 且时间戳最大的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最大的行的 `humidity` 值。
@@ -352,7 +373,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
查询`temperature` 列中最大值所在行的 `time` 值，以及`temperature` 列中最大值所在行的 `humidity` 值。
@@ -372,7 +393,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
查询`temperature` 列中最小值所在行的 `time` 值，以及`temperature` 列中最小值所在行的 `humidity` 值。
diff --git a/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md b/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md
index b1c772aac..2cb7130bc 100644
--- a/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md
+++ b/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_apache.md
@@ -155,30 +155,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 支持的聚合函数
-| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
-|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|------------------|
-| COUNT | 计算数据点数。 | 所有类型 | INT64 |
-| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
-| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
-| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
-| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
-| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
-| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
-| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
-| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
+|-----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|------------------|
+| COUNT | 计算数据点数。 | 所有类型 | INT64 |
+| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
+| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
+| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
+| APPROX_PERCENTILE | APPROX_PERCENTILE 函数用于计算数据集中指定百分位数的值，帮助快速了解数据分布情况（如中位数、四分位数等），支持基于权重的百分位数计算；若百分位数不指向精确位置，返回相邻数值在该位置的线性插值。内存占用与质心数量相关，可通过 compression 参数限定最大质心数量，误差可通过经验公式预估。注意：该函数自 V2.0.9-beta 起支持 | 单权重版本：APPROX_PERCENTILE (x, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
percentage：目标分位数，DOUBLE 类型。
带权重版本：APPROX_PERCENTILE (x, w, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
w：权重列，整型（与待计算列长度对齐，Null 或 0 表示该行忽略）；
percentage：目标分位数，DOUBLE 类型。 | 与待计算列 x 的类型相同 |
+| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
+| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
+| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
+| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
+| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
### 2.3 示例
@@ -271,8 +272,28 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+从`table1` 中，分别计算列 temperature 的90 分位数和列 humidity 的50 分位数（中位数），返回这两个近似百分位数值。
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+执行结果如下：
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+
+#### 2.3.7 First
查询`temperature`列、`humidity`列时间戳最小且不为 NULL 的值。
@@ -292,7 +313,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
查询`temperature`列、`humidity`列时间戳最大且不为 NULL 的值。
@@ -312,7 +333,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
查询 `temperature` 列中非 NULL 且时间戳最小的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最小的行的 `humidity` 值。
@@ -332,7 +353,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
查询`temperature` 列中非 NULL 且时间戳最大的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最大的行的 `humidity` 值。
@@ -352,7 +373,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
查询`temperature` 列中最大值所在行的 `time` 值，以及`temperature` 列中最大值所在行的 `humidity` 值。
@@ -372,7 +393,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
查询`temperature` 列中最小值所在行的 `time` 值，以及`temperature` 列中最小值所在行的 `humidity` 值。
diff --git a/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md b/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md
index 210eb2613..5ddef7e5c 100644
--- a/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md
+++ b/src/zh/UserGuide/latest-Table/SQL-Manual/Basis-Function_timecho.md
@@ -155,30 +155,31 @@ SELECT LEAST(temperature,humidity) FROM table2;
### 2.2 支持的聚合函数
-| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
-|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|------------------|
-| COUNT | 计算数据点数。 | 所有类型 | INT64 |
-| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
-| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
-| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
-| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
-| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
-| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
-| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
-| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
-| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
-| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
-| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| 函数名 | 功能描述 | 允许的输入类型 | 输出类型 |
+|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|
+| COUNT | 计算数据点数。 | 所有类型 | INT64 |
+| COUNT_IF | COUNT_IF(exp) 用于统计满足指定布尔表达式的记录行数 | exp 必须是一个布尔类型的表达式，例如 count_if(temperature>20) | INT64 |
+| APPROX_COUNT_DISTINCT | APPROX_COUNT_DISTINCT(x[,maxStandardError]) 函数提供 COUNT(DISTINCT x) 的近似值，返回不同输入值的近似个数。 | `x`：待计算列，支持所有类型；
`maxStandardError`：指定该函数应产生的最大标准误差，取值范围[0.0040625, 0.26]，未指定值时默认0.023。 | INT64 |
+| APPROX_MOST_FREQUENT | APPROX_MOST_FREQUENT(x, k, capacity) 函数用于近似计算数据集中出现频率最高的前 k 个元素。它返回一个JSON 格式的字符串，其中键是该元素的值，值是该元素对应的近似频率。（V 2.0.5.1 及以后版本支持） | `x`：待计算列，支持 IoTDB 现有所有的数据类型；
`k`：返回出现频率最高的 k 个值；
`capacity`: 用于计算的桶的数量，跟内存占用相关：其值越大误差越小，但占用内存更大，反之capacity值越小误差越大，但占用内存更小。 | STRING |
+| APPROX_PERCENTILE | APPROX_PERCENTILE 函数用于计算数据集中指定百分位数的值，帮助快速了解数据分布情况（如中位数、四分位数等），支持基于权重的百分位数计算；若百分位数不指向精确位置，返回相邻数值在该位置的线性插值。内存占用与质心数量相关，可通过 compression 参数限定最大质心数量，误差可通过经验公式预估。注意：该函数自 V2.0.9.1 起支持 | 单权重版本：APPROX_PERCENTILE (x, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
percentage：目标分位数，DOUBLE 类型。
带权重版本：APPROX_PERCENTILE (x, w, percentage)
x：待计算列，支持 INT32、INT64、FLOAT、DOUBLE、TIMESTAMP 等所有数字类型；
w：权重列，整型（与待计算列长度对齐，Null 或 0 表示该行忽略）；
percentage：目标分位数，DOUBLE 类型。 | 与待计算列 x 的类型相同 |
+| SUM | 求和。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| AVG | 求平均值。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| MAX | 求最大值。 | 所有类型 | 与输入类型一致 |
+| MIN | 求最小值。 | 所有类型 | 与输入类型一致 |
+| FIRST | 求时间戳最小且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| LAST | 求时间戳最大且不为 NULL 的值。 | 所有类型 | 与输入类型一致 |
+| STDDEV | STDDEV_SAMP 的别名，求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_POP | 求总体标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| STDDEV_SAMP | 求样本标准差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VARIANCE | VAR_SAMP 的别名，求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_POP | 求总体方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| VAR_SAMP | 求样本方差。 | INT32 INT64 FLOAT DOUBLE | DOUBLE |
+| EXTREME | 求具有最大绝对值的值。如果正值和负值的最大绝对值相等，则返回正值。 | INT32 INT64 FLOAT DOUBLE | 与输入类型一致 |
+| MODE | 求众数。注意： 1.输入序列的不同值个数过多时会有内存异常风险; 2.如果所有元素出现的频次相同，即没有众数，则随机返回一个元素; 3.如果有多个众数，则随机返回一个众数； 4. NULL 值也会被统计频次，所以即使输入序列的值不全为 NULL，最终结果也可能为 NULL。 | 所有类型 | 与输入类型一致 |
+| MAX_BY | MAX_BY(x, y) 求二元输入 x 和 y 在 y 最大时对应的 x 的值。MAX_BY(time, x) 返回 x 取最大值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| MIN_BY | MIN_BY(x, y) 求二元输入 x 和 y 在 y 最小时对应的 x 的值。MIN_BY(time, x) 返回 x 取最小值时对应的时间戳。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| FIRST_BY | FIRST_BY(x, y) 求当 y 为第一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
+| LAST_BY | LAST_BY(x, y) 求当 y 为最后一个不为 NULL 的值时，同一行里对应的 x 值。 | x 和 y 可以是任意类型 | 与第一个输入 x 的数据类型一致 |
### 2.3 示例
@@ -271,8 +272,28 @@ Total line number = 1
It costs 0.064s
```
+#### 2.3.6 Approx_Percentile
-#### 2.3.6 First
+从`table1` 中，分别计算列 temperature 的90 分位数和列 humidity 的50 分位数（中位数），返回这两个近似百分位数值。
+
+```SQL
+SELECT APPROX_PERCENTILE(temperature,0.9), APPROX_PERCENTILE(humidity,0.5) FROM table1;
+```
+
+执行结果如下：
+
+```SQL
++-----+-----+
+|_col0|_col1|
++-----+-----+
+| 90.0| 35.2|
++-----+-----+
+Total line number = 1
+It costs 0.206s
+```
+
+
+#### 2.3.7 First
查询`temperature`列、`humidity`列时间戳最小且不为 NULL 的值。
@@ -292,7 +313,7 @@ Total line number = 1
It costs 0.170s
```
-#### 2.3.7 Last
+#### 2.3.8 Last
查询`temperature`列、`humidity`列时间戳最大且不为 NULL 的值。
@@ -312,7 +333,7 @@ Total line number = 1
It costs 0.211s
```
-#### 2.3.8 First_by
+#### 2.3.9 First_by
查询 `temperature` 列中非 NULL 且时间戳最小的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最小的行的 `humidity` 值。
@@ -332,7 +353,7 @@ Total line number = 1
It costs 0.269s
```
-#### 2.3.9 Last_by
+#### 2.3.10 Last_by
查询`temperature` 列中非 NULL 且时间戳最大的行的 `time` 值，以及 `temperature` 列中非 NULL 且时间戳最大的行的 `humidity` 值。
@@ -352,7 +373,7 @@ Total line number = 1
It costs 0.070s
```
-#### 2.3.10 Max_by
+#### 2.3.11 Max_by
查询`temperature` 列中最大值所在行的 `time` 值，以及`temperature` 列中最大值所在行的 `humidity` 值。
@@ -372,7 +393,7 @@ Total line number = 1
It costs 0.172s
```
-#### 2.3.11 Min_by
+#### 2.3.12 Min_by
查询`temperature` 列中最小值所在行的 `time` 值，以及`temperature` 列中最小值所在行的 `humidity` 值。