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add function: approx_most_frequent #800

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33 changes: 27 additions & 6 deletions src/UserGuide/Master/Table/SQL-Manual/Basis-Function.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -161,6 +161,7 @@ SELECT LEAST(temperature,humidity) FROM table2;
| 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.<br>`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. | `x` : The column to be calculated, supporting all existing data types in IoTDB;<br> `k`: The number of top-k most frequent values to return;<br>`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 |
Expand Down Expand Up @@ -251,8 +252,28 @@ Total line number = 1
It costs 0.022s
```

#### 2.3.5 Approx_most_frequent

#### 2.3.5 First
Query the ​​top 2 most frequent values​​ in the `temperature` column of `table1`.

```sql
IoTDB> select approx_most_frequent(temperature,2,100) as topk from table1;
```

The execution result is as follows:

```sql
+-------------------+
| topk|
+-------------------+
|{"85.0":6,"90.0":5}|
+-------------------+
Total line number = 1
It costs 0.064s
```


#### 2.3.6 First

Finds the values with the smallest timestamp that are not NULL in the `temperature` and `humidity` columns.

Expand All @@ -272,7 +293,7 @@ Total line number = 1
It costs 0.170s
```

#### 2.3.6 Last
#### 2.3.7 Last

Finds the values with the largest timestamp that are not NULL in the `temperature` and `humidity` columns.

Expand All @@ -292,7 +313,7 @@ Total line number = 1
It costs 0.211s
```

#### 2.3.7 First_by
#### 2.3.8 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.

Expand All @@ -312,7 +333,7 @@ Total line number = 1
It costs 0.269s
```

#### 2.3.8 Last_by
#### 2.3.9 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.

Expand All @@ -332,7 +353,7 @@ Total line number = 1
It costs 0.070s
```

#### 2.3.9 Max_by
#### 2.3.10 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.

Expand All @@ -352,7 +373,7 @@ Total line number = 1
It costs 0.172s
```

#### 2.3.10 Min_by
#### 2.3.11 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.

Expand Down
33 changes: 27 additions & 6 deletions src/UserGuide/latest-Table/SQL-Manual/Basis-Function.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -161,6 +161,7 @@ SELECT LEAST(temperature,humidity) FROM table2;
| 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.<br>`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. | `x` : The column to be calculated, supporting all existing data types in IoTDB;<br> `k`: The number of top-k most frequent values to return;<br>`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 |
Expand Down Expand Up @@ -251,8 +252,28 @@ Total line number = 1
It costs 0.022s
```

#### 2.3.5 Approx_most_frequent

#### 2.3.5 First
Query the ​​top 2 most frequent values​​ in the `temperature` column of `table1`.

```sql
IoTDB> select approx_most_frequent(temperature,2,100) as topk from table1;
```

The execution result is as follows:

```sql
+-------------------+
| topk|
+-------------------+
|{"85.0":6,"90.0":5}|
+-------------------+
Total line number = 1
It costs 0.064s
```


#### 2.3.6 First

Finds the values with the smallest timestamp that are not NULL in the `temperature` and `humidity` columns.

Expand All @@ -272,7 +293,7 @@ Total line number = 1
It costs 0.170s
```

#### 2.3.6 Last
#### 2.3.7 Last

Finds the values with the largest timestamp that are not NULL in the `temperature` and `humidity` columns.

Expand All @@ -292,7 +313,7 @@ Total line number = 1
It costs 0.211s
```

#### 2.3.7 First_by
#### 2.3.8 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.

Expand All @@ -312,7 +333,7 @@ Total line number = 1
It costs 0.269s
```

#### 2.3.8 Last_by
#### 2.3.9 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.

Expand All @@ -332,7 +353,7 @@ Total line number = 1
It costs 0.070s
```

#### 2.3.9 Max_by
#### 2.3.10 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.

Expand All @@ -352,7 +373,7 @@ Total line number = 1
It costs 0.172s
```

#### 2.3.10 Min_by
#### 2.3.11 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.

Expand Down
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