druid-docs-cn/querying/having.md

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## Having过滤器(groupBy)
> [!WARNING]
> Apache Druid支持两种查询语言 [Druid SQL](druidsql.md) 和 [原生查询](makeNativeQueries.md)。该文档描述了原生查询中的一种查询方式。 对于Druid SQL中使用的该种类型的信息可以参考 [SQL文档](druidsql.md)。
having语法用来通过对聚合后的值指定特定条件来决定从GroupBy的结果中返回符合条件的行基本等价于SQL语法中的**HAVING**
Apache Druid支持下列类型的having语法
### 查询过滤器(Query Filters)
所有的[Druid查询过滤器](filters.md)都可以被用来使用在查询体的Having部分中。 一个查询过滤器的HavingSpec如下
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type" : "filter",
"filter" : <any Druid query filter>
}
}
```
例如,使用一个选择过滤器(selector filter)
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type" : "filter",
"filter" : {
"type": "selector",
"dimension" : "<dimension>",
"value" : "<dimension_value>"
}
}
}
```
对结果行的时间戳进行使用Having语法的时候也可以生效如同在 "__time" 字段上使用过滤器。
### 数值过滤器(Numeric Filters)
最简单的having子句是数字过滤器。数字过滤器可以用作过滤器的更复杂布尔表达式的基过滤器。
下面是having子句数字筛选器的示例
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "greaterThan",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
}
}
```
**等于(equalTo)**
`equalTo`过滤器根据指定的聚合后的值进行匹配,返回等于值的行,语法如下:
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "equalTo",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
}
}
```
这种方式等价于 `HAVING <aggregate> > <value>`
**大于(Greater Than)**
`greaterThan`过滤器根据指定的聚合后的值进行匹配,返回大于值的行,语法如下:
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "greaterThan",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
}
}
```
这种方式等价于 `HAVING <aggregate> > <value>`
**小于(Less Than)**
`lessThan`过滤器根据指定的聚合后的值进行匹配,返回大于值的行,语法如下:
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "lessThan",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
}
}
```
这种方式等价于 `HAVING <aggregate> < <value>`
### 维度选择过滤器(Dimension Selector Filter)
**dimSelector**
dimSelector过滤器根据维度值等于特定值来匹配行语法如下
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "dimSelector",
"dimension": "<dimension>",
"value": <dimension_value>
}
}
```
### 逻辑表达式过滤器(Logical Expression Filters)
**AND**
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "and",
"havingSpecs": [
{
"type": "greaterThan",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
},
{
"type": "lessThan",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
}
]
}
}
```
**OR**
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "or",
"havingSpecs": [
{
"type": "greaterThan",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
},
{
"type": "equalTo",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
}
]
}
}
```
**NOT**
```json
{
"queryType": "groupBy",
"dataSource": "sample_datasource",
...
"having":
{
"type": "not",
"havingSpec":
{
"type": "equalTo",
"aggregation": "<aggregate_metric>",
"value": <numeric_value>
}
}
}
```