2020-08-05 03:24:42 -04:00
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2020-08-05 03:24:42 -04:00
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## Timeseries查询
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> [!WARNING]
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> 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)。
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该类型的查询将会得到一个时间序列的查询结果，返回的是一个JSON对象数组，数组中的每一个对象表示被Timeseries查询所查的值。
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一个Timeseries查询的实例如下：
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```json
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{
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"queryType": "timeseries",
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"dataSource": "sample_datasource",
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"granularity": "day",
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"descending": "true",
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"filter": {
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"type": "and",
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"fields": [
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{ "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" },
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{ "type": "or",
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"fields": [
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{ "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" },
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{ "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" }
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]
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}
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]
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},
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"aggregations": [
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{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
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{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
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],
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"postAggregations": [
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{ "type": "arithmetic",
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"name": "sample_divide",
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"fn": "/",
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"fields": [
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{ "type": "fieldAccess", "name": "postAgg__sample_name1", "fieldName": "sample_name1" },
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{ "type": "fieldAccess", "name": "postAgg__sample_name2", "fieldName": "sample_name2" }
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]
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}
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],
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"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ]
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}
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```
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时间序列查询主要包括7个主要部分：
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| 属性 | 描述 | 是否必须 |
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|-|-|-|
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| `queryType` | 该字符串总是"timeseries"; 该字段告诉Apache Druid如何去解释这个查询 | 是 |
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| `dataSource` | 用来标识查询的的字符串或者对象，与关系型数据库中的表类似。查看[数据源](datasource.md)可以获得更多信息 | 是 |
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| `descending` | 是否对结果集进行降序排序,默认是`false`, 也就是升序排列 | 否 |
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| `intervals` | ISO-8601格式的JSON对象，定义了要查询的时间范围 | 是 |
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| `granularity` | 定义了查询结果的粒度，参见 [Granularity](granularity.md) | 是 |
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| `filter` | 参见 [Filters](filters.md) | 否 |
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| `aggregations` | 参见 [聚合](Aggregations.md)| 否 |
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| `postAggregations` | 参见[Post Aggregations](postaggregation.md) | 否 |
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| `limit` | 限制返回结果数量的整数值，默认是unlimited | 否 |
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| `context` | 可以被用来修改查询行为，包括 [Grand Total](#grand-total共计) 和 [Zero-filling](#zero-filling0填充)。详情可以看 [上下文参数](query-context.md)部分中的所有参数类型 | 否 |
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为了将所有数据集中起来，上面的查询将从"sample_datasource"表返回2个数据点，在 2012-01-01 和 2012-01-03 期间每天一个。每个数据点将是sample_fieldName1的longSum、sample_fieldName2的doubleSum以及sample_fieldName1除以sample_fieldName2的double结果。输出如下：
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```json
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[
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{
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"timestamp": "2012-01-01T00:00:00.000Z",
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"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
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},
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{
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"timestamp": "2012-01-02T00:00:00.000Z",
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"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
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}
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]
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```
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### Grand Total(共计)
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Druid可以在时间序列查询的结果集中增加一个额外的"总计"行，通过在上下文中增加 `"grandTotal":true`来启用该功能，例如：
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```json
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{
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"queryType": "timeseries",
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"dataSource": "sample_datasource",
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"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ],
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"granularity": "day",
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"aggregations": [
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{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
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{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
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],
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"context": {
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"grandTotal": true
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}
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}
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```
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总计行将显示为结果数组中的最后一行，并且没有时间戳。即使查询以"降序"模式运行，它也将是最后一行。总计行中的后聚合将基于总计聚合计算。
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### Zero-filling(0填充)
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Timeseries查询通常用零填充空的内部时间。例如，如果对间隔2012-01-01/2012-01-04发出"Day"粒度时间序列查询，并且2012-01-02不存在数据，则将收到：
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```json
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[
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{
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"timestamp": "2012-01-01T00:00:00.000Z",
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"result": { "sample_name1": <some_value> }
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},
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{
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"timestamp": "2012-01-02T00:00:00.000Z",
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"result": { "sample_name1": 0 }
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},
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{
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"timestamp": "2012-01-03T00:00:00.000Z",
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"result": { "sample_name1": <some_value> }
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}
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]
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```
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完全位于数据间隔之外的时间不是零填充的。
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可以使用上下文标志"skipEmptyBuckets"禁用所有零填充。在此模式下，将从结果中省略2012-01-02的数据点。
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设置了此上下文标志的查询如下所示：
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```json
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{
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"queryType": "timeseries",
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"dataSource": "sample_datasource",
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"granularity": "day",
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"aggregations": [
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{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" }
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],
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"intervals": [ "2012-01-01T00:00:00.000/2012-01-04T00:00:00.000" ],
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"context" : {
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"skipEmptyBuckets": "true"
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}
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}
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```
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