5.2 KiB
5.2 KiB
Timeseries查询
[!WARNING] Apache Druid支持两种查询语言: Druid SQL 和 原生查询。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。
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。
一个Timeseries查询的实例如下:
{
"queryType": "timeseries",
"dataSource": "sample_datasource",
"granularity": "day",
"descending": "true",
"filter": {
"type": "and",
"fields": [
{ "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" },
{ "type": "or",
"fields": [
{ "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" },
{ "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" }
]
}
]
},
"aggregations": [
{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
],
"postAggregations": [
{ "type": "arithmetic",
"name": "sample_divide",
"fn": "/",
"fields": [
{ "type": "fieldAccess", "name": "postAgg__sample_name1", "fieldName": "sample_name1" },
{ "type": "fieldAccess", "name": "postAgg__sample_name2", "fieldName": "sample_name2" }
]
}
],
"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ]
}
时间序列查询主要包括7个主要部分:
属性 | 描述 | 是否必须 |
---|---|---|
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 |
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。查看数据源可以获得更多信息 | 是 |
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 |
intervals |
ISO-8601格式的JSON对象,定义了要查询的时间范围 | 是 |
granularity |
定义了查询结果的粒度,参见 Granularity | 是 |
filter |
参见 Filters | 否 |
aggregations |
参见 聚合 | 否 |
postAggregations |
参见Post Aggregations | 否 |
limit |
限制返回结果数量的整数值,默认是unlimited | 否 |
context |
可以被用来修改查询行为,包括 Grand Total 和 Zero-filling。详情可以看 上下文参数部分中的所有参数类型 | 否 |
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:
[
{
"timestamp": "2012-01-01T00:00:00.000Z",
"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
},
{
"timestamp": "2012-01-02T00:00:00.000Z",
"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
}
]
Grand Total(共计)
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:
{
"queryType": "timeseries",
"dataSource": "sample_datasource",
"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ],
"granularity": "day",
"aggregations": [
{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
],
"context": {
"grandTotal": true
}
}
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。
Zero-filling(0填充)
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:
[
{
"timestamp": "2012-01-01T00:00:00.000Z",
"result": { "sample_name1": <some_value> }
},
{
"timestamp": "2012-01-02T00:00:00.000Z",
"result": { "sample_name1": 0 }
},
{
"timestamp": "2012-01-03T00:00:00.000Z",
"result": { "sample_name1": <some_value> }
}
]
完全位于数据间隔之外的时间不是零填充的。
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。
设置了此上下文标志的查询如下所示:
{
"queryType": "timeseries",
"dataSource": "sample_datasource",
"granularity": "day",
"aggregations": [
{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" }
],
"intervals": [ "2012-01-01T00:00:00.000/2012-01-04T00:00:00.000" ],
"context" : {
"skipEmptyBuckets": "true"
}
}