411 lines
10 KiB
Markdown
411 lines
10 KiB
Markdown
<!--toc-->
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<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script>
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<script>
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</script>
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## 查询粒度(Query granularities)
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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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粒度字段决定了数据如何被根据时间维度组织，或者如何按小时、天、分钟等进行聚合
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对于简单粒度可以使用字符串进行指定，或者对于任意粒度使用对象进行指定
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### **简单粒度(Simple Granularities)**
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简单粒度被指定为字符串和bucket时间戳（按UTC时间）（例如 days开始在UTC00：00）
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当前支持的粒度字符串有： `all`, `none`, `second`, `minute`, `fifteen_minute`, `thirty_minute`, `hour`, `day`, `week`, `month`, `quater` 和 `year`
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* `all`表示所有的数据都写入到一个bucket
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* `none`不存储数据（它实际上使用索引的粒度-这里的最小值是`none`，这意味着毫秒粒度）。目前不建议在[TimeseriesQuery](timeseriesquery.md) 中使用`none`（系统将尝试为所有不存在的毫秒生成0值，这通常是非常多的）。
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实例：
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假设有以下数据按秒的粒度摄入到Druid中：
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```json
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{"timestamp": "2013-08-31T01:02:33Z", "page": "AAA", "language" : "en"}
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{"timestamp": "2013-09-01T01:02:33Z", "page": "BBB", "language" : "en"}
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{"timestamp": "2013-09-02T23:32:45Z", "page": "CCC", "language" : "en"}
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{"timestamp": "2013-09-03T03:32:45Z", "page": "DDD", "language" : "en"}
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```
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当提交一个 `hour` 粒度的GroupBy查询时：
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```json
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{
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"queryType":"groupBy",
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"dataSource":"my_dataSource",
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"granularity":"hour",
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"dimensions":[
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"language"
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],
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"aggregations":[
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{
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"type":"count",
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"name":"count"
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}
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],
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"intervals":[
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"2000-01-01T00:00Z/3000-01-01T00:00Z"
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]
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}
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```
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将得到以下结果：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2013-08-31T01:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-01T01:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-02T23:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-03T03:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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} ]
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```
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可以注意到所有的空的buckets都被丢弃。
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如果查询粒度变为 `day`, 将会得到：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2013-08-31T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-01T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-02T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-03T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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} ]
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```
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如果查询粒度为 `none`, 将会得到和摄入的数据粒度一样的数据：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2013-08-31T01:02:33.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-01T01:02:33.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-02T23:32:45.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-03T03:32:45.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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} ]
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```
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**注意**：当查询时的 `granularity` 小于 [数据摄取](../DataIngestion/ingestion.md) 时候设置的 `queryGranularity`是不合理的，因为在存储的数据中没有更细粒度的数据了。 所以，当查询时设置的粒度小于摄取时设置的粒度时，Druid将基于`granularity`与`queryGranularity`相同的基础上进行生产结果。
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如果查询粒度更改为 `all`,将会在一个bucket中查到所以数据：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2000-01-01T00:00:00.000Z",
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"event" : {
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"count" : 4,
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"language" : "en"
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}
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} ]
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```
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### **持续时间粒度**
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持续时间粒度指定为精确的持续时间（毫秒），时间戳返回为UTC。持续时间粒度值以毫秒为单位。
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它们还支持指定可选的原点，该原点定义从何处开始计算时间段（默认为1970-01-01T00:00:00Z）。
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```json
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{"type": "duration", "duration": 7200000}
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```
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每两小时就有一次
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```json
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{"type": "duration", "duration": 3600000, "origin": "2012-01-01T00:30:00Z"}
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```
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在每小时30分时每一小时就有一次。
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实例：
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还是使用上边摄入的数据的例子，当提交一个24小时持续的GroupBy查询：
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```json
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{
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"queryType":"groupBy",
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"dataSource":"my_dataSource",
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"granularity":{"type": "duration", "duration": "86400000"},
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"dimensions":[
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"language"
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],
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"aggregations":[
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{
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"type":"count",
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"name":"count"
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}
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],
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"intervals":[
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"2000-01-01T00:00Z/3000-01-01T00:00Z"
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]
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}
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```
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将会得到：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2013-08-31T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-01T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-02T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-03T00:00:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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} ]
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```
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如果设置了查询粒度的起始时间为 `2012-01-01T00:30:00Z` :
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```json
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"granularity":{"type": "duration", "duration": "86400000", "origin":"2012-01-01T00:30:00Z"}
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```
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将会得到：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2013-08-31T00:30:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-01T00:30:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-02T00:30:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-03T00:30:00.000Z",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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} ]
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```
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可以注意到每个Bucket的起始时间都在30分钟。
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### **周期性粒度**
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周期粒度以 [ISO8601](https://en.wikipedia.org/wiki/ISO_8601) 格式指定为年、月、周、小时、分钟和秒（如P2W、P3M、PT1H30M、PT0.750S）的任意周期组合。它们支持指定一个时区来确定时段边界的起始位置以及返回的时间戳的时区。默认情况下，年份从1月1日开始，月份从1月1日开始，周从周一开始，除非指定了原点。
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时区是可选的，默认为UTC。 起始时间也是可选的，默认为在给定时区的1970-01-01T00:00:00
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```json
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{"type": "period", "period": "P2D", "timeZone": "America/Los_Angeles"}
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```
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这将在太平洋时区持续两天。
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```json
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{"type": "period", "period": "P3M", "timeZone": "America/Los_Angeles", "origin": "2012-02-01T00:00:00-08:00"}
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```
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在太平洋时区，三个月的季度定义为从2月份开始，这将是三个月的时间段。
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同样使用上边的示例数据，在太平洋时区下提交一个一天的周期的GroupBy查询：
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```json
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{
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"queryType":"groupBy",
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"dataSource":"my_dataSource",
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"granularity":{"type": "period", "period": "P1D", "timeZone": "America/Los_Angeles"},
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"dimensions":[
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"language"
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],
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"aggregations":[
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{
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"type":"count",
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"name":"count"
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}
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],
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"intervals":[
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"1999-12-31T16:00:00.000-08:00/2999-12-31T16:00:00.000-08:00"
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]
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}
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```
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将会得到：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2013-08-30T00:00:00.000-07:00",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-08-31T00:00:00.000-07:00",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-02T00:00:00.000-07:00",
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"event" : {
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"count" : 2,
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"language" : "en"
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}
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} ]
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```
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**注意**： 每一个bucket的时间戳都已经转换成太平洋时间。 `{"timestamp": "2013-09-02T23:32:45Z", "page": "CCC", "language" : "en"}`和`{"timestamp": "2013-09-03T03:32:45Z", "page": "DDD", "language" : "en"}`两行被合并到一个bucket中，是因为在太平洋时区下是同一天。
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同时也可以注意到，groupBy查询中的`intervals`不会被转换成指定的时区，时区只会在查询结果中生效。
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如果设置了粒度的起始时间为：`1970-01-01T20:30:00-08:00`:
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```json
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"granularity":{"type": "period", "period": "P1D", "timeZone": "America/Los_Angeles", "origin": "1970-01-01T20:30:00-08:00"}
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```
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将会得到：
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```json
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[ {
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"version" : "v1",
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"timestamp" : "2013-08-29T20:30:00.000-07:00",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-08-30T20:30:00.000-07:00",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-01T20:30:00.000-07:00",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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}, {
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"version" : "v1",
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"timestamp" : "2013-09-02T20:30:00.000-07:00",
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"event" : {
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"count" : 1,
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"language" : "en"
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}
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} ]
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```
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注意到，查询中指定的`origin`与时区无关，它仅仅决定了第一个粒度bucket的起始点，在这种情况下，`{"timestamp": "2013-09-02T23:32:45Z", "page": "CCC", "language" : "en"}` 和 `{"timestamp": "2013-09-03T03:32:45Z", "page": "DDD", "language" : "en"}` 数据行就不在一个bucket中了。
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### **支持的时区**
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时区是由[Joda Time Library](https://www.joda.org/)提供的， 其使用的是标准IANA时区。 详情可以查看 [Joda Time时区支持](http://joda-time.sourceforge.net/timezones.html)
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