使用 Flux 窗口化和聚合数据
此页面记录了早期版本的 InfluxDB OSS。InfluxDB OSS v2 是最新的稳定版本。请参阅等效的 InfluxDB v2 文档： 使用 Flux 窗口化和聚合数据。
使用时间序列数据执行的常见操作是将数据分组到时间窗口中，或“窗口化”数据，然后将窗口化值聚合为新值。本指南将引导您完成使用 Flux 窗口化和聚合数据的过程，并演示在此过程中数据的形状是如何变化的。
如果您刚刚开始使用 Flux 查询，请查看以下内容
以下示例深入介绍了窗口化和聚合数据所需的步骤。aggregateWindow() 函数为您执行这些操作，但了解在此过程中数据的形状变化有助于成功创建所需的输出。
数据集
在本指南中，定义一个变量来表示您的基本数据集。以下示例查询主机内存使用情况。
dataSet = from(bucket: "db/rp")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "used_percent"
)
|> drop(columns: ["host"])
此示例从返回的数据中删除 host 列，因为内存数据仅针对单个主机进行跟踪，并且简化了输出表。如果监控多个主机上的内存，则不建议删除 host 列，此操作是可选的。
dataSet 现在可以用来表示您的基本数据，它看起来类似于以下内容
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:00.000000000Z 71.11611366271973
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:10.000000000Z 67.39630699157715
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:20.000000000Z 64.16666507720947
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:30.000000000Z 64.19951915740967
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:40.000000000Z 64.2122745513916
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:50.000000000Z 64.22209739685059
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:00.000000000Z 64.6336555480957
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:10.000000000Z 64.16516304016113
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:20.000000000Z 64.18349742889404
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:30.000000000Z 64.20474052429199
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:40.000000000Z 68.65062713623047
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:50.000000000Z 67.20139980316162
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:00.000000000Z 70.9143877029419
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:10.000000000Z 64.14549350738525
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:20.000000000Z 64.15379047393799
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:30.000000000Z 64.1592264175415
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:40.000000000Z 64.18190002441406
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:50.000000000Z 64.28837776184082
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:00.000000000Z 64.29731845855713
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:10.000000000Z 64.36963081359863
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:20.000000000Z 64.37397003173828
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:30.000000000Z 64.44413661956787
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:40.000000000Z 64.42906856536865
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:50.000000000Z 64.44573402404785
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:00.000000000Z 64.48912620544434
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:10.000000000Z 64.49522972106934
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:20.000000000Z 64.48652744293213
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:30.000000000Z 64.49949741363525
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:40.000000000Z 64.4949197769165
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:50.000000000Z 64.49787616729736
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49816226959229
窗口化数据
使用 window() 函数 根据时间范围对数据进行分组。与 window() 一起传递的最常见参数是 every，它定义了窗口之间的时间间隔。还有其他参数可用，但在本例中，将基本数据集窗口化为一分钟的窗口。
dataSet
|> window(every: 1m)
every 参数支持所有 有效的持续时间单位，包括日历月 (1mo) 和年 (1y)。
每个时间窗口都输出在其自己的表中，其中包含窗口内的所有记录。
window() 输出表
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:00.000000000Z 71.11611366271973
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:10.000000000Z 67.39630699157715
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:20.000000000Z 64.16666507720947
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:30.000000000Z 64.19951915740967
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:40.000000000Z 64.2122745513916
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:50.000000000Z 64.22209739685059
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:00.000000000Z 64.6336555480957
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:10.000000000Z 64.16516304016113
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:20.000000000Z 64.18349742889404
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:30.000000000Z 64.20474052429199
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:40.000000000Z 68.65062713623047
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:50.000000000Z 67.20139980316162
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:00.000000000Z 70.9143877029419
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:10.000000000Z 64.14549350738525
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:20.000000000Z 64.15379047393799
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:30.000000000Z 64.1592264175415
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:40.000000000Z 64.18190002441406
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:50.000000000Z 64.28837776184082
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:00.000000000Z 64.29731845855713
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:10.000000000Z 64.36963081359863
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:20.000000000Z 64.37397003173828
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:30.000000000Z 64.44413661956787
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:40.000000000Z 64.42906856536865
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:50.000000000Z 64.44573402404785
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:00.000000000Z 64.48912620544434
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:10.000000000Z 64.49522972106934
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:20.000000000Z 64.48652744293213
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:30.000000000Z 64.49949741363525
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:40.000000000Z 64.4949197769165
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:50.000000000Z 64.49787616729736
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:55:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49816226959229
在 InfluxDB UI 中可视化时，每个窗口表都以不同的颜色显示。
聚合数据
聚合函数 获取表内所有行的值，并使用它们执行聚合操作。结果作为单行表中的新值输出。
由于窗口化数据被拆分为单独的表，因此聚合操作分别针对每个表运行，并输出仅包含聚合值的新表。
在本例中，使用 mean() 函数 输出每个窗口的平均值
dataSet
|> window(every: 1m)
|> mean()
mean() 输出表
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 65.88549613952637
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 65.50651391347249
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 65.30719598134358
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 64.39330975214641
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 64.49386278788249
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
2018-11-03T17:55:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 64.49816226959229
由于每个数据点都包含在自己的表中，因此在可视化时，它们显示为单个、不连接的点。
重新创建时间列
请注意，聚合输出表中没有 _time 列。 因为每个表中的记录聚合在一起，所以它们的时间戳不再适用，并且该列从组键和表中删除。
另请注意，_start 和 _stop 列仍然存在。它们表示时间窗口的下限和上限。
许多 Flux 函数都依赖于 _time 列。要在聚合函数之后进一步处理数据，您需要重新添加 _time。使用 duplicate() 函数 将 _start 或 _stop 列复制为新的 _time 列。
dataSet
|> window(every: 1m)
|> mean()
|> duplicate(column: "_stop", as: "_time")
duplicate() 输出表
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:51:00.000000000Z 65.88549613952637
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:52:00.000000000Z 65.50651391347249
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:53:00.000000000Z 65.30719598134358
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:54:00.000000000Z 64.39330975214641
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49386278788249
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:55:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49816226959229
“取消窗口化”聚合表
将聚合值保存在单独的表中通常不是您想要的数据格式。使用 window() 函数将数据“取消窗口化”为单个无限 (inf) 窗口。
dataSet
|> window(every: 1m)
|> mean()
|> duplicate(column: "_stop", as: "_time")
|> window(every: inf)
窗口化需要 _time 列，这就是为什么在聚合后需要重新创建 _time 列。
取消窗口化输出表
Table: keys: [_start, _stop, _field, _measurement]
_start:time _stop:time _field:string _measurement:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:00.000000000Z 65.88549613952637
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:00.000000000Z 65.50651391347249
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:00.000000000Z 65.30719598134358
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:00.000000000Z 64.39330975214641
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49386278788249
2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49816226959229
在单个表中包含聚合值后，可视化中的数据点将连接起来。
总结
您现在已经创建了一个窗口化和聚合数据的 Flux 查询。本指南中概述的数据转换过程应适用于所有聚合操作。
Flux 还提供了 aggregateWindow() 函数，它可以为您执行所有这些单独的函数。
以下 Flux 查询将返回相同的结果
aggregateWindow 函数
dataSet
|> aggregateWindow(every: 1m, fn: mean)
此页面是否对您有帮助？
感谢您的反馈！