351 lines
26 KiB
Markdown
351 lines
26 KiB
Markdown
---
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title: Window and aggregate data with Flux
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seotitle: Window and aggregate data in InfluxDB with Flux
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list_title: Window & aggregate
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description: >
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This guide walks through windowing and aggregating data with Flux and outlines
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how it shapes your data in the process.
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menu:
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enterprise_influxdb_v1:
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name: Window & aggregate
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parent: Query with Flux
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weight: 4
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list_query_example: aggregate_window
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canonical: /influxdb/v2/query-data/flux/window-aggregate/
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---
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A common operation performed with time series data is grouping data into windows of time,
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or "windowing" data, then aggregating windowed values into a new value.
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This guide walks through windowing and aggregating data with Flux and demonstrates
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how data is shaped in the process.
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If you're just getting started with Flux queries, check out the following:
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- [Get started with Flux](/enterprise_influxdb/v1/flux/get-started/) for a conceptual overview of Flux and parts of a Flux query.
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- [Execute queries](/enterprise_influxdb/v1/flux/guides/execute-queries/) to discover a variety of ways to run your queries.
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{{% note %}}
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The following example is an in-depth walk-through of the steps required to window and aggregate data.
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The [`aggregateWindow()` function](#summing-up) performs these operations for you, but understanding
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how data is shaped in the process helps to successfully create your desired output.
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{{% /note %}}
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## Data set
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For the purposes of this guide, define a variable that represents your base data set.
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The following example queries the memory usage of the host machine.
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```js
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dataSet = from(bucket: "db/rp")
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|> range(start: -5m)
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|> filter(fn: (r) => r._measurement == "mem" and r._field == "used_percent")
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|> drop(columns: ["host"])
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```
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{{% note %}}
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This example drops the `host` column from the returned data since the memory data
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is only tracked for a single host and it simplifies the output tables.
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Dropping the `host` column is optional and not recommended if monitoring memory
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on multiple hosts.
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{{% /note %}}
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`dataSet` can now be used to represent your base data, which will look similar to the following:
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{{% truncate %}}
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```
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:00.000000000Z 71.11611366271973
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:10.000000000Z 67.39630699157715
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:20.000000000Z 64.16666507720947
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:30.000000000Z 64.19951915740967
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:40.000000000Z 64.2122745513916
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:50:50.000000000Z 64.22209739685059
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:00.000000000Z 64.6336555480957
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:10.000000000Z 64.16516304016113
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:20.000000000Z 64.18349742889404
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:30.000000000Z 64.20474052429199
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:40.000000000Z 68.65062713623047
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:51:50.000000000Z 67.20139980316162
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:00.000000000Z 70.9143877029419
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:10.000000000Z 64.14549350738525
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:20.000000000Z 64.15379047393799
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:30.000000000Z 64.1592264175415
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:40.000000000Z 64.18190002441406
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:52:50.000000000Z 64.28837776184082
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:00.000000000Z 64.29731845855713
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:10.000000000Z 64.36963081359863
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:20.000000000Z 64.37397003173828
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:30.000000000Z 64.44413661956787
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:40.000000000Z 64.42906856536865
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:53:50.000000000Z 64.44573402404785
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:00.000000000Z 64.48912620544434
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:10.000000000Z 64.49522972106934
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:20.000000000Z 64.48652744293213
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:30.000000000Z 64.49949741363525
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:40.000000000Z 64.4949197769165
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:50.000000000Z 64.49787616729736
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49816226959229
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```
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{{% /truncate %}}
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## Windowing data
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Use the [`window()` function](/flux/v0/stdlib/universe/window)
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to group your data based on time bounds.
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The most common parameter passed with the `window()` is `every` which
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defines the duration of time between windows.
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Other parameters are available, but for this example, window the base data
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set into one minute windows.
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```js
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dataSet
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|> window(every: 1m)
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```
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{{% note %}}
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The `every` parameter supports all [valid duration units](/flux/v0/language/types/#duration-types),
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including **calendar months (`1mo`)** and **years (`1y`)**.
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{{% /note %}}
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Each window of time is output in its own table containing all records that fall within the window.
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{{% truncate %}}
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###### window() output tables
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```
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:00.000000000Z 71.11611366271973
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:10.000000000Z 67.39630699157715
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:20.000000000Z 64.16666507720947
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:30.000000000Z 64.19951915740967
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:40.000000000Z 64.2122745513916
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:50:50.000000000Z 64.22209739685059
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:00.000000000Z 64.6336555480957
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:10.000000000Z 64.16516304016113
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:20.000000000Z 64.18349742889404
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:30.000000000Z 64.20474052429199
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:40.000000000Z 68.65062713623047
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:51:50.000000000Z 67.20139980316162
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:00.000000000Z 70.9143877029419
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:10.000000000Z 64.14549350738525
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:20.000000000Z 64.15379047393799
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:30.000000000Z 64.1592264175415
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:40.000000000Z 64.18190002441406
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:52:50.000000000Z 64.28837776184082
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:00.000000000Z 64.29731845855713
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:10.000000000Z 64.36963081359863
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:20.000000000Z 64.37397003173828
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:30.000000000Z 64.44413661956787
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:40.000000000Z 64.42906856536865
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:53:50.000000000Z 64.44573402404785
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:00.000000000Z 64.48912620544434
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2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:10.000000000Z 64.49522972106934
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2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:20.000000000Z 64.48652744293213
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2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:30.000000000Z 64.49949741363525
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2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:40.000000000Z 64.4949197769165
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2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:54:50.000000000Z 64.49787616729736
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:55:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 2018-11-03T17:55:00.000000000Z 64.49816226959229
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```
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{{% /truncate %}}
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When visualized in the InfluxDB UI, each window table is displayed in a different color.
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## Aggregate data
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[Aggregate functions](/flux/v0/stdlib/universe) take the values
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of all rows in a table and use them to perform an aggregate operation.
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The result is output as a new value in a single-row table.
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Since windowed data is split into separate tables, aggregate operations run against
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each table separately and output new tables containing only the aggregated value.
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For this example, use the [`mean()` function](/flux/v0/stdlib/universe/mean)
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to output the average of each window:
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```js
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dataSet
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|> window(every: 1m)
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|> mean()
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```
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{{% truncate %}}
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###### mean() output tables
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```
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 65.88549613952637
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 65.50651391347249
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 65.30719598134358
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 64.39330975214641
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
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2018-11-03T17:54:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 64.49386278788249
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ----------------------------
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2018-11-03T17:55:00.000000000Z 2018-11-03T17:55:00.000000000Z used_percent mem 64.49816226959229
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```
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{{% /truncate %}}
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Because each data point is contained in its own table, when visualized,
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they appear as single, unconnected points.
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### Recreate the time column
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**Notice the `_time` column is not in the [aggregated output tables](#mean-output-tables).**
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Because records in each table are aggregated together, their timestamps no longer
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apply and the column is removed from the group key and table.
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Also notice the `_start` and `_stop` columns still exist.
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These represent the lower and upper bounds of the time window.
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Many Flux functions rely on the `_time` column.
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To further process your data after an aggregate function, you need to re-add `_time`.
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Use the [`duplicate()` function](/flux/v0/stdlib/universe/duplicate) to
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duplicate either the `_start` or `_stop` column as a new `_time` column.
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```js
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dataSet
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|> window(every: 1m)
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|> mean()
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|> duplicate(column: "_stop", as: "_time")
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```
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{{% truncate %}}
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###### duplicate() output tables
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```
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:50:00.000000000Z 2018-11-03T17:51:00.000000000Z used_percent mem 2018-11-03T17:51:00.000000000Z 65.88549613952637
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:51:00.000000000Z 2018-11-03T17:52:00.000000000Z used_percent mem 2018-11-03T17:52:00.000000000Z 65.50651391347249
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:52:00.000000000Z 2018-11-03T17:53:00.000000000Z used_percent mem 2018-11-03T17:53:00.000000000Z 65.30719598134358
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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2018-11-03T17:53:00.000000000Z 2018-11-03T17:54:00.000000000Z used_percent mem 2018-11-03T17:54:00.000000000Z 64.39330975214641
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Table: keys: [_start, _stop, _field, _measurement]
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_start:time _stop:time _field:string _measurement:string _time:time _value:float
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------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------------ ----------------------------
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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
|
|
```
|
|
{{% /truncate %}}
|
|
|
|
## "Unwindow" aggregate tables
|
|
Keeping aggregate values in separate tables generally isn't the format in which you want your data.
|
|
Use the `window()` function to "unwindow" your data into a single infinite (`inf`) window.
|
|
|
|
```js
|
|
dataSet
|
|
|> window(every: 1m)
|
|
|> mean()
|
|
|> duplicate(column: "_stop", as: "_time")
|
|
|> window(every: inf)
|
|
```
|
|
|
|
{{% note %}}
|
|
Windowing requires a `_time` column which is why it's necessary to
|
|
[recreate the `_time` column](#recreate-the-time-column) after an aggregation.
|
|
{{% /note %}}
|
|
|
|
###### Unwindowed output table
|
|
```
|
|
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
|
|
```
|
|
|
|
With the aggregate values in a single table, data points in the visualization are connected.
|
|
|
|
|
|
|
|
## Summing up
|
|
You have now created a Flux query that windows and aggregates data.
|
|
The data transformation process outlined in this guide should be used for all aggregation operations.
|
|
|
|
Flux also provides the [`aggregateWindow()` function](/flux/v0/stdlib/universe/aggregatewindow)
|
|
which performs all these separate functions for you.
|
|
|
|
The following Flux query will return the same results:
|
|
|
|
###### aggregateWindow function
|
|
```js
|
|
dataSet
|
|
|> aggregateWindow(every: 1m, fn: mean)
|
|
```
|