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## Tools for working with Flux
You have multiple [options for writing and running Flux queries](/v2.0/reference/flux/guides/executing-queries),
You have multiple [options for writing and running Flux queries](/v2.0/reference/flux/guides/execute-queries),
but as you're getting started, we recommend using the following:
### 1. Data Explorer

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---
_For a deeper dive into windowing and aggregating data with example data output for each transformation,
view the [Windowing and aggregating data](/v2.0/reference/flux/guides/windowing-aggregating) guide._
view the [Windowing and aggregating data](/v2.0/reference/flux/guides/window-aggregate) guide._
---

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---
title: Flux how-to guides
description: Helpful guides that walk through both common and complex tasks and use cases for Flux.
menu:
v2_0:
name: How-to guides
parent: Flux
weight: 3
---
## [Different ways to execute Flux queries](/v2.0/query-data/flux/guides/execute-queries)
A guide that covers the different options for executing Flux queries with InfluxDB and Chronograf v1.7+.
## [How to window and aggregate data with Flux](/v2.0/query-data/flux/guides/window-aggregate)
This guide walks through windowing and aggregating data with Flux and demonstrates
how data is shaped in the process.
## [How to group data with Flux](/v2.0/query-data/flux/guides/group-data)
This guide walks through grouping data in Flux with examples of how data is shaped in the process.
## [How to join data with Flux](/v2.0/query-data/flux/guides/join)
This guide walks through joining data with Flux, illustrating how joined data is output and how it can be used.
## [How to sort and limit data with Flux](/v2.0/query-data/flux/guides/sort-limit)
This guide walks through sorting and limiting data with Flux.
## [Regular expressions in Flux](/v2.0/query-data/flux/guides/regular-expressions)
This guide walks through using regular expressions in evaluation logic in Flux functions.

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---
title: Execute Flux queries
seotitle: Different ways to execute Flux queries
description: There are multiple ways to execute Flux queries include the InfluxDB user interface, CLI, and API.
menu:
v2_0:
name: Execute Flux queries
parent: How-to guides
weight: 1
---
There are multiple ways to execute Flux queries with InfluxDB and Chronograf v1.7+.
This guide covers the different options:
1. [Data Explorer](#data-explorer)
2. [Influx REPL](#influx-repl)
3. [Influx query command](#influx-query-command)
5. [InfluxDB API](#influxdb-api)
## Data Explorer
Flux queries can be built, executed, and visualized in InfluxDB UI's Data Explorer.
![Data Explorer with Flux](/img/flux-data-explorer.png)
## Influx REPL
The [`influx repl` command](/v2.0/reference/cli/influx/repl) starts an interactive
read-eval-print-loop (REPL) where you can write and execute Flux queries.
```bash
influx repl --org org-name
```
## Influx query command
You can pass Flux queries to the [`influx query` command](/v2.0/reference/cli/influx/query)
as either a file or raw Flux via stdin.
###### Run a Flux query from a file
```bash
influx query @/path/to/query.flux
```
###### Pass raw Flux via stdin pipe
```bash
influx query - # Return to open the pipe
data = from(bucket: "example-bucket") |> range(start: -10m) # ...
# ctrl-d to close the pipe and submit the query
```
## InfluxDB API
Flux can be used to query InfluxDB through InfluxDB's `/api/v2/query` endpoint.
Queried data is returned in annotated CSV format.
In your request, set the following:
- `accept` header to `application/csv`
- `content-type` header to `application/vnd.flux`
This allows you to POST the Flux query in plain text and receive the annotated CSV response.
Below is an example `curl` command that queries InfluxDB using Flux:
{{< code-tabs-wrapper >}}
{{% code-tabs %}}
[Multi-line](#)
[Single-line](#)
{{% /code-tabs %}}
{{% code-tab-content %}}
```bash
curl localhost:8086/api/v2/query -XPOST -sS \
-H 'accept:application/csv' \
-H 'content-type:application/vnd.flux' \
-d 'from(bucket:"telegraf")
|> range(start:-5m)
|> filter(fn:(r) => r._measurement == "cpu")'
```
{{% /code-tab-content %}}
{{% code-tab-content %}}
```bash
curl localhost:8086/api/v2/query -XPOST -sS -H 'accept:application/csv' -H 'content-type:application/vnd.flux' -d 'from(bucket:"telegraf") |> range(start:-5m) |> filter(fn:(r) => r._measurement == "cpu")'
```
{{% /code-tab-content %}}
{{< /code-tabs-wrapper >}}

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---
title: Group data with Flux
seotitle: How to group data with Flux
description: >
This guide walks through grouping data with Flux by providing examples and
illustrating how data is shaped throughout the process.
menu:
v2_0:
name: Group data
parent: How-to guides
weight: 3
---
With Flux, data can be grouped by any column in your queried data set.
"Grouping" is accomplished by partitioning data into tables where each row shares a common value for specified columns.
This guide walks through grouping data in Flux with examples of how data is shaped in the process.
## Group keys
Every table has a **group key** – a list of columns which for which every row in the table has the same value.
###### Example group key
```js
[_start, _stop, _field, _measurement, host]
```
Grouping data in Flux is essentially defining the group key of output tables.
Understanding how modifying group keys shapes output data is key to successfully
grouping and transforming data into your desired output.
## group() Function
Flux's [`group()` function](/v2.0/reference/flux/functions/transformations/group) defines the
group key for output tables, i.e. grouping records based on values for specific columns.
###### group() example
```js
dataStream
|> group(columns: ["cpu", "host"])
```
###### Resulting group key
```js
[cpu, host]
```
The `group()` function has the following parameters:
### by
An explicit method for defining the group key with an array of strings.
Only columns specified are included in the output group key.
### except
An implicit method for defining the group key with an array of strings.
All columns **except** those specified are included in the output group key.
### none
A boolean that removes all grouping and outputs everything as a single table.
---
_For more information, see the [`group()` function](/v2.0/reference/flux/functions/transformations/group)._
---
## Example grouping operations
To illustrate how grouping works, define a `dataSet` variable that queries System
CPU usage from the `telegraf/autogen` bucket.
Filter the `cpu` tag so it only returns results for each numbered CPU core.
### Data set
CPU used by system operations for all numbered CPU cores.
It uses a regular expression to filter only numbered cores.
```js
dataSet = from(bucket: "telegraf/autogen")
|> range(start: -2m)
|> filter(fn: (r) =>
r._field == "usage_system" and
r.cpu =~ /cpu[0-9*]/
)
|> drop(columns: ["host"])
```
> This example drops the `host` column from the returned data since the CPU data
> is only tracked for a single host and it simplifies the output tables.
> Don't drop the `host` column if monitoring multiple hosts.
{{% truncate %}}
```
Table: keys: [_start, _stop, _field, _measurement, cpu]
_start:time _stop:time _field:string _measurement:string cpu:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:34:00.000000000Z 7.892107892107892
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:34:10.000000000Z 7.2
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:34:20.000000000Z 7.4
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:34:30.000000000Z 5.5
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:34:40.000000000Z 7.4
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:34:50.000000000Z 7.5
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:35:00.000000000Z 10.3
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:35:10.000000000Z 9.2
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:35:20.000000000Z 8.4
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:35:30.000000000Z 8.5
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:35:40.000000000Z 8.6
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:35:50.000000000Z 10.2
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu0 2018-11-05T21:36:00.000000000Z 10.6
Table: keys: [_start, _stop, _field, _measurement, cpu]
_start:time _stop:time _field:string _measurement:string cpu:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:34:00.000000000Z 0.7992007992007992
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:34:10.000000000Z 0.7
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:34:20.000000000Z 0.7
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:34:30.000000000Z 0.4
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:34:40.000000000Z 0.7
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:34:50.000000000Z 0.7
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:35:00.000000000Z 1.4
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:35:10.000000000Z 1.2
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:35:20.000000000Z 0.8
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:35:30.000000000Z 0.8991008991008991
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:35:40.000000000Z 0.8008008008008008
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:35:50.000000000Z 0.999000999000999
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu1 2018-11-05T21:36:00.000000000Z 1.1022044088176353
Table: keys: [_start, _stop, _field, _measurement, cpu]
_start:time _stop:time _field:string _measurement:string cpu:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:34:00.000000000Z 4.1
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:34:10.000000000Z 3.6
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:34:20.000000000Z 3.5
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:34:30.000000000Z 2.6
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:34:40.000000000Z 4.5
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:34:50.000000000Z 4.895104895104895
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:35:00.000000000Z 6.906906906906907
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:35:10.000000000Z 5.7
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:35:20.000000000Z 5.1
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:35:30.000000000Z 4.7
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:35:40.000000000Z 5.1
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:35:50.000000000Z 5.9
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu2 2018-11-05T21:36:00.000000000Z 6.4935064935064934
Table: keys: [_start, _stop, _field, _measurement, cpu]
_start:time _stop:time _field:string _measurement:string cpu:string _time:time _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ---------------------- ------------------------------ ----------------------------
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:34:00.000000000Z 0.5005005005005005
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:34:10.000000000Z 0.5
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:34:20.000000000Z 0.5
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:34:30.000000000Z 0.3
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:34:40.000000000Z 0.6
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:34:50.000000000Z 0.6
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:35:00.000000000Z 1.3986013986013985
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:35:10.000000000Z 0.9
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:35:20.000000000Z 0.5005005005005005
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:35:30.000000000Z 0.7
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:35:40.000000000Z 0.6
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:35:50.000000000Z 0.8
2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z usage_system cpu cpu3 2018-11-05T21:36:00.000000000Z 0.9
```
{{% /truncate %}}
**Note that the group key is output with each table: `Table: keys: <group-key>`.**
![Group example data set](/img/flux/grouping-data-set.png)
### Group by CPU
Group the `dataSet` stream by the `cpu` column.
```js
dataSet
|> group(columns: ["cpu"])
```
This won't actually change the structure of the data since it already has `cpu` in the group key and is therefore grouped by `cpu`.
However, notice that it does change the group key:
{{% truncate %}}
###### Group by CPU output tables
```
Table: keys: [cpu]
cpu:string _stop:time _time:time _value:float _field:string _measurement:string _start:time
---------------------- ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 7.892107892107892 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:10.000000000Z 7.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:20.000000000Z 7.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:30.000000000Z 5.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:40.000000000Z 7.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:50.000000000Z 7.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:00.000000000Z 10.3 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:10.000000000Z 9.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:20.000000000Z 8.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:30.000000000Z 8.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:40.000000000Z 8.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:50.000000000Z 10.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu0 2018-11-05T21:36:00.000000000Z 2018-11-05T21:36:00.000000000Z 10.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [cpu]
cpu:string _stop:time _time:time _value:float _field:string _measurement:string _start:time
---------------------- ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 0.7992007992007992 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:10.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:20.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:30.000000000Z 0.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:40.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:50.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:00.000000000Z 1.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:10.000000000Z 1.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:20.000000000Z 0.8 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:30.000000000Z 0.8991008991008991 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:40.000000000Z 0.8008008008008008 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:50.000000000Z 0.999000999000999 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu1 2018-11-05T21:36:00.000000000Z 2018-11-05T21:36:00.000000000Z 1.1022044088176353 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [cpu]
cpu:string _stop:time _time:time _value:float _field:string _measurement:string _start:time
---------------------- ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 4.1 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:10.000000000Z 3.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:20.000000000Z 3.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:30.000000000Z 2.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:40.000000000Z 4.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:50.000000000Z 4.895104895104895 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:00.000000000Z 6.906906906906907 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:10.000000000Z 5.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:20.000000000Z 5.1 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:30.000000000Z 4.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:40.000000000Z 5.1 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:50.000000000Z 5.9 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu2 2018-11-05T21:36:00.000000000Z 2018-11-05T21:36:00.000000000Z 6.4935064935064934 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [cpu]
cpu:string _stop:time _time:time _value:float _field:string _measurement:string _start:time
---------------------- ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 0.5005005005005005 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:10.000000000Z 0.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:20.000000000Z 0.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:30.000000000Z 0.3 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:40.000000000Z 0.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:50.000000000Z 0.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:00.000000000Z 1.3986013986013985 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:10.000000000Z 0.9 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:20.000000000Z 0.5005005005005005 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:30.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:40.000000000Z 0.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:35:50.000000000Z 0.8 usage_system cpu 2018-11-05T21:34:00.000000000Z
cpu3 2018-11-05T21:36:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.9 usage_system cpu 2018-11-05T21:34:00.000000000Z
```
{{% /truncate %}}
The visualization remains the same.
![Group by CPU](/img/flux/grouping-data-set.png)
### Group by time
Grouping data by the `_time` column is a good illustration of how grouping changes the structure of your data.
```js
dataSet
|> group(columns: ["_time"])
```
When grouping by `_time`, all records that share a common `_time` value are grouped into individual tables.
So each output table represents a single point in time.
{{% truncate %}}
###### Group by time output tables
```
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:34:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 7.892107892107892 usage_system cpu cpu0
2018-11-05T21:34:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.7992007992007992 usage_system cpu cpu1
2018-11-05T21:34:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 4.1 usage_system cpu cpu2
2018-11-05T21:34:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.5005005005005005 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:34:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 7.2 usage_system cpu cpu0
2018-11-05T21:34:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu cpu1
2018-11-05T21:34:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 3.6 usage_system cpu cpu2
2018-11-05T21:34:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.5 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:34:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 7.4 usage_system cpu cpu0
2018-11-05T21:34:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu cpu1
2018-11-05T21:34:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 3.5 usage_system cpu cpu2
2018-11-05T21:34:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.5 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:34:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 5.5 usage_system cpu cpu0
2018-11-05T21:34:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.4 usage_system cpu cpu1
2018-11-05T21:34:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 2.6 usage_system cpu cpu2
2018-11-05T21:34:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.3 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:34:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 7.4 usage_system cpu cpu0
2018-11-05T21:34:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu cpu1
2018-11-05T21:34:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 4.5 usage_system cpu cpu2
2018-11-05T21:34:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.6 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:34:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 7.5 usage_system cpu cpu0
2018-11-05T21:34:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu cpu1
2018-11-05T21:34:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 4.895104895104895 usage_system cpu cpu2
2018-11-05T21:34:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.6 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:35:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 10.3 usage_system cpu cpu0
2018-11-05T21:35:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 1.4 usage_system cpu cpu1
2018-11-05T21:35:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 6.906906906906907 usage_system cpu cpu2
2018-11-05T21:35:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 1.3986013986013985 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:35:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 9.2 usage_system cpu cpu0
2018-11-05T21:35:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 1.2 usage_system cpu cpu1
2018-11-05T21:35:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 5.7 usage_system cpu cpu2
2018-11-05T21:35:10.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.9 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:35:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 8.4 usage_system cpu cpu0
2018-11-05T21:35:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.8 usage_system cpu cpu1
2018-11-05T21:35:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 5.1 usage_system cpu cpu2
2018-11-05T21:35:20.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.5005005005005005 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:35:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 8.5 usage_system cpu cpu0
2018-11-05T21:35:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.8991008991008991 usage_system cpu cpu1
2018-11-05T21:35:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 4.7 usage_system cpu cpu2
2018-11-05T21:35:30.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:35:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 8.6 usage_system cpu cpu0
2018-11-05T21:35:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.8008008008008008 usage_system cpu cpu1
2018-11-05T21:35:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 5.1 usage_system cpu cpu2
2018-11-05T21:35:40.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.6 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:35:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 10.2 usage_system cpu cpu0
2018-11-05T21:35:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.999000999000999 usage_system cpu cpu1
2018-11-05T21:35:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 5.9 usage_system cpu cpu2
2018-11-05T21:35:50.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.8 usage_system cpu cpu3
Table: keys: [_time]
_time:time _start:time _stop:time _value:float _field:string _measurement:string cpu:string
------------------------------ ------------------------------ ------------------------------ ---------------------------- ---------------------- ---------------------- ----------------------
2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 10.6 usage_system cpu cpu0
2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 1.1022044088176353 usage_system cpu cpu1
2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 6.4935064935064934 usage_system cpu cpu2
2018-11-05T21:36:00.000000000Z 2018-11-05T21:34:00.000000000Z 2018-11-05T21:36:00.000000000Z 0.9 usage_system cpu cpu3
```
{{% /truncate %}}
Because each timestamp is a structured as a separate table, when visualized, they appear as individual, unconnected points.
Even though there are multiple records per timestamp, it will only visualize the last record of the table.
![Group by time](/img/flux/grouping-by-time.png)
> With some further processing, you could calculate the average CPU usage across all CPUs per point
> of time and group them into a single table, but we won't cover that in this example.
> If you're interested in running and visualizing this yourself, here's what the query would look like:
>
```js
dataSet
|> group(columns: ["_time"])
|> mean()
|> group(columns: ["_value", "_time"], mode: "except")
```
## Group by CPU and time
Group by the `cpu` and `_time` columns.
```js
dataSet
|> group(columns: ["cpu", "_time"])
```
This outputs a table for every unique `cpu` and `_time` combination:
{{% truncate %}}
###### Group by CPU and time output tables
```
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:00.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 7.892107892107892 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:00.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.7992007992007992 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:00.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 4.1 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:00.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.5005005005005005 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:10.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 7.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:10.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:10.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 3.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:10.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:20.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 7.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:20.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:20.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 3.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:20.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:30.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 5.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:30.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:30.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 2.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:30.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.3 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:40.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 7.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:40.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:40.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 4.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:40.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:50.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 7.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:50.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:50.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 4.895104895104895 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:34:50.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:00.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 10.3 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:00.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 1.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:00.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 6.906906906906907 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:00.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 1.3986013986013985 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:10.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 9.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:10.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 1.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:10.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 5.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:10.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.9 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:20.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 8.4 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:20.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.8 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:20.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 5.1 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:20.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.5005005005005005 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:30.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 8.5 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:30.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.8991008991008991 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:30.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 4.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:30.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.7 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:40.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 8.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:40.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.8008008008008008 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:40.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 5.1 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:40.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:50.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 10.2 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:50.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 0.999000999000999 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:50.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 5.9 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:35:50.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.8 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:36:00.000000000Z cpu0 2018-11-05T21:36:00.000000000Z 10.6 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:36:00.000000000Z cpu1 2018-11-05T21:36:00.000000000Z 1.1022044088176353 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:36:00.000000000Z cpu2 2018-11-05T21:36:00.000000000Z 6.4935064935064934 usage_system cpu 2018-11-05T21:34:00.000000000Z
Table: keys: [_time, cpu]
_time:time cpu:string _stop:time _value:float _field:string _measurement:string _start:time
------------------------------ ---------------------- ------------------------------ ---------------------------- ---------------------- ---------------------- ------------------------------
2018-11-05T21:36:00.000000000Z cpu3 2018-11-05T21:36:00.000000000Z 0.9 usage_system cpu 2018-11-05T21:34:00.000000000Z
```
{{% /truncate %}}
When visualized, tables appear as individual, unconnected points.
![Group by CPU and time](/img/flux/grouping-by-cpu-time.png)
Grouping by `cpu` and `_time` is a good illustration of how grouping works.
## In conclusion
Grouping is a powerful way to shape your data into your desired output format.
It modifies the group keys of output tables, grouping records into tables that
all share common values within specified columns.

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---
title: Create histograms with Flux
seotitle: How to create histograms with Flux
description: This guide walks through using the histogram() function to create cumulative histograms with Flux.
menu:
v2_0:
name: Create histograms
parent: How-to guides
weight: 7
---
Histograms provide valuable insight into the distribution of your data.
This guide walks through using Flux's `histogram()` function to transform your data into a **cumulative histogram**.
## histgram() function
The [`histogram()` function](/v2.0/reference/flux/functions/transformations/histogram) approximates the
cumulative distribution of a dataset by counting data frequencies for a list of "bins."
A **bin** is simply a range in which a data point falls.
All data points that are less than or equal to the bound are counted in the bin.
In the histogram output, a column is added (le) that represents the upper bounds of of each bin.
Bin counts are cumulative.
```js
from(bucket:"telegraf/autogen")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "used_percent"
)
|> histogram(bins: [0.0, 10.0, 20.0, 30.0])
```
> Values output by the `histogram` function represent points of data aggregated over time.
> Since values do not represent single points in time, there is no `_time` column in the output table.
## Bin helper functions
Flux provides two helper functions for generating histogram bins.
Each generates and outputs an array of floats designed to be used in the `histogram()` function's `bins` parameter.
### linearBins()
The [`linearBins()` function](/v2.0/reference/flux/functions/misc/linearbins) generates a list of linearly separated floats.
```js
linearBins(start: 0.0, width: 10.0, count: 10)
// Generated list: [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, +Inf]
```
### logarithmicBins()
The [`logarithmicBins()` function](/v2.0/reference/flux/functions/misc/logarithmicbins) generates a list of exponentially separated floats.
```js
logarithmicBins(start: 1.0, factor: 2.0, count: 10, infinty: true)
// Generated list: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, +Inf]
```
## Examples
### Generating a histogram with linear bins
```js
from(bucket:"telegraf/autogen")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "used_percent"
)
|> histogram(
bins: linearBins(
start:65.5,
width: 0.5,
count: 20,
infinity:false
)
)
```
###### Output table
```
Table: keys: [_start, _stop, _field, _measurement, host]
_start:time _stop:time _field:string _measurement:string host:string le:float _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------ ---------------------------- ----------------------------
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 65.5 5
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 66 6
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 66.5 8
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 67 9
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 67.5 9
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 68 10
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 68.5 12
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 69 12
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 69.5 15
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 70 23
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 70.5 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 71 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 71.5 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 72 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 72.5 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 73 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 73.5 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 74 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 74.5 30
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 75 30
```
### Generating a histogram with logarithmic bins
```js
from(bucket:"telegraf/autogen")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "used_percent"
)
|> histogram(
bins: logarithmicBins(
start:0.5,
factor: 2.0,
count: 10,
infinity:false
)
)
```
###### Output table
```
Table: keys: [_start, _stop, _field, _measurement, host]
_start:time _stop:time _field:string _measurement:string host:string le:float _value:float
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------ ---------------------------- ----------------------------
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 0.5 0
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 1 0
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 2 0
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 4 0
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 8 0
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 16 0
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 32 0
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 64 2
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 128 30
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 256 30
```

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@ -0,0 +1,300 @@
---
title: Join data with Flux
seotitle: How to join data with Flux
description: This guide walks through joining data with Flux and outlines how it shapes your data in the process.
menu:
v2_0:
name: Join data
parent: How-to guides
weight: 5
---
The [`join()` function](/v2.0/reference/flux/functions/transformations/join) merges two or more
input streams whose values are equal on a set of common columns into a single output stream.
Flux allows you to join on any columns common between two data streams and opens the door
for operations such as cross-measurement joins and math across measurements.
To illustrate a join operation, use data captured by Telegraf and and stored in
InfluxDB with a default TICK stack installation - memory usage and processes.
In this guide, we'll join two data streams, one representing memory usage and the other representing the
total number of running processes, then calculate the average memory usage per running process.
## Define stream variables
In order to perform a join, you must have two streams of data.
Assign a variable to each data stream.
### Memory used variable
Define a `memUsed` variable that filters on the `mem` measurement and the `used` field.
This returns the amount of memory (in bytes) used.
###### memUsed stream definition
```js
memUsed = from(bucket: "telegraf/autogen")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "used"
)
```
{{% truncate %}}
###### memUsed data output
```
Table: keys: [_start, _stop, _field, _measurement, host]
_start:time _stop:time _field:string _measurement:string host:string _time:time _value:int
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------ ------------------------------ --------------------------
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:00.000000000Z 10956333056
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:10.000000000Z 11014008832
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:20.000000000Z 11373428736
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:30.000000000Z 11001421824
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:40.000000000Z 10985852928
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:50:50.000000000Z 10992279552
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:00.000000000Z 11053568000
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:10.000000000Z 11092242432
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:20.000000000Z 11612774400
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:30.000000000Z 11131961344
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:40.000000000Z 11124805632
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:51:50.000000000Z 11332464640
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:00.000000000Z 11176923136
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:10.000000000Z 11181068288
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:20.000000000Z 11182579712
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:30.000000000Z 11238862848
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:40.000000000Z 11275296768
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:52:50.000000000Z 11225411584
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:00.000000000Z 11252690944
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:10.000000000Z 11227029504
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:20.000000000Z 11201646592
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:30.000000000Z 11227897856
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:40.000000000Z 11330428928
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:53:50.000000000Z 11347976192
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:00.000000000Z 11368271872
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:10.000000000Z 11269623808
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:20.000000000Z 11295637504
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:30.000000000Z 11354423296
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:40.000000000Z 11379687424
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:54:50.000000000Z 11248926720
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z used mem host1.local 2018-11-06T05:55:00.000000000Z 11292524544
```
{{% /truncate %}}
### Total processes variable
Define a `procTotal` variable that filters on the `processes` measurement and the `total` field.
This returns the number of running processes.
###### procTotal stream definition
```js
procTotal = from(bucket: "telegraf/autogen")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "processes" and
r._field == "total"
)
```
{{% truncate %}}
###### procTotal data output
```
Table: keys: [_start, _stop, _field, _measurement, host]
_start:time _stop:time _field:string _measurement:string host:string _time:time _value:int
------------------------------ ------------------------------ ---------------------- ---------------------- ------------------------ ------------------------------ --------------------------
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:00.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:20.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:30.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:40.000000000Z 469
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:50:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:00.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:20.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:30.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:40.000000000Z 469
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:51:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:00.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:20.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:30.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:40.000000000Z 472
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:52:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:00.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:20.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:30.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:40.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:53:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:00.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:10.000000000Z 470
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:20.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:30.000000000Z 473
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:40.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:54:50.000000000Z 471
2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z total processes host1.local 2018-11-06T05:55:00.000000000Z 471
```
{{% /truncate %}}
## Join the two data streams
With the two data streams defined, use the `join()` function to join them together.
`join()` requires two parameters:
##### `tables`
A map of tables to join with keys by which they will be aliased.
In the example below, `mem` is the alias for `memUsed` and `proc` is the alias for `procTotal`.
##### `on`
An array of strings defining the columns on which the tables will be joined.
_**Both tables must have all columns defined in this list.**_
```js
join(
tables: {mem:memUsed, proc:procTotal},
on: ["_time", "_stop", "_start", "host"]
)
```
{{% truncate %}}
###### Joined output table
```
Table: keys: [_field_mem, _field_proc, _measurement_mem, _measurement_proc, _start, _stop, host]
_field_mem:string _field_proc:string _measurement_mem:string _measurement_proc:string _start:time _stop:time host:string _time:time _value_mem:int _value_proc:int
---------------------- ---------------------- ----------------------- ------------------------ ------------------------------ ------------------------------ ------------------------ ------------------------------ -------------------------- --------------------------
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:00.000000000Z 10956333056 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:10.000000000Z 11014008832 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:20.000000000Z 11373428736 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:30.000000000Z 11001421824 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:40.000000000Z 10985852928 469
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:50.000000000Z 10992279552 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:00.000000000Z 11053568000 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:10.000000000Z 11092242432 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:20.000000000Z 11612774400 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:30.000000000Z 11131961344 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:40.000000000Z 11124805632 469
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:50.000000000Z 11332464640 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:00.000000000Z 11176923136 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:10.000000000Z 11181068288 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:20.000000000Z 11182579712 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:30.000000000Z 11238862848 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:40.000000000Z 11275296768 472
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:50.000000000Z 11225411584 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:00.000000000Z 11252690944 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:10.000000000Z 11227029504 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:20.000000000Z 11201646592 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:30.000000000Z 11227897856 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:40.000000000Z 11330428928 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:50.000000000Z 11347976192 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:00.000000000Z 11368271872 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:10.000000000Z 11269623808 470
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:20.000000000Z 11295637504 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:30.000000000Z 11354423296 473
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:40.000000000Z 11379687424 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:50.000000000Z 11248926720 471
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:55:00.000000000Z 11292524544 471
```
{{% /truncate %}}
Notice the output table includes the following columns:
- `_field_mem`
- `_field_proc`
- `_measurement_mem`
- `_measurement_proc`
- `_value_mem`
- `_value_proc`
These represent the columns with values unique to the two input tables.
## Calculate and create a new table
With the two streams of data joined into a single table, use the [`map()` function](/v2.0/reference/flux/functions/transformations/map)
to build a new table by mapping the existing `_time` column to a new `_time` column and dividing `_value_mem` by `_value_proc`
and mapping it to a new `_value` column.
```js
join(tables: {mem:memUsed, proc:procTotal}, on: ["_time", "_stop", "_start", "host"])
|> map(fn: (r) => ({
_time: r._time,
_value: r._value_mem / r._value_proc
}))
```
{{% truncate %}}
###### Mapped table
```
Table: keys: [_field_mem, _field_proc, _measurement_mem, _measurement_proc, _start, _stop, host]
_field_mem:string _field_proc:string _measurement_mem:string _measurement_proc:string _start:time _stop:time host:string _time:time _value:int
---------------------- ---------------------- ----------------------- ------------------------ ------------------------------ ------------------------------ ------------------------ ------------------------------ --------------------------
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:00.000000000Z 23311346
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:10.000000000Z 23434061
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:20.000000000Z 24147407
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:30.000000000Z 23407280
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:40.000000000Z 23423993
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:50:50.000000000Z 23338173
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:00.000000000Z 23518229
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:10.000000000Z 23600515
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:20.000000000Z 24708030
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:30.000000000Z 23685024
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:40.000000000Z 23720267
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:51:50.000000000Z 24060434
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:00.000000000Z 23730197
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:10.000000000Z 23789506
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:20.000000000Z 23792722
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:30.000000000Z 23861704
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:40.000000000Z 23888340
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:52:50.000000000Z 23833145
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:00.000000000Z 23941895
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:10.000000000Z 23887296
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:20.000000000Z 23833290
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:30.000000000Z 23838424
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:40.000000000Z 24056112
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:53:50.000000000Z 24093367
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:00.000000000Z 24136458
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:10.000000000Z 23977922
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:20.000000000Z 23982245
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:30.000000000Z 24005123
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:40.000000000Z 24160695
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:54:50.000000000Z 23883071
used total mem processes 2018-11-06T05:50:00.000000000Z 2018-11-06T05:55:00.000000000Z Scotts-MacBook-Pro.local 2018-11-06T05:55:00.000000000Z 23975635
```
{{% /truncate %}}
This table represents the average amount of memory in bytes per running process.
## Real world example
The following function calculates the batch sizes written to an InfluxDB cluster by joining
fields from `httpd` and `write` measurements in order to compare `pointReq` and `writeReq`.
The results are grouped by cluster ID so you can make comparisons across clusters.
```js
batchSize = (cluster_id, start=-1m, interval=10s) => {
httpd = from(bucket:"telegraf")
|> range(start:start)
|> filter(fn:(r) =>
r._measurement == "influxdb_httpd" and
r._field == "writeReq" and
r.cluster_id == cluster_id
)
|> aggregateWindow(every: interval, fn: mean)
|> derivative(nonNegative:true,unit:60s)
write = from(bucket:"telegraf")
|> range(start:start)
|> filter(fn:(r) =>
r._measurement == "influxdb_write" and
r._field == "pointReq" and
r.cluster_id == cluster_id
)
|> aggregateWindow(every: interval, fn: max)
|> derivative(nonNegative:true,unit:60s)
return join(
tables:{httpd:httpd, write:write},
on:["_time","_stop","_start","host"]
)
|> map(fn:(r) => ({
_time: r._time,
_value: r._value_httpd / r._value_write,
}))
|> group(columns: cluster_id)
}
batchSize(cluster_id: "enter cluster id here")
```

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---
title: Use regular expressions in Flux
seotitle: How to use regular expressions in Flux
description: This guide walks through using regular expressions in evaluation logic in Flux functions.
menu:
v2_0:
name: Regular expressions
parent: How-to guides
weight: 7
---
Regular expressions (regexes) are incredibly powerful when matching patterns in large collections of data.
With Flux, regular expressions are primarily used for evaluation logic in operations such as filtering rows,
dropping and keeping columns, state detection, etc.
This guide shows how to use regular expressions in your Flux scripts.
## Go regular expression syntax
Flux uses Go's [regexp package](https://golang.org/pkg/regexp/) for regular expression search.
The links [below](#helpful-links) provide information about Go's regular expression syntax.
## Regular expression operators
Flux provides two comparison operators for use with regular expressions.
#### `=~`
When the expression on the left **MATCHES** the regular expression on the right, this evaluates to `true`.
#### `!~`
When the expression on the left **DOES NOT MATCH** the regular expression on the right, this evaluates to `true`.
## Regular expressions in Flux
When using regex matching in your Flux scripts, enclose your regular expressions with `/`.
The following is the basic regex comparison syntax:
###### Basic regex comparison syntax
```js
expression =~ /regex/
expression !~ /regex/
```
## Examples
### Use a regex to filter by tag value
The following example filters records by the `cpu` tag.
It only keeps records for which the `cpu` is either `cpu0`, `cpu1`, or `cpu2`.
```js
from(bucket: "telegraf/autogen")
|> range(start: -15m)
|> filter(fn: (r) =>
r._measurement == "cpu" and
r._field == "usage_user" and
r.cpu =~ /cpu[0-2]/
)
```
### Use a regex to filter by field key
The following example excludes records that do not have `_percent` in a field key.
```js
from(bucket: "telegraf/autogen")
|> range(start: -15m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field =~ /_percent/
)
```
### Drop columns matching a regex
The following example drops columns whose names do not being with `_`.
```js
from(bucket: "telegraf/autogen")
|> range(start: -15m)
|> filter(fn: (r) => r._measurement == "mem")
|> drop(fn: (col) => col !~ /_.*/)
```
## Helpful links
##### Syntax documentation
[regexp Syntax GoDoc](https://godoc.org/regexp/syntax)
[RE2 Syntax Overview](https://github.com/google/re2/wiki/Syntax)
##### Go regex testers
[Regex Tester - Golang](https://regex-golang.appspot.com/assets/html/index.html)
[Regex101](https://regex101.com/)

47
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---
title: Sort and limit data with Flux
seotitle: How to sort and limit data with Flux
description: >
This guide walks through sorting and limiting data with Flux and outlines how
it shapes your data in the process.
menu:
v2_0:
name: Sort and limit data
parent: How-to guides
weight: 6
---
The [`sort()`function](/v2.0/reference/flux/functions/transformations/sort) orders the records within each table. The following example orders system uptime first by region, then host, then value.
```js
from(bucket:"telegraf/autogen")
|> range(start:-12h)
|> filter(fn: (r) =>
r._measurement == "system" and
r._field == "uptime"
)
|> sort(columns:["region", "host", "_value"])
```
The [`limit()` function](/v2.0/reference/flux/functions/transformations/limit) limit the number of records in output tables to a fixed number (n). The following example shows up to 10 records from the past hour.
```js
from(bucket:"telegraf/autogen")
|> range(start:-1h)
|> limit(n:10)
```
You can use `sort()` and `limit()` together to show the top N records. The example below returns the 10 top system uptime values sorted first by region, then host, then value.
```js
from(bucket:"telegraf/autogen")
|> range(start:-12h)
|> filter(fn: (r) =>
r._measurement == "system" and
r._field == "uptime"
)
|> sort(columns:["region", "host", "_value"])
|> limit(n:10)
```
You now have created a Flux query that sorts and limits data. Flux also provides the [`top()`](/v2.0/reference/flux/functions/transformations/selectors/top) and [`bottom()`](/v2.0/reference/flux/functions/transformations/selectors/bottom) functions to perform both of these functions at the same time.

331
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---
title: Window and aggregate data with Flux
seotitle: How to window and aggregate data with Flux
description: >
This guide walks through windowing and aggregating data with Flux and outlines
how it shapes your data in the process.
menu:
v2_0:
name: Window and aggregate data
parent: How-to guides
weight: 2
---
A common operation performed with time series data is grouping data into windows of time,
or "windowing" data, then aggregating windowed values into a new value.
This guide walks through windowing and aggregating data with Flux and demonstrates
how data is shaped in the process.
> The following example is an in-depth walk through of the steps required to window and aggregate data.
> The [`aggregateWindow()` function](#summing-up) performs these operations for you, but understanding
> how data is shaped in the process helps to successfully create your desired output.
## Data set
For the purposes of this guide, define a variable that represents your base data set.
The following example queries the memory usage of the host machine.
```js
dataSet = from(bucket: "telegraf/autogen")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "mem" and
r._field == "used_percent"
)
|> drop(columns: ["host"])
```
> This example drops the `host` column from the returned data since the memory data
> is only tracked for a single host and it simplifies the output tables.
> Dropping the `host` column is column is optional and not recommended if monitoring memory
> on multiple hosts.
`dataSet` can now be used to represent your base data, which will look similar to the following:
{{% truncate %}}
```
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
```
{{% /truncate %}}
## Windowing data
Use the [`window()` function](/v2.0/reference/flux/functions/transformations/window) to group your data based on time bounds.
The most common parameter passed with the `window()` is `every` which defines the duration of time between windows.
Other parameters are available, but for this example, window the base data set into one minute windows.
```js
dataSet
|> window(every: 1m)
```
Each window of time is output in its own table containing all records that fall within the window.
{{% truncate %}}
###### window() output tables
```
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
```
{{% /truncate %}}
When visualized in [Chronograf](/chronograf/latest/), each window table is displayed in a different color.
![Windowed data](/img/flux/simple-windowed-data.png)
## Aggregate data
[Aggregate functions](/v2.0/reference/flux/functions/transformations/aggregates) take the values
of all rows in a table and use them to perform an aggregate operation.
The result is output as a new value in a single-row table.
Since windowed data is split into separate tables, aggregate operations run against
each table separately and output new tables containing only the aggregated value.
For this example, use the [`mean()` function](/v2.0/reference/flux/functions/transformations/aggregates/mean)
to output the average of each window:
```js
dataSet
|> window(every: 1m)
|> mean()
```
{{% truncate %}}
###### mean() output tables
```
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
```
{{% /truncate %}}
Because each data point is contained in its own table, when visualized,
they appear as single, unconnected points.
![Aggregated windowed data](/img/flux/simple-windowed-aggregate-data.png)
### Recreate the time column
**Notice the `_time` column is not in the [aggregated output tables](#mean-output-tables).**
Because records in each table are aggregated together, their timestamps no longer
apply and the column is removed from the group key and table.
Also notice the `_start` and `_stop` columns still exist.
These represent the lower and upper bounds of the time window.
Many Flux functions rely on the `_time` column.
To further process your data after an aggregate function, you need to add `_time` back in.
Use the [`duplicate()` function](/v2.0/reference/flux/functions/transformations/duplicate) to
duplicate either the `_start` or `_stop` column as a new `_time` column.
```js
dataSet
|> window(every: 1m)
|> mean()
|> duplicate(column: "_stop", as: "_time")
```
{{% truncate %}}
###### duplicate() output tables
```
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
```
{{% /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)
```
> Windowing requires a `_time` column which is why it's necessary to
> [recreate the `_time` column](#recreate-the-time-column) after an aggregation.
###### 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.
![Unwindowed aggregate data](/img/flux/simple-unwindowed-data.png)
## 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](/v2.0/reference/flux/functions/transformations/aggregates/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)
```