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Merge pull request #336 from influxdata/query/histogram-vis-info 

Histogram visualization in the histogram guide
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Scott Anderson 2019-07-22 13:32:01 -06:00 committed by GitHub
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@ -58,9 +58,30 @@ logarithmicBins(start: 1.0, factor: 2.0, count: 10, infinty: true)
// Generated list: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, +Inf] // Generated list: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, +Inf]
``` ```
## Histogram visualization
The [Histogram visualization type](/v2.0/visualize-data/visualization-types/histogram/)
creates histograms by automatically generating bins and groups within bins using
columns available in query results.
{{< img-hd src="/img/2-0-visualizations-histogram-example.png" alt="Histogram visualization" />}}
[Histogram visualization controls](/v2.0/visualize-data/visualization-types/histogram/#histogram-controls)
let you specify the number of bins and how to define groups within bins.
### Histogram visualization data structure
Because the Histogram visualization creates bins and bin segments based on settings
specified in the visualization controls, query results **should not** be structured
as histogram data.
{{% note %}}
Output of the [`histogram()` function](#histogram-function) is **not** compatible
with the Histogram visualization type.
View the example [below](#visualize-error-counts-by-severity-over-time).
{{% /note %}}
## Examples ## Examples
### Generating a histogram with linear bins ### Generate a histogram with linear bins
```js ```js
from(bucket:"example-bucket") from(bucket:"example-bucket")
|> range(start: -5m) |> range(start: -5m)
@ -105,7 +126,7 @@ Table: keys: [_start, _stop, _field, _measurement, host]
2018-11-07T22:19:58.423358000Z 2018-11-07T22:24:58.423358000Z used_percent mem Scotts-MacBook-Pro.local 75 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 ### Generate a histogram with logarithmic bins
```js ```js
from(bucket:"example-bucket") from(bucket:"example-bucket")
|> range(start: -5m) |> range(start: -5m)
@ -139,3 +160,21 @@ Table: keys: [_start, _stop, _field, _measurement, host]
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 128 30
2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 256 30 2018-11-07T22:23:36.860664000Z 2018-11-07T22:28:36.860664000Z used_percent mem Scotts-MacBook-Pro.local 256 30
``` ```
### Visualize error counts by severity over time
Use the [Telegraf Syslog plugin](Telegraf Syslog plugin) to collect error information from your system.
Query the `severity_code` field in the `syslog` measurement:
```js
from(bucket: "example-bucket")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) =>
r._measurement == "syslog" and
r._field == "severity_code"
)
```
In the Histogram visualization options, select `_time` as the **X Column**
and `severity` as the **Group By** option:
{{< img-hd src="/img/2-0-visualizations-histogram-errors.png" alt="Logs by severity histogram" />}}