mirror of
https://github.com/grafana/grafana.git
synced 2025-07-29 17:52:59 +08:00
414d536186
* refactors config panels and visualizations, corrects relrefs * adds an alias * Remove some old content * moves visualizations topic to the root * moves out panels and visualization topics to the root * adds move and resize panel to add/organize panel; creates a create dashboard topic under build dashboards; adjusts context of add a panel to be from within an existing dashboard * updates aliases * creates search at root, moves dashboard preview to search, creates standalone search dashboard topic * moves Set dashboard time range to use-dashboards, creates modify dashboard settings and adds moves Modify dashboard time settings to that topic * moves existing query-options topic from working with panels to configure-panel-visualizations, moves panel time overrides and timeshift content to query options * Moving things to better category, fixing links, improving ordering * Move panel inspector to main panel topic * completes partial fix of relrefs * relref fixes con't * restructures remaining panels topics * more relref fixes * Minor fix * Minor tweak * finishes fixing relrefs Co-authored-by: Torkel Ödegaard <torkel@grafana.com> Co-authored-by: Jack Baldry <jack.baldry@grafana.com>
71 lines
3.6 KiB
Markdown
71 lines
3.6 KiB
Markdown
---
|
|
aliases:
|
|
- /docs/grafana/latest/basics/intro-histograms/
|
|
- /docs/grafana/latest/getting-started/intro-histograms/
|
|
description: An introduction to histograms and heatmaps
|
|
keywords:
|
|
- grafana
|
|
- heatmap
|
|
- panel
|
|
- documentation
|
|
- histogram
|
|
title: Histograms and heatmaps
|
|
weight: 700
|
|
---
|
|
|
|
# Introduction to histograms and heatmaps
|
|
|
|
A histogram is a graphical representation of the distribution of numerical data. It groups values into buckets
|
|
(sometimes also called bins) and then counts how many values fall into each bucket.
|
|
|
|
Instead of graphing the actual values, histograms graph the buckets. Each bar represents a bucket,
|
|
and the bar height represents the frequency (such as count) of values that fell into that bucket's interval.
|
|
|
|
## Histogram example
|
|
|
|
This _histogram_ shows the value distribution of a couple of time series. You can easily see that
|
|
most values land between 240-300 with a peak between 260-280.
|
|
|
|
|
|
|
|
Here is an example showing height distribution of people.
|
|
|
|
{{< figure src="/static/img/docs/histogram-panel/histogram-example-v8-0.png" max-width="625px" caption="Bar chart example" >}}
|
|
|
|
For more information about histogram visualization options, refer to [Histogram]({{< relref "../panels-visualizations/visualizations/histogram/" >}}).
|
|
|
|
Histograms only look at _value distributions_ over a specific time range. The problem with histograms is that you cannot see any trends or changes in the distribution over time. This is where heatmaps become useful.
|
|
|
|
## Heatmaps
|
|
|
|
A _heatmap_ is like a histogram, but over time, where each time slice represents its own histogram. Instead of using bar height as a representation of frequency, it uses cells, and colors the cell proportional to the number of values in the bucket.
|
|
|
|
In this example, you can clearly see what values are more common and how they trend over time.
|
|
|
|
|
|
|
|
For more information about heatmap visualization options, refer to [Heatmap]({{< relref "../panels-visualizations/visualizations//heatmap/" >}}).
|
|
|
|
## Pre-bucketed data
|
|
|
|
There are a number of data sources supporting histogram over time, like Elasticsearch (by using a Histogram bucket
|
|
aggregation) or Prometheus (with [histogram](https://prometheus.io/docs/concepts/metric_types/#histogram) metric type
|
|
and _Format as_ option set to Heatmap). But generally, any data source could be used as long as it meets the requirement
|
|
that it either returns series with names representing bucket bounds, or that it returns series sorted by the bounds
|
|
in ascending order.
|
|
|
|
## Raw data vs aggregated
|
|
|
|
If you use the heatmap with regular time series data (not pre-bucketed), then it's important to keep in mind that your data
|
|
is often already aggregated by your time series backend. Most time series queries do not return raw sample data,
|
|
but instead include a group by time interval or maxDataPoints limit coupled with an aggregation function (usually average).
|
|
|
|
This all depends on the time range of your query of course. But the important point is to know that the histogram bucketing
|
|
that Grafana performs might be done on already aggregated and averaged data. To get more accurate heatmaps, it is better
|
|
to do the bucketing during metric collection, or to store the data in Elasticsearch or any other data source which
|
|
supports doing histogram bucketing on the raw data.
|
|
|
|
If you remove or lower the group by time (or raise maxDataPoints) in your query to return more data points, your heatmap will be
|
|
more accurate, but this can also be very CPU and memory taxing for your browser, possibly causing hangs or crashes if the number of
|
|
data points becomes unreasonably large.
|