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* Some documentation fixes * Address review comments * Fixed lint issues using prettifier. Co-authored-by: achatterjee-grafana <aparajita.chatterjee@grafana.com>
64 lines
3.5 KiB
Markdown
64 lines
3.5 KiB
Markdown
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title = "Histograms and heatmaps"
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description = "An introduction to histograms and heatmaps"
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keywords = ["grafana", "heatmap", "panel", "documentation", "histogram"]
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aliases = ["/docs/grafana/latest/getting-started/intro-histograms"]
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weight = 700
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# Introduction to histograms and heatmaps
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A histogram is a graphical representation of the distribution of numerical data. It groups values into buckets
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(sometimes also called bins) and then counts how many values fall into each bucket.
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Instead of graphing the actual values, histograms graph the buckets. Each bar represents a bucket,
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and the bar height represents the frequency (such as count) of values that fell into that bucket's interval.
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## Histogram example
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This _histogram_ shows the value distribution of a couple of time series. You can easily see that
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most values land between 240-300 with a peak between 260-280.
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Here is an example showing height distribution of people.
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{{< figure src="/static/img/docs/histogram-panel/histogram-example-v8-0.png" max-width="625px" caption="Bar chart example" >}}
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For more information about histogram visualization options, refer to [Histogram]({{< relref "../visualizations/histogram.md" >}}).
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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.
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## Heatmaps
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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.
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In this example, you can clearly see what values are more common and how they trend over time.
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For more information about heatmap visualization options, refer to [Heatmap]({{< relref "../visualizations/heatmap.md" >}}).
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## Pre-bucketed data
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There are a number of data sources supporting histogram over time, like Elasticsearch (by using a Histogram bucket
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aggregation) or Prometheus (with [histogram](https://prometheus.io/docs/concepts/metric_types/#histogram) metric type
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and _Format as_ option set to Heatmap). But generally, any data source could be used as long as it meets the requirement
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that it either returns series with names representing bucket bounds, or that it returns series sorted by the bounds
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in ascending order.
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## Raw data vs aggregated
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If you use the heatmap with regular time series data (not pre-bucketed), then it's important to keep in mind that your data
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is often already aggregated by your time series backend. Most time series queries do not return raw sample data,
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but instead include a group by time interval or maxDataPoints limit coupled with an aggregation function (usually average).
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This all depends on the time range of your query of course. But the important point is to know that the histogram bucketing
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that Grafana performs might be done on already aggregated and averaged data. To get more accurate heatmaps, it is better
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to do the bucketing during metric collection, or to store the data in Elasticsearch or any other data source which
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supports doing histogram bucketing on the raw data.
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If you remove or lower the group by time (or raise maxDataPoints) in your query to return more data points, your heatmap will be
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more accurate, but this can also be very CPU and memory taxing for your browser, possibly causing hangs or crashes if the number of
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data points becomes unreasonably large.
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