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Using heatmaps

New to running queries in Honeycomb? Check out the introduction to building queries!

Heatmaps are a visualization that shows the statistical distribution of the values in a dataset column over time.

Take the graph below, which shows the statistical distribution of roundtrip_dur over the selected time period:

Example heatmap visualization

Each of the vertical columns in that graph is a histogram for that time bucket. The color is chosen based on the number of events fall into that range of values, blue at the low end, red at the high end.

How to make a heatmap  🔗

To add a heatmap to a query, click in the Calculate clause and scroll down:

gif of how to add a heatmap

When to use them  🔗

Heatmaps look best when you have a lot of events to visualize, and where the spread of values is wide enough to see some differentiation, but not complete noise.

Any column representing a duration or size is a perfect fit, but any column you might run a percentile or average calculation on may benefit from being rendered as a heatmap as well.

The Rollover  🔗

The rollover for heatmaps is different than for the normal line and stacked graphs:

time bucket for heatmaps

“This time bucket” shows you what the histogram is for the column under the time bar. You can see those outliers represented as small bumps on the right hand side of the histogram (from about 0.8k and 1.0k.)

“Entire time range” shows you the merged histogram for all data within the time range displayed in the graph. Note the difference in y-axes, and the smoother shape for “Entire time range.”

How heatmaps interact with other features  🔗

Breakdowns  🔗

One of the most powerful features of Honeycomb is the ability to group results based on values in columns. Heatmaps work well with this. Take the query below, where we’ve grouped by endpoint. By default the heatmap of all endpoints is shown (approximating what you’d see if you did the query without the breakdown.)

breaking down with a heatmap

Note that the rollover displays independent histograms for each group. This makes it especially easy to see the reason for the bimodal distribution: /account accounts for the lower of the two peaks, and /dashboard for the upper.

Just as we highlight the corresponding line in line graphs as you mouse over the summary table, we also show the heatmap corresponding to that group:

highlighting in a heatmap

You can clearly see the difference between /account and /dashboard peaks by mousing over their lines - they’re almost completely disjoint (the values don’t, or barely overlap). If you watch closely, you can also see the outliers (from about 10:30 to 10:50) appear only when hovering over the line for /login.