Heatmap

A heat map shows how a numeric metric varies across two dimensions using color intensity. Use it to spot hotspots and patterns.

A heat map visualizes how a numeric metric varies across two dimensions using color intensity. It helps you spot hot spots, spikes, and distribution patterns.

It’s ideal for:

• log volume per app per namespace

• traffic distribution

• error concentration

• workload imbalance

What a heat map represents

Each colored cell corresponds to one (X, Y) combination.

• Column: X-axis category

• Row: Y-axis category

• Cell color: metric intensity (value column)

• Color scale: low → high values

Heat map

Configure a heat map

Use the Plot panel to choose axes and display settings.

Heat map options

Use these options to control how the heat map renders.

Option	Purpose	Typical values / notes
X-axis column	First (horizontal) dimension.	Categorical field. Examples: exported_namespace, cluster, region. You can also use timestamp for time buckets.
Y-axis column	Second (vertical) dimension.	Categorical field. Examples: app, service, pod, host.
Value column	Metric used for coloring (intensity).	Must be numeric. Examples: value, request_count, error_count, bytes_sent. Duplicate (X, Y) pairs are aggregated.
Apply	Applies changes to the widget.	Changes do not render until you click Apply.

Key rules: X-axis and Y-axis should be categorical fields, and Value column must be numeric. If your dataset has duplicate (X, Y) pairs, they will be aggregated.

When to use a heat map

Use heat maps when you want to analyze:

• which app produces most traffic

• which namespace is overloaded

• where errors are concentrated

• workload imbalance across services

They work best when your data has two categorical dimensions and you want fast visual pattern recognition.