Trace Graph

Trace Graph renders a live, interactive service-dependency map built from your trace data. Each node represents a service, and edges show the call relationships between them. Use it to understand your architecture, spot bottlenecks, and identify failing dependencies at a glance.

Getting Started

Navigate to Traces → Trace Graph in the sidebar. The graph populates automatically from trace spans observed in the selected time range.

Reading the Graph

Nodes

Each node represents a service (identified by the service.name span attribute). Nodes are color-coded by health:

Color	Meaning
Green	Healthy — low error rate
Yellow / Orange	Warning — elevated error rate
Red	Critical — high error rate

Edges

Directed arrows between nodes indicate that one service calls another. The arrow direction follows the request flow (caller → callee).

Sidebar Filters

The left-hand sidebar provides several filter controls:

• Environment — Filter by deployment environment (e.g. production, staging).
• Cluster — Filter by Kubernetes cluster.
• Health status — Show only Healthy, Warning, or Critical services.
• Service search — Type to find a specific service by name.
• Service checkboxes — Toggle individual services on or off to simplify the view.
• Hide services with no connections — Remove isolated nodes from the graph.
• Compare with previous period — Enable comparison stats to see how metrics have changed.

Time Range

Use the time picker in the sidebar to change the observation window (maximum 24 hours). A shorter window shows recent call patterns; a longer window provides a broader picture.

Interacting with the Graph

• Pan and zoom — Use the mouse wheel to zoom and drag to pan. The toolbar buttons (Zoom In, Zoom Out, Fit View) are also available.
• Click a node — Opens a detail panel showing the service's error rate, latency percentiles, and throughput.
• Hover an edge — Displays the request rate and error rate for that specific call path.

Use Cases

• Architecture discovery — Visualize how microservices interact without reading code or config files.
• Incident triage — During an outage, identify which upstream or downstream dependency is the root cause.
• Performance analysis — Find hot paths where multiple services converge and latency compounds.
• Dependency auditing — Verify that services only communicate with expected dependencies.

Best Practices

• Use a short time range (15 minutes) for real-time incident investigation.
• Use a longer range (1–24 hours) for architecture review and dependency mapping.
• Filter by cluster when you have multiple environments to avoid mixing production and staging traffic.

Related Pages

• Trace Lookup — Find a specific trace by its Trace ID.
• Trace Explorer — Search and filter traces across services.