OpenObserve collects distributed traces through OpenTelemetry instrumentation. The platform accepts traces via the OTLP protocol, supporting both manual and automatic instrumentation methods. For services already instrumented with OpenTelemetry, you can configure the exporter to send traces directly to OpenObserve. The system maintains trace context across service boundaries using W3C trace context standards.

Each trace consists of spans representing operations across services. Spans include essential information such as operation name, timestamp, duration, and service name. Additional context is captured through span attributes, events, and links. The system preserves parent-child relationships between spans, enabling full request path visualization. Error information and stack traces are automatically captured for failed operations.

OpenObserve processes traces to provide multiple analysis perspectives. The trace view shows the complete request timeline with nested spans and timing information. Service maps are generated automatically from trace data, showing dependencies and interaction patterns. The query engine supports filtering traces by service, operation, duration, and custom attributes. You can analyze latency distributions and error rates across services.

The platform provides several visualization options for trace data. The trace timeline view shows the hierarchical relationship between spans with precise timing. Service maps display the topology of your distributed system based on actual request flows. The dashboard system supports custom visualizations for trace metrics, including latency histograms and error rate trends.

Trace sampling is configurable to balance data volume with observability needs. The system supports head-based sampling at collection time and tail-based sampling for more sophisticated scenarios. You can define sampling rules based on service names, operation types, or custom attributes. The platform maintains statistical accuracy while reducing storage requirements through intelligent sampling strategies.

OpenObserve implements a powerful query engine for trace analysis. You can search traces using service names, operation names, duration thresholds, and custom attributes. The system supports complex queries combining multiple criteria with logical operators. Trace queries can be saved and reused across dashboards. The query language allows for aggregations and statistical analysis of trace data.

The platform automatically generates service dependency information from trace data. Service maps show both direct and indirect dependencies between components. The system calculates key metrics like request rates, error rates, and latency percentiles for each service interaction. You can analyze dependency changes over time and identify critical paths in your architecture.

OpenObserve optimizes trace storage using columnar format and efficient compression. Retention periods are configurable per data source, allowing you to balance storage costs with analysis needs. The system supports tiered storage for different trace retention requirements. Historical traces can be accessed efficiently through the query interface while maintaining quick access to recent data.