Distributed Tracing

Gain end-to-end visibility into your distributed systems with powerful tracing capabilities and seamless OpenTelemetry integration.

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Why Use OpenObserve for Tracing?

Transform your distributed system observability with efficient trace collection, comprehensive analysis, and intuitive visualization tools designed for modern architectures.

Trace Collection
Service Analysis
Trace Analysis
Optimized Storage
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Trace Collection

OpenTelemetry Integration

Seamlessly collect traces through native OTLP(Opentelemetry Protocol). Instrument applications once and export traces directly to OpenObserve—no vendor lock-in.

Auto-instrumentation

Capture distributed traces using zero-code auto-instrumentation configured by OpenObserve collector

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Service Analysis

Dependency Mapping

Automatically generate service maps showing real-time interactions between microservices.

Performance Insights

Identify slow endpoints with latency heatmaps and error trend analysis. Compare trace durations across service versions to catch regressions early.

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Trace Analysis

Detailed Spans

Inspect individual spans with nested timing data, tags, and logs. Jump from aggregated metrics to specific problematic traces in one click.

Context Propagation

Track requests across service boundaries with W3C trace context support. Visualize full distributed transaction paths with flame graphs and waterfall diagrams.

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Optimized Storage

Efficient Storage

Store traces at 1/140th the cost of Elasticsearch using Apache Parquet columnar format. Achieve efficient data compression without extremely high query performance.

Flexible Retention

Define custom retention policies without the need for tiered storage. OpenObserve uses object store for hot tier storage.

Get Started with Distributed Tracing

Begin monitoring your distributed systems with OpenObserve. Start with the free tier or schedule a demo.

Fair and transparent pricing

Only pay for what you use.

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Openobserve Cloud Free Tier

Monthly Limits:

  • iconIngestion - 50 GB logs, 50 GB metrics , 50 GB traces
  • iconQuery volume - 200 GB
  • iconPipelines - 50 GB of Data Processing
  • icon1K RUM & Session Replay
  • icon1K Action Script Runs
  • icon3 Users
  • icon7-Days Retention
Get started for free

Get started in minutes—no credit card required.

Distributed Tracing FAQs

How does OpenObserve collect traces?

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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.

What information is captured in traces?

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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.

How does trace analysis work?

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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.

What visualization capabilities are available?

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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.

How does OpenObserve handle trace sampling?

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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.

What query capabilities are available for traces?

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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.

How does service dependency analysis work?

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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.

What retention and storage options are available?

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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.

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