tracing▌

Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains reference files covering setup, instrumentation, span types, and production deployment.

Trace downstream data lineage to assess change impact before modifying tables or DAGs. \n \n Identifies direct consumers of a target table or DAG through source code search, view dependencies, and BI tool connections \n Builds a full dependency tree mapping all downstream impacts, from tables to dashboards to ML models \n Categorizes dependencies by criticality (critical, high, medium, low) to prioritize stakeholder communication and testing \n Generates an impact report with risk assessment, af

Set up distributed tracing infrastructure with Jaeger or Zipkin to track requests across microservices and identify performance bottlenecks.

Implement correlation IDs and distributed tracing to track requests across multiple services and understand system behavior.

Configure Sentry's performance monitoring to track transactions and spans.

Comprehensive guide to tracing Claude Code sessions in Braintrust, including sub-agent correlation.

Bugs often manifest deep in the call stack (git init in wrong directory, file created in wrong location, database opened with wrong path). Your instinct is to fix where the error appears, but that's treating a symptom.

Trace upstream data lineage to identify sources, DAGs, and dependencies feeding a table or column. \n \n Supports tracing three target types: tables, columns, and DAGs; uses Airflow DAG source code and task inspection to find producing pipelines \n Handles SQL sources (FROM clauses), external systems (S3, Postgres, Salesforce, HTTP APIs), and file-based sources; recursively traces upstream chains \n Includes column-level tracing through direct mappings, transformations, and aggregations in DAG c

Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.

Track requests across microservices to identify latency, dependencies, and failure points. \n \n Supports Jaeger and Tempo backends with OpenTelemetry instrumentation for Python, Node.js, and Go \n Includes trace structure concepts (traces, spans, context, tags, logs) and automatic service dependency graph generation \n Provides sampling strategies (probabilistic, rate-limiting, adaptive) to control tracing overhead in production \n Covers context propagation via HTTP headers, trace analysis que