Distributed Tracing
A method of tracking requests as they flow through multiple services in a distributed system, recording timing and metadata at each step to enable end-to-end performance analysis and debugging.
In a microservices architecture, a single user request might touch 10 or more services. Distributed tracing assigns a unique trace ID to each request and propagates it across service boundaries. Each service records a span with timing data and metadata, building a complete picture of the request's journey through the system.
Tools like Jaeger, Zipkin, and cloud-native solutions (AWS X-Ray, Google Cloud Trace) collect and visualize these traces. A trace waterfall view shows exactly where time is spent: 50ms in the API gateway, 200ms waiting for the recommendation model, 30ms in the database, revealing that the model call is the bottleneck.
For AI applications, distributed tracing is invaluable for debugging complex inference pipelines. A RAG request might involve query embedding, vector search, document retrieval, context assembly, LLM inference, and response parsing. Without tracing, identifying which step caused a slow response requires guesswork. With tracing, the latency bottleneck is immediately visible and actionable.
Related Terms
A/B Testing
A controlled experiment comparing two or more variants to determine which performs better on a defined metric, using statistical methods to ensure reliable results.
Feature Flag
A software mechanism that enables or disables features at runtime without deploying new code, used for gradual rollouts, A/B testing, and targeting specific user segments.
MLOps
The set of practices combining machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production.
Model Serving
The infrastructure and systems that host trained ML models and handle inference requests in production, optimizing for latency, throughput, and cost.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords, typically powered by embedding-based similarity comparison.
CI/CD (Continuous Integration / Continuous Deployment)
An automated software practice where code changes are continuously integrated into a shared repository, tested, and deployed to production, reducing manual intervention and accelerating delivery cycles.