Monitoring & Observability
Distributed Tracing Implementation
When a user reports that the dashboard is slow, distributed tracing shows you the exact call chain: the API took 2s because the database query took 1.5s because it's doing a full table scan on a 50M row table. We implement distributed tracing across your services with OpenTelemetry, proper context propagation, and correlation with your existing logs and metrics.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
What We Deliver
Distributed tracing across all your services with OpenTelemetry SDK instrumentation, trace context propagation across HTTP, gRPC, and message queues, trace collection via OpenTelemetry Collector, storage in Jaeger, Tempo, or your APM provider, trace-to-log and trace-to-metric correlation in Grafana, and sampling strategies that balance visibility with cost.
OpenTelemetry Standard
We use OpenTelemetry — the CNCF standard for telemetry — for all instrumentation. OTel SDKs are available for every major language and provide automatic instrumentation for HTTP frameworks, database drivers, cache clients, and message brokers. The OpenTelemetry Collector acts as a telemetry pipeline — receiving, processing, and exporting traces to your backend of choice.
Context Propagation
Distributed tracing only works if trace context (trace ID, span ID, flags) propagates across every service boundary. We configure W3C Trace Context propagation headers for HTTP, gRPC metadata for RPC calls, and message attributes for async processing via queues. Context propagation works across languages — a Node.js service calling a Python service calling a Go service all share the same trace.
Trace Analysis Patterns
We set up trace analysis workflows for common debugging scenarios: finding slow endpoints (sort by duration), identifying error sources (filter by status code), discovering N+1 queries (look for repeated database spans), detecting sequential calls that could be parallelized, and tracking performance regressions by comparing traces before and after deployments.
Correlation with Logs & Metrics
Trace IDs are injected into log messages so you can jump from a trace span to the exact log lines it produced. Exemplars link metrics (histograms, counters) to specific traces. In Grafana, this enables a workflow: see a latency spike on a dashboard, click to see exemplar traces, click a span to see its logs. Full observability without context switching.
How It Works
Purchase the engagement, submit your async brief with your service architecture and tracing goals, and receive a complete distributed tracing implementation within 5–7 business days. SDK integration guides, collector configuration, and dashboards included.
Why Anubiz Engineering
100% async — no calls, no meetings
Delivered in days, not weeks
Full documentation included
Production-grade from day one
Security-first approach
Post-delivery support included
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.