Day 39 of 60
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Production & continuous
Distributed tracing & observability
Past three services, debugging without traces is folklore. With them, you read the call graph like a story.
ProblemErrors in distributed systems are invisible without correlation.
How it works
Every request gets a trace ID. Every span emits structured data. A trace UI shows the whole call graph. Pairs with metrics + logs (the three pillars).
What it catches
Cross-service latency, dependency cascades, hot spots. Required for debugging anything past three services.
Tools
OpenTelemetry · OSS Jaeger · OSS Tempo · OSS Honeycomb · SaaS
Verdict by project size
Small
Opt
Medium
Rec
Large
Must
Extra-large
Must
Cost
| Project size | Setup | Maint / mo | Tool / mo | CI / run |
|---|---|---|---|---|
| Small <10k LOC | 1d | 1h | $0 | , |
| Medium 10–100k LOC | 3d | 5h | $200 | , |
| Large 100k–1M LOC | 15d | 30h | $3k | , |
| Extra-large >1M LOC | 60d | 150h | $20k | , |
Setup = engineer-days to first useful run ·
Maint = engineer-hours / month at steady state ·
Tool = out-of-pocket $ / month ·
CI = minutes added (or saved) per pipeline run
Lifecycle & ownership
When in lifecycle
Release Operate Observe
Continuous in prod · Always-on, observing real traffic.
Who owns it
SRE / DevOps / Platform
CI/CD, observability, reliability
Collaborates with: Developer
Reference implementations
-
OpenTelemetry Demo
Realistic microservice demo instrumented with traces, metrics, and logs.
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Jaeger HotROD
Distributed tracing demo app with realistic service interactions.
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OpenTelemetry Collector examples
Collector pipelines for exporting traces, metrics, and logs.
Quick check
Distributed tracing becomes effectively required past…
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