OpenTelemetry for Production: Unified Observability

The Observability Challenge

Modern distributed systems are complex. A single user request might traverse dozens of services. When something goes wrong, finding the root cause requires visibility across the entire request path.

Traditional monitoring tools - separate solutions for metrics, logs, and traces - create data silos. Correlating information across tools is manual and slow. OpenTelemetry unifies observability under a single standard.

Why OpenTelemetry?

OpenTelemetry is a vendor-neutral standard for observability data. Benefits include:

Vendor independence: Instrument once, export to any backend. Switch from Datadog to Honeycomb without changing application code.

Unified signals: Traces, metrics, and logs share context. Correlate a spike in error rate with the exact traces that failed.

Automatic instrumentation: Libraries for common frameworks instrument HTTP, databases, and messaging with zero code changes.

Community momentum: CNCF project with broad industry support. The standard is here to stay.

The Three Pillars

Traces: Follow a request across services. Each span represents a unit of work. Parent-child relationships show the call graph. Find slow operations and error sources.

Metrics: Aggregate measurements over time. Request rates, error percentages, latency histograms. Alerting and dashboards.

Logs: Detailed event records. Structured logs with trace context enable correlation with traces.

The power is in correlation. Jump from a metric alert to relevant traces to detailed logs without context switching.

Instrumentation Strategy

Auto-instrumentation: Start here. SDK plugins automatically trace HTTP requests, database queries, and message queue operations. Get visibility with minimal code.

Manual instrumentation: Add custom spans for business operations. Wrap important functions to understand their performance and error rates.

Semantic conventions: Use standard attribute names. service.name, http.method, db.system - consistency enables cross-service analysis and vendor tooling.

Sampling: Managing Volume

High-traffic systems generate enormous trace volumes. Sending everything is expensive and often unnecessary.

Head-based sampling: Decide at trace start whether to sample. Simple but may miss interesting traces.

Tail-based sampling: Collect everything, decide what to keep after the trace completes. Keep all errors and slow traces. More complex but captures what matters.

Adaptive sampling: Adjust rates based on traffic. Sample more during low traffic, less during peaks.

A common strategy: 100% of errors, 100% of slow requests, 10% of everything else.

Cost Management

Observability costs can explode without controls:

Set sampling rates based on value, not just volume

Use attribute filtering to drop high-cardinality data

Aggregate metrics where individual events are not needed

Implement retention policies - old data is rarely queried

Monitor your observability costs like any other resource

Best Practices

Correlate all signals: Shared trace IDs across logs, metrics, traces

Sample strategically: Keep interesting data, drop noise

Use semantic conventions: Standard attributes enable tooling

Start with auto-instrumentation: Add manual spans for business logic

Monitor costs: Observability can be expensive at scale

Set context propagation: W3C Trace Context for cross-service correlation