Observability, logging & monitoring¶

This document outlines a comprehensive document classification for observability, logging, and monitoring signals within a software system.

This document classifies observability into two major areas:

1. System / Technical observability
2. Business / Functional observability

The goal is to provide clear visibility into both system health and business outcomes.

1. System / Technical observability¶

Focus:
Is the system healthy, reliable, performant, and debuggable?

Primary Consumers:
Engineering, SRE, Platform, DevOps teams

1.1 Infrastructure observability¶

Scope: Cloud, VMs, containers, networking

• CPU, memory, disk, and IO utilization
• Node / VM availability and uptime
• Kubernetes:
  • Pod restarts, crash loops, OOMKills
  • Node pressure (memory, disk)
  • Deployment health and replica count
• Network latency, packet loss
• Ingress / egress errors
• Autoscaling events

1.2 Application observability¶

Scope: Application runtime behavior

Metrics¶

• Request rate (RPS)
• Error rate (4xx / 5xx)
• Latency (p50, p95, p99)
• Thread pool usage
• JVM metrics (heap usage, GC pauses)

Logs¶

• Structured logs (e.g., JSON)
• Log levels (INFO, WARN, ERROR)
• Exception stack traces
• Correlation IDs / Trace IDs
• Startup and configuration logs

Traces¶

• Distributed traces across services
• Span duration and timing
• Downstream dependency latency
• Error propagation across services

1.3 Dependency & integration observability¶

Scope: Internal and external dependencies

• Database query latency
• Connection pool saturation
• Cache hit / miss ratio
• Message queue lag (Kafka, RabbitMQ, etc.)
• External API success / failure rates
• Circuit breaker states (open / half-open)

1.4 Reliability & resilience signals¶

Scope: System stability and failure handling

• Retry counts
• Timeouts
• Failover events
• Circuit breaker activations
• Rate limiting triggers
• Bulkhead saturation

1.5 Security & compliance observability¶

Scope: System-level security signals

• Authentication and authorization failures
• Suspicious access patterns
• Certificate expiration
• Secrets access
• Audit logs (who accessed what and when)

1.6 Operational alerts¶

Scope: Actionable alerts for engineering teams

• SLA / SLO violations
• Threshold-based alerts
• Anomaly detection alerts
• Error budget burn-rate alerts
• On-call paging alerts

2. Business / Functional observability¶

Focus:
Are users successful? Are business processes working as expected?

Primary Consumers:
Product, Operations, Business stakeholders

2.1 User experience & journey observability¶

Scope: End-user behavior and experience

• Page load time and API response time (user-perceived)
• Mobile and browser performance
• Session duration
• Drop-off points in user journeys
• Error rates per user flow
• Feature usage frequency

2.2 Business process monitoring¶

Scope: End-to-end business workflows

Examples:

• Order creation → payment → fulfillment
• Signup → verification → activation
• Data ingestion → processing → reporting

Metrics:

• Success vs failure rate
• Processing time per step
• Workflow bottlenecks
• Reprocessing and retries

2.3 Domain & business metrics¶

Scope: Core business KPIs

• Orders per hour / day
• Revenue per transaction
• Conversion rate
• Active users (DAU / MAU)
• Payment success rate
• Refund and cancellation rates

These metrics must align with agreed business definitions.

2.4 Functional error monitoring¶

Scope: Business-meaningful failures

• Payment declined (categorized by reason)
• Inventory unavailable
• Business rule validation failures
• Data quality issues (missing, invalid, inconsistent data)
• SLA breaches for business operations

2.5 Data observability¶

Scope: Trust and reliability of business data

• Data freshness and timeliness
• Completeness (missing or partial data)
• Accuracy and validity checks
• Schema drift
• Pipeline failures impacting reports or dashboards

2.6 Customer impact & support Signals¶

Scope: Downstream business and customer impact

• Customer-impacting incidents
• Support tickets correlated with system errors
• Impact radius (number of users affected)
• Duration of customer-visible outages

2.7 Business alerts & notifications¶

Scope: Business-level notifications

• Sudden drop in transactions
• Spike in failed payments
• Unusual user behavior
• Regulatory threshold breaches
• Revenue anomalies

3. Cross-Cutting dimensions¶

These dimensions apply to both System and Business observability.

3.1 Correlation¶

• Logs ↔ Metrics ↔ Traces
• Business events linked to technical signals
• Correlation via Trace ID, Order ID, User ID

3.2 Ownership¶

• Clear ownership per signal
• Defined escalation paths
• Runbooks and remediation guides

3.3 SLIs & SLOs¶

• Technical SLOs (availability, latency)
• Business SLOs (checkout success rate, data freshness)

3.4 Visualization & reporting¶

• Engineering dashboards
• Business and executive dashboards
• Incident timelines and postmortems

4. Summary¶