concept#Observability#Reliability#DevOps#Software Engineering

Troubleshooting

A structured process for rapid detection, diagnosis and remediation of technical faults in systems and workflows.

Troubleshooting is the structured process for identifying, diagnosing, and resolving technical failures in systems and workflows.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeArchitectural
Organizational maturityIntermediate

Technical context

Integrations

Monitoring systems (e.g. Prometheus, Datadog)Logging/tracing platforms (ELK, Jaeger)Incident management (PagerDuty, Opsgenie)

Principles & goals

Principles

Observability as prerequisiteHypothesis-driven approachReproduce before fix

Value stream stage

Run

Organizational level

Team, Domain, Enterprise

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Wrong hypotheses delay resolution
Temporary workarounds instead of sustainable fixes
Knowledge remains in silos instead of documentation

Best practices

Reduce alert noise, define SLO-oriented alerts
Document reproduction steps and context in tickets
Use automated diagnostic tools and checklists

I/O & resources

Inputs

Monitoring metrics and dashboards
Structured logs and traces
Runbooks, playbooks and system documentation

Outputs

Restored functionality
Root‑cause analysis and permanent fixes
Updated runbooks and preventive measures

Resources

Description

Troubleshooting is the structured process for identifying, diagnosing, and resolving technical failures in systems and workflows. It uses hypothesis-driven investigation, analysis of logs, metrics and traces, and targeted remediation to restore service. The practice also captures root causes and improves system observability and resilience.

✔Benefits

Faster service restoration
Improved system understanding and prevention
Documented knowledge and runbooks

✖Limitations

Not always immediately reproducible
Dependence on metric and log quality
May require organizational coordination

Trade-offs

Metrics

Mean Time To Resolve (MTTR)
Average time from incident onset to full resolution.
Time To Detect (TTD)
Average time until a problem is detected by monitoring or users.
Number of reopened incidents
Count or rate of incidents that reoccur after an apparent fix.

Examples & implementations

Latency spike after feature rollout

After a feature launch API latencies rose; cause was an unoptimized query in a new code path.

Worker crashes sporadically

Intermittent null pointer in a third-party library caused restarts; reproducible with specific input.

CI tests fail after dependency upgrade

An upgraded test library changed assumptions; tests had to be adapted or the library rolled back.

Implementation steps

Establish visibility: instrument relevant metrics, logs and traces

Create and make runbooks and playbooks accessible

Define escalation and communication paths

Establish regular post-mortems and knowledge transfer

⚠️ Technical debt & bottlenecks

Technical debt

Incomplete or inconsistent logs
Missing tests for reproducing failures
Outdated runbooks and obsolete playbooks

Known bottlenecks

Insufficient telemetryKnowledge silos in teamComplex inter-service dependencies

Misuse examples

Only applying quick fixes repeatedly without addressing root cause
Ignoring monitoring data and relying solely on user reports
Denying privileged access so diagnosis becomes impossible

Typical traps

Making assumptions too early before collecting data
Hunter mentality: fix short-term only instead of documenting
Missing context in alerts (owner, runbook)

Required skills

Log and metrics analysisSystem and network diagnosisHypothesis formation and experimental testing

Architectural drivers

Observability and telemetrySLOs and availabilityFast feedback and release cycles

Constraints

• Limited access rights to production data
• Costs for long-term metrics and storage
• Regulatory constraints for data privacy