concept#Observability#Reliability#Metrics#Performance

Observability

Observability enables understanding the state of complex systems through metrics, logs, and traces.

Observability is crucial for monitoring IT systems.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeArchitectural
Organizational maturityAdvanced

Technical context

Integrations

KubernetesAWS CloudWatchDatadog

Principles & goals

Principles

Transparency.Real-time feedback.Less complexity.

Value stream stage

Run

Organizational level

Enterprise, Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Misinterpretation of metrics.
Data overload.
Unmet compliance requirements.

Best practices

Regular checks of systems.
Provide training for the team.
Set up automated alerts.

I/O & resources

Inputs

Access to Metrics
Access to Logs
Application Registration

Outputs

Reports on Application Performance
Analysis of Usage Trends
Error Diagnosis Reports

Resources

Description

Observability is crucial for monitoring IT systems. It provides insights into operational and application performance through metrics, logs, and traces. This information helps quickly identify issues and enhance system reliability.

✔Benefits

Improved issue detection.
Increased system reliability.
Faster troubleshooting.

✖Limitations

Dependence on data quality.
High implementation costs.
Complex integration.

Trade-offs

Metrics

Response Time
The time taken by a system to respond to a request.
Error Rate
The percentage of requests that return an error.
System Availability
The percentage of time during which the system is fully operational.

Examples & implementations

Utility Company

A utility company uses observability to monitor network systems and perform error analysis.

E-commerce Platform

An e-commerce platform uses observability to analyze user behavior in real-time.

Banking Software

Banking software implements observability to ensure transaction security and detect anomalies.

Implementation steps

Select monitoring tools.

Configure tools and integrations.

Analyze metrics and logs.

⚠️ Technical debt & bottlenecks

Technical debt

Non-optimized log storage.
Outdated monitoring tools.
Lack of automation in data collection.

Known bottlenecks

Data Overload.Insufficient Software Integration.Lack of Expertise.

Misuse examples

Using outdated metrics.
Ignoring alerts.
Lack of documentation of integration steps.

Typical traps

Too many metrics analyzed simultaneously.
Insufficient consideration of user feedback.
Lack of clear responsibilities.

Required skills

Knowledge of cloud architectureAbility to analyze dataFamiliarity with APIs

Architectural drivers

Meeting service level agreements.Customer satisfaction.Rapid failure review.

Constraints

• Budget constraints.
• Technological incompatibility.
• Access restrictions to data.