A living reliability engineering lab where systems observe themselves, run controlled failure experiments, and document learning automatically over time.
Distributed systems rarely fail in predictable ways. Reliability is not achieved by design alone — it comes from observation, experimentation, and iteration.
This lab is built to model that mindset by continuously generating:
- Daily reliability observations
- Weekly chaos engineering experiments
- Long-term engineering learning artifacts
Instead of static documentation, this repository grows into a historical record of how a system behaves, evolves, and improves.
The system automatically generates telemetry snapshots that capture reliability signals such as latency trends, error patterns, and service behavior.
Daily reports summarize system status, observations, and actionable improvement opportunities.
Weekly experiment logs simulate controlled failures to test system resilience and recovery behavior.
All experiments, observations, and decisions are stored as structured artifacts to prevent knowledge loss.
Generates:
- Metrics snapshot
- Reliability journal entry
Generates:
- Chaos experiment log
- Hypothesis and learning record
Reliability is validated through recorded system behavior.
Controlled failure experiments help uncover hidden weaknesses.
Learning is captured automatically and consistently.
System health is evaluated across time, not just at deployment.
This lab is intentionally designed to evolve and may later include:
- Real monitoring integrations (Prometheus / Grafana)
- Incident postmortem automation
- Reliability score tracking
- Service-level objective validation
- Automated remediation experiments
This repository is not just code — it is a reliability journal that answers:
How does a distributed system actually behave, and what did we learn from it?
Built as part of ongoing reliability engineering and distributed systems research.