Project 7: StatefulSet Failover and Backup Drills
Prove data durability and service recoverability through repeatable failover and restore exercises.
Quick Reference
| Attribute | Value |
|---|---|
| Difficulty | Expert |
| Time Estimate | 14-24 hours |
| Main Programming Language | YAML + shell |
| Alternative Programming Languages | Go, Python |
| Coolness Level | Level 3 - Production Realism |
| Business Potential | 4. Reliability Multiplier |
| Prerequisites | StatefulSet semantics, PVC lifecycle, backup tooling |
| Key Topics | failover, restore testing, RTO/RPO, integrity validation |
1. Learning Objectives
- Operate stateful workloads with explicit recovery objectives.
- Execute backup and restore with integrity verification.
- Measure and improve RTO/RPO.
- Build a game-day runbook for recurring resilience drills.
2. All Theory Needed (Per-Concept Breakdown)
2.1 Stateful Identity and Storage Semantics
Fundamentals
Stateful workloads need stable identity and persistent storage across pod restarts and rescheduling.
Deep Dive into the concept
StatefulSet ordering and stable network IDs simplify clustered applications but add operational constraints. Storage class behavior, attachment delays, and failure-domain boundaries all affect recovery time. Reliability requires deliberate design for pod disruption, node failure, and storage backend incidents.
2.2 Backup vs Restore Confidence
Fundamentals
A successful backup job is not proof of recoverability.
Deep Dive into the concept
Restore verification must include application-level integrity, not only snapshot completion. Runbooks should include pre-restore checks, restore actions, post-restore validations, and rollback options if restore quality fails.
3. Project Specification
3.1 What You Will Build
A stateful lab with:
- controlled failure injection
- scheduled backups
- tested restore workflows
- RTO/RPO measurement report
3.2 Functional Requirements
- Deploy stateful workload with durable volume claims.
- Trigger failover scenario and observe service behavior.
- Run backup job and restore into validation environment.
- Produce integrity and recovery timing evidence.
3.3 Non-Functional Requirements
- Performance: restore within target RTO.
- Reliability: repeatable runbook with consistent results.
- Usability: runbook executable by on-call engineer.
3.7 Real World Outcome
$ ./state-lab failover --node worker-1
event: primary rescheduled
service_impact: transient retry spike (under 30s)
$ ./state-lab backup --name nightly-001
backup_status: success
$ ./state-lab restore --snapshot nightly-001
restore_status: success
integrity_checks: pass
rto: 7m12s
rpo: <=5m
4. Solution Architecture
4.1 High-Level Design
stateful workload -> backup scheduler -> snapshot store -> restore validator -> report
4.2 Key Components
| Component | Responsibility | Key Decisions |
|---|---|---|
| Stateful workload | durable service behavior | identity + storage model |
| Backup engine | capture recoverable snapshots | schedule and retention policy |
| Restore validator | verify post-restore correctness | application-level invariants |
| Reporter | RTO/RPO metrics | audit-friendly evidence |
5. Implementation Guide
5.3 The Core Question You’re Answering
“Can this workload fail and recover without violating data and availability guarantees?”
5.6 Milestones
- Baseline stateful deployment.
- Failure injection and failover observation.
- Backup + restore runbook.
- Integrity and timing report.
5.9 Definition of Done
- Failover scenario completed with measured impact.
- Backup and restore tested end-to-end.
- Integrity checks pass after restore.
- RTO/RPO documented and reviewed.