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Is a Data Recovery Plan Considered for Failure Scenarios?

Type: DeepDive
Category: Availability
Audience: SREs, backend engineers, data platform owners managing fault recovery

šŸ” What This Perspective Covers

Backups are not recovery.
What matters is whether you can bring the system back to a safe and consistent stateā€”under pressure.

This perspective examines whether data-level restoration plans are explicitly designed and aligned with real-world failure cases.

āš ļø Failure Patterns

  • Backups exist but recovery steps are undocumented
  • Full restore is possible but takes hoursā€”no SLA defined
  • Only infra is recovered; business logic state is broken
  • Restore causes duplicate records or version conflict
  • RTO/RPO is undefined, untested, or unrealistic

āœ… Design Patterns for Recovery

šŸ”§ Technical Mechanisms

  • Point-in-time restore from WAL or snapshots
  • Restore specific tenant data without full rollback
  • Rehydrate indexes or caches from durable storage
  • Idempotent replay of external sync steps
  • Rollback incomplete transactional writes

šŸ›  Operational Practices

  • Document exact restore paths per failure class
  • Automate restore playbooks for high-risk systems
  • Test restore regularly under simulated pressure
  • Include logs, blobs, and async queues in recovery scope
  • Align RTO/RPO with actual SLA expectations

šŸ§  Principle

You didnā€™t ā€œrecoverā€ if you came back broken.
Recovery means: consistent, complete, and usable.

ā“ FAQ

  • Q: Isnā€™t backup enough?
    A: No. Backup is storage. Recovery is reassembly under pressure.

  • Q: How often should we test recovery?
    A: For critical paths: quarterly. For infra-wide: at least twice a year.