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System Design Masterclass · Reliability Resilience
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In April 2022, Atlassian ran a cleanup script that deleted 883 customer Cloud sites. Their published RTO was a few hours. The actual recovery took 14 days. They had backups. They had automation. What they did not have was a tested, rehearsed restore pipeline that worked at the scale of the disaster.
Every backup strategy starts with the full backup. It is the one artifact that stands on its own. No chains, no dependencies, no replay logic. One file, one restore command, one complete copy of your data. Incremental and differential strategies both depend on a healthy full backup underneath them. If the full is corrupt, nothing built on top of it survives.
Full backups are expensive in storage and slow to create, but they are the only backup type that does not require anything else to restore. That independence is why every production system takes them, even when incremental and differential strategies handle the daily work.
A full backup is a byte-for-byte copy of every table, index, and metadata structure in the database at a single point in time. Nothing is skipped, nothing is delta-encoded, nothing depends on a previous backup existing.
| Property | Full Backup |
|---|---|
| What it copies | Every byte of every data file |
| Storage per run | Equal to the entire database size |
| Restore requires | Only this single backup file |
| Restore complexity | One step: load the file |
| Typical frequency | Weekly for most workloads, daily for Tier-0 |
| Creation speed | Slow, reads every page on disk |
| Recovery speed | Fast, no chain assembly or replay |
The core tools used in production: pg_basebackup for Postgres, Percona XtraBackup for MySQL, BACKUP DATABASE for Server, and mongodump for MongoDB. Cloud-managed databases like Aurora and Cloud SQL handle full backups through storage-layer snapshots instead.