A matching row count can still hide a bad recovery. It tells you how many records a query returned—not whether the records are intact, internally consistent, accessible to the application, or reconciled with related systems. The title describes a first-person engineering story, but no account here establishes the tool’s identity, implementation, database, or triggering incident. What can be explained precisely is why row counts alone are a weak recovery test, and how to validate a restore in practice.
What a row count can—and cannot—prove
A count is a useful signal: it can reveal missing records when the expected and recovered totals differ. But matching totals do not establish that the same records were restored, that field values are correct, or that relationships between records remain valid. Nor do they show that an application can read and use the recovered data.
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For example, two systems can each contain the expected number of rows while disagreeing about which customer owns an order. A count can match even when values are corrupted, records are duplicated while others are missing, or data in a dependent system no longer corresponds. The specific failure behind the title is not documented, so these are general examples—not claims about the author’s incident.
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The AWS Well-Architected Framework calls restoring a backup without querying or retrieving data to confirm usability a common anti-pattern: “Restoring a backup, but not querying or retrieving any data to check that the restoration is usable.” AWS Well-Architected Framework, REL09-BP04
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Validate the recovered data, not just the restore command
A restore drill should follow the operational recovery process in a safe location, then check whether the recovered data is usable for its intended workload. AWS recommends defining validation criteria for each data source and checking availability, corruption, accessibility, and data loss against recovery objectives. Possible criteria include data type, format, checksum, size, custom validation logic, or comparison with values recorded at backup time. AWS Well-Architected Framework recovery-testing guidance
- Restore through the process you intend to rely on. Record whether the procedure completes and how long it takes; a successful command is only one checkpoint.
- Query or retrieve representative data. Confirm that the restored system can make the data available to the intended users or application, not merely that files exist.
- Apply checks suited to the data. Use counts as one signal, and add checks such as checksums, expected formats or sizes, schema checks, and custom business rules where appropriate.
- Reconcile related sources. If the workload spans databases, files, or services, compare linked values across them instead of validating each source in isolation.
- Retain the results where auditability matters. Record what was checked, when it was checked, and the outcome.
A useful recovery test also measures whether the system meets its recovery time objective (RTO)—how quickly it must be restored—and recovery point objective (RPO)—how much data loss the business can tolerate. A technically valid restore that takes too long or recovers data from an unacceptably old point may still fail the recovery requirement.
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Choose checks by the failure they can detect
Validation methods have different scopes. A check that detects damaged backup media does not necessarily detect a logical inconsistency that was already present when the backup was made, and neither automatically proves that an application behaves correctly.
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| Check | What it can help detect | Important limit |
|---|---|---|
| Backup checksum | Certain corruption in a backup stream or data pages, when the relevant checksum is present and verified. | It does not prove that the data was logically correct before backup or usable by the application after restore. |
| Archive or block validation | Some corrupt blocks, missing files, or problems that affect whether a backup can be restored, depending on the database and validation method. | Successful archive verification is not equivalent to database consistency or application validation. |
| Manifest comparison | Missing files or checksum mismatches between a backup and its server-generated manifest. | It may not cover every check that the running server performs when using the backup. |
| Counts and custom application checks | Missing or unexpected records, invalid values, broken relationships, or workload-specific failures when the checks are designed to test them. | A total count alone can miss substitutions, incorrect values, and mismatches with other systems. |
| Cross-system reconciliation | Disagreements between related records or values held in separate sources. | It requires expected relationships or comparison values and does not replace checks of each source’s integrity and accessibility. |
What database verification tools actually establish
SQL Server: checksum verification depends on the backup
SQL Server backup checksums are optional. Restore operations, including RESTORE VERIFYONLY, can verify backup checksums when they are present. If a backup has no checksum, restore proceeds without that checksum verification mechanism; it is not evidence that the data is free of corruption. Microsoft also warns that generating and verifying checksums can affect performance, so factor the cost into the backup and recovery design. Microsoft SQL Server documentation on checksum behavior
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Oracle RMAN: validation covers specific backup conditions
Oracle Database 19c RMAN validation can check for corrupt blocks and missing files and assess whether backups can be restored. Those checks address backup and file conditions; they do not by themselves establish that application-level rules or relationships across systems are correct. Oracle Database 19c RMAN validation documentation
SAP IQ: a readable archive can preserve an existing inconsistency
SAP IQ 16.2 SP00 distinguishes archive verification from database consistency checking. If the database was already inconsistent when backed up, a successful RESTORE VERIFY does not repair that condition or prove the restored data is consistent. SAP IQ 16.2 SP00 restore verification documentation
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PostgreSQL: the manifest is valuable but not exhaustive
In PostgreSQL 18, pg_verifybackup checks a pg_basebackup against the server-generated backup manifest. This can identify missing files or checksum mismatches, but PostgreSQL documents that it cannot perform every check a running server will make when using the backup. Treat it as one layer in a restore test, not a substitute for starting the recovered system and exercising relevant queries. PostgreSQL 18 pg_verifybackup documentation
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There is no single check that proves every backup is good. Define success in terms of what the workload must recover, then choose checks that address different failure modes. For a system with dependent data sources, that can mean combining a checksum or manifest check with record counts, schema or application rules, and cross-system reconciliation. AWS Life Sciences Lens guidance recommends combinations such as these and retaining verification evidence for regulated life-sciences workloads; that is workload-specific guidance, not a universal legal requirement. AWS Well-Architected Life Sciences Lens
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Run the checks in a way that does not endanger production data, and account for their runtime and operational impact. Microsoft specifically notes that checksum generation and verification can affect performance. Retain enough evidence to understand which backup was tested, what validation ran, and whether the result met the workload’s recovery objectives.
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