database-backup-and-point-in-time-restore-testing v1.0.0A

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---
name: database-backup-and-point-in-time-restore-testing
description: Quantitative database reliability engine for auditing Write-Ahead Log
  (WAL) continuous archiving, simulating Point-in-Time Recovery (PITR) to exact timestamps,
  and verifying RPO/RTO SLAs.
domain: Infrastructure & Operations
subdomain: Database Disaster Recovery
tags:
- database-backup
- pitr-restore
- wal-archiving
- rpo-rto-testing
- disaster-recovery
- timescaledb
- postgresql
- data-integrity
brokers_frameworks:
- PostgreSQL WAL
- TimescaleDB
- Python Dataclasses
version: "1.0.0"
author: algo-trading-skills-contributors
license: Apache-2.0
---

## When to Use

Use this skill in trading database operations, TimescaleDB/PostgreSQL administration, and disaster recovery automated testing. Algorithmic trading systems depend on continuous data integrity across order books, trade logs, and account balances. In the event of a buggy deployment, database corruption, or regional cloud outage, database administrators must restore the database to an exact millisecond timestamp $T_{\text{target}}$ prior to corruption. This module simulates base snapshot restoration, WAL log replay, and audits RPO ($\le 60\text{s}$) and RTO ($\le 15\text{m}$) SLAs.

## Prerequisites

- Full Base Snapshot records (`snapshot_id`, `timestamp_ms`, `state_checksum`).
- WAL Log Sequence records (`lsn_id`, `timestamp_ms`, `table_name`, `operation`, `row_data`).
- Target recovery SLA parameters (`max_allowed_rpo_sec`, `max_allowed_rto_min`).

## Workflow

1. **Snapshot & WAL Ingestion**:
   - Ingest base backup snapshot $S_0$ and sequence of WAL log records.
2. **Target Timestamp PITR Restoration**:
   - Identify latest base snapshot prior to target timestamp $T_{\text{target}}$.
   - Replay WAL log records sequentially up to $T_{\text{target}}$.
3. **RPO & RTO SLA Audit**:
   - Compute RPO: $\text{RPO}_{\text{sec}} = \frac{T_{\text{target}} - T_{\text{last\_wal}}}{1000}$.
   - Compute RTO: Measure actual elapsed restoration duration in minutes.
4. **Data Integrity & Checksum Verification**:
   - Verify table row counts and payload checksums match expected state at $T_{\text{target}}$.
5. **Audit Report Generation**: Output structured `PitrBackupAuditReport`.

> Full procedure: see `references/workflows.md`.
> Standards reference: see `references/standards.md`.
> Printable pre-flight checklist: see `assets/checklist.md`.

## Common Pitfalls

- **Un-tested Backup Restoration**: Creating daily backups without regularly testing automated PITR restoration, discovering corrupted WAL files during an active outage.
- **Relying on Weekly Full Backups Alone**: Relying only on night/weekly full backups without WAL continuous archiving, exposing the trading desk to up to 24 hours of lost trade data ($\text{RPO} = 24\text{h}$).
- **Ignoring Application-Level Parity**: Verifying database engine startup without checking if order book trade ledgers reconcile with broker balances.

## Verification

- Instantiate `PitrBackupTesterEngine`. Generate 1,000 trade WAL records spanning timestamp $1000\text{ms}$ to $5000\text{ms}$. Execute PITR restoration to target $T_{\text{target}} = 3500\text{ms}$. Verify engine restores exact database state at $3500\text{ms}$ (ignoring WAL records $> 3500\text{ms}$), verifies checksums, and confirms RPO/RTO SLAs.
- Run `python scripts/test_pitr_backup_tester.py`.

## Related Skills

- `cross-region-data-replication-lag-monitoring`
- `exchange-gateway-redundancy-and-failover-testing`
---