Let’s break down exactly how we took an AWS-hosted Oracle monster from glacial to lightning-quick, and why the client immediately signed us for Phase 2.
1. The Performance Crisis
“Users can’t log in, invoices spin forever, and our dashboards keep refreshing.” (TestABC)
The client’s Smart‑Invoice solution supports regulated finance workloads across the EU. On a Monday morning their team reported that:
- Certain invoice queries took over five hours to complete.
- Users experienced intermittent log‑in failures and page refresh loops.
- AWS costs had risen sharply as auto‑scaling attempted to hide the latency.
The immediate priority was clear: restore predictable performance without disrupting day‑to‑day operations or compliance obligations.
2. Our Approach
Phase 1 — Targeted Diagnostics
- Collected AWR/ASH reports, CloudWatch metrics, and Oracle Performance Insights.
- Ranked SQL statements by elapsed time and logical reads – one query consumed 62 % of database time.
- Captured Apache thread dumps at peak load to align wait events with database blocking.
Phase 2 — Focused Remediation
- Query refactoring: replaced Cartesian joins with indexed sub‑queries; applied selective parallel‑execution hints.
- Partition strategy: range‑partitioned
INVOICE_TXNbyinvoice_date, with regional sub‑partitions, eliminating full‑table scans. - Read caching: introduced Redis for static lookup tables, reducing unnecessary round‑trips.
- Infrastructure tuning: migrated to an RDS db.m6i.large instance and GP3 volumes; net cost held flat after right‑sizing.
Phase 3 — Validation and Safeguards
- Replayed production traffic at 2× volume in a staging environment.
- Monitored p95 latency, CPU, and buffer‑get trends – all targets achieved.
- Deployed Terraform guardrails and CloudWatch alarms to prevent regression.
Total user‑visible downtime during rollout: 0 minutes.
3. Roll‑Out Timeline
| Week | Deliverable |
| 1–2 | Comprehensive performance audit and remediation roadmap |
| 3–5 | Query refactor, index creation, and partition deployment— longest query reduced to two minutes |
| 6 | Redis cache implementation and connection‑pool tuning— thread starvation resolved |
| 7 | High‑load testing— 15‑second benchmark verified |
| 8 | Production cut‑over, run‑book hand‑off, and operator training |
4. Outcomes
- 99.9 % reduction in worst‑case query time (5 hours ➜ 15 seconds).
- 93 % faster average API response, enhancing user satisfaction and regulatory audit readiness.
- 18 % lower monthly AWS spend by eliminating reactive over‑scaling.
- Immediate client confidence, resulting in a 40 % contract expansion to modernise additional systems.
“The improvement is dramatic. We now resolve invoices in seconds and can focus on new features instead of fire‑fighting.”
— CTO, Smart‑Invoice Provider
5. Key Lessons
- Measure before you change. Data from AWR and CloudWatch provided an unambiguous starting point.
- Partitioning is essential for time‑series workloads. Logical design can outperform hardware upgrades.
- Visible results build trust. Sharing benchmark data at each milestone kept stakeholders aligned and decisive.
6. Next Step
If slow Oracle queries are limiting your roadmap, schedule a free 30‑minute performance review with our expert team.
We’ll analyse one critical query and outline practical optimisation steps you can apply immediately.
