Database Query Optimization. @database-query-optimization

When the database is slow, everything is slow. This sprint profiles a defined workload — the queries, indexes, and access patterns behind your worst latency — and fixes the offenders in ranked order of total cost, measured on production-shaped data.

The work spans query rewrites, index design, safe schema adjustments, connection and pooling configuration, and the application-side patterns that generate bad load in the first place. The finish state: the agreed workload meeting its latency targets, with slow-query monitoring in place to catch the next regression.

Workload profiling

Slow-query logs, execution plans, and statistics captured for the defined workload, with offenders ranked by total cost — frequency times duration, not just worst case.

Index & query rewrites

Missing and redundant indexes corrected, and the worst queries rewritten with measured before/after on production-shaped data.

Access-pattern fixes

The application-side patterns that generate bad load — N+1s, over-fetching, chatty transactions — fixed at the source.

Verification & guardrails

Targets verified under load, slow-query monitoring in place, and indexing guidelines handed to the team.

1. Scope. Capture the workload, rank slow queries by total cost, and set targets.
2. Build. Apply index, query, schema, and access-pattern fixes with measured before/after.
3. Handover. Results report, slow-query monitoring, and indexing guidelines for the team.

The Database Query Optimization is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Is this the right item if our database engine is end-of-life?

No — that is the Legacy Database Modernization menu item. This sprint tunes a workload on the engine you have; the two combine well in sequence, migration first.

What access do you need?

Query statistics and slow logs from production or a representative replica, plus the application repository for access-pattern fixes.

What is the natural next step for read-heavy workloads?

If profiling shows a high-read pattern that indexes alone cannot serve, the Cache Architecture Implementation menu item adds a deliberate caching layer on top of the tuned workload.