tomevault-io DB Index Strategy
Overview
Use this skill to choose indexes that improve real workloads while controlling write and storage costs.
Scope Boundaries
- Query latency hotspots are driven by access-path inefficiency.
- New query patterns or sort requirements are introduced.
- Existing indexes show redundancy, bloat, or unstable plans.
Core Judgments
- Query-path priority by business impact and frequency.
- Composite key order by filter selectivity and sort usage.
- Covering/partial/functional index suitability by engine capability.
- Write amplification and maintenance cost tolerance.
Practitioner Heuristics
- Indexes should be justified by concrete query families, not single ad hoc queries.
- Composite index order follows filter-first then sort semantics.
- Prefer fewer high-value indexes over broad index proliferation.
- Consider plan stability across parameter distribution, not one explain sample.
Workflow
- Rank critical queries by frequency, latency impact, and SLA relevance.
- Inspect current plans and identify scans, misestimation, and sort spills.
- Propose candidate indexes with expected plan effects.
- Evaluate write/maintenance/storage impact for each candidate.
- Decide new, modified, and removable indexes as one portfolio.
- Record assumptions that require re-evaluation as data distribution changes.
Common Failure Modes
- Indexes tuned for one tenant or parameter pattern only.
- Redundant indexes remain after query evolution.
- Index design ignores heavy write paths and causes throughput collapse.
Failure Conditions
- Stop when index choices are not tied to prioritized workload.
- Stop when write amplification risk is unbounded.
- Escalate when plan stability remains poor after viable candidates.
Converted and distributed by TomeVault — claim your Tome and manage your conversions.