Database Query Optimization With AI

TL;DR

• AI is good at suggesting indexes, rewriting queries, and explaining execution plans.
• AI doesn't know your data distribution, growth, or "we're decommissioning this in 6 months."
• Use AI for ideas. Verify with EXPLAIN (or equivalent) and real workload. Never deploy blind.

Query optimization feels like magic when AI suggests a change and the query gets 10x faster. It also feels like magic when AI suggests a change and prod catches fire. Here's how to get the former without the latter.

Where AI Helps

Index Suggestions

Prompt: "This query is slow. Suggest indexes."

What you get: Index recommendations based on WHERE and JOIN columns. Often reasonable.

What you verify: Do you already have similar indexes? What's the write load? More indexes = slower writes. AI doesn't know your write/read ratio. Also: index size. A 50-column index might not fit in memory. AI won't check.

Query Rewrites

Prompt: "Optimize this query." [paste slow SQL]

What you get: Alternative formulations. Maybe use EXISTS instead of IN. Maybe avoid SELECT *. Sometimes correct. Sometimes wrong.

What you verify: Run EXPLAIN (or equivalent). Compare before/after. AI doesn't execute. It suggests. Execution plans can surprise you.

Execution Plan Explanation

Prompt: "Explain this execution plan."

What you get: Plain-English breakdown. "This is a sequential scan." "This join is expensive." Helpful for learning and triage.

Caveat: AI explains what it sees. It might miss nuances of your specific database version or config.

Where AI Falls Short

Data Distribution

• "Add an index on status." — If status has 3 values and they're evenly distributed, the index might not help. If 99% of rows have status='active', the index is useless. AI doesn't know your data.
• "Partition by date." — Are your queries date-scoped? Do you have old partitions to archive? AI suggests patterns. You validate fit.

Growth and Capacity

• "This query will scale." — Will it? At 10M rows? 1B? AI doesn't profile. It assumes.
• "Use this indexing strategy." — How big will the index get? Will it fit in cache? AI doesn't know your hardware.

Schema and Model

• "Normalize this." — Sometimes normalization helps. Sometimes denormalization does. AI has preferences. Your access patterns decide.
• "Add this column." — Migration cost? Downtime? AI suggests. You own the rollout.

Database-Specific Quirks

• Postgres vs. MySQL vs. Oracle: Syntax and optimizer behavior differ. AI might give you Postgres advice when you're on Oracle. Always check.
• Version differences: EXPLAIN output format, new optimizer features — AI training data has a cutoff. Verify for your version.

How to Use AI for Query Optimization

1. Get suggestions. Paste the query, the plan, maybe table stats. Ask for options.
2. Verify before applying. Run EXPLAIN. Benchmark. Test in staging. Never deploy optimization blind.
3. Add context to your prompt. "We have 100M rows, 80% read, and we're on Postgres 15." Better input = better suggestions.
4. Own the trade-offs. More indexes = slower writes. Materialized views = staleness. AI suggests. You decide what's acceptable.

-- AI suggests: Use EXISTS instead of IN for better performance
SELECT * FROM orders o
WHERE EXISTS (
SELECT 1 FROM customers c WHERE c.id = o.customer_id AND c.status = 'active'
);

-- You run EXPLAIN. Check: Does it actually use an index?
-- Does your data distribution make this faster? AI suggested. You validated.

Do This Next

1. Take one slow query from your system. Ask AI to optimize it. Run EXPLAIN before and after. Did the suggestion help? What would you change?
2. Document one "AI doesn't know" fact about your data — distribution, growth, or migration plans. Use it to sanity-check future AI suggestions.