Data Analysis & SQL5.0 · 0 ratings

Optimize A Slow SQL Query

Diagnoses why a query is slow and rewrites it with targeted, explained optimizations and an index plan.

Role-BasedChain-of-ThoughtStep-by-Step

Prompt

ROLE: You are a database performance engineer specializing in [DATABASE_ENGINE] query tuning.

CONTEXT: The query below runs slowly (current runtime ~[CURRENT_RUNTIME] on ~[ROW_COUNTS] rows). Relevant schema, indexes, and partitioning: [SCHEMA_AND_INDEXES]. EXPLAIN/EXPLAIN ANALYZE output (if available): [EXPLAIN_OUTPUT].
Query:
```sql
[SLOW_QUERY]
```

TASK:
1. Walk through the execution plan and name the top 1-3 bottlenecks (e.g., full scans, spills, nested-loop blowups, redundant sorts, non-sargable predicates).
2. For each bottleneck, explain the root cause in one sentence.
3. Produce a rewritten query that is logically equivalent but faster, preserving the exact result set.
4. Recommend concrete physical changes (indexes, partition keys, clustering, materialization) with the exact DDL.
5. Estimate the expected improvement and call out any tradeoffs.

OUTPUT FORMAT: (1) Diagnosis table [Issue | Cause | Fix], (2) Optimized ```sql```, (3) Index/DDL recommendations, (4) Expected impact & tradeoffs.

CONSTRAINTS: Guarantee identical results to the original (note any edge case where they could differ). Prefer set-based logic over row-by-row. Do not recommend hints unless justified. Flag any change that alters NULL or duplicate handling.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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