Query Optimization Calculator

Step-by-Step Guide

1. 1Enter Execution Times: Type your query's execution time in milliseconds before and after optimization.
2. 2Add Rows Scanned (Optional): Enter rows scanned before and after to calculate rows reduction percentage.
3. 3Add Daily Executions (Optional): Enter how often the query runs per day to estimate cumulative savings.
4. 4Select Optimization Type and Engine: Choose the technique used and your database engine for a descriptive report.
5. 5Review and Export: Check the improvement, speed multiplier, rating, and savings, then copy, download, or share the result.

Key Features

• ✓Live calculation with a 150ms debounced update
• ✓Animated before/after performance comparison bars
• ✓Speed multiplier and rows reduction calculations
• ✓Daily, monthly, and yearly cumulative savings projections
• ✓Color-coded Poor-to-Outstanding optimization rating
• ✓11 optimization types and 9 database engines
• ✓Sample presets for common optimization scenarios
• ✓Copy result, full report, and shareable link independently
• ✓Download CSV, JSON, and print-friendly report
• ✓Shareable calculation URL using query parameters
• ✓Calculation history — save and reload past results
• ✓Auto-saves your last session and restores it on return
• ✓All processing runs locally — no data leaves your browser

Pro Tips

• 💡Measure execution time under realistic conditions (warm cache, typical load) for both before and after to get a fair comparison.
• 💡Add rows scanned data whenever possible — a large rows reduction alongside a modest time improvement often signals more optimization headroom remains.
• 💡Use daily execution count for frequently-run queries — a 25ms saving looks small until you see it's 16.7 hours saved per month at scale.
• 💡Take multiple measurements and use the median or average execution time rather than a single run, since query timing can vary.
• 💡Track optimization type and database engine in your report so the history stays useful when comparing techniques over time.

Common Mistakes to Avoid

• ✕Don't compare a cold-cache run against a warm-cache run — the difference may reflect caching, not the optimization itself.
• ✕Don't treat this calculator as a query analyzer — it estimates from numbers you provide and does not execute or analyze SQL.
• ✕Don't ignore a high rows-scanned reduction with a low time improvement — it may mean the query is now bottlenecked elsewhere (e.g., network or serialization).
• ✕Don't assume optimization gains scale linearly with traffic — always re-measure under realistic production load, not just a dev environment.
• ✕Don't forget to account for optimizations that trade write speed for read speed (like adding an index) when evaluating overall impact.