Optimizing Databutton-Generated Code for Performance

Python data processing with pandas may use apply() instead of vectorized operations. FastAPI endpoints may load entire database tables into Python for filtering instead of using SQL WHERE clauses. React visualization components may re-render large chart datasets on every parent update

Move data filtering to SQL — let the database do what it's good at. Replace pandas apply() with vectorized numpy/pandas operations. Add database indexes on time-series and commonly filtered columns. Implement data aggregation at the database level before returning to the API. Memoize expensive chart computations with useMemo

Before optimizing, measure. Use your browser's Lighthouse audit to get a baseline score. Check Core Web Vitals (LCP, FID, CLS) — these directly affect your Google ranking. Use the Performance tab in DevTools to identify which components take the longest to render. For server-side performance, add timing logs to API routes to find slow endpoints.

The fastest way to improve performance: optimize and lazy-load images, enable compression on your server, remove unused CSS and JavaScript, implement code splitting so users only download code for the page they're viewing, and add proper caching headers for static assets. These changes alone can cut load times significantly.

If your Databutton app uses a database, slow queries are often the biggest performance bottleneck. Add indexes on columns you filter or sort by. Use SELECT to fetch only the columns you need instead of SELECT *. Implement pagination for lists — loading 10,000 rows at once will always be slow. Consider adding a caching layer for data that doesn't change frequently.