Memory Optimization Skill for Streamlit + Supabase Apps

What problem does it solve?

This Skill addresses memory bloat in Streamlit applications, particularly when deploying to Streamlit Community Cloud or integrating with Supabase, ensuring apps run efficiently and within resource limits.

Core Features & Use Cases

• Streamlit Memory Management: Understand Streamlit's execution model, session state, and caching mechanisms to prevent memory leaks.
• DataFrame Optimization: Techniques like downcasting data types, loading only necessary columns, and avoiding redundant data copies.
• Server-Side Operations: Leverage Supabase for efficient data filtering, aggregation via functions, and cursor-based pagination to reduce client-side memory load.
• Use Case: Debugging a Streamlit app that crashes due to high memory usage on Streamlit Community Cloud by implementing caching strategies and optimizing DataFrame operations.

Quick Start

Optimize the memory usage of your Streamlit application by applying caching strategies and reducing DataFrame memory footprints.