optimizing-streamlit-performance

Speed up Streamlit dashboards by applying @st.cache_data to expensive data loads and @st.cache_resource to initialization objects. This avoids recomputing heavy operations during UI reruns, keeping apps responsive with large datasets.

Streamlit apps rerun entirely on every widget interaction, triggering unnecessary data recomputations. Isolate interactive UI elements using st.fragment to prevent full script reruns and reduce latency for interactive widgets.

Cache database connections in Streamlit using @st.cache_resource. This speeds up initialization and reduces latency by persisting the connection object across reruns instead of recreating it on every interaction.

Optimize Streamlit UI fragments by refactoring interactive elements into st.fragment decorators. This isolates UI updates, preventing the entire application from rerunning and keeping interactive widgets snappy for better user experience.

Yes, Streamlit caching works for large datasets by combining @st.cache_data with pre-computation strategies. Precompute heavy work outside the main UI loop and cache the results to serve data quickly during interactive reruns.

Use st.fragment instead of caching when the bottleneck is UI reruns rather than data processing. Fragments isolate widget interactions to prevent full script execution, while caching targets expensive data loads and initialization tasks.