optimizing-streamlit-performance

To optimize Streamlit app performance, isolate UI components using @st.fragment and batch inputs with st.form to prevent full application reruns on every interaction. This ensures only specific parts of the app rerun, significantly reducing unnecessary computation and improving speed.

The best way to cache data loading in Streamlit is by applying the @st.cache_data decorator to memoize function calls. This prevents redundant computations during interactive data filtering, while @st.cache_resource efficiently manages resource loading to speed up initial load times.

Streamlit fragments work by using the @st.fragment decorator to isolate specific UI components like metrics displays. When a user interacts with a filtered data section, only that isolated fragment reruns instead of triggering a full application rerun, ensuring efficient rendering and resource management.

Yes, you can manage memory bloat in Streamlit dashboards by batching user interactions with st.form to trigger a single rerun upon submission. Additionally, conditional rendering with st.toggle or st.segmented_control optimizes loading by rendering heavy content only when necessary.

Yes, this optimization approach works for interactive data applications with large datasets by leveraging caching for data loading and fragments for isolated metrics displays. It addresses slow reruns and excessive computation by applying Streamlit's built-in decorators and layout elements.