Description
Under the Interactive Visualizations and Data Analysis tabs, the system processes cluster mutations and exploratory data analytics dynamically. When processing large batches of synthetic spatial data, rendering highly complex 3D scatter plots or iterative outlier detection charts continuously forces Streamlit to rerun the entire script. This triggers heavy CPU/Memory usage, leading to UI lag and potential tab crashes on lower-end machines.
Points:-
1.) Implement Streamlit caching strategies (@st.cache_data or @st.cache_resource) on functions responsible for parsing the main dataset and executing clustering math.
2.) Optimize plotting instances (e.g., if using Plotly or Seaborn) to reuse figures rather than instantiating clean, heavy canvas components on every sidebar value change.
3.) Introduce an option to downsample data or limit preview records when rendering highly intensive 3D planetary distance maps.
Description
Under the Interactive Visualizations and Data Analysis tabs, the system processes cluster mutations and exploratory data analytics dynamically. When processing large batches of synthetic spatial data, rendering highly complex 3D scatter plots or iterative outlier detection charts continuously forces Streamlit to rerun the entire script. This triggers heavy CPU/Memory usage, leading to UI lag and potential tab crashes on lower-end machines.
Points:-
1.) Implement Streamlit caching strategies (
@st.cache_dataor@st.cache_resource) on functions responsible for parsing the main dataset and executing clustering math.2.) Optimize plotting instances (e.g., if using Plotly or Seaborn) to reuse figures rather than instantiating clean, heavy canvas components on every sidebar value change.
3.) Introduce an option to downsample data or limit preview records when rendering highly intensive 3D planetary distance maps.