sda.dashboard.pipeline

Data Pipeline Framework for SDA Dashboard.

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This module implements a simplified, efficient pipeline architecture using Flask-Caching:

1. Load (& validate) → 2. Filter columns → 3. Filter rows → 4. Plot

The Flask-Caching Pipeline provides: - Automatic memoization of pipeline stages - Native Flask/Dash integration - Easy cache invalidation - Production-ready scaling (Redis support) - Elimination of callback loops through proper caching

Submodules

• sda.dashboard.pipeline.flask_cache_pipeline

Classes

FlaskCachePipeline

Flask-caching based pipeline for SDA Dashboard.

Package Contents

class sda.dashboard.pipeline.FlaskCachePipeline(app, verbose=False)

Flask-caching based pipeline for SDA Dashboard.

verbose = False

error_handler

cache

load_data(test_name)

Load and validate test data (cached).

select_columns(test_name, user_selection=None)

Apply column selection logic (cached).

configure_filters(test_name, user_selection=None)

Configure filters for selected columns (cached).

apply_filters(test_name, user_selection=None, applied_filters=None)

Apply row filters to data (cached).

Filter logic: Start with everything, remove what we don’t want. - filter_value contains the SELECTED values (what user wants to keep) - We exclude the UNSELECTED values (all options minus selected values) - User interaction: unselect values to remove them from view

create_plot(test_name, user_selection=None, applied_filters=None, plot_config=None)

Create plot from filtered data (cached).

clear_cache(test_name=None)

Clear cache for specific test or all cache.

get_cache_stats()

Get cache statistics.