lmiranda
c9cf744d84
Implement full 5-tab Toronto Neighbourhood Dashboard with real data connectivity: Dashboard Structure: - Overview tab with livability scores and rankings - Housing tab with affordability metrics - Safety tab with crime statistics - Demographics tab with population/income data - Amenities tab with parks, schools, transit Figure Factories (portfolio_app/figures/): - bar_charts.py: ranking, stacked, horizontal bars - scatter.py: scatter plots, bubble charts - radar.py: spider/radar charts - demographics.py: donut, age pyramid, income distribution Service Layer (portfolio_app/toronto/services/): - neighbourhood_service.py: queries dbt marts for all tab data - geometry_service.py: generates GeoJSON from PostGIS - Graceful error handling when database unavailable Callbacks (portfolio_app/pages/toronto/callbacks/): - map_callbacks.py: choropleth updates, map click handling - chart_callbacks.py: supporting chart updates - selection_callbacks.py: dropdown handlers, KPI updates Data Pipeline (scripts/data/): - load_toronto_data.py: orchestration script with CLI flags Lessons Learned: - Graceful error handling in service layers - Modular callback structure for multi-tab dashboards - Figure factory pattern for reusable charts Closes: #64, #65, #66, #67, #68, #69, #70 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
35 lines
1.5 KiB
Markdown
35 lines
1.5 KiB
Markdown
# Sprint 9-10 - Graceful Error Handling in Service Layers
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## Context
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Building the Toronto Neighbourhood Dashboard with a service layer that queries PostgreSQL/PostGIS dbt marts to provide data to Dash callbacks.
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## Problem
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Initial service layer implementation let database connection errors propagate as unhandled exceptions. When the PostGIS Docker container was unavailable (common on ARM64 systems where the x86_64 image fails), the entire dashboard would crash instead of gracefully degrading.
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## Solution
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Wrapped database queries in try/except blocks to return empty DataFrames/lists/dicts when the database is unavailable:
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```python
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def _execute_query(sql: str, params: dict | None = None) -> pd.DataFrame:
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try:
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engine = get_engine()
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with engine.connect() as conn:
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return pd.read_sql(text(sql), conn, params=params)
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except Exception:
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return pd.DataFrame()
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```
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This allows:
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1. Dashboard to load and display empty states
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2. Development/testing without running database
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3. Graceful degradation in production
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## Prevention
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- **Always design service layers with graceful degradation** - assume external dependencies can fail
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- **Return empty collections, not exceptions** - let UI components handle empty states
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- **Test without database** - verify the app doesn't crash when DB is unavailable
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- **Consider ARM64 compatibility** - PostGIS images may not support all platforms
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## Tags
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python, postgresql, service-layer, error-handling, dash, graceful-degradation, arm64
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