2748c606e9
- Fix pipeline granularity: add market_year to cleaned/serving SQL models - Add DuckDB data access layer with async query functions (analytics.py) - Build Chart.js dashboard: supply/demand, STU ratio, top producers, YoY table - Add country comparison page with multi-select picker - Replace items CRUD with read-only commodity API (list, metrics, countries, CSV) - Configure BeanFlows plan tiers (Free/Starter/Pro) with feature gating - Rewrite public pages for coffee market intelligence positioning - Remove boilerplate items schema, update health check for DuckDB - Add test suite: 139 tests passing (dashboard, API, billing) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
107 lines
3.1 KiB
SQL
107 lines
3.1 KiB
SQL
MODEL (
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name serving.commodity_metrics,
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kind INCREMENTAL_BY_TIME_RANGE (
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time_column ingest_date
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),
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start '2006-08-01',
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cron '@daily'
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);
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-- CTE to calculate country-level derived metrics
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WITH country_metrics AS (
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SELECT
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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market_year,
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ingest_date,
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Production,
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Imports,
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Exports,
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Total_Distribution,
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Ending_Stocks,
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-- Derived metrics per country, mirroring Python script
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(Production + Imports - Exports) AS Net_Supply,
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(Exports - Imports) AS Trade_Balance,
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(Production + Imports - Exports) - Total_Distribution AS Supply_Demand_Balance,
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-- Handle division by zero for Stock-to-Use Ratio
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(Ending_Stocks / NULLIF(Total_Distribution, 0)) * 100 AS Stock_to_Use_Ratio_pct,
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-- Calculate Production YoY percentage change using a window function
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(Production - LAG(Production, 1, 0) OVER (PARTITION BY commodity_code, country_code ORDER BY market_year, ingest_date)) / NULLIF(LAG(Production, 1, 0) OVER (PARTITION BY commodity_code, country_code ORDER BY market_year, ingest_date), 0) * 100 AS Production_YoY_pct
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FROM cleaned.psdalldata__commodity_pivoted
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),
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global_aggregates AS (
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SELECT
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commodity_code,
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commodity_name,
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NULL::TEXT AS country_code, -- Use NULL for global aggregates
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'Global' AS country_name,
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market_year,
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ingest_date,
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SUM(Production) AS Production,
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SUM(Imports) AS Imports,
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SUM(Exports) AS Exports,
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SUM(Total_Distribution) AS Total_Distribution,
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SUM(Ending_Stocks) AS Ending_Stocks
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FROM cleaned.psdalldata__commodity_pivoted
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GROUP BY
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commodity_code,
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commodity_name,
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market_year,
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ingest_date
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),
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-- CTE to calculate derived metrics for global aggregates
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global_metrics AS (
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SELECT
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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market_year,
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ingest_date,
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Production,
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Imports,
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Exports,
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Total_Distribution,
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Ending_Stocks,
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(Production + Imports - Exports) AS Net_Supply,
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(Exports - Imports) AS Trade_Balance,
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(Production + Imports - Exports) - Total_Distribution AS Supply_Demand_Balance,
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(Ending_Stocks / NULLIF(Total_Distribution, 0)) * 100 AS Stock_to_Use_Ratio_pct,
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(Production - LAG(Production, 1, 0) OVER (PARTITION BY commodity_code ORDER BY market_year, ingest_date)) / NULLIF(LAG(Production, 1, 0) OVER (PARTITION BY commodity_code ORDER BY market_year, ingest_date), 0) * 100 AS Production_YoY_pct
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FROM global_aggregates
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)
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-- Combine country-level and global-level data into a single output
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SELECT
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commodity_code,
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commodity_name,
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country_code,
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country_name,
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market_year,
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ingest_date,
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Production,
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Imports,
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Exports,
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Total_Distribution,
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Ending_Stocks,
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Net_Supply,
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Trade_Balance,
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Supply_Demand_Balance,
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Stock_to_Use_Ratio_pct,
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Production_YoY_pct
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FROM (
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SELECT
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*
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FROM country_metrics
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UNION ALL
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SELECT
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*
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FROM global_metrics
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) AS combined_data
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ORDER BY
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commodity_name,
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country_name,
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market_year,
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ingest_date;
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