242 lines
8.3 KiB
PL/PgSQL
242 lines
8.3 KiB
PL/PgSQL
-- Custom PostgreSQL functions used by the metrics pipeline
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-- These must exist in the database before running calculate-metrics-new.js
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--
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-- To install/update: psql -d inventory_db -f functions.sql
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-- All functions use CREATE OR REPLACE so they are safe to re-run.
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-- =============================================================================
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-- safe_divide: Division helper that returns a default value instead of erroring
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-- on NULL or zero denominators.
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-- =============================================================================
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CREATE OR REPLACE FUNCTION public.safe_divide(
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numerator numeric,
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denominator numeric,
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default_value numeric DEFAULT NULL::numeric
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)
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RETURNS numeric
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LANGUAGE plpgsql
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IMMUTABLE
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AS $function$
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BEGIN
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IF denominator IS NULL OR denominator = 0 THEN
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RETURN default_value;
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ELSE
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RETURN numerator / denominator;
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END IF;
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END;
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$function$;
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-- =============================================================================
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-- std_numeric: Standardized rounding helper for consistent numeric precision.
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-- =============================================================================
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CREATE OR REPLACE FUNCTION public.std_numeric(
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value numeric,
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precision_digits integer DEFAULT 2
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)
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RETURNS numeric
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LANGUAGE plpgsql
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IMMUTABLE
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AS $function$
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BEGIN
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IF value IS NULL THEN
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RETURN NULL;
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ELSE
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RETURN ROUND(value, precision_digits);
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END IF;
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END;
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$function$;
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-- =============================================================================
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-- calculate_sales_velocity: Daily sales velocity adjusted for stockout days.
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-- Ensures at least 14-day denominator for products with sales to avoid
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-- inflated velocity from short windows.
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-- =============================================================================
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CREATE OR REPLACE FUNCTION public.calculate_sales_velocity(
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sales_30d integer,
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stockout_days_30d integer
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)
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RETURNS numeric
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LANGUAGE plpgsql
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IMMUTABLE
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AS $function$
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BEGIN
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RETURN sales_30d /
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NULLIF(
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GREATEST(
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30.0 - stockout_days_30d,
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CASE
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WHEN sales_30d > 0 THEN 14.0 -- If we have sales, ensure at least 14 days denominator
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ELSE 30.0 -- If no sales, use full period
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END
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),
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0
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);
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END;
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$function$;
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-- =============================================================================
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-- get_weighted_avg_cost: Weighted average cost from receivings up to a given date.
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-- Uses all non-canceled receivings (no row limit) weighted by quantity.
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-- =============================================================================
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CREATE OR REPLACE FUNCTION public.get_weighted_avg_cost(
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p_pid bigint,
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p_date date
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)
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RETURNS numeric
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LANGUAGE plpgsql
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STABLE
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AS $function$
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DECLARE
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weighted_cost NUMERIC;
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BEGIN
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SELECT
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CASE
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WHEN SUM(qty_each) > 0 THEN SUM(cost_each * qty_each) / SUM(qty_each)
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ELSE NULL
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END INTO weighted_cost
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FROM receivings
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WHERE pid = p_pid
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AND received_date <= p_date
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AND status != 'canceled';
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RETURN weighted_cost;
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END;
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$function$;
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-- =============================================================================
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-- classify_demand_pattern: Classifies demand based on average demand and
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-- coefficient of variation (CV). Standard inventory classification:
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-- zero: no demand
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-- stable: CV <= 0.2 (predictable, easy to forecast)
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-- variable: CV <= 0.5 (some variability, still forecastable)
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-- sporadic: low volume + high CV (intermittent demand)
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-- lumpy: high volume + high CV (unpredictable bursts)
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-- =============================================================================
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CREATE OR REPLACE FUNCTION public.classify_demand_pattern(
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avg_demand numeric,
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cv numeric
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)
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RETURNS character varying
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LANGUAGE plpgsql
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IMMUTABLE
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AS $function$
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BEGIN
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IF avg_demand IS NULL OR cv IS NULL THEN
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RETURN NULL;
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ELSIF avg_demand = 0 THEN
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RETURN 'zero';
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ELSIF cv <= 0.2 THEN
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RETURN 'stable';
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ELSIF cv <= 0.5 THEN
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RETURN 'variable';
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ELSIF avg_demand < 1.0 THEN
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RETURN 'sporadic';
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ELSE
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RETURN 'lumpy';
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END IF;
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END;
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$function$;
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-- =============================================================================
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-- detect_seasonal_pattern: Detects seasonality by comparing monthly average
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-- sales across the last 12 months. Uses coefficient of variation across months
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-- and peak-to-average ratio to classify patterns.
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--
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-- Returns:
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-- seasonal_pattern: 'none', 'moderate', or 'strong'
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-- seasonality_index: peak month avg / overall avg * 100 (100 = no seasonality)
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-- peak_season: name of peak month (e.g. 'January'), or NULL if none
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-- =============================================================================
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CREATE OR REPLACE FUNCTION public.detect_seasonal_pattern(p_pid bigint)
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RETURNS TABLE(seasonal_pattern character varying, seasonality_index numeric, peak_season character varying)
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LANGUAGE plpgsql
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STABLE
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AS $function$
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DECLARE
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v_monthly_cv NUMERIC;
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v_max_month_avg NUMERIC;
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v_overall_avg NUMERIC;
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v_monthly_stddev NUMERIC;
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v_peak_month_num INT;
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v_data_months INT;
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v_seasonality_index NUMERIC;
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v_seasonal_pattern VARCHAR;
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v_peak_season VARCHAR;
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BEGIN
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-- Gather monthly average sales over the last 12 months
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SELECT
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COUNT(*),
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AVG(month_avg),
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STDDEV(month_avg),
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MAX(month_avg)
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INTO v_data_months, v_overall_avg, v_monthly_stddev, v_max_month_avg
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FROM (
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SELECT EXTRACT(MONTH FROM snapshot_date) AS mo, AVG(units_sold) AS month_avg
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FROM daily_product_snapshots
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WHERE pid = p_pid AND snapshot_date >= CURRENT_DATE - INTERVAL '365 days'
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GROUP BY EXTRACT(MONTH FROM snapshot_date)
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) monthly;
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-- Need at least 3 months of data for meaningful seasonality detection
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IF v_data_months < 3 OR v_overall_avg IS NULL OR v_overall_avg = 0 THEN
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RETURN QUERY SELECT 'none'::VARCHAR, 100::NUMERIC, NULL::VARCHAR;
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RETURN;
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END IF;
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-- CV of monthly averages
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v_monthly_cv := v_monthly_stddev / v_overall_avg;
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-- Find peak month number
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SELECT EXTRACT(MONTH FROM snapshot_date)::INT INTO v_peak_month_num
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FROM daily_product_snapshots
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WHERE pid = p_pid AND snapshot_date >= CURRENT_DATE - INTERVAL '365 days'
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GROUP BY EXTRACT(MONTH FROM snapshot_date)
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ORDER BY AVG(units_sold) DESC
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LIMIT 1;
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-- Seasonality index: peak month avg / overall avg * 100
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v_seasonality_index := ROUND((v_max_month_avg / v_overall_avg * 100)::NUMERIC, 2);
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IF v_monthly_cv > 0.5 AND v_seasonality_index > 150 THEN
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v_seasonal_pattern := 'strong';
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v_peak_season := TRIM(TO_CHAR(TO_DATE(v_peak_month_num::TEXT, 'MM'), 'Month'));
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ELSIF v_monthly_cv > 0.3 AND v_seasonality_index > 120 THEN
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v_seasonal_pattern := 'moderate';
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v_peak_season := TRIM(TO_CHAR(TO_DATE(v_peak_month_num::TEXT, 'MM'), 'Month'));
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ELSE
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v_seasonal_pattern := 'none';
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v_peak_season := NULL;
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v_seasonality_index := 100;
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END IF;
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RETURN QUERY SELECT v_seasonal_pattern, v_seasonality_index, v_peak_season;
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END;
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$function$;
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-- =============================================================================
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-- category_hierarchy: Materialized view providing a recursive category tree
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-- with ancestor paths for efficient rollup queries.
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--
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-- Refresh after category changes: REFRESH MATERIALIZED VIEW category_hierarchy;
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-- =============================================================================
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-- DROP MATERIALIZED VIEW IF EXISTS category_hierarchy;
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-- CREATE MATERIALIZED VIEW category_hierarchy AS
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-- WITH RECURSIVE cat_tree AS (
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-- SELECT cat_id, name, type, parent_id,
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-- cat_id AS root_id, 0 AS level, ARRAY[cat_id] AS path
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-- FROM categories
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-- WHERE parent_id IS NULL
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-- UNION ALL
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-- SELECT c.cat_id, c.name, c.type, c.parent_id,
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-- ct.root_id, ct.level + 1, ct.path || c.cat_id
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-- FROM categories c
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-- JOIN cat_tree ct ON c.parent_id = ct.cat_id
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-- )
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-- SELECT cat_id, name, type, parent_id, root_id, level, path,
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-- (SELECT array_agg(unnest ORDER BY unnest DESC)
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-- FROM unnest(cat_tree.path) unnest
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-- WHERE unnest <> cat_tree.cat_id) AS ancestor_ids
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-- FROM cat_tree;
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--
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-- CREATE UNIQUE INDEX ON category_hierarchy (cat_id);
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