4e82907a70
Propagates the conformed city key (city_slug) from dim_venues through the full pricing pipeline, eliminating 3 fragile LOWER(TRIM(...)) fuzzy string joins with deterministic key joins. Changes (cascading, task-by-task): - dim_venues: add city_slug computed column (REGEXP_REPLACE slug derivation) - dim_venue_capacity: join foundation.dim_venues instead of stg_playtomic_venues; carry city_slug alongside country_code/city - fct_daily_availability: carry city_slug from dim_venue_capacity - venue_pricing_benchmarks: carry city_slug from fct_daily_availability; add to venue_stats GROUP BY and final SELECT/GROUP BY - city_market_profile: join vpb on city_slug = city_slug (was LOWER(TRIM)) - planner_defaults: add city_slug to city_benchmarks CTE; join on city_slug - pseo_city_pricing: join city_market_profile on city_slug (was LOWER(TRIM)) - pipeline_routes._DAG: dim_venue_capacity now depends on dim_venues, not stg_playtomic_venues Result: dim_venues.city_slug → dim_cities.(country_code, city_slug) forms a fully conformed geographic hierarchy with no fuzzy string comparisons. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
84 lines
3.2 KiB
SQL
84 lines
3.2 KiB
SQL
-- Daily venue-level availability, pricing, occupancy, and revenue estimates.
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-- Aggregates fct_availability_slot (event-grain fact) into per-venue per-day
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-- statistics, then calculates occupancy against capacity from dim_venue_capacity.
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--
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-- Recheck-aware deduplication lives in fct_availability_slot — this model only
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-- reads the already-deduplicated best-snapshot slots.
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--
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-- Occupancy = 1 - (available_court_hours / capacity_court_hours_per_day)
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-- Revenue estimate = booked_court_hours × avg_price_of_available_slots
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--
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-- Peak hours defined as 17:00–21:00 (captures main evening rush across markets).
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MODEL (
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name foundation.fct_daily_availability,
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kind FULL,
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cron '@daily',
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grain (snapshot_date, tenant_id)
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);
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WITH slot_agg AS (
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SELECT
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a.snapshot_date,
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a.tenant_id,
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COUNT(*) AS available_slot_count,
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COUNT(DISTINCT a.resource_id) AS courts_with_availability,
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-- Each available start_time represents a 60-min bookable window
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ROUND(COUNT(*) * 1.0, 2) AS available_court_hours,
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-- Pricing stats (60-min slots only)
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ROUND(MEDIAN(a.price_amount), 2) AS median_price,
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ROUND(AVG(a.price_amount), 2) AS avg_price,
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MIN(a.price_amount) AS min_price,
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MAX(a.price_amount) AS max_price,
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-- Peak: 17:00–21:00 (is_peak flag computed once in fct_availability_slot)
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ROUND(MEDIAN(a.price_amount) FILTER (WHERE a.is_peak), 2) AS median_price_peak,
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-- Off-peak: everything outside 17:00–21:00
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ROUND(MEDIAN(a.price_amount) FILTER (WHERE NOT a.is_peak), 2) AS median_price_offpeak,
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MAX(a.price_currency) AS price_currency,
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MAX(a.captured_at_utc) AS captured_at_utc
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FROM foundation.fct_availability_slot a
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GROUP BY a.snapshot_date, a.tenant_id
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)
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SELECT
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sa.snapshot_date,
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sa.tenant_id,
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cap.country_code,
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cap.city,
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cap.city_slug,
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cap.active_court_count,
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cap.capacity_court_hours_per_day,
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sa.available_slot_count,
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sa.courts_with_availability,
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sa.available_court_hours,
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-- Occupancy: (capacity - available) / capacity
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CASE
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WHEN cap.capacity_court_hours_per_day > 0
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THEN ROUND(
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1.0 - (sa.available_court_hours / cap.capacity_court_hours_per_day),
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4
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)
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ELSE NULL
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END AS occupancy_rate,
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-- Estimated booked court-hours
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ROUND(
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GREATEST(cap.capacity_court_hours_per_day - sa.available_court_hours, 0),
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2
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) AS booked_court_hours,
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-- Estimated daily revenue: booked hours × avg price
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ROUND(
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GREATEST(cap.capacity_court_hours_per_day - sa.available_court_hours, 0)
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* sa.avg_price,
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2
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) AS estimated_revenue_eur,
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-- Pricing
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sa.median_price,
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sa.avg_price,
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sa.min_price,
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sa.max_price,
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sa.median_price_peak,
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sa.median_price_offpeak,
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sa.price_currency,
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sa.captured_at_utc
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FROM slot_agg sa
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JOIN foundation.dim_venue_capacity cap ON sa.tenant_id = cap.tenant_id
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