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>
60 lines
2.7 KiB
SQL
60 lines
2.7 KiB
SQL
-- Per-city pricing and occupancy benchmarks from Playtomic availability data.
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-- Aggregates venue-level daily metrics (last 30 days) into city-level benchmarks.
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-- Consumed by: planner defaults (pre-fill), city market profile, SEO articles.
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--
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-- Minimum data threshold: venues with >= 3 days of observations.
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MODEL (
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name serving.venue_pricing_benchmarks,
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kind FULL,
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cron '@daily',
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grain (country_code, city)
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);
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WITH venue_stats AS (
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-- Aggregate last 30 days per venue
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SELECT
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da.tenant_id,
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da.country_code,
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da.city,
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da.city_slug,
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da.price_currency,
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AVG(da.occupancy_rate) AS avg_occupancy_rate,
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MEDIAN(da.median_price) AS median_hourly_rate,
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MEDIAN(da.median_price_peak) AS median_peak_rate,
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MEDIAN(da.median_price_offpeak) AS median_offpeak_rate,
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AVG(da.estimated_revenue_eur) AS avg_daily_revenue,
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MAX(da.active_court_count) AS court_count,
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COUNT(DISTINCT da.snapshot_date) AS days_observed
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FROM foundation.fct_daily_availability da
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WHERE TRY_CAST(da.snapshot_date AS DATE) >= CURRENT_DATE - INTERVAL '30 days'
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AND da.occupancy_rate IS NOT NULL
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AND da.occupancy_rate BETWEEN 0 AND 1.5
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GROUP BY da.tenant_id, da.country_code, da.city, da.city_slug, da.price_currency
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HAVING COUNT(DISTINCT da.snapshot_date) >= 3
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)
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SELECT
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country_code,
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city,
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city_slug,
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price_currency,
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COUNT(*) AS venue_count,
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-- Pricing benchmarks
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ROUND(MEDIAN(median_hourly_rate), 2) AS median_hourly_rate,
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ROUND(MEDIAN(median_peak_rate), 2) AS median_peak_rate,
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ROUND(MEDIAN(median_offpeak_rate), 2) AS median_offpeak_rate,
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ROUND(PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY median_hourly_rate), 2) AS hourly_rate_p25,
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ROUND(PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY median_hourly_rate), 2) AS hourly_rate_p75,
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-- Occupancy benchmarks
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ROUND(MEDIAN(avg_occupancy_rate), 4) AS median_occupancy_rate,
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ROUND(AVG(avg_occupancy_rate), 4) AS avg_occupancy_rate,
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-- Revenue benchmarks (per venue per day)
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ROUND(MEDIAN(avg_daily_revenue), 2) AS median_daily_revenue_per_venue,
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-- Court mix
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ROUND(MEDIAN(court_count), 0)::INTEGER AS median_court_count,
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-- Data quality
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SUM(days_observed) AS total_venue_days_observed,
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CURRENT_DATE AS refreshed_date
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FROM venue_stats
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GROUP BY country_code, city, city_slug, price_currency
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