7737b79230
- Add maximum_object_size=128MB to read_json for 14K-venue tenants file - Rewrite opening_hours to use UNION ALL unpivot (DuckDB struct dynamic access) - Add seed file guard for availability model (empty result on first run) - Fix snapshot_date VARCHAR→DATE comparison in venue_pricing_benchmarks - Fix export_serving to resolve SQLMesh physical tables from view definitions (SQLMesh views reference "local" catalog unavailable outside its context) - Add pyarrow dependency for Arrow-based cross-connection data transfer Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
58 lines
2.6 KiB
SQL
58 lines
2.6 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.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.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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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, price_currency
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