feat(admin): add pipeline diagnostic script + extraction card UX improvements
- Add scripts/check_pipeline.py: read-only diagnostic for pricing pipeline row counts, date range analysis, HAVING filter impact, join coverage - Add description field to all 12 workflows in workflows.toml - Parse and display descriptions on extraction status cards - Show spinner + "Running" state with blue-tinted card border - Display start time with "running..." text for active extractions Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -7,6 +7,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
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## [Unreleased]
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### Added
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- **Pipeline diagnostic script** (`scripts/check_pipeline.py`) — read-only script that reports row counts at every layer of the pricing pipeline (staging → foundation → serving), date range analysis, HAVING filter impact, and join coverage. Run on prod to diagnose empty serving tables.
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- **Extraction card descriptions** — each workflow card on the admin pipeline page now shows a one-line description explaining what the data source is (e.g. "EU geographic boundaries (NUTS2 polygons) from Eurostat GISCO"). Descriptions defined in `workflows.toml`.
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- **Running state indicator** — extraction cards show a spinner + "Running" label with a blue-tinted border when an extraction is actively running, replacing the plain Run button. Cards also display the start time with "running..." text.
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- **Interactive Leaflet maps** — geographic visualization across 4 key placements using self-hosted Leaflet 1.9.4 (GDPR-safe, no CDN):
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- **Markets hub** (`/markets`): country bubble map with circles sized by total venues, colored by avg market score (green ≥ 60, amber 30-60, red < 30). Click navigates to country overview.
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- **Country overview articles**: city bubble map loads after article render, auto-fits bounds, click navigates to city page. Bubbles colored by market score.
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@@ -8,54 +8,67 @@
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# entry — optional: function name if not "main" (default: "main")
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# depends_on — optional: list of workflow names that must run first
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# proxy_mode — optional: "round-robin" (default) or "sticky"
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# description — optional: human-readable one-liner shown in the admin UI
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[overpass]
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module = "padelnomics_extract.overpass"
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schedule = "monthly"
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description = "Padel court locations from OpenStreetMap via Overpass API"
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[overpass_tennis]
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module = "padelnomics_extract.overpass_tennis"
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schedule = "monthly"
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description = "Tennis court locations from OpenStreetMap via Overpass API"
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[eurostat]
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module = "padelnomics_extract.eurostat"
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schedule = "monthly"
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description = "City population data from Eurostat Urban Audit"
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[geonames]
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module = "padelnomics_extract.geonames"
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schedule = "monthly"
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description = "Global city/town gazetteer from GeoNames (pop >= 1K)"
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[playtomic_tenants]
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module = "padelnomics_extract.playtomic_tenants"
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schedule = "daily"
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description = "Padel venue directory from Playtomic (names, locations, courts)"
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[playtomic_availability]
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module = "padelnomics_extract.playtomic_availability"
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schedule = "daily"
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depends_on = ["playtomic_tenants"]
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description = "Morning availability snapshots — slot-level pricing per venue"
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[playtomic_recheck]
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module = "padelnomics_extract.playtomic_availability"
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entry = "main_recheck"
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schedule = "0,30 6-23 * * *"
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depends_on = ["playtomic_availability"]
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description = "Intraday availability rechecks for occupancy tracking"
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[census_usa]
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module = "padelnomics_extract.census_usa"
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schedule = "monthly"
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description = "US city/place population from Census Bureau ACS"
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[census_usa_income]
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module = "padelnomics_extract.census_usa_income"
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schedule = "monthly"
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description = "US county median household income from Census Bureau ACS"
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[eurostat_city_labels]
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module = "padelnomics_extract.eurostat_city_labels"
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schedule = "monthly"
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description = "City code-to-name mapping for Eurostat Urban Audit cities"
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[ons_uk]
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module = "padelnomics_extract.ons_uk"
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schedule = "monthly"
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description = "UK local authority population estimates from ONS"
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[gisco]
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module = "padelnomics_extract.gisco"
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schedule = "monthly"
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description = "EU geographic boundaries (NUTS2 polygons) from Eurostat GISCO"
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200
scripts/check_pipeline.py
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200
scripts/check_pipeline.py
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@@ -0,0 +1,200 @@
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"""
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Diagnostic script: check row counts at every layer of the pricing pipeline.
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Run on prod via SSH:
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DUCKDB_PATH=/opt/padelnomics/data/lakehouse.duckdb uv run python scripts/check_pipeline.py
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Or locally:
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DUCKDB_PATH=data/lakehouse.duckdb uv run python scripts/check_pipeline.py
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Read-only — never writes to the database.
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"""
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import os
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import sys
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import duckdb
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DUCKDB_PATH = os.environ.get("DUCKDB_PATH", "data/lakehouse.duckdb")
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PIPELINE_TABLES = [
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("staging", "stg_playtomic_availability"),
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("foundation", "fct_availability_slot"),
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("foundation", "dim_venue_capacity"),
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("foundation", "fct_daily_availability"),
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("serving", "venue_pricing_benchmarks"),
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("serving", "pseo_city_pricing"),
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]
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def main():
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if not os.path.exists(DUCKDB_PATH):
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print(f"ERROR: {DUCKDB_PATH} not found")
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sys.exit(1)
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con = duckdb.connect(DUCKDB_PATH, read_only=True)
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print(f"Database: {DUCKDB_PATH}")
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print(f"DuckDB version: {con.execute('SELECT version()').fetchone()[0]}")
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print()
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# ── Row counts at each layer ──────────────────────────────────────────
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print("=" * 60)
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print("PIPELINE ROW COUNTS")
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print("=" * 60)
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for schema, table in PIPELINE_TABLES:
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# SQLMesh may use __<env> suffixed physical tables
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# Try the logical name first, then scan for physical tables
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candidates = [f"{schema}.{table}"]
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try:
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phys = con.execute(
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f"SELECT table_schema || '.' || table_name "
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f"FROM information_schema.tables "
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f"WHERE table_name LIKE '{table}%' "
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f"ORDER BY table_name"
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).fetchall()
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for (name,) in phys:
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if name not in candidates:
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candidates.append(name)
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except Exception:
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pass
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for fqn in candidates:
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try:
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(count,) = con.execute(f"SELECT COUNT(*) FROM {fqn}").fetchone()
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print(f" {fqn:50s} {count:>10,} rows")
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except Exception as e:
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print(f" {fqn:50s} ERROR: {e}")
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# ── Date range in fct_daily_availability ──────────────────────────────
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print()
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print("=" * 60)
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print("DATE RANGE: fct_daily_availability")
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print("=" * 60)
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try:
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row = con.execute("""
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SELECT
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MIN(snapshot_date) AS min_date,
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MAX(snapshot_date) AS max_date,
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COUNT(DISTINCT snapshot_date) AS distinct_days,
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CURRENT_DATE AS today,
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CURRENT_DATE - INTERVAL '30 days' AS window_start
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FROM foundation.fct_daily_availability
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""").fetchone()
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if row:
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min_date, max_date, days, today, window_start = row
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print(f" Min snapshot_date: {min_date}")
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print(f" Max snapshot_date: {max_date}")
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print(f" Distinct days: {days}")
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print(f" Today: {today}")
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print(f" 30-day window start: {window_start}")
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if max_date and str(max_date) < str(window_start):
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print()
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print(" *** ALL DATA IS OUTSIDE THE 30-DAY WINDOW ***")
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print(" This is why venue_pricing_benchmarks is empty.")
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except Exception as e:
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print(f" ERROR: {e}")
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# ── HAVING filter impact in venue_pricing_benchmarks ──────────────────
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print()
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print("=" * 60)
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print("HAVING FILTER IMPACT (venue_pricing_benchmarks)")
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print("=" * 60)
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try:
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row = con.execute("""
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WITH venue_stats AS (
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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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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
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)
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SELECT
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COUNT(*) AS total_venues,
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COUNT(*) FILTER (WHERE days_observed >= 3) AS venues_passing_having,
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COUNT(*) FILTER (WHERE days_observed < 3) AS venues_failing_having,
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MAX(days_observed) AS max_days,
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MIN(days_observed) AS min_days
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FROM venue_stats
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""").fetchone()
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if row:
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total, passing, failing, max_d, min_d = row
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print(f" Venues in 30-day window: {total}")
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print(f" Venues with >= 3 days (PASSING): {passing}")
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print(f" Venues with < 3 days (FILTERED): {failing}")
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print(f" Max days observed: {max_d}")
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print(f" Min days observed: {min_d}")
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if total == 0:
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print()
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print(" *** NO VENUES IN 30-DAY WINDOW — check fct_daily_availability dates ***")
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except Exception as e:
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print(f" ERROR: {e}")
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# ── Occupancy rate distribution ───────────────────────────────────────
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print()
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print("=" * 60)
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print("OCCUPANCY RATE DISTRIBUTION (fct_daily_availability)")
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print("=" * 60)
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try:
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rows = con.execute("""
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SELECT
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CASE
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WHEN occupancy_rate IS NULL THEN 'NULL'
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WHEN occupancy_rate < 0 THEN '< 0 (invalid)'
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WHEN occupancy_rate > 1.5 THEN '> 1.5 (filtered)'
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WHEN occupancy_rate <= 0.25 THEN '0 – 0.25'
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WHEN occupancy_rate <= 0.50 THEN '0.25 – 0.50'
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WHEN occupancy_rate <= 0.75 THEN '0.50 – 0.75'
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ELSE '0.75 – 1.0+'
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END AS bucket,
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COUNT(*) AS cnt
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FROM foundation.fct_daily_availability
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GROUP BY 1
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ORDER BY 1
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""").fetchall()
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for bucket, cnt in rows:
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print(f" {bucket:25s} {cnt:>10,}")
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except Exception as e:
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print(f" ERROR: {e}")
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# ── dim_venue_capacity join coverage ──────────────────────────────────
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print()
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print("=" * 60)
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print("JOIN COVERAGE: fct_availability_slot → dim_venue_capacity")
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print("=" * 60)
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try:
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row = con.execute("""
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SELECT
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COUNT(DISTINCT a.tenant_id) AS slot_tenants,
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COUNT(DISTINCT c.tenant_id) AS capacity_tenants,
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COUNT(DISTINCT a.tenant_id) - COUNT(DISTINCT c.tenant_id) AS missing_capacity
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FROM foundation.fct_availability_slot a
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LEFT JOIN foundation.dim_venue_capacity c ON a.tenant_id = c.tenant_id
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""").fetchone()
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if row:
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slot_t, cap_t, missing = row
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print(f" Tenants in fct_availability_slot: {slot_t}")
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print(f" Tenants with capacity match: {cap_t}")
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print(f" Tenants missing capacity: {missing}")
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if missing and missing > 0:
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print(f" *** {missing} tenants dropped by INNER JOIN to dim_venue_capacity ***")
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except Exception as e:
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print(f" ERROR: {e}")
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con.close()
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print()
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print("Done.")
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if __name__ == "__main__":
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main()
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@@ -540,6 +540,7 @@ def _load_workflows() -> list[dict]:
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"schedule": schedule,
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"schedule_label": schedule_label,
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"depends_on": config.get("depends_on", []),
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"description": config.get("description", ""),
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})
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return workflows
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@@ -16,8 +16,9 @@
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{% set wf = row.workflow %}
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{% set run = row.run %}
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{% set stale = row.stale %}
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<div style="border:1px solid #E2E8F0;border-radius:10px;padding:0.875rem;background:#FAFAFA">
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<div class="flex items-center gap-2 mb-2">
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{% set is_running = run and run.status == 'running' and not stale %}
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<div style="border:1px solid {% if is_running %}#93C5FD{% else %}#E2E8F0{% endif %};border-radius:10px;padding:0.875rem;background:{% if is_running %}#EFF6FF{% else %}#FAFAFA{% endif %}">
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<div class="flex items-center gap-2 mb-1">
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{% if not run %}
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<span class="status-dot pending"></span>
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{% elif stale %}
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@@ -33,6 +34,15 @@
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{% if stale %}
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<span class="badge-warning" style="font-size:10px;padding:1px 6px;margin-left:auto">stale</span>
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{% endif %}
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{% if is_running %}
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<span class="btn btn-sm ml-auto"
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style="padding:2px 8px;font-size:11px;opacity:0.6;cursor:default;pointer-events:none">
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<svg class="spinner-icon" width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3">
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<path d="M12 2a10 10 0 0 1 10 10" stroke-linecap="round"/>
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</svg>
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Running
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</span>
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{% else %}
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<button type="button"
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class="btn btn-sm ml-auto"
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style="padding:2px 8px;font-size:11px"
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@@ -41,9 +51,17 @@
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hx-swap="outerHTML"
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hx-vals='{"extractor": "{{ wf.name }}", "csrf_token": "{{ csrf_token() }}"}'
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hx-confirm="Run {{ wf.name }} extractor?">Run</button>
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{% endif %}
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</div>
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{% if wf.description %}
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<p class="text-xs text-slate" style="margin-top:2px;margin-bottom:4px">{{ wf.description }}</p>
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{% endif %}
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<p class="text-xs text-slate">{{ wf.schedule_label }}</p>
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{% if run %}
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{% if is_running %}
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<p class="text-xs mt-1" style="color:#2563EB">
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Started {{ run.started_at[:16].replace('T', ' ') if run.started_at else '—' }} — running...
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</p>
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{% elif run %}
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<p class="text-xs mono text-slate-dark mt-1">{{ run.started_at[:16].replace('T', ' ') if run.started_at else '—' }}</p>
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{% if run.status == 'failed' and run.error_message %}
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<p class="text-xs text-danger mt-1" style="font-family:monospace;word-break:break-all">
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Reference in New Issue
Block a user