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Author SHA1 Message Date
Deeman
add5f8ddfa fix(extract): correct lc_lci_lev lcstruct filter value
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2026-03-05 17:39:37 +01:00
Deeman
15ca316682 fix(extract): correct lc_lci_lev lcstruct filter value
D1_D2_A_HW doesn't exist in the API; use D1_D4_MD5 (total labour cost
= compensation + taxes - subsidies).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-05 17:32:49 +01:00
Deeman
103ef73cf5 fix(pipeline): eurostat filter bugs + supervisor uses sqlmesh plan
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2026-03-05 17:19:21 +01:00
Deeman
aa27f14f3c fix(pipeline): eurostat filter bugs + supervisor uses sqlmesh plan
- nrg_pc_203: add missing unit=KWH filter (API returns 2 units)
- lc_lci_lev: fix currency→unit filter dimension name
- supervisor: use `sqlmesh plan prod --auto-apply` instead of
  `sqlmesh run` so new/changed models are detected automatically

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-05 17:19:12 +01:00
Deeman
8205744444 chore: remove accidentally committed .claire/ worktree directory
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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-05 17:10:48 +01:00
Deeman
1cbefe349c add env var 2026-03-05 17:08:52 +01:00
Deeman
003f19e071 fix(pipeline): handle DuckDB catalog naming in diagnostic script 2026-03-05 17:07:52 +01:00
Deeman
c3f15535b8 fix(pipeline): handle DuckDB catalog naming in diagnostic script
The lakehouse.duckdb file uses catalog "lakehouse" not "local", causing
SQLMesh logical views to break. Script now auto-detects the catalog via
USE and falls back to physical tables when views fail.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-05 17:06:44 +01:00
Deeman
fcb8ec4227 merge: pipeline diagnostic script + extraction card UX improvements
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2026-03-05 15:40:16 +01:00
Deeman
6b7fa45bce 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>
2026-03-05 15:40:12 +01:00
8 changed files with 344 additions and 11 deletions

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@@ -3,6 +3,7 @@ APP_NAME=ENC[AES256_GCM,data:ldJf4P0iD9ziMVg=,iv:hiVl2whhd02yZCafzBfbxX5/EU/suvz
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@@ -63,7 +64,7 @@ sops_age__list_1__map_enc=-----BEGIN AGE ENCRYPTED FILE-----\nYWdlLWVuY3J5cHRpb2
sops_age__list_1__map_recipient=age1wjepykv3glvsrtegu25tevg7vyn3ngpl607u3yjc9ucay04s045s796msw
sops_age__list_2__map_enc=-----BEGIN AGE ENCRYPTED FILE-----\nYWdlLWVuY3J5cHRpb24ub3JnL3YxCi0+IFgyNTUxOSBFeHhaOURNZnRVMEwxNThu\nUjF4Q0kwUXhTUE1QSzZJbmpubnh3RnpQTmdvCjRmWWxpNkxFUmVGb3NRbnlydW5O\nWEg3ZXJQTU4vcndzS2pUQXY3Q0ttYjAKLS0tIE9IRFJ1c2ZxbGVHa2xTL0swbGN1\nTzgwMThPUDRFTWhuZHJjZUYxOTZrU00KY62qrNBCUQYxwcLMXFEnLkwncxq3BPJB\nKm4NzeHBU87XmPWVrgrKuf+PH1mxJlBsl7Hev8xBTy7l6feiZjLIvQ==\n-----END AGE ENCRYPTED FILE-----\n
sops_age__list_2__map_recipient=age1c783ym2q5x9tv7py5d28uc4k44aguudjn03g97l9nzs00dd9tsrqum8h4d
sops_lastmodified=2026-03-01T20:26:09Z
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sops_unencrypted_suffix=_unencrypted
sops_version=3.12.1

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@@ -6,7 +6,17 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
## [Unreleased]
### Fixed
- **Pipeline diagnostic script** (`scripts/check_pipeline.py`) — handle DuckDB catalog naming quirk where `lakehouse.duckdb` uses catalog `lakehouse` instead of `local`, causing SQLMesh logical views to break. Script now auto-detects the catalog via `USE`, and falls back to querying physical tables (`sqlmesh__<schema>.<table>__<hash>`) when views fail.
- **Eurostat gas prices extractor** — `nrg_pc_203` filter missing `unit` dimension (API returns both KWH and GJ_GCV); now filters to `KWH`.
- **Eurostat labour costs extractor** — `lc_lci_lev` used non-existent `currency` filter dimension; corrected to `unit: EUR`.
- **Supervisor transform step** — changed `sqlmesh run` to `sqlmesh plan prod --auto-apply` so new/modified models are detected and applied automatically.
### Added
- **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.
- **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`.
- **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.
- **Interactive Leaflet maps** — geographic visualization across 4 key placements using self-hosted Leaflet 1.9.4 (GDPR-safe, no CDN):
- **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.
- **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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@@ -63,15 +63,15 @@ DATASETS: dict[str, dict] = {
"time_dim": "time",
},
"nrg_pc_203": {
# Gas prices for non-household consumers, EUR/GJ, excl. taxes
"filters": {"freq": "S", "nrg_cons": "GJ1000-9999", "currency": "EUR", "tax": "I_TAX"},
# Gas prices for non-household consumers, EUR/kWh, excl. taxes
"filters": {"freq": "S", "nrg_cons": "GJ1000-9999", "unit": "KWH", "currency": "EUR", "tax": "I_TAX"},
"geo_dim": "geo",
"time_dim": "time",
},
"lc_lci_lev": {
# Labour cost levels EUR/hour — NACE N (administrative/support services)
# Stored in dim_countries for future staffed-scenario calculations.
"filters": {"lcstruct": "D1_D2_A_HW", "nace_r2": "N", "currency": "EUR"},
# D1_D4_MD5 = compensation of employees + taxes - subsidies (total labour cost)
"filters": {"lcstruct": "D1_D4_MD5", "nace_r2": "N", "unit": "EUR"},
"geo_dim": "geo",
"time_dim": "time",
},

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@@ -33,10 +33,10 @@ do
DUCKDB_PATH="${DUCKDB_PATH:-/data/padelnomics/lakehouse.duckdb}" \
uv run --package padelnomics_extract extract
# Transform
# Transform — plan detects new/changed models; run only executes existing plans.
LANDING_DIR="${LANDING_DIR:-/data/padelnomics/landing}" \
DUCKDB_PATH="${DUCKDB_PATH:-/data/padelnomics/lakehouse.duckdb}" \
uv run --package sqlmesh_padelnomics sqlmesh run --select-model "serving.*"
uv run sqlmesh -p transform/sqlmesh_padelnomics plan prod --auto-apply
# Export serving tables to analytics.duckdb (atomic swap).
# The web app detects the inode change on next query — no restart needed.

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@@ -8,54 +8,67 @@
# entry — optional: function name if not "main" (default: "main")
# depends_on — optional: list of workflow names that must run first
# proxy_mode — optional: "round-robin" (default) or "sticky"
# description — optional: human-readable one-liner shown in the admin UI
[overpass]
module = "padelnomics_extract.overpass"
schedule = "monthly"
description = "Padel court locations from OpenStreetMap via Overpass API"
[overpass_tennis]
module = "padelnomics_extract.overpass_tennis"
schedule = "monthly"
description = "Tennis court locations from OpenStreetMap via Overpass API"
[eurostat]
module = "padelnomics_extract.eurostat"
schedule = "monthly"
description = "City population data from Eurostat Urban Audit"
[geonames]
module = "padelnomics_extract.geonames"
schedule = "monthly"
description = "Global city/town gazetteer from GeoNames (pop >= 1K)"
[playtomic_tenants]
module = "padelnomics_extract.playtomic_tenants"
schedule = "daily"
description = "Padel venue directory from Playtomic (names, locations, courts)"
[playtomic_availability]
module = "padelnomics_extract.playtomic_availability"
schedule = "daily"
depends_on = ["playtomic_tenants"]
description = "Morning availability snapshots — slot-level pricing per venue"
[playtomic_recheck]
module = "padelnomics_extract.playtomic_availability"
entry = "main_recheck"
schedule = "0,30 6-23 * * *"
depends_on = ["playtomic_availability"]
description = "Intraday availability rechecks for occupancy tracking"
[census_usa]
module = "padelnomics_extract.census_usa"
schedule = "monthly"
description = "US city/place population from Census Bureau ACS"
[census_usa_income]
module = "padelnomics_extract.census_usa_income"
schedule = "monthly"
description = "US county median household income from Census Bureau ACS"
[eurostat_city_labels]
module = "padelnomics_extract.eurostat_city_labels"
schedule = "monthly"
description = "City code-to-name mapping for Eurostat Urban Audit cities"
[ons_uk]
module = "padelnomics_extract.ons_uk"
schedule = "monthly"
description = "UK local authority population estimates from ONS"
[gisco]
module = "padelnomics_extract.gisco"
schedule = "monthly"
description = "EU geographic boundaries (NUTS2 polygons) from Eurostat GISCO"

290
scripts/check_pipeline.py Normal file
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@@ -0,0 +1,290 @@
"""
Diagnostic script: check row counts at every layer of the pricing pipeline.
Run on prod via SSH:
DUCKDB_PATH=/opt/padelnomics/data/lakehouse.duckdb uv run python scripts/check_pipeline.py
Or locally:
DUCKDB_PATH=data/lakehouse.duckdb uv run python scripts/check_pipeline.py
Read-only — never writes to the database.
Handles the DuckDB catalog naming quirk: when the file is named lakehouse.duckdb,
the catalog is "lakehouse" not "local". SQLMesh views may reference the wrong catalog,
so we fall back to querying physical tables (sqlmesh__<schema>.<table>__<hash>).
"""
import os
import sys
import duckdb
DUCKDB_PATH = os.environ.get("DUCKDB_PATH", "data/lakehouse.duckdb")
PIPELINE_TABLES = [
("staging", "stg_playtomic_availability"),
("foundation", "fct_availability_slot"),
("foundation", "dim_venue_capacity"),
("foundation", "fct_daily_availability"),
("serving", "venue_pricing_benchmarks"),
("serving", "pseo_city_pricing"),
]
def _use_catalog(con):
"""Detect and USE the database catalog so schema-qualified queries work."""
catalogs = [
row[0]
for row in con.execute(
"SELECT catalog_name FROM information_schema.schemata"
).fetchall()
]
# Pick the non-system catalog (not 'system', 'temp', 'memory')
user_catalogs = [c for c in set(catalogs) if c not in ("system", "temp", "memory")]
if user_catalogs:
catalog = user_catalogs[0]
con.execute(f"USE {catalog}")
return catalog
return None
def _find_physical_table(con, schema, table):
"""Find the SQLMesh physical table name for a logical table.
SQLMesh stores physical tables as:
sqlmesh__<schema>.<schema>__<table>__<hash>
"""
sqlmesh_schema = f"sqlmesh__{schema}"
try:
rows = con.execute(
"SELECT table_schema, table_name "
"FROM information_schema.tables "
f"WHERE table_schema = '{sqlmesh_schema}' "
f"AND table_name LIKE '{schema}__{table}%' "
"ORDER BY table_name "
"LIMIT 1"
).fetchall()
if rows:
return f"{rows[0][0]}.{rows[0][1]}"
except Exception:
pass
return None
def _query_table(con, schema, table):
"""Try logical view first, fall back to physical table. Returns (fqn, count) or (fqn, error_str)."""
logical = f"{schema}.{table}"
try:
(count,) = con.execute(f"SELECT COUNT(*) FROM {logical}").fetchone()
return logical, count
except Exception:
pass
physical = _find_physical_table(con, schema, table)
if physical:
try:
(count,) = con.execute(f"SELECT COUNT(*) FROM {physical}").fetchone()
return f"{physical} (physical)", count
except Exception as e:
return f"{physical} (physical)", f"ERROR: {e}"
return logical, "ERROR: view broken, no physical table found"
def _query_sql(con, sql, schema_tables):
"""Execute SQL, falling back to rewritten SQL using physical table names if views fail.
schema_tables: list of (schema, table) tuples used in the SQL, in order of appearance.
The SQL must use {schema}.{table} format for these references.
"""
try:
return con.execute(sql)
except Exception:
# Rewrite SQL to use physical table names
rewritten = sql
for schema, table in schema_tables:
physical = _find_physical_table(con, schema, table)
if physical:
rewritten = rewritten.replace(f"{schema}.{table}", physical)
else:
raise
return con.execute(rewritten)
def main():
if not os.path.exists(DUCKDB_PATH):
print(f"ERROR: {DUCKDB_PATH} not found")
sys.exit(1)
con = duckdb.connect(DUCKDB_PATH, read_only=True)
print(f"Database: {DUCKDB_PATH}")
print(f"DuckDB version: {con.execute('SELECT version()').fetchone()[0]}")
catalog = _use_catalog(con)
if catalog:
print(f"Catalog: {catalog}")
print()
# ── Row counts at each layer ──────────────────────────────────────────
print("=" * 60)
print("PIPELINE ROW COUNTS")
print("=" * 60)
for schema, table in PIPELINE_TABLES:
fqn, result = _query_table(con, schema, table)
if isinstance(result, int):
print(f" {fqn:55s} {result:>10,} rows")
else:
print(f" {fqn:55s} {result}")
# ── Date range in fct_daily_availability ──────────────────────────────
print()
print("=" * 60)
print("DATE RANGE: fct_daily_availability")
print("=" * 60)
try:
row = _query_sql(
con,
"""
SELECT
MIN(snapshot_date) AS min_date,
MAX(snapshot_date) AS max_date,
COUNT(DISTINCT snapshot_date) AS distinct_days,
CURRENT_DATE AS today,
CURRENT_DATE - INTERVAL '30 days' AS window_start
FROM foundation.fct_daily_availability
""",
[("foundation", "fct_daily_availability")],
).fetchone()
if row:
min_date, max_date, days, today, window_start = row
print(f" Min snapshot_date: {min_date}")
print(f" Max snapshot_date: {max_date}")
print(f" Distinct days: {days}")
print(f" Today: {today}")
print(f" 30-day window start: {window_start}")
if max_date and str(max_date) < str(window_start):
print()
print(" *** ALL DATA IS OUTSIDE THE 30-DAY WINDOW ***")
print(" This is why venue_pricing_benchmarks is empty.")
except Exception as e:
print(f" ERROR: {e}")
# ── HAVING filter impact in venue_pricing_benchmarks ──────────────────
print()
print("=" * 60)
print("HAVING FILTER IMPACT (venue_pricing_benchmarks)")
print("=" * 60)
try:
row = _query_sql(
con,
"""
WITH venue_stats AS (
SELECT
da.tenant_id,
da.country_code,
da.city,
COUNT(DISTINCT da.snapshot_date) AS days_observed
FROM foundation.fct_daily_availability da
WHERE TRY_CAST(da.snapshot_date AS DATE) >= CURRENT_DATE - INTERVAL '30 days'
AND da.occupancy_rate IS NOT NULL
AND da.occupancy_rate BETWEEN 0 AND 1.5
GROUP BY da.tenant_id, da.country_code, da.city
)
SELECT
COUNT(*) AS total_venues,
COUNT(*) FILTER (WHERE days_observed >= 3) AS venues_passing_having,
COUNT(*) FILTER (WHERE days_observed < 3) AS venues_failing_having,
MAX(days_observed) AS max_days,
MIN(days_observed) AS min_days
FROM venue_stats
""",
[("foundation", "fct_daily_availability")],
).fetchone()
if row:
total, passing, failing, max_d, min_d = row
print(f" Venues in 30-day window: {total}")
print(f" Venues with >= 3 days (PASSING): {passing}")
print(f" Venues with < 3 days (FILTERED): {failing}")
print(f" Max days observed: {max_d}")
print(f" Min days observed: {min_d}")
if total == 0:
print()
print(" *** NO VENUES IN 30-DAY WINDOW — check fct_daily_availability dates ***")
except Exception as e:
print(f" ERROR: {e}")
# ── Occupancy rate distribution ───────────────────────────────────────
print()
print("=" * 60)
print("OCCUPANCY RATE DISTRIBUTION (fct_daily_availability)")
print("=" * 60)
try:
rows = _query_sql(
con,
"""
SELECT
CASE
WHEN occupancy_rate IS NULL THEN 'NULL'
WHEN occupancy_rate < 0 THEN '< 0 (invalid)'
WHEN occupancy_rate > 1.5 THEN '> 1.5 (filtered)'
WHEN occupancy_rate <= 0.25 THEN '0 0.25'
WHEN occupancy_rate <= 0.50 THEN '0.25 0.50'
WHEN occupancy_rate <= 0.75 THEN '0.50 0.75'
ELSE '0.75 1.0+'
END AS bucket,
COUNT(*) AS cnt
FROM foundation.fct_daily_availability
GROUP BY 1
ORDER BY 1
""",
[("foundation", "fct_daily_availability")],
).fetchall()
for bucket, cnt in rows:
print(f" {bucket:25s} {cnt:>10,}")
except Exception as e:
print(f" ERROR: {e}")
# ── dim_venue_capacity join coverage ──────────────────────────────────
print()
print("=" * 60)
print("JOIN COVERAGE: fct_availability_slot → dim_venue_capacity")
print("=" * 60)
try:
row = _query_sql(
con,
"""
SELECT
COUNT(DISTINCT a.tenant_id) AS slot_tenants,
COUNT(DISTINCT c.tenant_id) AS capacity_tenants,
COUNT(DISTINCT a.tenant_id) - COUNT(DISTINCT c.tenant_id) AS missing_capacity
FROM foundation.fct_availability_slot a
LEFT JOIN foundation.dim_venue_capacity c ON a.tenant_id = c.tenant_id
""",
[
("foundation", "fct_availability_slot"),
("foundation", "dim_venue_capacity"),
],
).fetchone()
if row:
slot_t, cap_t, missing = row
print(f" Tenants in fct_availability_slot: {slot_t}")
print(f" Tenants with capacity match: {cap_t}")
print(f" Tenants missing capacity: {missing}")
if missing and missing > 0:
print(f" *** {missing} tenants dropped by INNER JOIN to dim_venue_capacity ***")
except Exception as e:
print(f" ERROR: {e}")
con.close()
print()
print("Done.")
if __name__ == "__main__":
main()

View File

@@ -540,6 +540,7 @@ def _load_workflows() -> list[dict]:
"schedule": schedule,
"schedule_label": schedule_label,
"depends_on": config.get("depends_on", []),
"description": config.get("description", ""),
})
return workflows

View File

@@ -16,8 +16,9 @@
{% set wf = row.workflow %}
{% set run = row.run %}
{% set stale = row.stale %}
<div style="border:1px solid #E2E8F0;border-radius:10px;padding:0.875rem;background:#FAFAFA">
<div class="flex items-center gap-2 mb-2">
{% set is_running = run and run.status == 'running' and not stale %}
<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 %}">
<div class="flex items-center gap-2 mb-1">
{% if not run %}
<span class="status-dot pending"></span>
{% elif stale %}
@@ -33,6 +34,15 @@
{% if stale %}
<span class="badge-warning" style="font-size:10px;padding:1px 6px;margin-left:auto">stale</span>
{% endif %}
{% if is_running %}
<span class="btn btn-sm ml-auto"
style="padding:2px 8px;font-size:11px;opacity:0.6;cursor:default;pointer-events:none">
<svg class="spinner-icon" width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3">
<path d="M12 2a10 10 0 0 1 10 10" stroke-linecap="round"/>
</svg>
Running
</span>
{% else %}
<button type="button"
class="btn btn-sm ml-auto"
style="padding:2px 8px;font-size:11px"
@@ -41,9 +51,17 @@
hx-swap="outerHTML"
hx-vals='{"extractor": "{{ wf.name }}", "csrf_token": "{{ csrf_token() }}"}'
hx-confirm="Run {{ wf.name }} extractor?">Run</button>
{% endif %}
</div>
{% if wf.description %}
<p class="text-xs text-slate" style="margin-top:2px;margin-bottom:4px">{{ wf.description }}</p>
{% endif %}
<p class="text-xs text-slate">{{ wf.schedule_label }}</p>
{% if run %}
{% if is_running %}
<p class="text-xs mt-1" style="color:#2563EB">
Started {{ run.started_at[:16].replace('T', ' ') if run.started_at else '—' }} — running...
</p>
{% elif run %}
<p class="text-xs mono text-slate-dark mt-1">{{ run.started_at[:16].replace('T', ' ') if run.started_at else '—' }}</p>
{% if run.status == 'failed' and run.error_message %}
<p class="text-xs text-danger mt-1" style="font-family:monospace;word-break:break-all">