fix(pipeline): use sqlmesh plan --auto-apply instead of run

Missed the Python supervisor module — same fix as supervisor.sh and
worker.py.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

infra/supervisor/supervisor.sh

@@ -33,10 +33,10 @@ do
         DUCKDB_PATH="${DUCKDB_PATH:-/data/padelnomics/lakehouse.duckdb}" \
             uv run --package padelnomics_extract extract
 
-        # Transform — run evaluates missing daily intervals for incremental models.
+        # Transform — plan detects new/modified/deleted models and applies changes.
         LANDING_DIR="${LANDING_DIR:-/data/padelnomics/landing}" \
         DUCKDB_PATH="${DUCKDB_PATH:-/data/padelnomics/lakehouse.duckdb}" \
-            uv run sqlmesh -p transform/sqlmesh_padelnomics run prod
+            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.

src/padelnomics/supervisor.py

@@ -247,10 +247,10 @@ def run_shell(cmd: str, timeout_seconds: int = SUBPROCESS_TIMEOUT_SECONDS) -> tu
 
 
 def run_transform() -> None:
-    """Run SQLMesh — evaluates missing daily intervals."""
+    """Run SQLMesh — detects new/modified/deleted models and applies changes."""
     logger.info("Running SQLMesh transform")
     ok, err = run_shell(
-        "uv run sqlmesh -p transform/sqlmesh_padelnomics run prod",
+        "uv run sqlmesh -p transform/sqlmesh_padelnomics plan prod --auto-apply",
     )
     if not ok:
         send_alert(f"[transform] {err}")

transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql

@@ -215,7 +215,7 @@ SELECT
   l.location_slug,
   l.lat,
   l.lon,
-  h3_latlng_to_cell(l.lat, l.lon, 4)                                    AS h3_cell_res4,
+  h3_latlng_to_cell(l.lat, l.lon, 5)                                    AS h3_cell_res5,
   l.admin1_code,
   l.admin2_code,
   l.population,

transform/sqlmesh_padelnomics/models/serving/location_profiles.sql

@@ -20,7 +20,7 @@
 --   "Where should I build a padel court?"
 --   Computed for ALL locations — zero-court locations score highest on supply gap.
 --   H3 catchment methodology: addressable market and supply gap use a regional
---   H3 catchment (res-4 cell + 6 neighbours, ~462km², ~15-18km radius).
+--   H3 catchment (res-5 cell + 6 neighbours, ~24km radius).
 --
 --   25 pts  addressable market — log-scaled catchment population, ceiling 500K
 --   20 pts  economic power     — income PPS, normalised to 35,000
@@ -63,30 +63,30 @@ base AS (
     l.padel_venues_per_100k,
     l.nearest_padel_court_km,
     l.tennis_courts_within_25km,
-    l.h3_cell_res4
+    l.h3_cell_res5
   FROM foundation.dim_locations l
 ),
--- Aggregate population and court counts per H3 cell (res 4, ~10km edge).
+-- Aggregate population and court counts per H3 cell (res 5, ~8.5km edge).
--- Grouping by cell first (~30-50K distinct cells vs 140K locations) keeps the
+-- Grouping by cell first (~50-80K distinct cells vs 140K locations) keeps the
 -- subsequent lateral join small.
 hex_stats AS (
   SELECT
-    h3_cell_res4,
+    h3_cell_res5,
     SUM(population)        AS hex_population,
     SUM(padel_venue_count) AS hex_padel_courts
   FROM foundation.dim_locations
-  GROUP BY h3_cell_res4
+  GROUP BY h3_cell_res5
 ),
 -- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
--- Effective catchment: ~462km², ~15-18km radius — realistic driving distance.
+-- Effective catchment: ~24km radius — realistic driving distance.
 catchment AS (
   SELECT
     l.geoname_id,
     SUM(hs.hex_population)   AS catchment_population,
     SUM(hs.hex_padel_courts) AS catchment_padel_courts
   FROM base l,
-  LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res4, 1)) AS cell) ring
+  LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res5, 1)) AS cell) ring
-  JOIN hex_stats hs ON hs.h3_cell_res4 = ring.cell
+  JOIN hex_stats hs ON hs.h3_cell_res5 = ring.cell
   GROUP BY l.geoname_id
 ),
 -- Match dim_cities via (country_code, geoname_id) to get city_slug + exact venue count.

uv.lock

@@ -150,6 +150,11 @@ dependencies = [
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web/src/padelnomics/worker.py

@@ -737,9 +737,9 @@ async def handle_run_extraction(payload: dict) -> None:
 
 @task("run_transform")
 async def handle_run_transform(payload: dict) -> None:
-    """Run SQLMesh transform (prod run) in the background.
+    """Run SQLMesh transform (prod plan + apply) in the background.
 
-    Shells out to `uv run sqlmesh -p transform/sqlmesh_padelnomics run prod`.
+    Shells out to `uv run sqlmesh -p transform/sqlmesh_padelnomics plan prod --auto-apply`.
     2-hour absolute timeout — same as extraction.
     """
     import subprocess
@@ -748,7 +748,7 @@ async def handle_run_transform(payload: dict) -> None:
     repo_root = Path(__file__).resolve().parents[4]
     result = await asyncio.to_thread(
         subprocess.run,
-        ["uv", "run", "sqlmesh", "-p", "transform/sqlmesh_padelnomics", "run", "prod"],
+        ["uv", "run", "sqlmesh", "-p", "transform/sqlmesh_padelnomics", "plan", "prod", "--auto-apply"],
         capture_output=True,
         text=True,
         timeout=7200,
@@ -803,7 +803,7 @@ async def handle_run_pipeline(payload: dict) -> None:
         ),
         (
             "transform",
-            ["uv", "run", "sqlmesh", "-p", "transform/sqlmesh_padelnomics", "run", "prod"],
+            ["uv", "run", "sqlmesh", "-p", "transform/sqlmesh_padelnomics", "plan", "prod", "--auto-apply"],
             7200,
         ),
         (