perf: resolve all P1–P5 performance issues from code review

P1 (backend/data_loader.py): Add load_latest_school_data() which pre-computes
the one-row-per-school latest-year snapshot (groupby, prev-year trend merge)
once at startup instead of on every /api/schools request. get_schools route
now starts from the cached snapshot rather than rebuilding it.

S3 (backend/app.py): Wrap synchronous geocode_single_postcode() call in
asyncio.to_thread() so postcode lookups no longer block the uvicorn event
loop. Admin reload endpoint also uses to_thread for both cache primes.

P2 (nextjs-app/components/HomeView.tsx): Add mapParamsRef guard so switching
back to map view does not re-fetch 500 schools when search params haven't
changed. Reset ref on new searches so fresh data is always fetched.

P3 (nextjs-app/lib/chartSetup.ts): Extract Chart.js registration into a
shared side-effect module. ComparisonChart and PerformanceChart now import
it instead of each calling ChartJS.register() independently.

P4 (backend/database.py): Remove unnecessary db.commit() from the read-only
get_db_session() context manager — saves a DB round-trip on every request.

P5 (backend/database.py): Add pool_recycle=1800 to SQLAlchemy engine to
prevent stale TCP connections from accumulating in long-running processes.

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

backend/data_loader.py

@@ -242,6 +242,8 @@ def load_school_data_as_dataframe() -> pd.DataFrame:
 
 # Cache for DataFrame
 _df_cache: Optional[pd.DataFrame] = None
+# Pre-computed latest-year snapshot (one row per school, with prev-year trend columns)
+_df_latest_cache: Optional[pd.DataFrame] = None
 
 
 def load_school_data() -> pd.DataFrame:
@@ -260,10 +262,60 @@ def load_school_data() -> pd.DataFrame:
     return _df_cache
 
 
+def load_latest_school_data() -> pd.DataFrame:
+    """Return a cached one-row-per-school DataFrame at the latest available year.
+
+    The expensive groupby / merge / prev-year trend computation runs once at
+    startup (or after a cache clear) rather than on every search request.
+    Per-request filters (phase, gender, LA …) should be applied to the returned
+    DataFrame's copy; they must NOT modify the cached object.
+    """
+    global _df_latest_cache
+    if _df_latest_cache is not None:
+        return _df_latest_cache
+
+    df = load_school_data()
+    if df.empty:
+        return df
+
+    # Schools that have no performance rows (PRUs, new schools, etc.)
+    df_no_perf = df[df["year"].isna()].drop_duplicates(subset=["urn"])
+    df_with_perf = df[df["year"].notna()]
+
+    # Reduce to the latest year per school
+    latest_year = df_with_perf.groupby("urn")["year"].max().reset_index()
+    df_latest = df_with_perf.merge(latest_year, on=["urn", "year"])
+
+    # Attach previous-year metrics for trend arrows (second-latest year per school)
+    df_sorted = df_with_perf.sort_values(["urn", "year"], ascending=[True, False])
+    df_prev = df_sorted.groupby("urn").nth(1).reset_index()
+    if not df_prev.empty and "rwm_expected_pct" in df_prev.columns:
+        prev_rwm = df_prev[["urn", "rwm_expected_pct"]].rename(
+            columns={"rwm_expected_pct": "prev_rwm_expected_pct"}
+        )
+        if "attainment_8_score" in df_prev.columns:
+            prev_rwm = prev_rwm.merge(
+                df_prev[["urn", "attainment_8_score"]].rename(
+                    columns={"attainment_8_score": "prev_attainment_8_score"}
+                ),
+                on="urn",
+                how="outer",
+            )
+        df_latest = df_latest.merge(prev_rwm, on="urn", how="left")
+
+    # Merge back schools with no performance data
+    df_latest = pd.concat([df_latest, df_no_perf], ignore_index=True)
+
+    print(f"Latest-snapshot cache built: {len(df_latest)} schools")
+    _df_latest_cache = df_latest
+    return _df_latest_cache
+
+
 def clear_cache():
     """Clear all caches."""
-    global _df_cache
+    global _df_cache, _df_latest_cache
     _df_cache = None
+    _df_latest_cache = None
 
 
 # =============================================================================