perf: resolve all P1–P5 performance issues from code review
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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>
This commit is contained in:
+11
-33
@@ -24,6 +24,7 @@ from .config import settings
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from .data_loader import (
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clear_cache,
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load_school_data,
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load_latest_school_data,
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geocode_single_postcode,
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get_supplementary_data,
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search_schools_typesense,
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@@ -223,6 +224,8 @@ async def lifespan(app: FastAPI):
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print("Warning: No data in marts. Run the annual EES pipeline to populate KS2 data.")
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else:
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print(f"Data loaded successfully: {len(df)} records.")
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# Pre-compute the latest-year snapshot so the first search request is fast
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await asyncio.to_thread(load_latest_school_data)
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try:
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_sitemap_xml = build_sitemap()
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n = _sitemap_xml.count("<url>")
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@@ -321,44 +324,17 @@ async def get_schools(
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phase = sanitize_search_input(phase)
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postcode = validate_postcode(postcode)
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df = load_school_data()
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# Load the pre-computed latest-year snapshot (cached after first request / startup).
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# This avoids rebuilding the expensive groupby + prev-year merge on every search.
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df_latest = load_latest_school_data()
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if df.empty:
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if df_latest.empty:
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return {"schools": [], "total": 0, "page": page, "page_size": 0}
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# Use configured default if not specified
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if page_size is None:
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page_size = settings.default_page_size
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# Schools with no performance data (special schools, PRUs, newly opened, etc.)
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# have NULL year from the LEFT JOIN — keep them but skip the groupby/trend logic.
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df_no_perf = df[df["year"].isna()].drop_duplicates(subset=["urn"])
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df = df[df["year"].notna()]
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# Get unique schools (latest year data for each)
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latest_year = df.groupby("urn")["year"].max().reset_index()
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df_latest = df.merge(latest_year, on=["urn", "year"])
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# Calculate trend by comparing to previous year
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# Get second-latest year for each school
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df_sorted = df.sort_values(["urn", "year"], ascending=[True, False])
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df_prev = df_sorted.groupby("urn").nth(1).reset_index()
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if not df_prev.empty and "rwm_expected_pct" in df_prev.columns:
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prev_rwm = df_prev[["urn", "rwm_expected_pct"]].rename(
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columns={"rwm_expected_pct": "prev_rwm_expected_pct"}
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)
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if "attainment_8_score" in df_prev.columns:
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prev_rwm = prev_rwm.merge(
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df_prev[["urn", "attainment_8_score"]].rename(
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columns={"attainment_8_score": "prev_attainment_8_score"}
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),
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on="urn", how="outer"
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)
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df_latest = df_latest.merge(prev_rwm, on="urn", how="left")
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# Merge back schools with no performance data
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df_latest = pd.concat([df_latest, df_no_perf], ignore_index=True)
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# Phase filter — uses PHASE_GROUPS so all-through/middle schools appear
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# in the correct phase(s) rather than being invisible to both filters.
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if phase:
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@@ -404,7 +380,8 @@ async def get_schools(
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# Location-based search (uses pre-geocoded data from database)
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search_coords = None
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if postcode:
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coords = geocode_single_postcode(postcode)
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# Offload the synchronous HTTP call to a thread so the event loop stays free
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coords = await asyncio.to_thread(geocode_single_postcode, postcode)
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if coords:
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search_coords = coords
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schools_df = schools_df.copy()
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@@ -907,7 +884,8 @@ async def reload_data(
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Requires X-API-Key header with valid admin API key.
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"""
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clear_cache()
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load_school_data()
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await asyncio.to_thread(load_school_data)
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await asyncio.to_thread(load_latest_school_data)
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return {"status": "reloaded"}
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+53
-1
@@ -242,6 +242,8 @@ def load_school_data_as_dataframe() -> pd.DataFrame:
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# Cache for DataFrame
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_df_cache: Optional[pd.DataFrame] = None
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# Pre-computed latest-year snapshot (one row per school, with prev-year trend columns)
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_df_latest_cache: Optional[pd.DataFrame] = None
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def load_school_data() -> pd.DataFrame:
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@@ -260,10 +262,60 @@ def load_school_data() -> pd.DataFrame:
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return _df_cache
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def load_latest_school_data() -> pd.DataFrame:
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"""Return a cached one-row-per-school DataFrame at the latest available year.
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The expensive groupby / merge / prev-year trend computation runs once at
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startup (or after a cache clear) rather than on every search request.
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Per-request filters (phase, gender, LA …) should be applied to the returned
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DataFrame's copy; they must NOT modify the cached object.
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"""
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global _df_latest_cache
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if _df_latest_cache is not None:
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return _df_latest_cache
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df = load_school_data()
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if df.empty:
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return df
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# Schools that have no performance rows (PRUs, new schools, etc.)
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df_no_perf = df[df["year"].isna()].drop_duplicates(subset=["urn"])
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df_with_perf = df[df["year"].notna()]
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# Reduce to the latest year per school
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latest_year = df_with_perf.groupby("urn")["year"].max().reset_index()
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df_latest = df_with_perf.merge(latest_year, on=["urn", "year"])
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# Attach previous-year metrics for trend arrows (second-latest year per school)
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df_sorted = df_with_perf.sort_values(["urn", "year"], ascending=[True, False])
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df_prev = df_sorted.groupby("urn").nth(1).reset_index()
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if not df_prev.empty and "rwm_expected_pct" in df_prev.columns:
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prev_rwm = df_prev[["urn", "rwm_expected_pct"]].rename(
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columns={"rwm_expected_pct": "prev_rwm_expected_pct"}
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)
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if "attainment_8_score" in df_prev.columns:
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prev_rwm = prev_rwm.merge(
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df_prev[["urn", "attainment_8_score"]].rename(
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columns={"attainment_8_score": "prev_attainment_8_score"}
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),
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on="urn",
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how="outer",
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)
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df_latest = df_latest.merge(prev_rwm, on="urn", how="left")
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# Merge back schools with no performance data
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df_latest = pd.concat([df_latest, df_no_perf], ignore_index=True)
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print(f"Latest-snapshot cache built: {len(df_latest)} schools")
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_df_latest_cache = df_latest
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return _df_latest_cache
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def clear_cache():
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"""Clear all caches."""
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global _df_cache
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global _df_cache, _df_latest_cache
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_df_cache = None
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_df_latest_cache = None
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# =============================================================================
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+2
-5
@@ -15,6 +15,7 @@ engine = create_engine(
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pool_size=10,
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max_overflow=20,
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pool_pre_ping=True,
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pool_recycle=1800, # recycle connections every 30 min to avoid stale TCP
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echo=False,
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)
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@@ -34,13 +35,9 @@ def get_db():
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@contextmanager
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def get_db_session():
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"""Context manager for non-FastAPI contexts."""
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"""Context manager for non-FastAPI contexts (read-only)."""
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db = SessionLocal()
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try:
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yield db
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db.commit()
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except Exception:
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db.rollback()
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raise
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finally:
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db.close()
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@@ -6,31 +6,11 @@
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'use client';
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import { Line } from 'react-chartjs-2';
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import {
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Chart as ChartJS,
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CategoryScale,
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LinearScale,
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PointElement,
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LineElement,
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Title,
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Tooltip,
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Legend,
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ChartOptions,
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} from 'chart.js';
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import { ChartOptions } from 'chart.js';
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import '@/lib/chartSetup';
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import type { ComparisonData } from '@/lib/types';
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import { CHART_COLORS, formatAcademicYear } from '@/lib/utils';
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// Register Chart.js components
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ChartJS.register(
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CategoryScale,
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LinearScale,
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PointElement,
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LineElement,
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Title,
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Tooltip,
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Legend
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);
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interface ComparisonChartProps {
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comparisonData: Record<string, ComparisonData>;
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metric: string;
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@@ -74,6 +74,7 @@ export function HomeView({ initialSchools, filters, totalSchools }: HomeViewProp
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const [mapSchools, setMapSchools] = useState<School[]>([]);
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const [isLoadingMap, setIsLoadingMap] = useState(false);
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const prevSearchParamsRef = useRef(searchParams.toString());
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const mapParamsRef = useRef<string>('');
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const [geoState, setGeoState] = useState<'idle' | 'requesting' | 'error'>('idle');
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const [geoError, setGeoError] = useState<string | null>(null);
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const [chipDays, setChipDays] = useState<(number | null)[]>(ADMISSIONS_CHIPS.map(() => null));
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@@ -88,11 +89,12 @@ export function HomeView({ initialSchools, filters, totalSchools }: HomeViewProp
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|| (!currentPhase && secondaryCount > primaryCount);
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const isMixedView = primaryCount > 0 && secondaryCount > 0 && !currentPhase;
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// Reset pagination state when search params change
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// Reset pagination and map cache when search params change
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useEffect(() => {
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const newParamsStr = searchParams.toString();
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if (newParamsStr !== prevSearchParamsRef.current) {
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prevSearchParamsRef.current = newParamsStr;
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mapParamsRef.current = ''; // allow map to re-fetch for new search
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setAllSchools(initialSchools.schools);
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setCurrentPage(initialSchools.page);
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setHasMore(initialSchools.total_pages > 1);
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@@ -105,9 +107,13 @@ export function HomeView({ initialSchools, filters, totalSchools }: HomeViewProp
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setSelectedMapSchool(null);
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}, [resultsView, searchParams]);
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// Fetch all schools within radius when map view is active
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// Fetch all schools within radius when map view is active.
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// Guard with a ref so toggling back to map never re-fetches the same params.
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useEffect(() => {
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if (resultsView !== 'map' || !isLocationSearch) return;
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const paramsKey = searchParams.toString();
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if (paramsKey === mapParamsRef.current) return;
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mapParamsRef.current = paramsKey;
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setIsLoadingMap(true);
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const params: Record<string, any> = {};
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searchParams.forEach((value, key) => { params[key] = value; });
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@@ -6,24 +6,12 @@
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'use client';
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import { Line } from 'react-chartjs-2';
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import {
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Chart as ChartJS,
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CategoryScale,
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LinearScale,
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PointElement,
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LineElement,
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Title,
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Tooltip,
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Legend,
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ChartOptions,
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ChartDataset,
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} from 'chart.js';
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import { ChartOptions, ChartDataset } from 'chart.js';
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import '@/lib/chartSetup';
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import type { SchoolResult } from '@/lib/types';
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import { formatAcademicYear } from '@/lib/utils';
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import styles from './PerformanceChart.module.css';
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ChartJS.register(CategoryScale, LinearScale, PointElement, LineElement, Title, Tooltip, Legend);
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interface NationalByYear {
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year: number;
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primary: Record<string, number>;
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@@ -0,0 +1,28 @@
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/**
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* Single Chart.js registration point.
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* Import this module (side-effect import) in any file that uses react-chartjs-2.
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* Chart.js is idempotent about duplicate registrations, but importing the
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* registration from one place ensures every primitive is only bundled once
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* and the register() call is not duplicated at runtime.
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*/
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import {
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Chart as ChartJS,
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CategoryScale,
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LinearScale,
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PointElement,
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LineElement,
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Title,
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Tooltip,
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Legend,
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} from 'chart.js';
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ChartJS.register(
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CategoryScale,
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LinearScale,
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PointElement,
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LineElement,
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Title,
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Tooltip,
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Legend,
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);
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