Compare commits

..
Author SHA1 Message Date
TudorandClaude Fable 5 d677b54533 fix(pipeline): cache invalidation for IDACI DAG too; curl timeouts
PR Checks / Frontend Typecheck + Tests (pull_request) Successful in 9m37s
PR Checks / Backend Smoke (pull_request) Successful in 6s
PR Checks / Build Backend (no push) (pull_request) Successful in 18s
PR Checks / Build Frontend (no push) (pull_request) Successful in 48s
PR Checks / Build Pipeline (no push) (pull_request) Successful in 35s
PR Checks / AI Code Review (Claude) (pull_request) Successful in 1m40s
Addresses AI-review findings: the annual IDACI DAG also rebuilds a mart
(fact_deprivation) and needs the reload; curl gets connect/max timeouts
so an unreachable backend fails fast instead of hanging the task.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-09 21:26:45 +01:00
TudorandClaude Fable 5 c353e36072 fix(pipeline): actually invalidate the backend cache after data rebuilds
PR Checks / Frontend Typecheck + Tests (pull_request) Successful in 9m40s
PR Checks / Backend Smoke (pull_request) Successful in 6s
PR Checks / Build Backend (no push) (pull_request) Successful in 16s
PR Checks / Build Frontend (no push) (pull_request) Successful in 51s
PR Checks / Build Pipeline (no push) (pull_request) Successful in 36s
PR Checks / AI Code Review (Claude) (pull_request) Failing after 1m20s
The daily/monthly/annual DAG docstring promised an Invalidate Cache step
that never existed — after a marts rebuild the backend kept serving its
startup-cached (possibly empty) DataFrame until a container restart.
Add a POST /api/admin/reload task at the end of each pipeline DAG,
mirroring the sitemap DAG's admin-call pattern.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-09 21:12:01 +01:00
tudor 84dfc6c1bb Merge pull request 'feat: GIAS classification fields stored as codes, translated in code' (#24) from feat/gias-code-dictionaries into main
Deploy (staging -> E2E gate -> production) / Build Backend (FastAPI) (push) Successful in 19s
Deploy (staging -> E2E gate -> production) / Build Frontend (Next.js) (push) Successful in 52s
Deploy (staging -> E2E gate -> production) / Build Pipeline (Meltano + dbt + Airflow) (push) Successful in 1m16s
Deploy (staging -> E2E gate -> production) / Deploy to Staging (push) Successful in 1s
Deploy (staging -> E2E gate -> production) / E2E Journeys against Staging (push) Failing after 4m47s
Deploy (staging -> E2E gate -> production) / Promote to Production (push) Has been skipped
Reviewed-on: #24
2026-07-09 13:32:43 +00:00
3 changed files with 60 additions and 108 deletions
+11 -52
View File
@@ -262,66 +262,25 @@ assert "NULL AS has_sixth_form" in str(_MAIN_QUERY_NO_SIXTH_FORM), (
"expected replacement of 's.has_sixth_form,' to have taken effect" "expected replacement of 's.has_sixth_form,' to have taken effect"
) )
# Fallback used when marts.dim_school predates the GIAS code-dictionary
# migration (i.e. the nightly dbt pipeline hasn't rebuilt the mart yet on
# this DB, so it still has the old name columns instead of *_code columns).
_MAIN_QUERY_LEGACY_NAMES = str(_MAIN_QUERY)
_LEGACY_NAME_REPLACEMENTS = [
("s.phase_code,", "s.phase,"),
("s.school_type_code,", "s.school_type,"),
(
"s.religious_character_code,",
"s.religious_character AS religious_denomination,",
),
("s.status_code,", "s.status,"),
("s.admissions_policy_code,", "s.admissions_policy,"),
]
for _old, _new in _LEGACY_NAME_REPLACEMENTS:
assert _old in _MAIN_QUERY_LEGACY_NAMES, (
f"expected {_old!r} to be present in _MAIN_QUERY before replacement"
)
_MAIN_QUERY_LEGACY_NAMES = _MAIN_QUERY_LEGACY_NAMES.replace(_old, _new)
_MAIN_QUERY_LEGACY_NAMES = text(_MAIN_QUERY_LEGACY_NAMES)
_GIAS_CODE_COLUMN_NAMES = (
"phase_code",
"school_type_code",
"religious_character_code",
"status_code",
"admissions_policy_code",
)
def load_school_data_as_dataframe() -> pd.DataFrame: def load_school_data_as_dataframe() -> pd.DataFrame:
"""Load all school + KS2 data as a pandas DataFrame.""" """Load all school + KS2 data as a pandas DataFrame."""
try: try:
df = pd.read_sql(_MAIN_QUERY, engine) df = pd.read_sql(_MAIN_QUERY, engine)
except sqlalchemy.exc.ProgrammingError as exc: except sqlalchemy.exc.ProgrammingError as exc:
if any(col in str(exc) for col in _GIAS_CODE_COLUMN_NAMES): if "has_sixth_form" not in str(exc):
logging.getLogger(__name__).warning(
"marts predate the GIAS code migration — falling back to "
"legacy name-column query: %s",
exc,
)
try:
df = pd.read_sql(_MAIN_QUERY_LEGACY_NAMES, engine)
except Exception as exc2:
print(f"Warning: Could not load school data from marts: {exc2}")
return pd.DataFrame()
elif "has_sixth_form" in str(exc):
logging.getLogger(__name__).warning(
"marts.dim_school is missing has_sixth_form (pipeline hasn't "
"rebuilt the mart yet on this DB) — retrying without it: %s",
exc,
)
try:
df = pd.read_sql(_MAIN_QUERY_NO_SIXTH_FORM, engine)
except Exception as exc2:
print(f"Warning: Could not load school data from marts: {exc2}")
return pd.DataFrame()
else:
print(f"Warning: Could not load school data from marts: {exc}") print(f"Warning: Could not load school data from marts: {exc}")
return pd.DataFrame() return pd.DataFrame()
logging.getLogger(__name__).warning(
"marts.dim_school is missing has_sixth_form (pipeline hasn't "
"rebuilt the mart yet on this DB) — retrying without it: %s",
exc,
)
try:
df = pd.read_sql(_MAIN_QUERY_NO_SIXTH_FORM, engine)
except Exception as exc2:
print(f"Warning: Could not load school data from marts: {exc2}")
return pd.DataFrame()
except Exception as exc: except Exception as exc:
print(f"Warning: Could not load school data from marts: {exc}") print(f"Warning: Could not load school data from marts: {exc}")
return pd.DataFrame() return pd.DataFrame()
-52
View File
@@ -42,55 +42,3 @@ def test_missing_code_columns_are_a_noop():
out = translate_gias_code_columns(df) out = translate_gias_code_columns(df)
assert out.iloc[0]["phase"] == "Primary" assert out.iloc[0]["phase"] == "Primary"
assert out.iloc[0]["status"] == "Open" assert out.iloc[0]["status"] == "Open"
def test_load_school_data_survives_premigration_marts(monkeypatch):
"""Real prod state until the nightly pipeline first rebuilds the mart with
the GIAS code columns: marts.dim_school still has the old name columns
(phase, school_type, religious_character, status, admissions_policy)
instead of the new *_code columns. The first query raises UndefinedColumn
on s.phase_code; load_school_data_as_dataframe must retry with the
legacy name-column query rather than swallow the error and return (and
then have load_school_data cache) an empty DataFrame."""
import sqlalchemy.exc
from backend import data_loader
data_loader._df_cache = None
data_loader._df_latest_cache = None
good_df = pd.DataFrame(
[
{
"urn": 1,
"school_name": "Legacy School",
"phase": "Primary",
"school_type": "Academy",
"status": "Open",
}
]
)
calls = []
def fake_read_sql(query, con):
calls.append(query)
if len(calls) == 1:
raise sqlalchemy.exc.ProgrammingError(
"(psycopg2.errors.UndefinedColumn) column s.phase_code does not exist",
None,
None,
)
return good_df.copy()
monkeypatch.setattr(data_loader.pd, "read_sql", fake_read_sql)
try:
df = data_loader.load_school_data_as_dataframe()
finally:
data_loader._df_cache = None
data_loader._df_latest_cache = None
assert len(calls) == 2, "must retry with the legacy name-column query variant"
assert calls[1] is data_loader._MAIN_QUERY_LEGACY_NAMES
assert not df.empty
assert df["phase"].iloc[0] == "Primary"
assert df["status"].iloc[0] == "Open"
+49 -4
View File
@@ -38,6 +38,31 @@ default_args = {
"retry_delay": timedelta(minutes=5), "retry_delay": timedelta(minutes=5),
} }
# The backend caches the marts DataFrame at startup; after any rebuild the
# cache must be invalidated or the API serves stale (or empty) data until the
# container restarts.
INVALIDATE_CACHE_CMD = """
set -e
BACKEND_URL="${BACKEND_URL:-http://backend:80}"
ADMIN_KEY="${ADMIN_API_KEY:-changeme}"
echo "Calling $BACKEND_URL/api/admin/reload ..."
response=$(curl -s -o /tmp/reload_response.json -w "%{http_code}" \\
--connect-timeout 10 --max-time 120 \\
-X POST "$BACKEND_URL/api/admin/reload" \\
-H "X-API-Key: $ADMIN_KEY" \\
-H "Content-Type: application/json")
echo "HTTP status: $response"
cat /tmp/reload_response.json
if [ "$response" != "200" ]; then
echo "ERROR: backend cache reload failed (HTTP $response)"
exit 1
fi
"""
# ── Daily DAG (GIAS + downstream) ────────────────────────────────────── # ── Daily DAG (GIAS + downstream) ──────────────────────────────────────
@@ -91,7 +116,12 @@ print(f'Validation passed: {{count}} GIAS rows')
bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py", bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py",
) )
extract_group >> validate_raw >> dbt_build >> sync_typesense invalidate_cache = BashOperator(
task_id="invalidate_cache",
bash_command=INVALIDATE_CACHE_CMD,
)
extract_group >> validate_raw >> dbt_build >> sync_typesense >> invalidate_cache
# ── Monthly DAG (Ofsted) ─────────────────────────────────────────────── # ── Monthly DAG (Ofsted) ───────────────────────────────────────────────
@@ -121,7 +151,12 @@ with DAG(
bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py", bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py",
) )
extract_ofsted >> dbt_build_ofsted >> sync_typesense_ofsted invalidate_cache_ofsted = BashOperator(
task_id="invalidate_cache",
bash_command=INVALIDATE_CACHE_CMD,
)
extract_ofsted >> dbt_build_ofsted >> sync_typesense_ofsted >> invalidate_cache_ofsted
# ── Annual DAG (EES: KS2, KS4, Census, Admissions) ─────────────────── # ── Annual DAG (EES: KS2, KS4, Census, Admissions) ───────────────────
@@ -153,7 +188,12 @@ with DAG(
bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py", bash_command=f"cd {PIPELINE_DIR} && python scripts/sync_typesense.py",
) )
extract_ees_group >> dbt_build_ees >> sync_typesense_ees invalidate_cache_ees = BashOperator(
task_id="invalidate_cache",
bash_command=INVALIDATE_CACHE_CMD,
)
extract_ees_group >> dbt_build_ees >> sync_typesense_ees >> invalidate_cache_ees
# ── Annual DAG (IDACI Deprivation) ──────────────────────────────────── # ── Annual DAG (IDACI Deprivation) ────────────────────────────────────
@@ -178,4 +218,9 @@ with DAG(
bash_command=f"cd {PIPELINE_DIR}/transform && {DBT_BIN} build --profiles-dir . --target production --select stg_idaci+ fact_deprivation+", bash_command=f"cd {PIPELINE_DIR}/transform && {DBT_BIN} build --profiles-dir . --target production --select stg_idaci+ fact_deprivation+",
) )
extract_idaci >> dbt_build_idaci invalidate_cache_idaci = BashOperator(
task_id="invalidate_cache",
bash_command=INVALIDATE_CACHE_CMD,
)
extract_idaci >> dbt_build_idaci >> invalidate_cache_idaci