Captures the grill-with-docs decisions for the full-SAP-17.1 mapper completion:
rebaseline-to-10.2 (restores Rebaselining trigger (a)), calc-affecting fields
coupled with the sap_version flip, and growing Elmhurst-anchored validation.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Route the Google Solar client through the shared call_with_retry with
full jitter (de-synchronises the 32 concurrent containers per Google's
"avoid synchronised requests" guidance), honouring Retry-After, a 60s max
backoff (rides out the 600 QPM per-minute window), and 6 bounded retries.
429/5xx/transport errors are transient; other 4xx propagate immediately;
404-entity-not-found stays BuildingInsightsNotFoundError. On exhaustion a
TransientHttpError surfaces so the subtask fails and is re-triggered (no
silent degrade).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Group pending property IDs by postcode and pack them into ~BATCH_SIZE
messages, never splitting a postcode, so each SQS message drives one
batched modelling_e2e Lambda invocation. Adds a completed-since skip
filter and a properties cap.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Even after batching the data writes, the handler still wrote to the DB per
property through the orchestrator's SubTask bookkeeping: create + start +
complete each self-committed, and _cascade re-listed every sibling and re-saved
the parent on every transition — ~5 writes per property plus an O(N^2) cascade.
- TaskOrchestrator.run_subtasks: create all children in one INSERT, run each
(failures isolated per child), then persist all terminal states in one bulk
save and cascade the parent once. Children go WAITING -> terminal; the
transient IN_PROGRESS row is never written.
- SubTaskRepository.create_many / save_many (bulk INSERT / bulk fetch + update).
- _cascade short-circuits when the Task is already FAILED (terminal) — skips the
sibling roll-up entirely.
- modelling_e2e handler fans out via run_subtasks instead of per-property
create_child_subtask + run_subtask.
Per N-property batch the SubTask bookkeeping drops from ~5N writes + an O(N^2)
cascade to ~2 writes + 1 cascade.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The handler fired ~2+2N read round-trips and N+N write transactions per
SQS batch, pinning RDS CPU under ~32 concurrent containers on pool_size=1.
Reads: merge the duplicate property query and add overrides_for_many /
SolarRepository.get_many so overrides, solar, and property rows each load
in one query (2+2N -> 3).
Writes: buffer each modelled property's persistence intent in memory
(_PropertyWrite) during the loop, then flush the whole batch in one
PostgresUnitOfWork with a single commit, and run the baseline orchestrator
once for all written ids (N+N -> 2 transactions). Per-property modelling
failures stay isolated in the loop; the batch write is all-or-nothing and
retried via SQS (saves are idempotent upserts).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
An Economy-7 storage dwelling now prices heating at the 0.20-day/0.80-
night blend through cert -> calculator -> bill, instead of raising
UnpricedFuel and aborting the modelling_e2e batch.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Surface the hot-water (Table 13 / HP-DHW), secondary (direct-acting),
main-2 and ALL_OTHER_USES High-Rate Fractions on CalculatorInputs from
the same Table 12a helpers the SAP cost path uses, so Bill Derivation's
day/night split matches the rating's exactly.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The invocation is architecturally one DB connection at a time (read up front,
sequential write Units of Work, overrides resolved on the unit's own session).
Keep that as the design intent, but back it with NullPool instead of a fixed
pool_size=1 pool: each checkout opens a fresh connection and closes it on return,
so there is no pool slot to exhaust.
The difference is the failure mode if a path ever regresses and holds two
Sessions at once. A pool_size=1/max_overflow=0 pool turns that into a hard
30s dead-lock that fails the whole invocation ("QueuePool limit of size 1
overflow 0 reached, connection timed out"). NullPool instead opens a transient
second connection for that instant and the Lambda keeps running. The design
target stays one connection; NullPool just keeps it alive if we slip.
The single-connection invariant itself is still enforced in the Unit of Work
(overrides read on the unit's own session) and pinned by the regression test,
which uses its own strict pool_size=1 engine so it asserts the architecture
regardless of the production NullPool choice.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The modelling_e2e Lambda runs on a single-connection pool (pool_size=1,
max_overflow=0) so one invocation uses one Postgres connection. But re-hydrating
a Property through PostgresUnitOfWork resolved its Landlord Overrides through a
PropertyOverridesPostgresReader built from the unit's session *factory* — which
opens a brand-new Session per call. While the unit's own read transaction was
still open (PropertyPostgresRepository.get_many had checked out the connection),
that second Session asked the pool for a second connection, found none, and timed
out after 30s:
QueuePool limit of size 1 overflow 0 reached, connection timed out, timeout 30.00
The baseline stage (PropertyBaselineOrchestrator.run -> uow.property.get_many ->
landlord overrides) hit this on every invocation.
Read the overrides on the unit's OWN session instead. property_overrides is
committed reference data, so reading it inside the unit's transaction sees the
same rows and keeps the invocation on one connection. Extract the query/mapping
into a shared helper and add OpenSessionPropertyOverridesReader (reads on a
caller-owned, already-open session without closing it) for the unit; the
standalone PropertyOverridesPostgresReader still opens its own short session for
use outside a unit.
Regression test pins the invariant with a real pool_size=1/max_overflow=0 engine:
without the fix it reproduces the exact QueuePool timeout.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The modelling_e2e Lambda held up to ~4 concurrent Postgres connections per
invocation: the read Session stayed open across the write loop (the catalogue
was queried live and overrides were read per-Property), each per-Property Unit
of Work opened a second, and the TaskOrchestrator ran on its own NullPool
engine — so the pool needed pool_size=2 + max_overflow=1 just for the modelling
work. Under 32 concurrent containers that approached RDS max_connections.
Restructure the handler to read everything up front — overrides, Scenario, an
in-memory catalogue snapshot, and stored Solar — through one short-lived read
Session, close it, then write each Property in a sequential Unit of Work. The
read and write Sessions no longer overlap, so the engine drops to pool_size=1,
max_overflow=0. Fold the orchestrator onto the same pooled engine: its repos
commit on every save, releasing the connection between bookkeeping calls, so it
holds none during the work. One invocation now uses one connection at a time.
The catalogue becomes a per-invocation snapshot (MaterialSnapshotRepository),
mirroring ProductPostgresRepository.get exactly — same drift mapping, lowest-id
pick, and errors — but priced after the Session closes. Transaction isolation
is preserved: per-Property writes and orchestrator bookkeeping keep their own
independent transactions, just drawn sequentially from a single connection.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Resolves the ADR-0014 off-peak deferral: off-peak is a property of the
meter (every electric end use splits day/night by its own High-Rate
Fraction, a calculator output), not a per-end-use fuel. Adds the
Off-Peak Meter and High-Rate Fraction glossary terms.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
