de71f9abb6
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> |
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| .devcontainer | ||
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| .idea | ||
| .vscode | ||
| applications | ||
| asset_list | ||
| backend | ||
| backlog | ||
| datatypes | ||
| deployment/terraform | ||
| docs | ||
| domain | ||
| epr_data_exports | ||
| etl | ||
| harness | ||
| infrastructure | ||
| model_data/requirements | ||
| orchestration | ||
| recommendations | ||
| repositories | ||
| sap worksheets | ||
| scripts | ||
| sfr/principal_pitch | ||
| survey_report | ||
| tests | ||
| utilities | ||
| utils | ||
| .coveragerc | ||
| .dockerignore | ||
| .gitignore | ||
| __init__.py | ||
| ara_backend_design.md | ||
| BaseUtility.py | ||
| CLAUDE.md | ||
| conftest.py | ||
| CONTEXT.md | ||
| devcontainer.sh | ||
| Dockerfile.test | ||
| Dockerfile.test.dockerignore | ||
| Makefile | ||
| MEMORY.md | ||
| next_claude_prompt.txt | ||
| P960-0001-001431-2.pdf | ||
| package-lock.json | ||
| package.json | ||
| playground.py.local-backup | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| pytest.ini | ||
| README.md | ||
| run_lambda_local.sh | ||
| serverless.yml | ||
| Summary_001431-3.pdf | ||
| test.requirements.txt | ||
| tox.ini | ||
| UBIQUITOUS_LANGUAGE.md | ||
Model Repository
This repository contains the code pertaining to the development of the data science and machine learning products being utilised by Hestia.
The different folders in this repository relate to services that can be used independently, or can be imported and used as part of a larger application
Getting Started
Prerequisites
Dev Container Setup
This repo uses a Docker Compose-based dev container. The model-backend service joins a shared-dev Docker network so it can communicate with other local services (e.g. a frontend container) running on your machine.
VS Code users: The initializeCommand in devcontainer.json creates the shared-dev network automatically before the container starts. No manual step required — just open the repo and select Reopen in Container.
Non-VS Code / CI workflows: Run the following once before starting the container:
make dev-setup
This is idempotent and safe to re-run if the network already exists.
Folders
backend/
This folder contains the code for the fastapi backend service, which provides an interface to much of the functionality in this repository, for the frontend
model_data/
This folder contains related to the reading and preparation of assessment model data, including pulling out epc attributes
Testing
All tests can be run, against the configuration in pytest.ini running
pytest
This will run the complete panel of tests and report on coverage in the locations specified by the pytest.ini file.
To run tests in a specific service, e.g. inside of model_data, simply run
pytest --cov-config=model_data/.coveragerc --cov=model_data
This will produce the test results and coverage reports