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17b9ae08eb
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> |
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| .. | ||
| local_handler | ||
| Dockerfile | ||
| errors.py | ||
| handler.py | ||
| modelling_e2e_trigger_body.py | ||
| requirements.txt | ||