Postgres Fan-Out Flow

End-to-end example: examples/postgres_fanout_flow.py

This flow demonstrates: - Creating tables (via SQLAlchemy if installed, or raw SQL fallback) - Seeding data - Dynamic fan-out to query per-category stats concurrently - Upserting an aggregated summary - Reading back the results

Install

# Postgres connector
poetry install -E connectors-postgres
# Optional SQLAlchemy helpers (engine/session/ORM)
poetry install -E connectors-sqlalchemy
# or everything
poetry install -E connectors-all

Environment (profile: EXAMPLE)

export AUTO_WORKFLOW_CONNECTORS_POSTGRES_EXAMPLE__DSN="postgresql://postgres:postgres@127.0.0.1:5432/postgres"

Tip: In CI and local dev with Docker, the repo includes test_helpers/docker-compose.yml and a wait-for-postgres.sh script.

Run

poetry run python examples/postgres_fanout_flow.py

Expected behavior: - Creates etl_input and etl_summary tables if missing - Seeds sample rows into etl_input - Computes aggregates per category using fan-out - Upserts into etl_summary and prints the summary rows

If SQLAlchemy is installed, the example will use ORM for schema and session; otherwise, it falls back to raw SQL.