# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Project Overview

Materia is a commodity data analytics platform (product: **BeanFlows.coffee**) for coffee traders. It's a uv workspace monorepo: multiple extraction packages, a SQL transformation pipeline, a web app, and a CLI for local pipeline execution.

## Commands

```bash
# Install dependencies
uv sync --all-packages

# Lint & format
ruff check .            # Check
ruff check --fix .      # Auto-fix
ruff format .           # Format

# Tests
uv run pytest tests/ -v --cov=src/materia         # CLI/Python tests
cd transform/sqlmesh_materia && uv run sqlmesh test  # SQLMesh model tests

# Run a single test
uv run pytest tests/test_cli.py::test_name -v

# SQLMesh (from repo root)
uv run sqlmesh -p transform/sqlmesh_materia plan              # Plans to dev_<username> by default
uv run sqlmesh -p transform/sqlmesh_materia plan prod          # Production
uv run sqlmesh -p transform/sqlmesh_materia test               # Run model tests
uv run sqlmesh -p transform/sqlmesh_materia format             # Format SQL

# CLI
uv run materia pipeline run extract|transform|export_serving
uv run materia pipeline list
uv run materia secrets list
uv run materia secrets test

# Supervisor status (production)
uv run python src/materia/supervisor.py status

# CSS (Tailwind)
make css-build     # one-shot build
make css-watch     # watch mode

# Secrets
make secrets-decrypt-dev   # decrypt .env.dev.sops → .env (local dev)
make secrets-decrypt-prod  # decrypt .env.prod.sops → .env
make secrets-edit-dev      # edit dev secrets in $EDITOR
make secrets-edit-prod     # edit prod secrets in $EDITOR
```

## Architecture

**Workspace packages** (`pyproject.toml` → `[tool.uv.workspace]`):
- `extract/extract_core/` — Shared extraction utilities: state tracking (SQLite), HTTP helpers, atomic file writes
- `extract/psdonline/` — USDA PSD Online data (ZIP → gzip CSV)
- `extract/cftc_cot/` — CFTC Commitments of Traders (weekly)
- `extract/coffee_prices/` — KC=F futures prices
- `extract/ice_stocks/` — ICE warehouse stocks + aging reports
- `extract/openmeteo/` — Daily weather for 12 coffee-growing regions (Open-Meteo ERA5, no API key)
- `transform/sqlmesh_materia/` — 3-layer SQL transformation pipeline (DuckDB)
- `src/materia/` — CLI (Typer): pipeline execution, secrets, version
- `web/` — Quart + HTMX web app (BeanFlows.coffee dashboard)

**Data flow:**
```
USDA API     → extract → /data/materia/landing/psd/{year}/{month}/{etag}.csv.gzip
CFTC API     → extract → /data/materia/landing/cot/{year}/{date}.csv.gz
Yahoo/prices → extract → /data/materia/landing/prices/{symbol}/{date}.json.gz
ICE API      → extract → /data/materia/landing/ice_stocks/{date}.csv.gz
Open-Meteo   → extract → /data/materia/landing/weather/{location_id}/{year}/{date}.json.gz
         → rclone timer syncs landing/ to R2 every 6 hours
         → SQLMesh staging → foundation → serving → /data/materia/lakehouse.duckdb
         → export_serving pipeline → /data/materia/analytics.duckdb (web app)
         → Web app reads analytics.duckdb (read-only, per-thread)
```

**SQLMesh 3-layer model structure** (`transform/sqlmesh_materia/models/`):
1. `staging/` — Type casting, lookup joins, basic cleansing (reads landing directly)
2. `foundation/` — Business logic, pivoting, **conformed dimensions** (ontology), facts
3. `serving/` — Analytics-ready aggregates for the web app

**Foundation layer is the ontology.** `dim_commodity` conforms identifiers across all sources:
- Each row = one commodity (e.g. Arabica coffee)
- Columns: `usda_commodity_code`, `cftc_contract_market_code`, `ice_stock_report_code`, `ticker` (KC=F), etc.
- New data sources add columns to existing dims, not new tables
- Facts join to dims via surrogate keys (MD5 hash keys generated in staging)

**Two-DuckDB architecture:**
- `lakehouse.duckdb` (`DUCKDB_PATH`) — SQLMesh exclusive write; never opened by web app
- `analytics.duckdb` (`SERVING_DUCKDB_PATH`) — read-only serving copy for web app
- Why not `serving.duckdb`: DuckDB derives catalog name from filename stem — "serving" would collide with the "serving" schema inside
- `export_serving` pipeline copies `serving.*` tables via Arrow + atomic rename after each transform
- Web app uses per-thread connections (`threading.local`) with inode-based reopen on rotation

**Extraction pattern** — one workspace package per data source:
- All packages depend on `extract_core` (shared state tracking, HTTP, file writes)
- Landing zone is immutable and content-addressed: `{LANDING_DIR}/{source}/{partitions}/{hash}.ext`
- State tracked in SQLite at `{LANDING_DIR}/.state.sqlite` (WAL mode, OLTP — not DuckDB)
- Query state: `sqlite3 data/landing/.state.sqlite "SELECT * FROM extraction_runs ORDER BY run_id DESC LIMIT 20"`

**Adding a new data source:**
```bash
# Create package
uv init --package extract/new_source
uv add --package new_source extract-core niquests

# Add entry function in extract/new_source/src/new_source/execute.py
# Register in infra/supervisor/workflows.toml
# Add staging + foundation models in transform/sqlmesh_materia/models/
```

**Supervisor** (`src/materia/supervisor.py`):
- Croniter-based scheduling with named presets: `hourly`, `daily`, `weekly`, `monthly`
- Workflow registry: `infra/supervisor/workflows.toml`
- Dependency-wave execution: independent workflows run in parallel (ThreadPoolExecutor)
- Each tick: git pull (tag-based) → due extractors → SQLMesh → export_serving → web deploy if changed
- Crash-safe: systemd `Restart=always` + 10-minute backoff on tick failure

**CI/CD** (`.gitlab/.gitlab-ci.yml`) — pull-based, no SSH:
- `test` stage: pytest, sqlmesh test, web pytest
- `tag` stage: creates `v${CI_PIPELINE_IID}` tag after tests pass (master branch only)
- Supervisor polls for new tags every 60s, checks out latest, runs `uv sync`
- No SSH keys or deploy credentials in CI — only `CI_JOB_TOKEN` (built-in)

**CLI modules** (`src/materia/`):
- `cli.py` — Typer app with subcommands: pipeline, secrets, version
- `pipelines.py` — Local subprocess pipeline execution with bounded timeouts
- `secrets.py` — SOPS+age integration (decrypts `.env.prod.sops`)

**Infrastructure** (`infra/`):
- Pulumi IaC for Cloudflare R2 buckets
- Python supervisor + systemd service
- rclone systemd timer for landing data backup to R2
- `setup_server.sh` — one-time server init (age keypair generation)
- `bootstrap_supervisor.sh` — full server setup from scratch

## Secrets management (SOPS + age)

| File | Purpose |
|------|---------|
| `.env.dev.sops` | Dev defaults (safe values, local paths) |
| `.env.prod.sops` | Production secrets (encrypted) |
| `.sops.yaml` | Maps file patterns to age public keys |
| `age-key.txt` | Server age keypair (gitignored, generated by `setup_server.sh`) |

```bash
make secrets-decrypt-dev   # decrypt dev secrets → .env (local dev)
make secrets-edit-prod     # edit prod secrets in $EDITOR
```

`web/deploy.sh` auto-decrypts `.env.prod.sops` → `web/.env` on each deploy.
`src/materia/secrets.py` decrypts on-demand via subprocess call to `sops`.

**Adding the server key (new server setup):**
1. Run `infra/setup_server.sh` on the server — prints the age public key
2. Add the public key to `.sops.yaml` on your workstation
3. Run `sops updatekeys .env.prod.sops`
4. Commit + push

## uv workspace management

```bash
# Install everything (run from repo root)
uv sync --all-packages --all-groups

# Create a new extraction package
uv init --package extract/new_source
uv add --package new_source extract-core niquests

# Add a dependency to an existing package
uv add --package materia croniter
uv add --package beanflows duckdb

# Run a command in a specific package context
uv run --package new_source python -c "import new_source"
```

Always use `uv` CLI to manage dependencies — never edit `pyproject.toml` manually for dependency changes.

## Coding Philosophy

Read `coding_philosophy.md` for the full guide. Key points:

- **Simple, procedural code** — Functions over classes, no inheritance hierarchies, no "Manager" patterns
- **Data-oriented** — Use dicts/lists/tuples, not objects hiding data behind getters
- **Keep logic in SQL** — Let DuckDB do the heavy lifting, don't pull data into Python to transform it
- **Build minimum that works** — No premature abstraction, three examples before generalizing
- **Explicit over implicit** — No framework magic, no metaprogramming, no hidden behavior
- **Question every dependency** — Can you write it simply yourself? Are you using 5% of a large framework?

## Key Configuration

- **Python 3.13** (`.python-version`)
- **Ruff**: double quotes, spaces, E501 ignored (formatter handles line length)
- **SQLMesh**: DuckDB dialect, `@daily` cron, start date `2025-07-07`, default env `dev_{{ user() }}`
- **Storage**: Local NVMe (`LANDING_DIR`, `DUCKDB_PATH`, `SERVING_DUCKDB_PATH`), R2 for backup via rclone
- **Secrets**: SOPS + age (`.env.*.sops` files, Makefile targets)
- **CI**: GitLab CI — test → tag (pull-based deploy, no SSH)
- **Pre-commit hooks**: installed via `pre-commit install`

## Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `LANDING_DIR` | `data/landing` | Root directory for extracted landing data |
| `DUCKDB_PATH` | `local.duckdb` | Path to the SQLMesh lakehouse database (exclusive write) |
| `SERVING_DUCKDB_PATH` | `analytics.duckdb` | Path to the serving DB (read by web app) |
| `ALERT_WEBHOOK_URL` | _(empty)_ | ntfy.sh URL for supervisor failure alerts |
| `SUPERVISOR_GIT_PULL` | _(unset)_ | Set to any value to enable tag-based git pull in supervisor |