c1d00dcdc4
Remove distributed R2/Iceberg/SSH pipeline architecture in favor of
local subprocess execution with NVMe storage. Landing data backed up
to R2 via rclone timer.
- Strip Iceberg catalog, httpfs, boto3, paramiko, prefect, pyarrow
- Pipelines run via subprocess.run() with bounded timeouts
- Extract writes to {LANDING_DIR}/psd/{year}/{month}/{etag}.csv.gzip
- SQLMesh reads LANDING_DIR variable, writes to DUCKDB_PATH
- Delete unused provider stubs (ovh, scaleway, oracle)
- Add rclone systemd timer for R2 backup every 6h
- Update supervisor to run pipelines with env vars
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
103 lines
4.5 KiB
Markdown
103 lines
4.5 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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Materia is a commodity data analytics platform (product: **BeanFlows.coffee**) for coffee traders. It's a uv workspace monorepo with three packages: extraction (USDA PSD data), SQL transformation (SQLMesh + DuckDB), and a CLI for worker management and local pipeline execution.
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## Commands
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```bash
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# Install dependencies
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uv sync
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# Lint & format
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ruff check . # Check
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ruff check --fix . # Auto-fix
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ruff format . # Format
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# Tests
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uv run pytest tests/ -v --cov=src/materia # CLI/Python tests
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cd transform/sqlmesh_materia && uv run sqlmesh test # SQLMesh model tests
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# Run a single test
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uv run pytest tests/test_cli.py::test_name -v
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# Extract data
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LANDING_DIR=data/landing uv run extract_psd
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# SQLMesh (from repo root)
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uv run sqlmesh -p transform/sqlmesh_materia plan # Plans to dev_<username> by default
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uv run sqlmesh -p transform/sqlmesh_materia plan prod # Production
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uv run sqlmesh -p transform/sqlmesh_materia test # Run model tests
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uv run sqlmesh -p transform/sqlmesh_materia format # Format SQL
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# CLI
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uv run materia pipeline run extract|transform
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uv run materia pipeline list
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uv run materia worker create|destroy|list
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uv run materia secrets get
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```
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## Architecture
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**Workspace packages** (`pyproject.toml` → `tool.uv.workspace`):
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- `extract/psdonline/` — Downloads USDA PSD Online data, normalizes ZIP→gzip CSV, writes to local landing directory
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- `transform/sqlmesh_materia/` — 4-layer SQL transformation pipeline (local DuckDB)
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- `src/materia/` — CLI (Typer) for pipeline execution, worker management, secrets
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- `web/` — Future web frontend
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**Data flow:**
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```
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USDA API → extract → /data/materia/landing/psd/{year}/{month}/{etag}.csv.gzip
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→ rclone cron syncs landing/ to R2
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→ SQLMesh raw → staging → cleaned → serving → /data/materia/lakehouse.duckdb
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→ Web app reads lakehouse.duckdb (read-only)
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```
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**SQLMesh 4-layer model structure** (`transform/sqlmesh_materia/models/`):
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1. `raw/` — Immutable source reads (read_csv from landing directory)
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2. `staging/` — Type casting, lookup joins, basic cleansing
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3. `cleaned/` — Business logic, pivoting, integration
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4. `serving/` — Analytics-ready facts, dimensions, aggregates
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**CLI modules** (`src/materia/`):
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- `cli.py` — Typer app with subcommands: worker, pipeline, secrets, version
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- `workers.py` — Hetzner cloud instance management (for ad-hoc compute)
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- `pipelines.py` — Local subprocess pipeline execution with bounded timeouts
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- `secrets.py` — Pulumi ESC integration for environment secrets
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**Infrastructure** (`infra/`):
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- Pulumi IaC for Cloudflare R2 buckets and Hetzner compute
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- Supervisor systemd service for always-on orchestration (pulls git, runs pipelines)
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- rclone systemd timer for landing data backup to R2
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## Coding Philosophy
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Read `coding_philosophy.md` for the full guide. Key points:
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- **Simple, procedural code** — Functions over classes, no inheritance hierarchies, no "Manager" patterns
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- **Data-oriented** — Use dicts/lists/tuples, not objects hiding data behind getters
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- **Keep logic in SQL** — Let DuckDB do the heavy lifting, don't pull data into Python to transform it
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- **Build minimum that works** — No premature abstraction, three examples before generalizing
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- **Explicit over implicit** — No framework magic, no metaprogramming, no hidden behavior
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- **Question every dependency** — Can you write it simply yourself? Are you using 5% of a large framework?
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## Key Configuration
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- **Python 3.13** (`.python-version`)
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- **Ruff**: double quotes, spaces, E501 ignored (formatter handles line length)
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- **SQLMesh**: DuckDB dialect, `@daily` cron, start date `2025-07-07`, default env `dev_{{ user() }}`
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- **Storage**: Local NVMe (`LANDING_DIR`, `DUCKDB_PATH`), R2 for backup via rclone
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- **Secrets**: Pulumi ESC (`esc run beanflows/prod -- <cmd>`)
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- **CI**: GitLab CI (`.gitlab/.gitlab-ci.yml`) — runs pytest and sqlmesh test on push/MR
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- **Pre-commit hooks**: installed via `pre-commit install`
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## Environment Variables
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `LANDING_DIR` | `data/landing` | Root directory for extracted landing data |
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| `DUCKDB_PATH` | `local.duckdb` | Path to the DuckDB lakehouse database |
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