fastapifromfrictionless 0.2.1

Creating FastAPIs from Frictionless Data Packages

fastapi-from-frictionless

Status: Work in Progress — API and generated output are subject to change.

Overview

fastapifromfrictionless is a Python scaffolding tool that reads Frictionless Data Package schema files and generates a fully-functional FastAPI + SQLModel application — including models, CRUD endpoints, and dynamic query support — with no hand-written boilerplate.

The driving goal is to bridge the gap between familiar flat-file workflows (Excel workbooks, CSV files, Frictionless packages) and a production-grade relational database with a queryable REST API. Data stewards continue working in Excel; the package handles ingestion, validation, and API synchronization behind the scenes.

Core capabilities:

• Generate models.py, app.py, and database.py from *.schema.yaml files
• Automatic SQLModel class hierarchy per schema (base, table, create, update, public, public-with-relations)
• Full CRUD + pagination + recent-records endpoints per resource
• Excel workbook as a data-entry interface — create or update API records from a .xlsx file
• Frictionless package wraps the workbook for field-level validation before ingestion
• CLI entry point: fastapifromfrictionless generate <schema-folder>

Requirements

• Python >= 3.10
• frictionless
• FastAPI
• SQLModel
• SQLAlchemy
• fastapi-querybuilder
• jinja2
• requests
• pandas
• xlsxwriter

An ASGI server such as uvicorn is required to run the generated application.

Installation

pip install -e .

Quick Start

1. Generate application files from schemas

Place *.schema.yaml files in a folder (see doc/data/ for examples), then run:

fastapifromfrictionless generate path/to/schemas --output path/to/output

Or from Python:

from fastapifromfrictionless.load import build_database

build_database(schema_folder="path/to/schemas", db_filename="app.db")

2. Start the generated API

cd path/to/output
uvicorn app:app --reload

3. Generated endpoints

For each schema resource (e.g. sensor), the generated app exposes:

Method	Path	Description
POST	/sensor	Create a record
GET	/sensor/all	List all (paginated: ?offset=0&limit=100)
GET	/sensor/recent	Most recent records (?limit=10)
GET	/sensor/{id}	Get one by primary key
PATCH	/sensor/{id}	Update a record
DELETE	/sensor/{id}	Delete a record
GET	/sensor/query	Dynamic filter query (FK schemas only)
GET	/excel/export	Download all data as .xlsx
POST	/excel/import	Upload and sync an .xlsx workbook

4. Configuration via environment variables

Variable	Default	Description
DATABASE_URL	sqlite:///database.db	Database connection URL (overrides SQLite default)
ALLOWED_ORIGINS	*	Comma-separated CORS allowed origins
API_KEY	(unset)	If set, all requests require X-API-Key: <value> header
SCHEMA_FOLDER	.	Path to *.schema.yaml files (used by Excel import/export)
API_URL	http://localhost:8000	Base URL of this app (used by Excel export)

5. Excel data workflow

from fastapifromfrictionless.load import empty_excel, create_package, update_api_from_package

# Create a blank workbook with one sheet per schema
empty_excel(schema_folder="path/to/schemas", output_filepath="data.xlsx")

# Fill in data, then validate and sync to the API
create_package(folder="path/to/schemas", filename="data.xlsx")
update_api_from_package(api_url="http://localhost:8000", package_file="data.package.yaml")

# Or export all current API data to Excel
from fastapifromfrictionless.load import dump_to_excel
dump_to_excel(api_url="http://localhost:8000", schema_folder="path/to/schemas", output_filepath="export.xlsx")

6. CLI reference

fastapifromfrictionless generate <schema_folder> [options]

Options:
  --output DIR      Output directory (default: current directory)
  --db FILENAME     SQLite database filename (default: database.db)
  --dry-run         Print generated code to stdout without writing files
  --no-models       Skip generating models.py
  --no-app          Skip generating app.py
  --no-db           Skip generating database.py

See doc/quickstart.ipynb for a step-by-step walkthrough of the full deployment workflow.

Deployment

A ready-to-use Podman deployment lives in podman/. It spins up a PostGIS database and the generated FastAPI app as two containers — no manual setup beyond dropping schemas and filling in a .env file:

cd podman/
cp .env.example .env          # fill in passwords
# add *.schema.yaml files to schemas/
podman-compose up -d

See podman/README.md for full instructions.

Roadmap

Items are grouped by priority. Checked items are complete.

Foundation

• Generate SQLModel class hierarchies from Frictionless schemas
• Generate FastAPI CRUD + query endpoints per resource
• Generate database.py with SQLite engine setup
• Excel workbook as data-entry interface (create and update)
• Frictionless package wrapping for field-level validation
• Export API data back to Excel (dump all records to workbook)
• Validate schemas before code generation; surface clear error messages on malformed input

Code Quality & Reliability

• Add a test suite (unit tests for generators, integration tests for generated app)
• Add CI pipeline (lint, type-check, tests on push)
• Enforce type hints throughout; run mypy in CI
• Replace raw string generation with a templating engine (e.g. Jinja2) for maintainability
• Structured logging with configurable verbosity

Production Readiness

• Async database support (replace sync SQLModel sessions with async SQLAlchemy)
• Database migrations via Alembic instead of create_all
• Configuration management — accept settings via environment variables or a config file (database URL, allowed origins, etc.)
• CORS and security headers in generated app.py
• API key / token authentication (FastAPI security)
• Pagination on list endpoints
• Proper HTTP error responses with consistent JSON error bodies

Developer Experience

• CLI entry point (fastapifromfrictionless generate <schema-folder>) so users do not need to write Python
• Dry-run / preview mode that prints generated code without writing files
• Configurable output — opt in/out of specific endpoint types, choose database backend
• Package versioning and automated releases (GitHub Actions → PyPI)
• Expand documentation and examples in doc/

Optional / Future

• Support for PostgreSQL and other SQLAlchemy-compatible backends
• Podman/container deployment (compose.yaml + Dockerfile + PostGIS)
• Auto-generated front-end form from schema fields
• DrawIO ERD → Frictionless schema converter
• Default query routes for common patterns derived from schema metadata
• GET/POST file endpoints for uploading and downloading the Excel workbook directly via the API