fastapifromfrictionless 0.2.17

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.scaffolding 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, the generated app exposes the following endpoints (replace {resource} with the schema name, e.g. sensor, location, permit):

Method	Path	Description
POST	/{resource}	Create a record
POST	/{resource}s/bulk	Create many records in a single request (one commit)
GET	/{resource}/all	List all (paginated: ?offset=0&limit=100)
GET	/{resource}/recent	Most recent records (?limit=10)
GET	/{resource}/{id}	Get one by primary key
PATCH	/{resource}/{id}	Update a record
DELETE	/{resource}/{id}	Delete a record
GET	/{resource}/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	(required)	All requests require X-API-Key: <value> header. App refuses to start if unset unless ALLOW_NO_AUTH=true is also set.
ALLOW_NO_AUTH	(unset)	Set to true to start the app without API_KEY (dev only — logs a warning, do not use in production)
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.runtime import (
    empty_excel, create_package, update_api_from_package, dump_to_excel
)

# 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
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 container deployment lives in podman/. It spins up three containers — PostGIS database, pgAdmin web UI, and the generated FastAPI app — and works with Docker Desktop, Podman, or any docker compose-compatible tool on Linux, macOS, and Windows.

Multiple deployments can run simultaneously on the same machine by assigning each a unique SUBNET_BASE and port set (API_PORT, PGADMIN_PORT, DB_PORT).

The API image uses a two-stage build: Stage 1 installs the code-generator and produces FastAPI source from your schemas; Stage 2 installs only the runtime dependencies and serves the generated app. The pip install layer is cached, so schema-only rebuilds are fast.

cd podman/
cp .env.example .env          # set ports, passwords, and settings
# add *.schema.yaml files to schemas/
docker compose up -d          # or: podman-compose up -d

After schema changes, re-run docker compose build api then docker compose up -d api. To stop and clean up, run docker compose down.

See podman/README.md for full instructions.