FraiseQL v2 - Python Schema Authoring

Python decorators for authoring FraiseQL schemas

This package provides Python decorators to define GraphQL schemas that are compiled by the FraiseQL Rust engine.

Architecture

Python Decorators → schema.json → fraiseql-cli compile → schema.compiled.json → Rust Runtime

Important: This package is for schema authoring only. It does NOT provide runtime execution. The compiled schema is executed by the standalone Rust server.

Installation

pip install fraiseql

Quick Start

import fraiseql

# Define a GraphQL type
@fraiseql.type
class User:
    id: int
    name: str
    email: str
    created_at: str

# Define a query
@fraiseql.query(sql_source="v_user")
def users(limit: int = 10) -> list[User]:
    """Get all users with pagination."""
    pass

# Define a mutation
@fraiseql.mutation(sql_source="fn_create_user", operation="CREATE")
def create_user(name: str, email: str) -> User:
    """Create a new user."""
    pass

# Export schema to JSON
if __name__ == "__main__":
    fraiseql.export_schema("schema.json")

Compile Schema

# Compile schema.json to optimized schema.compiled.json
fraiseql-cli compile schema.json -o schema.compiled.json

# Start server with compiled schema
fraiseql-server --schema schema.compiled.json

Features

Type-safe: Python type hints map to GraphQL types
Database-backed: Queries map to SQL views, mutations to functions
Compile-time: All validation happens at compile time, zero runtime overhead
No FFI: Pure JSON output, no Python-Rust bindings needed
Analytics: Fact tables and aggregate queries for OLAP workloads

Analytics / Fact Tables

FraiseQL supports high-performance analytics via fact tables:

import fraiseql

# Define a fact table
@fraiseql.fact_table(
    table_name="tf_sales",
    measures=["revenue", "quantity", "cost"],
    dimension_paths=[
        {"name": "category", "json_path": "data->>'category'", "data_type": "text"},
        {"name": "region", "json_path": "data->>'region'", "data_type": "text"}
    ]
)
@fraiseql.type
class Sale:
    id: int
    revenue: float  # Measure (aggregatable)
    quantity: int   # Measure
    cost: float     # Measure
    customer_id: str  # Denormalized filter (indexed)
    occurred_at: str  # Denormalized filter (indexed)

# Define an aggregate query
@fraiseql.aggregate_query(
    fact_table="tf_sales",
    auto_group_by=True,
    auto_aggregates=True
)
@fraiseql.query
def sales_aggregate() -> list[dict]:
    """Aggregate sales with flexible grouping and filtering."""

This generates a GraphQL query that supports:

GROUP BY: Dimensions (category, region) and temporal buckets (occurred_at_day, occurred_at_month)
Aggregates: count, revenue_sum, revenue_avg, quantity_sum, etc.
WHERE: Pre-aggregation filters (customer_id, occurred_at range)
HAVING: Post-aggregation filters (revenue_sum_gt: 1000)
ORDER BY: Any aggregate or dimension
LIMIT/OFFSET: Pagination

Fact Table Pattern

-- Table name starts with tf_ (table fact)
CREATE TABLE tf_sales (
    id BIGSERIAL PRIMARY KEY,
    -- Measures: Numeric columns for fast aggregation
    revenue DECIMAL(10,2) NOT NULL,
    quantity INT NOT NULL,
    cost DECIMAL(10,2) NOT NULL,
    -- Dimensions: JSONB column for flexible GROUP BY
    data JSONB NOT NULL,
    -- Denormalized filters: Indexed columns for fast WHERE
    customer_id UUID NOT NULL,
    occurred_at TIMESTAMPTZ NOT NULL
);
CREATE INDEX ON tf_sales(customer_id);
CREATE INDEX ON tf_sales(occurred_at);

Key Principles:

Measures: SQL columns (numeric types) for fast aggregation
Dimensions: JSONB data column for flexible grouping
Denormalized Filters: Indexed SQL columns for fast WHERE clauses
No Joins: All dimensional data denormalized at ETL time

Type Mapping

Python Type	GraphQL Type
`int`	`Int`
`float`	`Float`
`str`	`String`
`bool`	`Boolean`
`list[T]`	`[T]`
`T \| None`	`T` (nullable)
Custom class	Object type

Documentation

Full documentation: https://fraiseql.readthedocs.io

License

MIT

Metadata

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