FraiseQL

Stable | Rust-Powered GraphQL for PostgreSQL

Python: 3.13+ | PostgreSQL: 13+

GraphQL for the LLM era. Simple. Powerful. Rust-fast

PostgreSQL returns JSONB. Rust transforms it. Zero Python overhead.

# Complete GraphQL API in 15 lines
import fraiseql
from fraiseql.fastapi import create_fraiseql_app

@fraiseql.type(sql_source="v_user", jsonb_column="data")
class User:
    """A user in the system.

    Fields:
        id: Unique user identifier
        name: User's full name
        email: User's email address
    """
    id: int
    name: str
    email: str

@fraiseql.query
async def users(info) -> list[User]:
    """Get all users."""
    db = info.context["db"]
    return await db.find("v_user")

app = create_fraiseql_app(
    database_url="postgresql://localhost/mydb",
    types=[User],
    queries=[users]
)

📌 FraiseQL Versions

FraiseQL has two versions with overlapping but complementary strengths:

v1 (This Repository: fraiseql-python)

Python framework with integrated Rust pipeline

• ✅ Stable, production-ready
• 🐍 Python 3.13+ native - write schema in Python decorators
• 🚀 Rust pipeline included - 7-10x faster JSON transformation
• 📡 FastAPI runtime - immediate deployment, hot reload, development-friendly
• 🗄️ PostgreSQL-only - optimized for PostgreSQL expertise
• 🔄 Iterative development - no build step, rapid feedback loops

Best for: Python teams, rapid iteration, existing PostgreSQL codebases, teams that want Rust performance without leaving Python ecosystem

Repository: fraiseql/fraiseql-python

v2 (Separate Repository: fraiseql)

Compiled Rust engine with polyglot schema authoring

• 🚀 Beta stage (v2.0.0-alpha.2) - feature-complete, actively developed
• 🔧 16+ language support - Python, TypeScript, Go, Java, Rust, etc. (same schema authoring experience)
• ⚡ Compile-time optimization - build-time schema validation, zero-cost abstractions
• 🛠️ CLI compiler - fraiseql-cli compile schema.json
• 🗄️ Multi-database - PostgreSQL, MySQL, SQLite, SQL Server
• 🎯 Maximum performance - no runtime interpretation, fully compiled

Best for: Teams with multiple language ecosystems, multi-database requirements, compile-time optimization, polyglot teams

Repository: fraiseql/fraiseql

Comparison

If you're using Python: Both versions work great! v1 for rapid iteration, v2 for compile-time guarantees and multi-DB future-proofing. Both are actively maintained.

Why FraiseQL?

• ⚡ Rust Pipeline - 7-10x faster JSON transformation, zero Python overhead
• 🔒 Secure by Design - Explicit field contracts prevent data leaks
• 🤖 AI-Native - LLMs generate correct code on first try
• 💰 Save $5-48K/year - Eliminate Redis, Sentry, APM tools
• 🔄 GraphQL Cascade - Automatic cache updates and side effect tracking
• ✨ Auto-populated mutations - status, message, errors handled automatically (50-60% less boilerplate)
• 🎯 Auto-wired query params - where, orderBy, limit, offset added automatically to list queries
• 📝 Auto-documentation - Attribute docstrings become GraphQL descriptions automatically
• 🔍 Advanced filtering - Full-text search, JSONB queries, array operations, regex
• 🧠 Vector search - pgvector integration for semantic search, RAG, recommendations (6 distance operators)
• 📋 GraphQL compliant - 85-90% GraphQL spec compliance with advanced fragment support

Is This For You?

✅ Perfect if you:

• Build high-performance APIs with PostgreSQL
• Want 7-10x faster JSON processing
• Need enterprise security & compliance
• Prefer database-first architecture
• Use LLMs for code generation

❌ Consider alternatives if:

• You need multi-database support (PostgreSQL-only)
• Building your first GraphQL API (use simpler frameworks)
• Don't use JSONB columns in PostgreSQL

How It Works

Traditional GraphQL (slow):

PostgreSQL → Rows → ORM deserialize → Python objects → GraphQL serialize → JSON → Response
            ╰─── Unnecessary roundtrips (2 conversions) ───╯

FraiseQL (fast):

PostgreSQL → JSONB → Rust field selection → HTTP Response
           ╰─ Zero Python overhead (1 conversion) ─╯

Why This Is Better

1. No ORM Overhead - Database returns final JSONB, Rust transforms it
2. No N+1 Queries - PostgreSQL composes everything in one query
3. Security Built-In - View defines exactly what's exposed (impossible to leak)
4. Recursion Safe - View structure prevents depth attacks naturally
5. AI-Friendly - SQL + Python are massively trained; no magic frameworks

Quick Start

pip install fraiseql
fraiseql init my-api
cd my-api
fraiseql dev

Your GraphQL API is live at http://localhost:8000/graphql 🎉

Next steps:

• 5-Minute Quickstart
• First Hour Guide - Build a complete blog API
• Understanding FraiseQL - Architecture deep-dive

Real Security, Not Theatre

The Problem (ORM-based frameworks)

class User(Base):  # SQLAlchemy
    id = Column(Integer)
    email = Column(String)
    password_hash = Column(String)  # ← Sensitive!
    api_key = Column(String)        # ← Sensitive!

@strawberry.type
class UserType:
    id: int
    email: str
    # Forgot to exclude password_hash and api_key!

Result: One mistake = data leak.

The Solution (FraiseQL)

-- PostgreSQL view defines what's exposed
CREATE VIEW v_user AS
SELECT id,
  jsonb_build_object('id', id, 'email', email) as data
FROM tb_user;
-- password_hash and api_key aren't in JSONB = impossible to leak

Result: Structure defines the contract. No way to accidentally expose fields.

Chaos Engineering & Resilience Testing

FraiseQL separates testing into two workflows:

Standard CI/CD: Validates that features work correctly Chaos Tests: Validates that system recovers from failures

→ Learn about chaos engineering strategy

Advanced Features

Specialized Type System (50+ scalar types)

from fraiseql.types import EmailAddress, PhoneNumber, IPv4, Money, LTree

@fraiseql.type(sql_source="v_users")
class User:
    email: EmailAddress      # Validated emails
    phone: PhoneNumber       # International phone numbers
    ip: IPv4                 # IP addresses with subnet operations
    balance: Money           # Currency with precision
    location: LTree          # Hierarchical paths

Trinity Identifiers

Three ID types for different purposes:

• pk_user (int): Internal DB key, not exposed
• id (UUID): Public API, stable, never changes
• identifier (str): Human-readable slug, SEO-friendly

GraphQL Cascade

Automatic cache invalidation when mutations change related data:

mutation {
  createPost(input: {...}) {
    post { id title }
    cascade {
      updated { __typename }     # What changed
      invalidations { queryName } # Which queries to invalidate
    }
  }
}

Enterprise Security Features

• KMS Integration: Vault, AWS KMS, GCP Cloud KMS
• Security Profiles: STANDARD, REGULATED, RESTRICTED (government-grade)
• SBOM Generation: Automated compliance (FedRAMP, NIS2, HIPAA, PCI-DSS)
• Audit Logging: Cryptographic chain (SHA-256 + HMAC)
• Row-Level Security: PostgreSQL RLS integration
• Rate Limiting: Per-endpoint and per-GraphQL operation

🔐 Security Configuration

🔍 Security Feature Implementation Status

Legend:

• ✅ Complete and verified
• ⚠️ Partial implementation or limited scope
• ❌ Not implemented or not production ready

Roadmap:

• Complete security profile enforcement (Issue #225)
• Add RBAC enforcement verification tests
• Unified field filtering for all query types
• Full security audit and penetration testing

Important: This matrix reflects current implementation status. Security features are under active development. Always verify features meet your requirements before production deployment. See Issue #225 for implementation progress.

Cost Savings: Replace 4 Services with 1 Database

📋 Software Bill of Materials (SBOM)

• Automated generation via fraiseql sbom generate
• Global compliance: US EO 14028, EU NIS2/CRA, PCI-DSS 4.0, ISO 27001
• CycloneDX 1.5 format with cryptographic signing
• CI/CD integration for continuous compliance

🔑 Key Management Service (KMS)

• HashiCorp Vault: Production-ready with transit engine
• AWS KMS: Native integration with GenerateDataKey
• GCP Cloud KMS: Envelope encryption support
• Local Provider: Development-only with warnings

🛡️ Security Profiles

• STANDARD: Default protections for general applications
• REGULATED: PCI-DSS/HIPAA/SOC 2 compliance
• RESTRICTED: Government, defence, critical infrastructure
  • 🇺🇸 FedRAMP, DoD, NIST 800-53
  • 🇪🇺 NIS2 Essential Entities, EU CRA
  • 🇨🇦 CPCSC (defence contractors)
  • 🇦🇺 Essential Eight Level 3
  • 🇸🇬 Singapore CII operators

📊 Observability

• OpenTelemetry tracing with sensitive data sanitization
• Security event logging
• Audit trail support

🔒 Advanced Security Controls

• Rate limiting for API endpoints and GraphQL operations
• CSRF protection for mutations and forms
• Security headers middleware for defense in depth
• Input validation and sanitization
• Field-level authorization with role inheritance
• Row-level security via PostgreSQL RLS

📋 KMS Architecture

Code Examples

Complete CRUD API

@fraiseql.input
class CreateUserInput:
    email: str  # AI sees exact input structure
    name: str

@fraiseql.success
class UserCreated:
    user_id: str  # AI sees success response
    # Note: @success auto-injects: status, message, updated_fields, id

@fraiseql.error
class ValidationError:
    error: str    # AI sees failure cases
    code: str = "VALIDATION_ERROR"

@fraiseql.mutation(function="fn_create_user", schema="public")
class CreateUser:
    input: CreateUserInput
    success: UserCreated
    failure: ValidationError  # Note: Use 'failure' field, not '@failure' decorator

# That's it! FraiseQL automatically:
# 1. Calls public.fn_create_user(input) with input as dict
# 2. Parses JSONB result into UserCreated or ValidationError

Why AI Loves This

• ✅ SQL + Python - Massively trained languages (no proprietary DSLs)
• ✅ JSONB everywhere - Clear data structures, obvious contracts
• ✅ Database functions - Complete context in one file
• ✅ Explicit logging - AI can trace execution without debugging
• ✅ No abstraction layers - What you see is what executes

Real Impact: Claude Code, GitHub Copilot, and ChatGPT generate correct FraiseQL code on first try.

📖 Core Concepts

New to FraiseQL? Understanding these core concepts will help you make the most of the framework:

📚 Concepts & Glossary - Essential terminology and mental models:

• CQRS Pattern - Separate read models (views) from write models (functions)
• Trinity Identifiers - Three-tier ID system (pk_*, id, identifier) for performance and UX
• JSONB Views - PostgreSQL composes data once, eliminating N+1 queries
• Database-First Architecture - Start with PostgreSQL, GraphQL follows
• Explicit Sync Pattern - Table views (tv_*) for complex queries

Quick links:

• Understanding FraiseQL - 10-minute architecture overview
• Database API - Connection pooling and query execution
• Types and Schema - Complete type system guide
• Filter Operators - Advanced PostgreSQL filtering (arrays, full-text search, JSONB, regex)

✨ See How Simple It Is

Complete CRUD API in 20 Lines

from uuid import UUID
from fraiseql import type, query, mutation, input, success

@fraiseql.type(sql_source="v_note", jsonb_column="data")
class Note:
    id: int
    title: str
    content: str | None

@fraiseql.query
async def notes(info) -> list[Note]:
    return await info.context["db"].find("v_note")

@fraiseql.query
async def note(info, id: UUID) -> Note | None:
    """Get a note by ID."""
    db = info.context["db"]
    return await db.find_one("v_note", id=id)

# Step 3: Define mutations
@fraiseql.input
class CreateNoteInput:
    title: str
    content: str | None = None

@fraiseql.mutation
class CreateNote:
    input: CreateNoteInput
    success: Note

app = create_fraiseql_app(
    database_url="postgresql://localhost/mydb",
    types=[Note],
    queries=[notes],
    mutations=[CreateNote]
)

Database-First Pattern

-- PostgreSQL view (composable, no N+1)
CREATE VIEW v_user AS
SELECT id,
  jsonb_build_object(
    'id', id,
    'name', name,
    'email', email,
    'posts', (
      SELECT jsonb_agg(...)
      FROM tb_post p
      WHERE p.user_id = tb_user.id
    )
  ) as data
FROM tb_user;

# Python type mirrors the view
@fraiseql.type(sql_source="v_user", jsonb_column="data")
class User:
    id: int
    name: str
    email: str
    posts: list[Post]  # Nested relations! No N+1 queries!

# Step 3: Query it
@fraiseql.query
async def users(info) -> list[User]:
    db = info.context["db"]
    return await db.find("v_user")

No ORM. No complex resolvers. PostgreSQL composes data, Rust transforms it.

Mutations with Business Logic

CREATE OR REPLACE FUNCTION fn_publish_post(p_post_id UUID) RETURNS JSONB AS $$
DECLARE
    v_post RECORD;
BEGIN
    -- Get post with user info (Trinity pattern: JOIN on pk_user)
    SELECT p.*, u.email as user_email
    INTO v_post
    FROM tb_post p
    JOIN tb_user u ON p.fk_user = u.pk_user  -- ✅ Trinity: INTEGER FK to pk_user
    WHERE p.id = p_post_id;

    -- Validate post exists
    IF NOT FOUND THEN
        RETURN jsonb_build_object('success', false, 'error', 'Post not found');
    END IF;

    -- Validate not already published
    IF v_post.published_at IS NOT NULL THEN
        RETURN jsonb_build_object('success', false, 'error', 'Post already published');
    END IF;

    -- Update post
    UPDATE tb_post
    SET published_at = NOW()
    WHERE id = p_post_id;

    -- Sync projection table
    PERFORM fn_sync_tv_post(p_post_id);

    -- Log event
    INSERT INTO audit_log (action, details)
    VALUES ('post_published', jsonb_build_object('post_id', p_post_id, 'user_email', v_post.user_email));

    -- Return success
    RETURN jsonb_build_object('success', true, 'post_id', p_post_id);
END;
$$ LANGUAGE plpgsql;

Business logic, validation, logging - all in the database function. Crystal clear for humans and AI.

Selective CASCADE Querying

Request only the CASCADE data you need:

mutation CreatePost($input: CreatePostInput!) {
  createPost(input: $input) {
    post { id title }

    # Option 1: No CASCADE (smallest payload)
    # Just omit the cascade field

    # Option 2: Metadata only
    cascade {
      metadata { affectedCount }
    }

    # Option 3: Full CASCADE
    cascade {
      updated { __typename id entity }
      deleted { __typename id }
      invalidations { queryName }
      metadata { affectedCount }
    }
  }
}

Performance: Not requesting CASCADE reduces response size by 2-10x.

💰 In PostgreSQL Everything

Replace 4 services with 1 database.

Cost Savings Calculator

How It Works

Caching (Replaces Redis)

from fraiseql.caching import PostgresCache

cache = PostgresCache(db_pool)
await cache.set("user:123", user_data, ttl=3600)

# Uses PostgreSQL UNLOGGED tables
# - No WAL overhead = fast writes
# - Shared across instances
# - TTL-based expiration
# - Pattern-based deletion

Error Tracking (Replaces Sentry)

from fraiseql.monitoring import init_error_tracker

tracker = init_error_tracker(db_pool, environment="production")
await tracker.capture_exception(error, context={...})

# Features:
# - Automatic error fingerprinting and grouping
# - Full stack trace capture
# - OpenTelemetry trace correlation
# - Custom notifications (Email, Slack, Webhook)

Observability (Replaces APM)

-- All traces and metrics stored in PostgreSQL
SELECT * FROM monitoring.traces
WHERE error_id = 'error-123'
  AND trace_id = 'trace-xyz';

Grafana Dashboards Pre-built dashboards in grafana/ query PostgreSQL directly:

• Error monitoring dashboard
• Performance metrics dashboard
• OpenTelemetry traces dashboard

Operational Benefits

• ✅ 70% fewer services to deploy and monitor
• ✅ One database to backup (not 4 separate systems)
• ✅ No Redis connection timeouts or cluster issues
• ✅ No Sentry quota surprises or rate limiting
• ✅ ACID guarantees for everything (no eventual consistency)
• ✅ Self-hosted - full control, no vendor lock-in

🏗️ Architecture Deep Dive

Rust-First Execution

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   GraphQL       │ →  │   PostgreSQL     │ →  │   Rust          │
│   Request       │    │   JSONB Query    │    │   Transform     │
│                 │    │                  │    │   (7-10x faster)│
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                        ↓
                                               ┌─────────────────┐
                                               │   FastAPI       │
                                               │   HTTP Response │
                                               └─────────────────┘

Unified path for all queries:

1. GraphQL query arrives at FastAPI
2. Python resolver calls PostgreSQL view/function
3. PostgreSQL returns pre-composed JSONB
4. Rust pipeline transforms JSONB based on GraphQL selection
5. FastAPI returns bytes directly (zero Python serialization)

CQRS Pattern

FraiseQL implements Command Query Responsibility Segregation:

┌─────────────────────────────────────┐
│         GraphQL API                 │
├──────────────────┬──────────────────┤
│   QUERIES        │   MUTATIONS      │
│   (Reads)        │   (Writes)       │
├──────────────────┼──────────────────┤
│  v_* views       │  fn_* functions  │
│  tv_* tables     │  tb_* tables     │
│  JSONB ready     │  Business logic  │
└──────────────────┴──────────────────┘

Queries use views:

• v_* - Real-time views with JSONB computation
• tv_* - Denormalized tables with generated JSONB columns (for complex queries)

Mutations use functions:

• fn_* - Business logic, validation, side effects
• tb_* - Base tables for data storage

📊 Detailed Architecture Diagrams

Key Innovations

1. Exclusive Rust Pipeline

• PostgreSQL → Rust → HTTP (no Python JSON processing)
• 7-10x faster JSON transformation vs Python
• No GIL contention, compiled performance

2. JSONB Views

• Database composes data once
• Rust selects fields based on GraphQL query
• No N+1 query problems

3. Table Views (tv_*)

-- Denormalized JSONB table with explicit sync
CREATE TABLE tv_user (
    id INT PRIMARY KEY,
    data JSONB NOT NULL,  -- Regular column, not generated
    updated_at TIMESTAMPTZ DEFAULT NOW()
);

-- Sync function populates tv_* from v_* view
CREATE FUNCTION fn_sync_tv_user(p_user_id INT) RETURNS VOID AS $$
BEGIN
    INSERT INTO tv_user (id, data)
    SELECT id, data FROM v_user WHERE id = p_user_id
    ON CONFLICT (id) DO UPDATE SET
        data = EXCLUDED.data,
        updated_at = NOW();
END;
$$ LANGUAGE plpgsql;

-- Mutations call sync explicitly
CREATE FUNCTION fn_create_user(p_name TEXT) RETURNS JSONB AS $$
DECLARE v_user_id INT;
BEGIN
    INSERT INTO tb_user (name) VALUES (p_name) RETURNING id INTO v_user_id;
    PERFORM fn_sync_tv_user(v_user_id);  -- ← Explicit sync call
    RETURN (SELECT data FROM tv_user WHERE id = v_user_id);
END;
$$ LANGUAGE plpgsql;

Benefits: Instant lookups, embedded relations, explicitly synchronized

4. Zero-Copy Response

• Direct RustResponseBytes to FastAPI
• No Python serialization overhead
• Optimal for high-throughput APIs

🎯 How FraiseQL Is Different

Execution Path Comparison

FraiseQL's Unique Advantages

• ✅ Database returns final structure (JSONB views)
• ✅ Rust handles field selection (compiled performance)
• ✅ No Python in hot path (zero serialization overhead)
• ✅ No ORM abstraction (SQL functions are business logic)
• ✅ Built-in recursion protection (view defines max depth, no middleware needed)
• ✅ Secure by design (explicit field contracts prevent data leaks)
• ✅ AI-readable (clear contracts, full context visible)
• ✅ PostgreSQL-native (caching, monitoring, APQ in one database)

🎯 Advanced Features

Automatic Persisted Queries (APQ)

Enterprise-grade APQ with pluggable storage backends:

from fraiseql import FraiseQLConfig

# Memory backend (zero configuration)
config = FraiseQLConfig(apq_storage_backend="memory")

# PostgreSQL backend (multi-instance coordination)
config = FraiseQLConfig(
    apq_storage_backend="postgresql",
    apq_storage_schema="apq_cache"
)

How it works:

1. Client sends query hash instead of full query
2. FraiseQL checks storage backend for cached query
3. PostgreSQL → Rust → HTTP (same fast path)
4. Bandwidth reduction with large queries

⚡ APQ Details

Specialized Type System

Advanced operators for network types, hierarchical data, ranges, and nested arrays:

query {
  servers(where: {
    ipAddress: { eq: "192.168.1.1" }          # → ::inet casting
    port: { gt: 1024 }                        # → ::integer casting
    location: { ancestor_of: "US.CA" }        # → ltree operations
    dateRange: { overlaps: "[2024-01-01,2024-12-31)" }

    # Nested array filtering with logical operators
    printServers(where: {
      AND: [
        { operatingSystem: { in: ["Linux", "Windows"] } }
        { OR: [
            { nTotalAllocations: { gte: 100 } }
            { NOT: { ipAddress: { isnull: true } } }
          ]
        }
      ]
    }) {
      hostname operatingSystem
    }
  }) {
    id name ipAddress port
  }
}

50+ Specialized Scalar Types:

Financial & Trading:

• CUSIP, ISIN, SEDOL, MIC, LEI - Security identifiers
• Money, Percentage, ExchangeRate - Financial values
• CurrencyCode, StockSymbol - Trading symbols

Network & Infrastructure:

• IPv4, IPv6, CIDR, MACAddress - Network addresses with subnet operations
• Hostname, DomainName, Port, EmailAddress - Internet identifiers
• APIKey, HashSHA256 - Security tokens

Geospatial & Location:

• Coordinate, Latitude, Longitude - Geographic coordinates with distance calculations
• PostalCode, Timezone - Location data

Business & Logistics:

• ContainerNumber, FlightNumber, TrackingNumber, VIN - Asset identifiers
• IBAN, LicensePlate - Financial & vehicle identifiers
• PhoneNumber, LocaleCode, LanguageCode - Contact & localization

Technical & Data:

• UUID, JSON, Date, DateTime, Time, DateRange - Standard types with validation
• LTree - Hierarchical data with ancestor/descendant queries
• SemanticVersion, Color, MIMEType, File, Image - Specialized formats
• HTML, Markdown - Rich text content

Advanced Filtering: Full-text search, JSONB queries, array operations, regex, vector similarity search on all types

Scalar Type Usage Examples

from fraiseql import type
from fraiseql.types import (
    EmailAddress, PhoneNumber, Money, Percentage,
    CUSIP, ISIN, IPv4, MACAddress, LTree, DateRange
)

@fraiseql.type(sql_source="v_financial_data")
class FinancialRecord:
    id: int
    email: EmailAddress           # Validated email addresses
    phone: PhoneNumber           # International phone numbers
    balance: Money               # Currency amounts with precision
    margin: Percentage           # Percentages (0.00-100.00)
    security_id: CUSIP | ISIN    # Financial instrument identifiers

@fraiseql.type(sql_source="v_network_device")
class NetworkDevice:
    id: int
    ip_address: IPv4             # IPv4 addresses with subnet operations
    mac_address: MACAddress      # MAC addresses with validation
    location: LTree              # Hierarchical location paths
    maintenance_window: DateRange # Date ranges with overlap queries

# Advanced filtering with specialized types
query {
  financialRecords(where: {
    balance: { gte: "1000.00" }           # Money comparison
    margin: { between: ["5.0", "15.0"] }   # Percentage range
    security_id: { eq: "037833100" }       # CUSIP validation
  }) {
    id balance margin security_id
  }

  networkDevices(where: {
    ip_address: { inSubnet: "192.168.1.0/24" }  # CIDR operations
    location: { ancestor_of: "US.CA.SF" }       # LTree hierarchy
    maintenance_window: { overlaps: "[2024-01-01,2024-12-31)" }
  }) {
    id ip_address location
  }
}

📖 Nested Array Filtering Guide

Enterprise Security

from fraiseql import authorized

@fraiseql.authorized(roles=["admin", "editor"])
@fraiseql.mutation
class DeletePost:
    """Only admins and editors can delete posts."""
    input: DeletePostInput
    success: DeleteSuccess
    failure: PermissionDenied

# Features:
# - Field-level authorization with role inheritance
# - Row-level security via PostgreSQL RLS
# - Unified audit logging with cryptographic chain (SHA-256 + HMAC)
# - Multi-tenant isolation
# - Rate limiting and CSRF protection

Trinity Identifiers

Three types of identifiers per entity for different purposes:

@fraiseql.type(sql_source="v_post")
class Post(TrinityMixin):
    """
    Trinity Pattern:
    - pk_post (int): Internal SERIAL key (NOT exposed, only in database)
    - id (UUID): Public API key (exposed, stable)
    - identifier (str): Human-readable slug (exposed, SEO-friendly)
    """

    # GraphQL exposed fields
    id: UUID                  # Public API (stable, secure)
    identifier: str | None    # Human-readable (SEO-friendly, slugs)
    title: str
    content: str
    # ... other fields

    # pk_post is NOT a field - accessed via TrinityMixin.get_internal_pk()

Why three?

• pk_*: Fast integer joins (PostgreSQL only, never in GraphQL schema)
• id: Public API stability (UUID, exposed, never changes)
• identifier: Human-friendly URLs (exposed, SEO, readability)

🚀 Get Started in 5 Minutes

# Install
pip install fraiseql

# Create project
fraiseql init my-api
cd my-api

# Setup database
createdb my_api
psql my_api < schema.sql

# Start server
fraiseql dev

Your GraphQL API is live at http://localhost:8000/graphql 🎉

Next Steps

• 📚 First Hour Guide - Build a complete blog API (60 minutes, hands-on)
• 🧠 Understanding FraiseQL - Architecture deep dive (10 minute read)
• ⚡ 5-Minute Quickstart - Copy, paste, run
• 📖 Full Documentation - Complete guides and references

Prerequisites

• Python 3.13+ (required for Rust pipeline integration and advanced type features)
• PostgreSQL 13+

📖 Detailed Installation Guide - Platform-specific instructions, troubleshooting

🚦 Is FraiseQL Right for You?

✅ Perfect For

• PostgreSQL-first teams already using PostgreSQL extensively
• Performance-critical APIs requiring efficient data access
• Multi-tenant SaaS with per-tenant isolation needs
• Cost-conscious startups ($5-48K annual savings vs traditional stack)
• AI-assisted development teams using Claude/Copilot/ChatGPT
• Operational simplicity - one database for everything
• Self-hosted infrastructure - full control, no vendor lock-in

❌ Consider Alternatives

• Multi-database support - FraiseQL is PostgreSQL-specific
• Simple CRUD APIs - Traditional REST may be simpler
• Non-PostgreSQL databases - FraiseQL requires PostgreSQL
• Microservices - Better for monolithic or database-per-service

🛠️ CLI Commands

# Project management
fraiseql init <name>           # Create new project
fraiseql dev                   # Development server with hot reload
fraiseql check                 # Validate schema and configuration

# Code generation
fraiseql generate schema       # Export GraphQL schema
fraiseql generate types        # Generate TypeScript definitions

# Database utilities
fraiseql sql analyze <query>   # Analyze query performance
fraiseql sql explain <query>   # Show PostgreSQL execution plan

Learn More

• Full Documentation - Comprehensive guides
• Architecture Decisions - Why we built it this way
• Performance Guide - Optimization strategies
• Examples - Real-world applications

Contributing

git clone https://github.com/fraiseql/fraiseql-python
cd fraiseql-python && make setup-dev
prek install  # 7-10x faster than pre-commit

→ Contributing Guide

About

FraiseQL is created by Lionel Hamayon (@evoludigit).

The Idea: What if PostgreSQL returned JSON directly instead of Python serializing it? No ORM. No N+1 queries. No Python overhead. Just Rust transforming JSONB to HTTP.

The Result: A GraphQL framework that's 7-10x faster and designed for the LLM era.

License

MIT License - see LICENSE

📋 Project Navigation

FraiseQL Ecosystem

This Repository: fraiseql-python (v1 - Python-based with Rust pipeline)

Quick Navigation

New to FraiseQL?

• Start here: First Hour Guide (60 min, hands-on)
• Architecture overview: Understanding FraiseQL (10 min read)
• Project structure: Strategic Overview

Exploring v2?

• v2 Repository: fraiseql/fraiseql
• v2 Documentation: fraiseql.readthedocs.io

Troubleshooting:

• Complete Version Roadmap - Version status and feature matrix

🚀 Get Started Now

Ready to build the most efficient GraphQL API in Python?

pip install fraiseql && fraiseql init my-api

📚 Documentation: github.com/fraiseql/fraiseql-python

🚀 PostgreSQL → Rust → Production