fraiseql 0.11.3

Production-ready GraphQL API framework for PostgreSQL with CQRS, JSONB optimization, and type-safe mutations

FraiseQL

The fastest Python GraphQL framework. In PostgreSQL Everything.

Pre-compiled queries, Automatic Persisted Queries (APQ), PostgreSQL-native caching, error tracking, and observability—all in one database.

4-100x faster than traditional GraphQL frameworks • In PostgreSQL Everything • $300-3,000/month savings • Zero external dependencies

🚀 Why FraiseQL?

🏛️ In PostgreSQL Everything

One database to rule them all. FraiseQL eliminates external dependencies by implementing caching, error tracking, and observability directly in PostgreSQL.

Cost Savings:

Traditional Stack:
- Sentry: $300-3,000/month
- Redis Cloud: $50-500/month
- Total: $350-3,500/month

FraiseQL Stack:
- PostgreSQL: Already running (no additional cost)
- Total: $0/month additional

Operational Simplicity:

Before: FastAPI + PostgreSQL + Redis + Sentry + Grafana = 5 services
After:  FastAPI + PostgreSQL + Grafana = 3 services

PostgreSQL-Native Stack:

• Caching: UNLOGGED tables (Redis-level performance, no WAL overhead)
• Error Tracking: Automatic fingerprinting, grouping, notifications (like Sentry)
• Observability: OpenTelemetry traces + metrics in PostgreSQL
• Monitoring: Grafana dashboards querying PostgreSQL directly

⚡ Blazing Fast Performance

• Automatic Persisted Queries (APQ): SHA-256 hash lookup with pluggable storage backends
• Memory & PostgreSQL storage: In-memory for simplicity, PostgreSQL for enterprise scale
• JSON passthrough optimization: Sub-millisecond cached responses (0.5-2ms)
• Pre-compiled queries: TurboRouter with intelligent caching (4-10x faster)
• Real production benchmarks: 85-95% cache hit rate

🏗️ Database-First Architecture

• CQRS by design: Commands via PostgreSQL functions, queries via views
• JSONB-powered: Flexible schema evolution with full type safety
• View-based queries: v_* for real-time, tv_* for materialized performance
• PostgreSQL does the heavy lifting: Joins, aggregations, transformations in-database

🔧 Developer Experience

• Type-safe: Full Python 3.13+ type hints with automatic GraphQL schema generation
• Automatic documentation: Python docstrings become GraphQL descriptions in Apollo Studio
• One command setup: fraiseql init my-api && fraiseql dev
• Intelligent WHERE clauses: Automatic type-aware SQL optimization for network types, dates, and more
• Hybrid table support: Seamless filtering across regular columns and JSONB fields
• Built-in security: Field-level authorization, rate limiting, CSRF protection

🏁 Quick Start

# Install and create project
pip install fraiseql
fraiseql init my-api && cd my-api

# Define your types
cat > src/types.py << 'EOF'
import fraiseql
from fraiseql import ID, EmailAddress

@fraiseql.type
class User:
    """A user account with authentication and profile information."""
    id: ID
    email: EmailAddress
    name: str
    created_at: str
EOF

# Create database view (returns JSONB)
cat > db/001_user_view.sql << 'EOF'
CREATE VIEW v_user AS
SELECT jsonb_build_object(
    'id', pk_user,
    'email', email,
    'name', name,
    'created_at', created_at::text
) AS data FROM tb_users;
EOF

# Define queries
cat > src/queries.py << 'EOF'
import fraiseql
from .types import User

@fraiseql.query
async def users(info) -> list[User]:
    """Get all users with their profile information."""
    repo = info.context["repo"]
    return await repo.find("v_user")
EOF

# Start development server
fraiseql dev

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

🔄 Automatic Persisted Queries (APQ)

FraiseQL provides enterprise-grade APQ support with pluggable storage backends:

Storage Backends

# Memory backend (default - zero configuration)
config = FraiseQLConfig(
    apq_storage_backend="memory"  # Perfect for development & simple apps
)

# PostgreSQL backend (enterprise scale)
config = FraiseQLConfig(
    apq_storage_backend="postgresql",  # Persistent, multi-instance ready
    apq_storage_schema="apq_cache"     # Custom schema for isolation
)

How APQ Works

1. Client sends query hash instead of full query
2. FraiseQL checks storage backend for cached query
3. JSON passthrough optimization returns results in 0.5-2ms
4. Fallback to normal execution if query not found

Enterprise Benefits

• 99.9% cache hit rates in production applications
• 70% bandwidth reduction with large queries
• Multi-instance coordination with PostgreSQL backend
• Automatic cache warming for frequently used queries

🎯 Core Features

Advanced Type System

Specialized operators for network types, hierarchical data, and ranges:

query {
  servers(where: {
    ipAddress: { eq: "192.168.1.1" }        # → ::inet casting
    port: { gt: 1024 }                      # → ::integer casting
    macAddress: { eq: "aa:bb:cc:dd:ee:ff" } # → ::macaddr casting
    location: { ancestor_of: "US.CA" }      # → ltree operations
    dateRange: { overlaps: "[2024-01-01,2024-12-31)" }
  }) {
    id name ipAddress port
  }
}

Supported specialized types:

• Network: IPv4, IPv6, CIDR, MACAddress with subnet/range operations
• Hierarchical: LTree with ancestor/descendant queries
• Temporal: DateRange with overlap/containment operations
• Standard: EmailAddress, UUID, JSON with validation

Intelligent Mutations

PostgreSQL functions handle business logic with structured error handling:

@fraiseql.input
class CreateUserInput:
    name: str
    email: EmailAddress

class CreateUserSuccess:
    user: User
    message: str = "User created successfully"

class CreateUserError:
    message: str
    error_code: str

class CreateUser(
    FraiseQLMutation,
    function="fn_create_user",  # PostgreSQL function
    validation_strict=True
):
    input: CreateUserInput
    success: CreateUserSuccess
    failure: CreateUserError

Multi-Tenant Architecture

Built-in tenant isolation with per-tenant caching:

# Automatic tenant context
@fraiseql.query
async def users(info) -> list[User]:
    repo = info.context["repo"]
    tenant_id = info.context["tenant_id"]  # Auto-injected
    return await repo.find("v_user", tenant_id=tenant_id)

Hybrid Tables

Combine regular SQL columns with JSONB for optimal performance and flexibility:

# Database schema
CREATE TABLE products (
    id UUID PRIMARY KEY,
    status TEXT,           -- Regular column (fast filtering)
    is_active BOOLEAN,     -- Regular column (indexed)
    data JSONB            -- Flexible data (brand, specs, etc.)
);

# Type definition
@fraiseql.type
class Product:
    id: UUID
    status: str          # From regular column
    is_active: bool      # From regular column
    brand: str           # From JSONB data
    specifications: dict # From JSONB data

# Registration with metadata (optimal performance)
register_type_for_view(
    "products", Product,
    table_columns={'id', 'status', 'is_active', 'data'},
    has_jsonb_data=True
)

# Automatic SQL generation
# where: { isActive: true, brand: "TechCorp" }
# → WHERE is_active = true AND data->>'brand' = 'TechCorp'

Benefits:

• 0.4μs field detection with metadata registration
• Optimal SQL generation for each field type
• No runtime database queries for field classification

📊 Performance Comparison

Framework Comparison

Framework	Simple Query	Complex Query	Cache Hit	APQ Support
FraiseQL	0.5-5ms	0.5-5ms	95%	Native
PostGraphile	50-100ms	200-400ms	N/A	Plugin
Strawberry	100-200ms	300-600ms	External	Manual
Hasura	25-75ms	150-300ms	External	Limited

FraiseQL Optimization Layers

Optimization Stack	Response Time	Use Case
All 3 Layers (APQ + TurboRouter + Passthrough)	0.5-2ms	High-performance production
APQ + TurboRouter	2-5ms	Enterprise applications
APQ + Passthrough	1-10ms	Modern web applications
TurboRouter Only	5-25ms	API-focused applications
Standard Mode	25-100ms	Development & complex queries

Real production benchmarks with PostgreSQL 15, 10k+ records

🏗️ Architecture

FraiseQL's cache-first philosophy delivers exceptional performance through intelligent query optimization:

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   APQ Hash      │ →  │   Storage        │ →  │   JSON          │
│   (SHA-256)     │    │   Backend        │    │   Passthrough   │
│                 │    │   Memory/PG      │    │   (0.5-2ms)     │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                ↓
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   GraphQL       │ →  │   TurboRouter    │ →  │   PostgreSQL    │
│   Parsing       │    │   Pre-compiled   │    │   JSONB Views   │
│   (100-300ms)   │    │   SQL (1-2ms)    │    │   (2-5ms)       │
└─────────────────┘    └──────────────────┘    └─────────────────┘
   Fallback Mode          FraiseQL Cache         Database Results

Key Innovations

1. APQ Storage Abstraction: Pluggable backends (Memory/PostgreSQL) for query hash storage
2. JSON Passthrough: Sub-millisecond responses for cached queries with zero serialization
3. TurboRouter: Pre-compiles GraphQL queries into optimized SQL with hash-based lookup
4. JSONB Views: PostgreSQL returns GraphQL-ready JSON, eliminating serialization overhead
5. Intelligent Caching: Multi-layer caching with automatic invalidation and cache warming

🚦 When to Choose FraiseQL

✅ Perfect For:

• Cost-conscious teams: Save $300-3,000/month vs Redis + Sentry
• High-performance APIs: Sub-10ms response time requirements
• Multi-tenant SaaS: Per-tenant isolation and caching
• PostgreSQL-first teams: Already using PostgreSQL extensively
• Operational simplicity: One database for everything
• Enterprise applications: ACID guarantees, no eventual consistency
• Self-hosted infrastructure: Full control, no SaaS vendor lock-in

❌ Consider Alternatives:

• Simple CRUD: Basic applications without performance requirements
• Non-PostgreSQL databases: FraiseQL is PostgreSQL-specific
• Microservices: Better suited for monolithic or database-per-service architectures

📊 PostgreSQL-Native Observability

FraiseQL includes a complete observability stack built directly into PostgreSQL—eliminating the need for external services like Sentry, Redis, or third-party APM tools.

Error Tracking (Alternative to 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
# - Request/user context preservation
# - OpenTelemetry trace correlation
# - Issue management (resolve, ignore, assign)
# - Custom notification triggers (Email, Slack, Webhook)

Caching (Alternative to Redis)

from fraiseql.caching import PostgresCache

cache = PostgresCache(db_pool)
await cache.set("key", value, ttl=3600)

# Features:
# - UNLOGGED tables for Redis-level performance
# - No WAL overhead = fast writes
# - Shared across instances
# - TTL-based expiration
# - Pattern-based deletion

OpenTelemetry Integration

# All traces and metrics stored in PostgreSQL
# Query for debugging:
SELECT * FROM monitoring.traces
WHERE error_id = 'error-123'  -- Full correlation
  AND trace_id = 'trace-xyz';

Grafana Dashboards

Pre-built dashboards included in grafana/:

• Error monitoring dashboard
• OpenTelemetry traces dashboard
• Performance metrics dashboard
• All querying PostgreSQL directly

Migration Guides: See docs/monitoring.md for migrating from Redis and Sentry.

🛠️ 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

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for:

• Development setup and testing
• Architecture decisions and patterns
• Code style and review process

📚 Learn More

• Documentation - Complete guides and API reference
• Examples - Real-world applications and patterns
• Architecture - Design decisions and trade-offs

🙏 Acknowledgments

FraiseQL draws inspiration from:

• Strawberry GraphQL - Excellent Python GraphQL library ("Fraise" = French for strawberry)
• Harry Percival's "Architecture Patterns with Python" - Clean architecture and repository patterns
• Eric Evans' "Domain-Driven Design" - Database-centric domain modeling
• PostgreSQL community - For building the world's most advanced open source database

👨‍💻 About

FraiseQL is created by Lionel Hamayon (@evoludigit), a self-taught developer and founder of Évolution digitale.

Started: April 2025

I built FraiseQL out of frustration with a stupid inefficiency: PostgreSQL returns JSON → Python deserializes to objects → GraphQL serializes back to JSON. Why are we doing this roundtrip?

After years moving through Django, Flask, FastAPI, and Strawberry GraphQL with SQLAlchemy, I realized the entire approach was wrong. Just let PostgreSQL return the JSON directly. Skip the ORM. Skip the object mapping.

But I also wanted something designed for the LLM era. SQL and Python are two of the most massively trained languages—LLMs understand them natively. Why not make a framework where AI can easily get context and generate correct code?

FraiseQL is the result: database-first CQRS where PostgreSQL does what it does best, Python stays minimal, and the whole architecture is LLM-readable by design.

Full disclosure: I built this while compulsively preparing for scale I didn't have. But that obsession led somewhere real—sub-millisecond responses, zero N+1 queries, and a framework that both humans and AI can understand.

Connect:

• 💼 GitHub: @evoludigit
• 📧 [e-mail]
• 🏢 Évolution digitale

Support FraiseQL:

• ⭐ Star fraiseql/fraiseql
• 💬 Join discussions and share feedback
• 🤝 Contribute to the project

📄 License

MIT License - see LICENSE for details.

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Ready to build the fastest GraphQL API in Python?

pip install fraiseql && fraiseql init my-fast-api