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Concepts & Glossary

Key concepts and terminology in FraiseQL.

Core Concepts

CQRS (Command Query Responsibility Segregation)

Separating read and write operations for optimal performance:

flowchart TB
    subgraph Client
        GQL[GraphQL Client]
    end

    subgraph FraiseQL["FraiseQL API"]
        Q[Queries]
        M[Mutations]
    end

    subgraph ReadPath["Read Path (Optimized)"]
        V[(v_* Views<br/>Pre-composed JSONB)]
        TV[(tv_* Table Views<br/>Denormalized)]
    end

    subgraph WritePath["Write Path (Transactional)"]
        FN[fn_* Functions<br/>Business Logic]
        TB[(tb_* Tables<br/>Normalized)]
    end

    GQL --> Q
    GQL --> M
    Q --> V
    Q --> TV
    M --> FN
    FN --> TB
    TB -.->|triggers/sync| V
    TB -.->|triggers/sync| TV
  • Commands (Writes): Mutations that modify data
  • Queries (Reads): Queries that fetch data from optimized views

Benefits: - Optimized read paths with PostgreSQL views - ACID transactions for writes

See Also: - CQRS Implementation - Complete CQRS blog example - Enterprise Patterns - Production CQRS with audit trails - Independent scaling of reads and writes

JSONB View Pattern

FraiseQL's core pattern for GraphQL types - database views return pre-composed JSONB:

Data Flow: 

┌─────────────────┐      ┌──────────────────┐      ┌─────────────────┐
│  tb_user        │  →   │   v_user         │  →   │  GraphQL        │
│ (base table)    │      │  (JSONB view)    │      │  Response       │
│                 │      │                  │      │                 │
│ pk_user: 1      │      │ SELECT           │      │ {               │
│ id: uuid-123    │      │ jsonb_build_     │      │   "id": "uuid"  │
│ identifier:     │      │   object(        │      │   "identifier": │
│   "alice"       │      │    'id', id,     │      │     "alice"     │
│ name: "Alice"   │      │    'identifier', │      │   "name": "..." │
│ email: a@b.com  │      │     identifier,  │      │ }               │
│                 │      │    'name', name  │      │                 │
└─────────────────┘      └──────────────────┘      └─────────────────┘

SQL Example (with Trinity Identifiers): 

-- Base table with trinity identifiers
CREATE TABLE tb_user (
    pk_user INT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,  -- Internal
    id UUID DEFAULT gen_random_uuid() UNIQUE NOT NULL,         -- Public API
    identifier TEXT UNIQUE NOT NULL,                            -- Human-readable
    name TEXT NOT NULL,
    email TEXT UNIQUE NOT NULL
);

-- JSONB view exposes only public identifiers
CREATE VIEW v_user AS
SELECT
    id,  -- Direct column for efficient WHERE filtering (WHERE id = $1)
    jsonb_build_object(
        'id', id,                    -- Public UUID (exposed in GraphQL)
        'identifier', identifier,    -- Human-readable slug (exposed in GraphQL)
        'name', name,
        'email', email
        -- Note: pk_user NOT in view (internal only)
    ) as data
FROM tb_user;

Python Type Definition: 

import fraiseql
import fraiseql
from fraiseql.types import ID

@fraiseql.type(sql_source="v_user")
class User:
    """User with trinity identifiers."""
    id: ID              # Public API identifier (stable, secure)
    identifier: str       # Human-readable slug (SEO-friendly)
    name: str
    email: str
    # Note: pk_user is NOT exposed in GraphQL type

Why This Pattern? - ✅ PostgreSQL composes JSONB - One query, no N+1 problems - ✅ Rust transforms efficiently - Compiled performance for field selection - ✅ Explicit field control - Only fields in JSONB are accessible - ✅ Security by design - Can't accidentally expose hidden columns - ✅ Trinity identifiers - Three ID types for different purposes (see below)

Trinity Identifiers

FraiseQL's pattern of using three identifier types per entity for optimal performance and usability:

The Three Identifiers:

CREATE TABLE tb_post (
    -- 1. pk_* - Internal primary key (NEVER exposed to GraphQL)
    pk_post INT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,

    -- 2. id - Public API identifier (ALWAYS exposed, stable)
    id UUID DEFAULT gen_random_uuid() UNIQUE NOT NULL,

    -- 3. identifier - Human-readable slug (OPTIONAL, for SEO)
    identifier TEXT UNIQUE,

    -- Other fields
    title TEXT NOT NULL,
    content TEXT
);

Purpose of Each Identifier:

Identifier Type Purpose Example Exposed in API? Use Case
pk_post Fast integer joins in PostgreSQL 1234 ❌ Never Database performance (JOINs, indexes)
id Stable public API identifier 550e8400-e29b-41d4-a716-446655440000 ✅ Always GraphQL queries, external integrations
identifier Human-readable SEO slug "my-first-post" ✅ Optional URLs, user-facing references

Why Three Identifiers?

  1. Performance (pk_*):
  2. Integer primary keys are faster for JOINs than UUIDs
  3. Smaller indexes, better cache locality
  4. Sequential IDs optimize B-tree performance

  5. Never exposed to prevent enumeration attacks

  6. Stability (id):

  7. UUIDs don't reveal database size or creation order
  8. Can be generated client-side (distributed systems)
  9. Stable even if slug changes

  10. Safe for public APIs

  11. Usability (identifier):

  12. SEO-friendly URLs: /posts/my-first-post vs /posts/550e8400...
  13. Human-readable references
  14. Can change without breaking API (id stays stable)
  15. Optional (not all entities need slugs)

View column pattern:

-- Leaf view (nothing references it) - only needs id for filtering
CREATE VIEW v_user AS
SELECT
    id,  -- For WHERE id = $1 filtering
    jsonb_build_object(
        'id', id,
        'name', name
    ) as data
FROM tb_user;

-- Referenced view - needs id AND pk_* for parent views to JOIN
CREATE VIEW v_post AS
SELECT
    id,       -- For WHERE id = $1 filtering
    pk_post,  -- For parent views to JOIN
    jsonb_build_object(
        'id', id,
        'identifier', identifier,
        'title', title,
        'content', content
    ) as data
FROM tb_post;

-- Parent view composing nested data using pk_post
CREATE VIEW v_user_with_posts AS
SELECT
    id,  -- For WHERE id = $1 filtering
    jsonb_build_object(
        'id', u.id,
        'name', u.name,
        'posts', (
            SELECT jsonb_agg(p.data)
            FROM v_post p
            JOIN tb_post tp ON tp.pk_post = p.pk_post
            WHERE tp.user_id = tb_user.pk_user
        )
    ) as data
FROM tb_user u;

Rule: - Always include id (public identifier) for WHERE filtering - Include pk_* only if other views need to JOIN to this view - Never include pk_* in JSONB (internal only)

import fraiseql
import fraiseql
from fraiseql.types import ID

@fraiseql.type(sql_source="v_post")
class Post:
    id: ID          # Public API - stable forever
    identifier: str   # Human-readable - can change
    title: str
    content: str
    # pk_post NOT exposed - internal only

Querying by Different Identifiers:

# Query by public UUID
query {
  post(id: "550e8400-e29b-41d4-a716-446655440000") {
    title
  }
}

# Query by human-readable identifier
query {
  post(identifier: "my-first-post") {
    title
  }
}

# pk_post is NEVER queryable from GraphQL (security)

Best Practices:

✅ Always use trinity pattern for entities with public APIs ✅ Never expose pk_ in GraphQL types (security risk) ✅ Use id for API contracts (stable, never changes) ✅ Use identifier for URLs (human-friendly, can update) ✅ Index all three* for query performance: 

CREATE INDEX idx_post_pk ON tb_post(pk_post);        -- Primary key (automatic)
CREATE UNIQUE INDEX idx_post_id ON tb_post(id);      -- API lookups
CREATE UNIQUE INDEX idx_post_identifier ON tb_post(identifier);  -- URL lookups

Projection Tables (tv_*)

Pattern: Manually-synced tables that cache pre-composed JSONB for instant GraphQL queries.

Projection tables (tv_*) are regular tables (NOT views!) that store materialized JSONB data:

When to use: - Read-heavy workloads (10:1+ read:write ratio) - Large datasets (>100k rows) where view JOINs are too slow - GraphQL APIs needing sub-millisecond response times - Acceptable write complexity for massive read performance gains

Architecture (3-layer CQRS):

-- Layer 1: Base tables (normalized, for writes)
CREATE TABLE tb_user (
    pk_user INT PRIMARY KEY,
    id UUID UNIQUE NOT NULL,
    name TEXT,
    email TEXT
);

CREATE TABLE tb_post (
    pk_post INT PRIMARY KEY,
    user_id INT REFERENCES tb_user(pk_user),
    title TEXT,
    content TEXT
);

-- Layer 2: Views (compose JSONB from base tables)
CREATE VIEW v_user AS
SELECT
    u.id,
    jsonb_build_object(
        'id', u.id,
        'name', u.name,
        'posts', (
            SELECT jsonb_agg(jsonb_build_object('id', p.id, 'title', p.title))
            FROM tb_post p
            WHERE p.user_id = u.pk_user
        )
    ) AS data
FROM tb_user u;

-- Layer 3: Projection tables (cache JSONB for fast reads)
CREATE TABLE tv_user (
    id UUID PRIMARY KEY,
    data JSONB NOT NULL,  -- Regular column (NOT GENERATED!)
    updated_at TIMESTAMPTZ DEFAULT NOW()
);

-- Sync function: copies v_user → tv_user
CREATE FUNCTION fn_sync_tv_user(p_id UUID) RETURNS VOID AS $$
BEGIN
    INSERT INTO tv_user (id, data)
    SELECT id, data FROM v_user WHERE id = p_id
    ON CONFLICT (id) DO UPDATE SET
        data = EXCLUDED.data,
        updated_at = NOW();
END;
$$ LANGUAGE plpgsql;

-- Mutation explicitly calls sync (CRITICAL!)
CREATE FUNCTION fn_create_user(p_name TEXT, p_email TEXT)
RETURNS JSONB AS $$
DECLARE v_user_id UUID;
BEGIN
    INSERT INTO tb_user (name, email)
    VALUES (p_name, p_email)
    RETURNING id INTO v_user_id;

    -- Explicitly sync to projection table
    PERFORM fn_sync_tv_user(v_user_id);

    RETURN (SELECT data FROM tv_user WHERE id = v_user_id);
END;
$$ LANGUAGE plpgsql;

⚠️ CRITICAL: Explicit Sync Required

Projection tables do NOT auto-update. Every mutation must call sync functions:

-- ✅ CORRECT
CREATE FUNCTION fn_update_user(...) RETURNS JSONB AS $$
BEGIN
    UPDATE tb_user SET name = p_name WHERE id = p_id;
    PERFORM fn_sync_tv_user(p_id);  -- Must call!
    RETURN (SELECT data FROM tv_user WHERE id = p_id);
END;
$$;

-- ❌ WRONG - Missing sync!
CREATE FUNCTION fn_update_user_broken(...) RETURNS JSONB AS $$
BEGIN
    UPDATE tb_user SET name = p_name WHERE id = p_id;
    -- tv_user will have stale data!
    RETURN (SELECT data FROM tv_user WHERE id = p_id);
END;
$$;

Benefits: - ✅ 100-200x faster reads - 0.05-0.5ms (vs 5-10ms for views) - ✅ Embedded relations - Nested data pre-composed - ✅ Works with Rust pipeline - JSONB → Rust → HTTP - ✅ No N+1 queries - Everything in one lookup

Trade-offs: - ❌ Write complexity - Mutations must call sync functions - ❌ Storage overhead - Duplicates data (1.5-2x) - ❌ Manual sync - Developer must remember - ⚠️ Not for high-write tables - Sync overhead

Python mapping: 

import fraiseql
from fraiseql.types import ID

@fraiseql.type(sql_source="tv_user", jsonb_column="data")
class User:
    id: ID
    name: str
    posts: list[Post]  # Pre-composed!

Common misconception: 

-- ❌ WRONG - PostgreSQL can't do this!
CREATE TABLE tv_user (
    id UUID PRIMARY KEY,
    data JSONB GENERATED ALWAYS AS (
        SELECT ... FROM tb_user ...  -- Can't reference other tables!
    ) STORED
);

Where GENERATED ALWAYS works: 

-- ✅ Same-row scalar extraction (for indexing)
CREATE TABLE tb_user (
    data JSONB,
    email TEXT GENERATED ALWAYS AS (lower(data->>'email')) STORED
);

See Projection Tables Example

GraphQL Concepts

Auto-Documentation

FraiseQL automatically extracts field descriptions from your Python code for GraphQL schema documentation.

Supported sources (priority order):

  1. Inline comments (highest priority)
  2. Annotated types with string metadata
  3. Docstring Fields sections (lowest priority)

Example: 

import fraiseql
import fraiseql
from typing import Annotated
from fraiseql.types import ID

@fraiseql.type(sql_source="v_user")
class User:
    """User account model.

    Fields:
        created_at: Account creation timestamp
    """
    id: ID  # Public API identifier (inline comment - highest priority)
    identifier: str  # Human-readable username
    name: Annotated[str, "User's full name"]  # Annotated type
    email: str
    created_at: datetime  # Uses docstring description

Generated GraphQL schema: 

type User {
  "Public API identifier"
  id: ID!

  "Human-readable username"
  identifier: String!

  "User's full name"
  name: String!

  email: String!

  "Account creation timestamp"
  createdAt: DateTime!
}

Auto-applied to: - ✅ Type fields (visible in Apollo Studio) - ✅ Where clause filter operators (eq, gt, contains, etc.) - ✅ Input type fields - ✅ Mutation parameters - ✅ Specialized type operators (network, LTree, coordinates)

Benefits: - No separate documentation files to maintain - Descriptions live next to type definitions - AI tools (Claude, Copilot) can see context - Apollo Studio shows helpful field hints

Type

Define your data models with trinity identifiers:

import fraiseql
import fraiseql
from fraiseql.types import ID

@fraiseql.type(sql_source="v_user")
class User:
    """User type with trinity identifiers."""
    id: ID          # Public API identifier (always exposed)
    identifier: str   # Human-readable slug (SEO-friendly)
    name: str
    email: str
    # pk_user is NOT exposed (internal only)

Without trinity pattern (simpler entities): 

import fraiseql
import fraiseql

@fraiseql.type(sql_source="v_note")
class Note:
    """Simple note without slug."""
    id: int           # Can use simple int if no public API needed
    title: str
    content: str

Query

Read operations:

import fraiseql

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

Mutation

Write operations (two patterns supported):

Simple pattern (function-based): 

import fraiseql
import fraiseql

@fraiseql.mutation
async def create_user(info, input: CreateUserInput) -> User:
    """Simple mutation that returns the type directly."""
    db = info.context["db"]
    # Call PostgreSQL function with business logic
    result = await db.execute_function("fn_create_user", {
        "name": input.name,
        "email": input.email
    })
    return await db.find_one("v_user", id=result["id"])

Class-based pattern (with success/failure): 

import fraiseql
import fraiseql

@fraiseql.mutation
class CreateUser:
    """Create a new user with explicit success/failure handling."""
    input: CreateUserInput
    success: CreateUserSuccess
    failure: ValidationError

    async def resolve(self, info):
        db = info.context["db"]
        # Call PostgreSQL function - all business logic in database
        result = await db.execute_function("fn_create_user", {
            "name": self.input.name,
            "email": self.input.email
        })

        # PostgreSQL function returns success/error indicator with user ID
        if result["success"]:
            user = await db.find_one("v_user", id=result["user_id"])
            return CreateUserSuccess(
                user=user,
                message=result.get("message", "User created")
            )
        return ValidationError(
            message=result["error"],
            code=result.get("code", "VALIDATION_ERROR")
        )

Corresponding PostgreSQL function: 

CREATE OR REPLACE FUNCTION fn_create_user(
    p_name TEXT,
    p_email TEXT
) RETURNS JSONB AS $$
DECLARE
    v_user_id UUID;
BEGIN
    -- Validation: Check email format using regex
    -- Pattern: local-part@domain.tld (basic RFC 5322 compliance)
    IF p_email !~ '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}$' THEN
        RETURN jsonb_build_object(
            'success', false,
            'error', 'Invalid email format',
            'code', 'INVALID_EMAIL'
        );
    END IF;

    -- Insert user
    INSERT INTO tb_user (name, email)
    VALUES (p_name, p_email)
    RETURNING id INTO v_user_id;

    -- Audit log
    INSERT INTO audit_log (action, details)
    VALUES ('user_created', jsonb_build_object('user_id', v_user_id));

    -- Return success with user data
    RETURN jsonb_build_object(
        'success', true,
        'user', jsonb_build_object(
            'id', v_user_id,
            'name', p_name,
            'email', p_email
        ),
        'message', 'User created successfully'
    );
END;
$$ LANGUAGE plpgsql;

Where Input Types

FraiseQL automatically generates strongly-typed WhereInput types for filtering with field-specific operators.

Basic operators (all types): - eq / neq - Equality checks - in / nin - List membership (NOT IN) - isnull - Null checks (true = IS NULL, false = IS NOT NULL)

Numeric operators (Int, Float, Decimal): - gt / gte - Greater than (or equal) - lt / lte - Less than (or equal)

String operators: - contains - Substring search (case-sensitive) - startswith - Prefix match - endswith - Suffix match

Date/DateTime operators: - All numeric operators (gt, gte, lt, lte) - in / nin for specific dates - isnull for optional date fields

Example - basic filtering: 

query {
  users(where: {
    status: { eq: "active" }
    age: { gte: 18 }
    email: { endswith: "@company.com" }
    deletedAt: { isnull: true }
  }) {
    name
    email
  }
}

Specialized Type Operators:

1. Coordinates (Geographic Filtering) 

query {
  stores(where: {
    location: {
      distance_within: {
        center: [37.7749, -122.4194]  # San Francisco
        radius: 5000  # meters
      }
    }
  }) {
    name
    address
    location
  }
}

2. Network Addresses (IP/CIDR Filtering) 

query {
  servers(where: {
    ipAddress: {
      inSubnet: "192.168.1.0/24"    # CIDR subnet matching
      isPrivate: true                # RFC 1918 private addresses
      isIPv4: true                   # IPv4 vs IPv6
    }
  }) {
    hostname
    ipAddress
  }
}

query {
  publicServers(where: {
    ipAddress: {
      inRange: { from: "10.0.0.1", to: "10.0.0.254" }
      isPublic: true
      NOT: { isLoopback: true }
    }
  }) {
    hostname
  }
}

Network classification operators: - inSubnet - IP within CIDR range - inRange - IP between from/to addresses - isPrivate / isPublic - RFC 1918 detection - isIPv4 / isIPv6 - IP version - isLoopback - 127.0.0.1 or ::1 - isMulticast - Multicast addresses - isBroadcast - 255.255.255.255 - isLinkLocal - 169.254.0.0/16 or fe80::/10 - isDocumentation - RFC 3849/5737 ranges - isReserved - Reserved/unspecified (0.0.0.0, ::) - isCarrierGrade - CGN range (100.64.0.0/10) - isSiteLocal - Site-local IPv6 (deprecated) - isUniqueLocal - Unique local IPv6 (fc00::/7) - isGlobalUnicast - Global unicast addresses

3. LTree (Hierarchical Paths) 

query {
  categories(where: {
    path: {
      ancestor_of: "Electronics.Computers.Laptops"  # All ancestor categories
      nlevel_gte: 2                                  # At least 2 levels deep
    }
  }) {
    name
    path
  }
}

query {
  subcategories(where: {
    path: {
      descendant_of: "Electronics"       # All subcategories
      matches_lquery: "Electronics.*"    # Pattern matching
    }
  }) {
    name
  }
}

LTree hierarchical operators: - ancestor_of - Path is ancestor of target - descendant_of - Path is descendant of target - matches_lquery - Pattern match with wildcards - matches_ltxtquery - Text search (AND/OR/NOT) - nlevel_eq / nlevel_gt / nlevel_gte / nlevel_lt / nlevel_lte - Path depth filtering

Logical Operators (All WhereInput Types)

Combine filters with AND, OR, NOT for complex queries:

query {
  users(where: {
    AND: [
      { status: { eq: "active" } }
      { OR: [
          { role: { eq: "admin" } }
          { AND: [
              { role: { eq: "editor" } }
              { verified: { eq: true } }
            ]
          }
        ]
      }
    ]
  }) {
    name
    role
  }
}

Nested array filtering: 

query {
  users(where: {
    posts: {  # Filter parent by nested array properties
      AND: [
        { status: { eq: "published" } }
        { views: { gte: 1000 } }
      ]
    }
  }) {
    name
    posts {
      title
      views
    }
  }
}

How it works: 1. FraiseQL inspects your type fields 2. Generates appropriate filter class per field type 3. Creates TypeWhereInput with logical operators 4. Converts GraphQL input to SQL WHERE clauses 5. PostgreSQL executes with proper type casting

Example generated type: 

import fraiseql
from fraiseql.types import ID

# Your type definition
@fraiseql.type(sql_source="v_server")
class Server:
    id: ID
    hostname: str
    ip_address: NetworkAddress  # Special type
    port: int
    location: Coordinate        # Special type

# FraiseQL auto-generates:
class ServerWhereInput:
    id: UUIDFilter | None
    hostname: StringFilter | None
    ip_address: NetworkAddressFilter | None  # Rich operators!
    port: IntFilter | None
    location: CoordinateFilter | None         # Distance queries!
    AND: list[ServerWhereInput] | None
    OR: list[ServerWhereInput] | None
    NOT: ServerWhereInput | None

Benefits: - ✅ Type-safe filtering - No runtime query errors - ✅ Field-specific operators - contains for strings, gt for numbers - ✅ Specialized types - Network, geographic, hierarchical queries - ✅ Logical operators - Complex AND/OR/NOT combinations - ✅ Apollo autocomplete - All operators visible in IDE - ✅ SQL injection safe - Parameterized queries always

Connection

Relay-style cursor-based pagination (built-in):

import fraiseql
from fraiseql import connection
from fraiseql.types.generic import Connection

@connection(
    node_type=User,
    default_page_size=20,
    max_page_size=100
)
async def users(info, first: int | None = None, after: str | None = None) -> Connection[User]:
    """Get paginated users - pagination handled automatically."""
    # Framework calls db.paginate() automatically
    # Returns Connection with nodes, pageInfo, totalCount

Configuration options: - node_type: The type being paginated (required) - view_name: Database view (defaults to v_<function_name>) - default_page_size: Default results per page (default: 20) - max_page_size: Maximum allowed page size (default: 100) - cursor_field: Field for cursor (default: "id") - include_total_count: Include total count (default: True)

Returned Connection type includes: - nodes: List of items (User[]) - pageInfo: Pagination info (hasNextPage, hasPreviousPage, startCursor, endCursor) - totalCount: Total number of items (optional)

Database Concepts

View

Read-optimized database views that compose JSONB for GraphQL:

Simple view (without trinity pattern): 

CREATE VIEW v_note AS
SELECT
    id,
    jsonb_build_object(
        'id', id,
        'title', title,
        'content', content,
        'created_at', created_at
    ) as data
FROM tb_note
WHERE deleted_at IS NULL;

View with trinity identifiers: 

CREATE VIEW v_user AS
SELECT
    id,  -- For WHERE id = $1 filtering
    jsonb_build_object(
        'id', id,                    -- Public UUID
        'identifier', identifier,    -- Human-readable slug
        'name', name,
        'email', email,
        'created_at', created_at
    ) as data
FROM tb_user
WHERE deleted_at IS NULL;

View that will be referenced by others (includes pk_*): 

CREATE VIEW v_post AS
SELECT
    id,       -- For WHERE id = $1 filtering
    pk_post,  -- For parent views to JOIN
    jsonb_build_object(
        'id', id,
        'title', title,
        'content', content
    ) as data
FROM tb_post;

Key points: - Always include id as direct column for efficient WHERE filtering - Include pk_* only if other views need to JOIN/reference this view - Never include pk_* in JSONB data column (internal only) - data column contains complete GraphQL response - Only fields in JSONB are exposed to GraphQL

Materialized View

Pre-computed aggregations:

CREATE MATERIALIZED VIEW user_stats AS
SELECT
    user_id,
    COUNT(*) as post_count,
    MAX(created_at) as last_post_at
FROM posts
GROUP BY user_id;

Index

Performance optimization:

CREATE INDEX idx_users_email ON users(email);

Performance Concepts

Query Complexity

Limiting query depth and breadth:

from fraiseql import ComplexityConfig

config = ComplexityConfig(
    max_complexity=1000,
    max_depth=10
)

APQ (Automatic Persisted Queries)

Cache GraphQL queries by SHA-256 hash to reduce bandwidth and improve performance.

How it works:

  1. First request: Client sends full query + SHA-256 hash
  2. Server: Stores query in cache, returns result
  3. Subsequent requests: Client sends only hash
  4. Server: Retrieves query from cache, executes, returns result

Benefits: - ✅ Bandwidth reduction - 90%+ for large queries (send 64-char hash vs full query) - ✅ Faster uploads - Especially on mobile networks - ✅ Query optimization - Server can optimize cached queries - ✅ Works with Rust pipeline - PostgreSQL → JSONB → Rust → HTTP (no slowdown)

See Also: - APQ Multi-tenant Example - APQ with tenant isolation

Configuration:

Memory backend (single instance): 

from fraiseql import FraiseQLConfig

config = FraiseQLConfig(
    apq_storage_backend="memory",  # Default - LRU cache
    apq_cache_size=1000             # Max cached queries
)

PostgreSQL backend (multi-instance): 

config = FraiseQLConfig(
    apq_storage_backend="postgresql",
    apq_storage_schema="apq_cache",     # Schema for cache table
    apq_cache_ttl=3600                   # TTL in seconds (optional)
)

# Creates table:
# CREATE TABLE apq_cache.persisted_queries (
#     query_hash TEXT PRIMARY KEY,
#     query_text TEXT NOT NULL,
#     created_at TIMESTAMPTZ DEFAULT NOW(),
#     last_used TIMESTAMPTZ DEFAULT NOW()
# );

Client usage (Apollo Client):

import { ApolloClient, InMemoryCache, HttpLink } from '@apollo/client';
import { createPersistedQueryLink } from '@apollo/client/link/persisted-queries';
import { sha256 } from 'crypto-hash';

const link = createPersistedQueryLink({ sha256 }).concat(
  new HttpLink({ uri: 'http://localhost:8000/graphql' })
);

const client = new ApolloClient({
  cache: new InMemoryCache(),
  link,
});

// First query: sends full query + hash
// Subsequent queries: sends only hash
const { data } = await client.query({
  query: GET_USERS,
  // Apollo automatically handles APQ protocol
});

Server logs: 

[APQ] Cache miss - storing query hash: 5d41402abc4b2a76b9719d911017c592
[APQ] Cache hit - executing query from hash: 5d41402abc4b2a76b9719d911017c592

When to use: - Large, complex queries (>1KB) - Mobile applications (limited bandwidth) - Multi-instance deployments (use PostgreSQL backend) - Production APIs with repeated queries

Storage backend comparison:

Feature Memory Backend PostgreSQL Backend
Multi-instance ❌ No ✅ Yes (shared cache)
Persistence ❌ Lost on restart ✅ Survives restarts
Performance ✅ Fastest ⚠️ Network overhead
Setup ✅ Zero config ⚠️ Requires migration
Use case Single instance Multi-instance/production

Monitoring:

from fraiseql.monitoring import apq_metrics

# Check APQ cache statistics
stats = await apq_metrics.get_stats()
print(f"Cache hits: {stats.hits}")
print(f"Cache misses: {stats.misses}")
print(f"Hit rate: {stats.hit_rate:.2%}")
print(f"Cached queries: {stats.total_queries}")

See also: - Multi-tenant APQ Setup

Rust JSON Pipeline

FraiseQL's exclusive architecture: PostgreSQL → Rust → HTTP

Traditional frameworks: 

PostgreSQL → Rows → ORM → Python objects → JSON serialize → Response
            ╰────────── Python overhead ──────────╯

FraiseQL: 

PostgreSQL → JSONB → Rust transform → HTTP Response
            ╰────── 7-10x faster ──────╯

Why Rust? - Compiled performance - No Python serialization overhead - 7-10x faster JSON processing - Rust handles field selection - Zero-copy path - Direct bytes to HTTP response - No GIL contention - Parallel request processing

Architectural advantage: - PostgreSQL composes JSONB once (no N+1 queries) - Rust selects only requested fields (respects GraphQL query) - No Python in the hot path (compiled speed for every request)

Security Concepts

Explicit Field Exposure (Security by Architecture)

FraiseQL prevents accidental data leaks through explicit JSONB view contracts:

The ORM security problem: 

# Traditional ORM - ALL columns loaded
class User(Base):
    id = Column(Integer)
    email = Column(String)
    password_hash = Column(String)  # Oops! Sensitive!
    api_key = Column(String)        # Oops! Sensitive!

# Easy to forget excluding fields
# One mistake = data leak

FraiseQL's explicit whitelisting: 

-- Only safe fields in JSONB view
CREATE VIEW v_user AS
SELECT
    pk_user,
    jsonb_build_object(
        'id', id,
        'email', email
        -- password_hash CANNOT be queried
        -- api_key CANNOT be queried
        -- Impossible to accidentally expose!
    ) as data
FROM tb_user;

Security benefits: - ✅ Whitelist-only - Only fields in JSONB are accessible - ✅ Database-enforced - PostgreSQL is the security boundary - ✅ Two-layer protection - SQL view + Python type - ✅ No accidental exposure - Can't query fields not in view - ✅ Trinity protection - pk_* never exposed (prevents enumeration)

Recursion Depth Protection

Views define maximum nesting depth structurally:

-- View defines max depth (no circular references possible)
CREATE VIEW v_user AS
SELECT
    pk_user,
    jsonb_build_object(
        'id', id,
        'posts', (
            SELECT jsonb_agg(jsonb_build_object(
                'id', p.id,
                'title', p.title
                -- NO 'author' field here
                -- Recursion IMPOSSIBLE
            ))
            FROM tb_post p
            WHERE p.user_id = tb_user.id
            LIMIT 100  -- Array size limit
        )
    ) as data
FROM tb_user;

Protection: - ✅ Fixed depth - Attackers can't exceed view definition - ✅ No middleware needed - GraphQL schema validates automatically - ✅ Array size limits - LIMIT clauses prevent huge responses - ✅ One query - No N+1 bomb attacks possible

Field-Level Authorization

Control access at the field level:

import fraiseql
from fraiseql import field, authorized

@field
@authorized(roles=["admin"])
def sensitive_field(user: User, info) -> str:
    """Only admins can access this field."""
    return user.sensitive_data

Rate Limiting

Prevent abuse:

from fraiseql.auth import RateLimitConfig

rate_limit = RateLimitConfig(
    requests_per_minute=100
)

Introspection Control

Disable schema introspection in production:

config = FraiseQLConfig(
    introspection_enabled=False
)