MongoDB Data Modeling Best Practices and Schema Design: Advanced Document Structure Optimization and Relationship Management for Scalable Applications
Modern applications require sophisticated data modeling strategies that can handle complex relationships, evolving schemas, and high-performance requirements while maintaining data consistency and query flexibility. Traditional relational modeling approaches often struggle with document-oriented data, nested structures, and the dynamic schema requirements of modern applications, leading to complex object-relational mapping, rigid schema constraints, and performance bottlenecks that limit application scalability and development velocity.
MongoDB provides comprehensive data modeling capabilities through flexible document structures, embedded relationships, and advanced schema design patterns that enable sophisticated data organization with optimal performance characteristics. Unlike traditional databases that enforce rigid table structures and require complex joins, MongoDB integrates data modeling directly into the document structure with native support for arrays, nested objects, and flexible schemas that adapt to application requirements.
The Traditional Relational Data Modeling Challenge
Conventional approaches to data modeling in relational systems face significant limitations when handling complex, hierarchical, and rapidly evolving data structures:
-- Traditional relational data modeling - rigid schema with complex relationship management
-- Basic user management with limited flexibility
CREATE TABLE users (
user_id SERIAL PRIMARY KEY,
username VARCHAR(255) UNIQUE NOT NULL,
email VARCHAR(255) UNIQUE NOT NULL,
-- Basic profile information (limited structure)
first_name VARCHAR(100),
last_name VARCHAR(100),
date_of_birth DATE,
phone_number VARCHAR(20),
-- Address information (denormalized for simplicity)
address_line_1 VARCHAR(255),
address_line_2 VARCHAR(255),
city VARCHAR(100),
state VARCHAR(100),
postal_code VARCHAR(20),
country VARCHAR(100),
-- Account metadata
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
last_login TIMESTAMP,
account_status VARCHAR(50) DEFAULT 'active',
-- Basic preferences (very limited)
preferred_language VARCHAR(10) DEFAULT 'en',
timezone VARCHAR(50) DEFAULT 'UTC',
-- Social media links (limited and rigid)
facebook_url VARCHAR(255),
twitter_url VARCHAR(255),
linkedin_url VARCHAR(255),
instagram_url VARCHAR(255)
);
-- Separate table for user profiles (normalized approach)
CREATE TABLE user_profiles (
profile_id SERIAL PRIMARY KEY,
user_id INTEGER REFERENCES users(user_id) ON DELETE CASCADE,
-- Extended profile information
bio TEXT,
website VARCHAR(255),
company VARCHAR(255),
job_title VARCHAR(255),
-- Skills and interests (very basic approach)
skills TEXT, -- Comma-separated values - not optimal
interests TEXT, -- Comma-separated values - not optimal
-- Professional information
years_of_experience INTEGER,
education_level VARCHAR(100),
-- Contact preferences
email_notifications BOOLEAN DEFAULT true,
sms_notifications BOOLEAN DEFAULT false,
marketing_emails BOOLEAN DEFAULT false,
-- Profile metadata
profile_completeness_percent DECIMAL(5,2) DEFAULT 0.0,
profile_visibility VARCHAR(50) DEFAULT 'public',
-- Profile customization (limited)
theme VARCHAR(50) DEFAULT 'default',
profile_picture_url VARCHAR(255),
cover_photo_url VARCHAR(255)
);
-- User posts with basic relationship management
CREATE TABLE posts (
post_id SERIAL PRIMARY KEY,
user_id INTEGER REFERENCES users(user_id) ON DELETE CASCADE,
-- Post content
title VARCHAR(500) NOT NULL,
content TEXT NOT NULL,
post_type VARCHAR(50) DEFAULT 'article',
-- Post metadata
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
published_at TIMESTAMP,
-- Post status and visibility
status VARCHAR(50) DEFAULT 'draft',
visibility VARCHAR(50) DEFAULT 'public',
-- SEO and categorization
slug VARCHAR(500) UNIQUE,
meta_description TEXT,
featured_image_url VARCHAR(255),
-- Engagement metrics (basic)
view_count INTEGER DEFAULT 0,
like_count INTEGER DEFAULT 0,
comment_count INTEGER DEFAULT 0,
share_count INTEGER DEFAULT 0,
-- Content flags
is_featured BOOLEAN DEFAULT false,
is_pinned BOOLEAN DEFAULT false,
allow_comments BOOLEAN DEFAULT true
);
-- Post categories (many-to-many relationship)
CREATE TABLE categories (
category_id SERIAL PRIMARY KEY,
category_name VARCHAR(255) UNIQUE NOT NULL,
category_slug VARCHAR(255) UNIQUE NOT NULL,
description TEXT,
parent_category_id INTEGER REFERENCES categories(category_id),
-- Category metadata
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
is_active BOOLEAN DEFAULT true,
sort_order INTEGER DEFAULT 0,
-- Category appearance
color VARCHAR(7), -- Hex color code
icon VARCHAR(100) -- Icon identifier
);
-- Post-category relationships (junction table)
CREATE TABLE post_categories (
post_id INTEGER REFERENCES posts(post_id) ON DELETE CASCADE,
category_id INTEGER REFERENCES categories(category_id) ON DELETE CASCADE,
PRIMARY KEY (post_id, category_id),
-- Relationship metadata
assigned_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
assigned_by INTEGER REFERENCES users(user_id)
);
-- Comments with hierarchical structure (self-referencing)
CREATE TABLE comments (
comment_id SERIAL PRIMARY KEY,
post_id INTEGER REFERENCES posts(post_id) ON DELETE CASCADE,
user_id INTEGER REFERENCES users(user_id) ON DELETE CASCADE,
parent_comment_id INTEGER REFERENCES comments(comment_id) ON DELETE CASCADE,
-- Comment content
content TEXT NOT NULL,
comment_type VARCHAR(50) DEFAULT 'text',
-- Comment metadata
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-- Comment status
status VARCHAR(50) DEFAULT 'published',
is_edited BOOLEAN DEFAULT false,
is_pinned BOOLEAN DEFAULT false,
-- Engagement
like_count INTEGER DEFAULT 0,
reply_count INTEGER DEFAULT 0,
-- Moderation
is_flagged BOOLEAN DEFAULT false,
moderation_status VARCHAR(50) DEFAULT 'approved'
);
-- Tags for flexible categorization (many-to-many)
CREATE TABLE tags (
tag_id SERIAL PRIMARY KEY,
tag_name VARCHAR(255) UNIQUE NOT NULL,
tag_slug VARCHAR(255) UNIQUE NOT NULL,
description TEXT,
-- Tag metadata
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
usage_count INTEGER DEFAULT 0,
is_trending BOOLEAN DEFAULT false,
-- Tag appearance
color VARCHAR(7)
);
-- Post-tag relationships
CREATE TABLE post_tags (
post_id INTEGER REFERENCES posts(post_id) ON DELETE CASCADE,
tag_id INTEGER REFERENCES tags(tag_id) ON DELETE CASCADE,
PRIMARY KEY (post_id, tag_id),
-- Relationship metadata
tagged_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
tagged_by INTEGER REFERENCES users(user_id),
relevance_score DECIMAL(3,2) DEFAULT 1.0
);
-- Complex query to retrieve post with all relationships (performance issues)
WITH post_data AS (
SELECT
p.post_id,
p.title,
p.content,
p.created_at,
p.status,
p.view_count,
p.like_count,
p.comment_count,
-- User information (requires join)
u.username,
u.email,
up.bio,
up.profile_picture_url,
-- Categories (requires aggregation)
STRING_AGG(DISTINCT c.category_name, ', ' ORDER BY c.category_name) as categories,
-- Tags (requires aggregation)
STRING_AGG(DISTINCT t.tag_name, ', ' ORDER BY t.tag_name) as tags
FROM posts p
JOIN users u ON p.user_id = u.user_id
LEFT JOIN user_profiles up ON u.user_id = up.user_id
LEFT JOIN post_categories pc ON p.post_id = pc.post_id
LEFT JOIN categories c ON pc.category_id = c.category_id
LEFT JOIN post_tags pt ON p.post_id = pt.post_id
LEFT JOIN tags t ON pt.tag_id = t.tag_id
WHERE p.status = 'published'
GROUP BY
p.post_id, p.title, p.content, p.created_at, p.status,
p.view_count, p.like_count, p.comment_count,
u.username, u.email, up.bio, up.profile_picture_url
),
comment_hierarchy AS (
-- Recursive CTE for nested comments (complex and performance-intensive)
WITH RECURSIVE comment_tree AS (
SELECT
c.comment_id,
c.post_id,
c.content,
c.created_at,
c.parent_comment_id,
u.username as commenter_username,
up.profile_picture_url as commenter_picture,
0 as depth,
CAST(c.comment_id as TEXT) as path
FROM comments c
JOIN users u ON c.user_id = u.user_id
LEFT JOIN user_profiles up ON u.user_id = up.user_id
WHERE c.parent_comment_id IS NULL
AND c.status = 'published'
UNION ALL
SELECT
c.comment_id,
c.post_id,
c.content,
c.created_at,
c.parent_comment_id,
u.username,
up.profile_picture_url,
ct.depth + 1,
ct.path || '.' || c.comment_id
FROM comments c
JOIN users u ON c.user_id = u.user_id
LEFT JOIN user_profiles up ON u.user_id = up.user_id
JOIN comment_tree ct ON c.parent_comment_id = ct.comment_id
WHERE c.status = 'published'
AND ct.depth < 5 -- Limit recursion depth
)
SELECT
post_id,
JSON_AGG(
JSON_BUILD_OBJECT(
'comment_id', comment_id,
'content', content,
'created_at', created_at,
'commenter_username', commenter_username,
'commenter_picture', commenter_picture,
'depth', depth,
'path', path
) ORDER BY path
) as comments_json
FROM comment_tree
GROUP BY post_id
)
SELECT
pd.post_id,
pd.title,
pd.content,
pd.created_at,
pd.username as author_username,
pd.bio as author_bio,
pd.profile_picture_url as author_picture,
pd.categories,
pd.tags,
pd.view_count,
pd.like_count,
pd.comment_count,
-- Comments as JSON (complex aggregation)
COALESCE(ch.comments_json, '[]'::json) as comments
FROM post_data pd
LEFT JOIN comment_hierarchy ch ON pd.post_id = ch.post_id
ORDER BY pd.created_at DESC;
-- Basic user activity analysis (multiple complex joins)
WITH user_activity AS (
SELECT
u.user_id,
u.username,
u.email,
u.created_at as user_created_at,
-- Post statistics
COUNT(DISTINCT p.post_id) as total_posts,
COUNT(DISTINCT CASE WHEN p.status = 'published' THEN p.post_id END) as published_posts,
SUM(p.view_count) as total_views,
SUM(p.like_count) as total_likes,
-- Comment statistics
COUNT(DISTINCT c.comment_id) as total_comments,
-- Category usage
COUNT(DISTINCT pc.category_id) as categories_used,
-- Tag usage
COUNT(DISTINCT pt.tag_id) as tags_used,
-- Activity timeline
MAX(GREATEST(p.created_at, c.created_at)) as last_activity_at,
-- Engagement metrics
AVG(p.view_count) as avg_views_per_post,
AVG(p.like_count) as avg_likes_per_post,
AVG(p.comment_count) as avg_comments_per_post
FROM users u
LEFT JOIN posts p ON u.user_id = p.user_id
LEFT JOIN comments c ON u.user_id = c.user_id
LEFT JOIN post_categories pc ON p.post_id = pc.post_id
LEFT JOIN post_tags pt ON p.post_id = pt.post_id
WHERE u.account_status = 'active'
GROUP BY u.user_id, u.username, u.email, u.created_at
),
engagement_analysis AS (
SELECT
ua.*,
-- Activity classification
CASE
WHEN ua.total_posts > 50 AND ua.total_comments > 100 THEN 'highly_active'
WHEN ua.total_posts > 10 AND ua.total_comments > 25 THEN 'moderately_active'
WHEN ua.total_posts > 0 OR ua.total_comments > 0 THEN 'low_activity'
ELSE 'inactive'
END as activity_level,
-- Content quality indicators
CASE
WHEN ua.avg_views_per_post > 1000 AND ua.avg_likes_per_post > 50 THEN 'high_quality'
WHEN ua.avg_views_per_post > 500 AND ua.avg_likes_per_post > 20 THEN 'good_quality'
WHEN ua.avg_views_per_post > 100 THEN 'average_quality'
ELSE 'low_engagement'
END as content_quality,
-- User tenure
EXTRACT(DAYS FROM CURRENT_TIMESTAMP - ua.user_created_at) as days_since_signup,
EXTRACT(DAYS FROM CURRENT_TIMESTAMP - ua.last_activity_at) as days_since_last_activity,
-- Productivity metrics
CASE
WHEN EXTRACT(DAYS FROM CURRENT_TIMESTAMP - ua.user_created_at) > 0 THEN
ua.total_posts / EXTRACT(DAYS FROM CURRENT_TIMESTAMP - ua.user_created_at)::DECIMAL
ELSE 0
END as posts_per_day,
-- Diversity metrics
CASE
WHEN ua.total_posts > 0 THEN ua.categories_used / ua.total_posts::DECIMAL
ELSE 0
END as category_diversity,
CASE
WHEN ua.total_posts > 0 THEN ua.tags_used / ua.total_posts::DECIMAL
ELSE 0
END as tag_diversity
FROM user_activity ua
)
SELECT
ea.username,
ea.activity_level,
ea.content_quality,
ea.total_posts,
ea.published_posts,
ROUND(ea.total_views, 0) as total_views,
ROUND(ea.total_likes, 0) as total_likes,
ea.total_comments,
-- Engagement metrics
ROUND(ea.avg_views_per_post, 1) as avg_views_per_post,
ROUND(ea.avg_likes_per_post, 1) as avg_likes_per_post,
ROUND(ea.avg_comments_per_post, 1) as avg_comments_per_post,
-- Activity metrics
ROUND(ea.posts_per_day, 3) as posts_per_day,
ROUND(ea.category_diversity, 2) as category_diversity,
ROUND(ea.tag_diversity, 2) as tag_diversity,
-- Time metrics
ea.days_since_signup,
ea.days_since_last_activity,
-- Recommendations
CASE
WHEN ea.activity_level = 'inactive' AND ea.days_since_signup < 30 THEN 'new_user_onboarding'
WHEN ea.activity_level = 'low_activity' AND ea.days_since_last_activity > 30 THEN 're_engagement_campaign'
WHEN ea.content_quality = 'high_quality' THEN 'featured_contributor'
WHEN ea.activity_level = 'highly_active' AND ea.content_quality != 'high_quality' THEN 'content_improvement_guidance'
ELSE 'continue_monitoring'
END as engagement_recommendation
FROM engagement_analysis ea
ORDER BY ea.total_views DESC, ea.total_posts DESC;
-- Problems with traditional relational data modeling:
-- 1. Rigid schema requiring extensive migrations for changes
-- 2. Complex joins across multiple tables for simple data retrieval
-- 3. Object-relational impedance mismatch for nested data structures
-- 4. Performance overhead from normalization and multiple table queries
-- 5. Difficulty modeling hierarchical and semi-structured data
-- 6. Limited flexibility for evolving application requirements
-- 7. Complex relationship management requiring junction tables
-- 8. Inefficient storage for sparse or optional data fields
-- 9. Challenging aggregation across related entities
-- 10. Maintenance complexity for schema evolution and data migration
MongoDB provides comprehensive data modeling capabilities with flexible document structures and embedded relationships:
// MongoDB Advanced Data Modeling - flexible document structures with optimized relationships
const { MongoClient, ObjectId } = require('mongodb');
// Comprehensive MongoDB Data Modeling Manager
class AdvancedDataModelingManager {
constructor(mongoUri, modelingConfig = {}) {
this.mongoUri = mongoUri;
this.client = null;
this.db = null;
// Data modeling configuration
this.config = {
// Schema validation settings
enableSchemaValidation: modelingConfig.enableSchemaValidation !== false,
strictValidation: modelingConfig.strictValidation || false,
validationLevel: modelingConfig.validationLevel || 'moderate',
// Document design preferences
embeddingStrategy: modelingConfig.embeddingStrategy || 'balanced', // balanced, aggressive, conservative
referencingThreshold: modelingConfig.referencingThreshold || 100, // Size threshold for referencing
denormalizationLevel: modelingConfig.denormalizationLevel || 'moderate',
// Performance optimization
enableIndexOptimization: modelingConfig.enableIndexOptimization !== false,
enableAggregationOptimization: modelingConfig.enableAggregationOptimization || false,
enableQueryPatternAnalysis: modelingConfig.enableQueryPatternAnalysis || false,
// Relationship management
cascadeDeletes: modelingConfig.cascadeDeletes || false,
maintainReferentialIntegrity: modelingConfig.maintainReferentialIntegrity || false,
enableRelationshipIndexing: modelingConfig.enableRelationshipIndexing !== false,
// Schema evolution
enableSchemaEvolution: modelingConfig.enableSchemaEvolution || false,
backwardCompatibility: modelingConfig.backwardCompatibility !== false,
versionedSchemas: modelingConfig.versionedSchemas || false
};
// Document schemas and relationships
this.documentSchemas = new Map();
this.relationshipMappings = new Map();
this.validationRules = new Map();
// Performance and optimization state
this.queryPatterns = new Map();
this.indexStrategies = new Map();
this.optimizationRecommendations = [];
this.initializeDataModeling();
}
async initializeDataModeling() {
console.log('Initializing advanced MongoDB data modeling...');
try {
// Connect to MongoDB
this.client = new MongoClient(this.mongoUri);
await this.client.connect();
this.db = this.client.db();
// Setup comprehensive user schema with embedded relationships
await this.defineUserSchema();
// Setup post schema with flexible content structure
await this.definePostSchema();
// Setup optimized indexes for performance
if (this.config.enableIndexOptimization) {
await this.setupOptimizedIndexes();
}
// Initialize schema validation if enabled
if (this.config.enableSchemaValidation) {
await this.applySchemaValidation();
}
console.log('Advanced data modeling initialized successfully');
} catch (error) {
console.error('Error initializing data modeling:', error);
throw error;
}
}
async defineUserSchema() {
console.log('Defining comprehensive user schema with embedded relationships...');
try {
const userSchema = {
// Schema metadata
schemaVersion: '1.0',
schemaName: 'user_profile',
lastUpdated: new Date(),
// Document structure
documentStructure: {
// Core identification
_id: 'ObjectId',
userId: 'string', // Application-level ID
username: 'string',
email: 'string',
// Personal information (embedded object)
profile: {
firstName: 'string',
lastName: 'string',
displayName: 'string',
bio: 'string',
dateOfBirth: 'date',
phoneNumber: 'string',
// Professional information
company: 'string',
jobTitle: 'string',
yearsOfExperience: 'number',
educationLevel: 'string',
// Skills and interests (arrays for flexibility)
skills: ['string'],
interests: ['string'],
languages: [
{
language: 'string',
proficiency: 'string' // beginner, intermediate, advanced, native
}
],
// Social media links (flexible object)
socialMedia: {
facebook: 'string',
twitter: 'string',
linkedin: 'string',
instagram: 'string',
github: 'string',
website: 'string'
},
// Profile media
profilePicture: {
url: 'string',
thumbnailUrl: 'string',
uploadedAt: 'date',
fileSize: 'number',
dimensions: {
width: 'number',
height: 'number'
}
},
coverPhoto: {
url: 'string',
uploadedAt: 'date',
fileSize: 'number'
}
},
// Contact information (embedded for locality)
contact: {
addresses: [
{
type: 'string', // home, work, billing, shipping
addressLine1: 'string',
addressLine2: 'string',
city: 'string',
state: 'string',
postalCode: 'string',
country: 'string',
isPrimary: 'boolean',
coordinates: {
latitude: 'number',
longitude: 'number'
}
}
],
phoneNumbers: [
{
type: 'string', // mobile, home, work
number: 'string',
countryCode: 'string',
isPrimary: 'boolean',
isVerified: 'boolean'
}
],
emailAddresses: [
{
email: 'string',
type: 'string', // primary, work, personal
isVerified: 'boolean',
isPrimary: 'boolean'
}
]
},
// Account settings and preferences (embedded)
settings: {
// Privacy settings
privacy: {
profileVisibility: 'string', // public, private, friends
emailVisible: 'boolean',
phoneVisible: 'boolean',
searchable: 'boolean'
},
// Notification preferences
notifications: {
email: {
posts: 'boolean',
comments: 'boolean',
mentions: 'boolean',
messages: 'boolean',
newsletter: 'boolean',
marketing: 'boolean'
},
push: {
posts: 'boolean',
comments: 'boolean',
mentions: 'boolean',
messages: 'boolean'
},
sms: {
security: 'boolean',
important: 'boolean'
}
},
// UI preferences
interface: {
theme: 'string', // light, dark, auto
language: 'string',
timezone: 'string',
dateFormat: 'string',
currency: 'string'
},
// Content preferences
content: {
defaultPostVisibility: 'string',
autoSaveEnabled: 'boolean',
contentLanguages: ['string']
}
},
// Activity tracking (embedded for performance)
activity: {
// Account lifecycle
createdAt: 'date',
updatedAt: 'date',
lastLoginAt: 'date',
lastActiveAt: 'date',
// Status information
status: 'string', // active, inactive, suspended, deleted
emailVerifiedAt: 'date',
phoneVerifiedAt: 'date',
// Statistics (denormalized for performance)
stats: {
totalPosts: 'number',
publishedPosts: 'number',
totalComments: 'number',
totalLikes: 'number',
totalViews: 'number',
followersCount: 'number',
followingCount: 'number',
// Calculated metrics
engagementRate: 'number',
averagePostViews: 'number',
profileCompleteness: 'number'
},
// Activity timeline (recent activities embedded)
recentActivities: [
{
type: 'string', // login, post_created, comment_posted, profile_updated
timestamp: 'date',
details: 'object', // Flexible details object
ipAddress: 'string',
userAgent: 'string'
}
]
},
// Authentication and security (embedded)
authentication: {
passwordHash: 'string',
passwordSalt: 'string',
lastPasswordChange: 'date',
// Two-factor authentication
twoFactorEnabled: 'boolean',
twoFactorSecret: 'string',
backupCodes: ['string'],
// Session management
activeSessions: [
{
sessionId: 'string',
createdAt: 'date',
lastActivityAt: 'date',
ipAddress: 'string',
userAgent: 'string',
deviceInfo: 'object'
}
],
// Security events
securityEvents: [
{
type: 'string', // login_attempt, password_change, suspicious_activity
timestamp: 'date',
details: 'object',
resolved: 'boolean'
}
]
},
// Content relationships (selective referencing for large collections)
content: {
// Recent posts (embedded for performance)
recentPosts: [
{
postId: 'ObjectId',
title: 'string',
createdAt: 'date',
status: 'string',
viewCount: 'number',
likeCount: 'number'
}
],
// Favorite posts (referenced due to potential size)
favoritePostIds: ['ObjectId'],
// Bookmarked content
bookmarks: [
{
contentId: 'ObjectId',
contentType: 'string', // post, comment, user
bookmarkedAt: 'date',
tags: ['string'],
notes: 'string'
}
]
},
// Social relationships (hybrid approach)
social: {
// Close relationships (embedded for performance)
following: [
{
userId: 'ObjectId',
username: 'string',
followedAt: 'date',
relationshipType: 'string' // friend, colleague, interest
}
],
// Large follower lists (referenced)
followerIds: ['ObjectId'],
// Social groups and communities
groups: [
{
groupId: 'ObjectId',
groupName: 'string',
role: 'string', // member, moderator, admin
joinedAt: 'date'
}
]
},
// Flexible metadata for extensibility
metadata: {
customFields: 'object', // Application-specific fields
tags: ['string'],
categories: ['string'],
source: 'string', // registration_source
referrer: 'string'
}
},
// Validation rules
validationRules: {
required: ['username', 'email', 'profile.firstName', 'profile.lastName'],
unique: ['username', 'email', 'userId'],
patterns: {
email: /^[^\s@]+@[^\s@]+\.[^\s@]+$/,
username: /^[a-zA-Z0-9_]{3,30}$/
},
ranges: {
'profile.yearsOfExperience': { min: 0, max: 70 },
'activity.stats.profileCompleteness': { min: 0, max: 100 }
}
},
// Index strategies for optimal performance
indexStrategies: [
{ fields: { username: 1 }, unique: true },
{ fields: { email: 1 }, unique: true },
{ fields: { userId: 1 }, unique: true },
{ fields: { 'activity.lastActiveAt': -1 } },
{ fields: { 'activity.createdAt': -1 } },
{ fields: { 'profile.skills': 1 } },
{ fields: { 'metadata.tags': 1 } },
// Compound indexes for common query patterns
{ fields: { 'activity.status': 1, 'activity.lastActiveAt': -1 } },
{ fields: { 'profile.company': 1, 'profile.jobTitle': 1 } },
{ fields: { 'settings.privacy.profileVisibility': 1, 'activity.stats.totalPosts': -1 } }
]
};
// Store schema definition
this.documentSchemas.set('users', userSchema);
console.log('User schema defined with embedded relationships and flexible structure');
} catch (error) {
console.error('Error defining user schema:', error);
throw error;
}
}
async definePostSchema() {
console.log('Defining flexible post schema with content optimization...');
try {
const postSchema = {
// Schema metadata
schemaVersion: '1.0',
schemaName: 'content_post',
lastUpdated: new Date(),
// Document structure optimized for content management
documentStructure: {
// Core identification
_id: 'ObjectId',
postId: 'string', // Application-level ID
slug: 'string', // URL-friendly identifier
// Author information (denormalized for performance)
author: {
userId: 'ObjectId',
username: 'string',
displayName: 'string',
profilePicture: 'string',
// Author stats (denormalized)
totalPosts: 'number',
followerCount: 'number',
verified: 'boolean'
},
// Content structure (flexible for different content types)
content: {
// Basic content information
title: 'string',
subtitle: 'string',
excerpt: 'string',
body: 'string', // Main content
contentType: 'string', // article, tutorial, review, announcement
// Rich content elements
media: [
{
type: 'string', // image, video, audio, embed
url: 'string',
thumbnailUrl: 'string',
caption: 'string',
altText: 'string',
dimensions: {
width: 'number',
height: 'number'
},
fileSize: 'number',
mimeType: 'string',
duration: 'number', // For video/audio
uploadedAt: 'date'
}
],
// Content structure and formatting
sections: [
{
type: 'string', // paragraph, heading, list, code, quote
content: 'string',
level: 'number', // For headings
language: 'string', // For code blocks
order: 'number'
}
],
// SEO and metadata
seo: {
metaTitle: 'string',
metaDescription: 'string',
keywords: ['string'],
canonicalUrl: 'string',
openGraphImage: 'string',
// Schema.org structured data
structuredData: 'object'
},
// Content settings
formatting: {
readingTime: 'number', // Estimated reading time in minutes
wordCount: 'number',
language: 'string',
rtlDirection: 'boolean'
}
},
// Publication and status management
publication: {
// Status workflow
status: 'string', // draft, review, published, archived, deleted
visibility: 'string', // public, private, unlisted, password_protected
password: 'string', // For password-protected posts
// Publishing timeline
createdAt: 'date',
updatedAt: 'date',
publishedAt: 'date',
scheduledPublishAt: 'date',
// Revision history (embedded for recent changes)
revisions: [
{
version: 'number',
changedAt: 'date',
changedBy: 'ObjectId',
changeType: 'string', // content, metadata, status
changesSummary: 'string',
previousTitle: 'string', // Track major changes
previousContent: 'string' // Last few versions only
}
],
// Publishing settings
allowComments: 'boolean',
allowSharing: 'boolean',
allowIndexing: 'boolean',
requireApproval: 'boolean'
},
// Categorization and tagging (embedded for performance)
taxonomy: {
// Categories (hierarchical structure)
categories: [
{
categoryId: 'ObjectId',
name: 'string',
slug: 'string',
level: 'number', // For hierarchical categories
parentCategory: 'string'
}
],
// Tags (flat structure for flexibility)
tags: [
{
tag: 'string',
relevanceScore: 'number',
addedBy: 'ObjectId',
addedAt: 'date'
}
],
// Custom taxonomies
customFields: {
difficulty: 'string', // For tutorials
estimatedTime: 'number', // For how-to content
targetAudience: 'string',
prerequisites: ['string']
}
},
// Engagement metrics (denormalized for performance)
engagement: {
// View statistics
views: {
total: 'number',
unique: 'number',
today: 'number',
thisWeek: 'number',
thisMonth: 'number',
// View sources
sources: {
direct: 'number',
social: 'number',
search: 'number',
referral: 'number'
}
},
// Interaction statistics
interactions: {
likes: 'number',
dislikes: 'number',
shares: 'number',
bookmarks: 'number',
// Comment statistics
comments: {
total: 'number',
approved: 'number',
pending: 'number',
spam: 'number'
}
},
// Engagement metrics
metrics: {
engagementRate: 'number',
averageTimeOnPage: 'number',
bounceRate: 'number',
socialShares: 'number'
},
// Top comments (embedded for performance)
topComments: [
{
commentId: 'ObjectId',
content: 'string',
author: {
userId: 'ObjectId',
username: 'string',
profilePicture: 'string'
},
createdAt: 'date',
likeCount: 'number',
isHighlighted: 'boolean'
}
]
},
// Comments (hybrid approach - recent embedded, full collection referenced)
comments: {
// Recent comments embedded for quick access
recent: [
{
commentId: 'ObjectId',
parentCommentId: 'ObjectId', // For threading
content: 'string',
// Author information (denormalized)
author: {
userId: 'ObjectId',
username: 'string',
displayName: 'string',
profilePicture: 'string'
},
// Comment metadata
createdAt: 'date',
updatedAt: 'date',
status: 'string', // approved, pending, spam, deleted
// Comment engagement
likeCount: 'number',
replyCount: 'number',
isEdited: 'boolean',
isPinned: 'boolean',
// Moderation
flags: ['string'],
moderationStatus: 'string'
}
],
// Statistics
statistics: {
totalComments: 'number',
approvedComments: 'number',
pendingComments: 'number',
lastCommentAt: 'date'
}
},
// Performance optimization data
performance: {
// Caching information
lastCached: 'date',
cacheVersion: 'string',
// Search optimization
searchTerms: ['string'], // Extracted keywords for search
searchBoost: 'number', // Manual search ranking boost
// Content analysis
sentiment: {
score: 'number', // -1 to 1
magnitude: 'number',
language: 'string'
},
readabilityScore: 'number',
complexity: 'string' // simple, moderate, complex
},
// Flexible metadata
metadata: {
customFields: 'object',
source: 'string', // web, mobile, api
importedFrom: 'string',
externalIds: 'object', // For integration with other systems
// A/B testing
experiments: [
{
experimentId: 'string',
variant: 'string',
startDate: 'date',
endDate: 'date'
}
]
}
},
// Validation rules for data integrity
validationRules: {
required: ['content.title', 'author.userId', 'publication.status'],
unique: ['slug', 'postId'],
patterns: {
slug: /^[a-z0-9-]+$/,
'content.contentType': /^(article|tutorial|review|announcement|news)$/
},
ranges: {
'content.formatting.readingTime': { min: 0, max: 300 },
'engagement.metrics.engagementRate': { min: 0, max: 100 }
}
},
// Index strategies optimized for content queries
indexStrategies: [
{ fields: { slug: 1 }, unique: true },
{ fields: { postId: 1 }, unique: true },
{ fields: { 'author.userId': 1, 'publication.publishedAt': -1 } },
{ fields: { 'publication.status': 1, 'publication.publishedAt': -1 } },
{ fields: { 'taxonomy.categories.name': 1 } },
{ fields: { 'taxonomy.tags.tag': 1 } },
// Text search index
{ fields: { 'content.title': 'text', 'content.body': 'text', 'taxonomy.tags.tag': 'text' } },
// Performance optimization indexes
{ fields: { 'engagement.views.total': -1, 'publication.publishedAt': -1 } },
{ fields: { 'publication.visibility': 1, 'engagement.views.total': -1 } },
{ fields: { 'content.contentType': 1, 'publication.publishedAt': -1 } }
]
};
// Store schema definition
this.documentSchemas.set('posts', postSchema);
console.log('Post schema defined with flexible content structure and performance optimization');
} catch (error) {
console.error('Error defining post schema:', error);
throw error;
}
}
async createOptimizedUserProfile(userData, profileData = {}) {
console.log(`Creating optimized user profile: ${userData.username}`);
try {
const userDocument = {
// Core identification
userId: userData.userId || new ObjectId().toString(),
username: userData.username,
email: userData.email,
// Personal information (embedded)
profile: {
firstName: profileData.firstName || '',
lastName: profileData.lastName || '',
displayName: profileData.displayName || `${profileData.firstName} ${profileData.lastName}`.trim(),
bio: profileData.bio || '',
dateOfBirth: profileData.dateOfBirth ? new Date(profileData.dateOfBirth) : null,
phoneNumber: profileData.phoneNumber || '',
// Professional information
company: profileData.company || '',
jobTitle: profileData.jobTitle || '',
yearsOfExperience: profileData.yearsOfExperience || 0,
educationLevel: profileData.educationLevel || '',
// Skills and interests
skills: profileData.skills || [],
interests: profileData.interests || [],
languages: profileData.languages || [
{ language: 'English', proficiency: 'native' }
],
// Social media links
socialMedia: {
facebook: profileData.socialMedia?.facebook || '',
twitter: profileData.socialMedia?.twitter || '',
linkedin: profileData.socialMedia?.linkedin || '',
instagram: profileData.socialMedia?.instagram || '',
github: profileData.socialMedia?.github || '',
website: profileData.socialMedia?.website || ''
},
// Profile media
profilePicture: profileData.profilePicture ? {
url: profileData.profilePicture.url,
thumbnailUrl: profileData.profilePicture.thumbnailUrl || profileData.profilePicture.url,
uploadedAt: new Date(),
fileSize: profileData.profilePicture.fileSize || 0,
dimensions: profileData.profilePicture.dimensions || { width: 0, height: 0 }
} : null
},
// Contact information
contact: {
addresses: profileData.addresses || [],
phoneNumbers: profileData.phoneNumbers || [],
emailAddresses: [
{
email: userData.email,
type: 'primary',
isVerified: false,
isPrimary: true
}
]
},
// Account settings with sensible defaults
settings: {
privacy: {
profileVisibility: 'public',
emailVisible: false,
phoneVisible: false,
searchable: true
},
notifications: {
email: {
posts: true,
comments: true,
mentions: true,
messages: true,
newsletter: false,
marketing: false
},
push: {
posts: true,
comments: true,
mentions: true,
messages: true
},
sms: {
security: true,
important: false
}
},
interface: {
theme: 'light',
language: 'en',
timezone: 'UTC',
dateFormat: 'MM/DD/YYYY',
currency: 'USD'
},
content: {
defaultPostVisibility: 'public',
autoSaveEnabled: true,
contentLanguages: ['en']
}
},
// Activity tracking
activity: {
createdAt: new Date(),
updatedAt: new Date(),
lastLoginAt: new Date(),
lastActiveAt: new Date(),
status: 'active',
emailVerifiedAt: null,
phoneVerifiedAt: null,
// Initialize statistics
stats: {
totalPosts: 0,
publishedPosts: 0,
totalComments: 0,
totalLikes: 0,
totalViews: 0,
followersCount: 0,
followingCount: 0,
engagementRate: 0,
averagePostViews: 0,
profileCompleteness: this.calculateProfileCompleteness(profileData)
},
recentActivities: [
{
type: 'account_created',
timestamp: new Date(),
details: { source: 'registration' }
}
]
},
// Authentication (placeholder - would be handled by auth system)
authentication: {
passwordHash: '', // Would be set by authentication system
passwordSalt: '',
lastPasswordChange: new Date(),
twoFactorEnabled: false,
activeSessions: [],
securityEvents: []
},
// Initialize content relationships
content: {
recentPosts: [],
favoritePostIds: [],
bookmarks: []
},
// Initialize social relationships
social: {
following: [],
followerIds: [],
groups: []
},
// Metadata
metadata: {
customFields: profileData.customFields || {},
tags: profileData.tags || [],
categories: profileData.categories || [],
source: profileData.source || 'direct_registration',
referrer: profileData.referrer || ''
}
};
// Insert user document
const result = await this.db.collection('users').insertOne(userDocument);
// Update activity statistics
await this.updateUserStatistics(result.insertedId);
return {
success: true,
userId: result.insertedId,
userDocument: userDocument,
profileCompleteness: userDocument.activity.stats.profileCompleteness
};
} catch (error) {
console.error(`Error creating user profile for ${userData.username}:`, error);
return {
success: false,
error: error.message,
username: userData.username
};
}
}
async createOptimizedPost(postData, authorId) {
console.log(`Creating optimized post: ${postData.title}`);
try {
// Get author information for denormalization
const author = await this.db.collection('users').findOne(
{ _id: new ObjectId(authorId) },
{
projection: {
username: 1,
'profile.displayName': 1,
'profile.profilePicture.url': 1,
'activity.stats.totalPosts': 1,
'activity.stats.followersCount': 1
}
}
);
if (!author) {
throw new Error('Author not found');
}
const postDocument = {
// Core identification
postId: postData.postId || new ObjectId().toString(),
slug: postData.slug || this.generateSlug(postData.title),
// Author information (denormalized)
author: {
userId: new ObjectId(authorId),
username: author.username,
displayName: author.profile?.displayName || author.username,
profilePicture: author.profile?.profilePicture?.url || '',
totalPosts: author.activity?.stats?.totalPosts || 0,
followerCount: author.activity?.stats?.followersCount || 0,
verified: false // Would be determined by verification system
},
// Content structure
content: {
title: postData.title,
subtitle: postData.subtitle || '',
excerpt: postData.excerpt || this.generateExcerpt(postData.body),
body: postData.body,
contentType: postData.contentType || 'article',
// Media content
media: postData.media || [],
// Content sections (for structured content)
sections: this.parseContentSections(postData.body),
// SEO optimization
seo: {
metaTitle: postData.seo?.metaTitle || postData.title,
metaDescription: postData.seo?.metaDescription || postData.excerpt,
keywords: postData.seo?.keywords || this.extractKeywords(postData.body),
canonicalUrl: postData.seo?.canonicalUrl || '',
openGraphImage: postData.featuredImage || ''
},
// Content formatting
formatting: {
readingTime: this.calculateReadingTime(postData.body),
wordCount: this.calculateWordCount(postData.body),
language: postData.language || 'en',
rtlDirection: postData.rtlDirection || false
}
},
// Publication settings
publication: {
status: postData.status || 'draft',
visibility: postData.visibility || 'public',
password: postData.password || '',
createdAt: new Date(),
updatedAt: new Date(),
publishedAt: postData.status === 'published' ? new Date() : null,
scheduledPublishAt: postData.scheduledPublishAt ? new Date(postData.scheduledPublishAt) : null,
revisions: [
{
version: 1,
changedAt: new Date(),
changedBy: new ObjectId(authorId),
changeType: 'content',
changesSummary: 'Initial post creation'
}
],
allowComments: postData.allowComments !== false,
allowSharing: postData.allowSharing !== false,
allowIndexing: postData.allowIndexing !== false,
requireApproval: postData.requireApproval || false
},
// Taxonomy
taxonomy: {
categories: (postData.categories || []).map(cat => ({
categoryId: new ObjectId(),
name: cat.name || cat,
slug: this.generateSlug(cat.name || cat),
level: cat.level || 1,
parentCategory: cat.parent || ''
})),
tags: (postData.tags || []).map(tag => ({
tag: typeof tag === 'string' ? tag : tag.name,
relevanceScore: typeof tag === 'object' ? tag.relevance : 1.0,
addedBy: new ObjectId(authorId),
addedAt: new Date()
})),
customFields: postData.customFields || {}
},
// Initialize engagement metrics
engagement: {
views: {
total: 0,
unique: 0,
today: 0,
thisWeek: 0,
thisMonth: 0,
sources: {
direct: 0,
social: 0,
search: 0,
referral: 0
}
},
interactions: {
likes: 0,
dislikes: 0,
shares: 0,
bookmarks: 0,
comments: {
total: 0,
approved: 0,
pending: 0,
spam: 0
}
},
metrics: {
engagementRate: 0,
averageTimeOnPage: 0,
bounceRate: 0,
socialShares: 0
},
topComments: []
},
// Initialize comments
comments: {
recent: [],
statistics: {
totalComments: 0,
approvedComments: 0,
pendingComments: 0,
lastCommentAt: null
}
},
// Performance data
performance: {
lastCached: null,
cacheVersion: '1.0',
searchTerms: this.extractSearchTerms(postData.title, postData.body),
searchBoost: postData.searchBoost || 1.0,
sentiment: this.analyzeSentiment(postData.body),
readabilityScore: this.calculateReadabilityScore(postData.body),
complexity: this.assessComplexity(postData.body)
},
// Metadata
metadata: {
customFields: postData.metadata || {},
source: postData.source || 'web',
importedFrom: postData.importedFrom || '',
externalIds: postData.externalIds || {},
experiments: postData.experiments || []
}
};
// Insert post document
const result = await this.db.collection('posts').insertOne(postDocument);
// Update author statistics
await this.updateAuthorStatistics(authorId, 'post_created');
// Update user's recent posts
await this.updateUserRecentPosts(authorId, result.insertedId, postDocument);
return {
success: true,
postId: result.insertedId,
postDocument: postDocument,
readingTime: postDocument.content.formatting.readingTime,
wordCount: postDocument.content.formatting.wordCount
};
} catch (error) {
console.error(`Error creating post '${postData.title}':`, error);
return {
success: false,
error: error.message,
title: postData.title
};
}
}
async performAdvancedQuery(queryOptions) {
console.log('Executing advanced MongoDB query with optimized document structure...');
try {
const {
collection,
filters = {},
projection = {},
sort = {},
limit = 50,
skip = 0,
includeRelated = false
} = queryOptions;
// Build aggregation pipeline for complex queries
const pipeline = [];
// Match stage
if (Object.keys(filters).length > 0) {
pipeline.push({ $match: filters });
}
// Add related data if requested
if (includeRelated && collection === 'posts') {
pipeline.push(
// Add full comment documents for recent comments
{
$lookup: {
from: 'comments',
localField: '_id',
foreignField: 'postId',
as: 'fullComments',
pipeline: [
{ $match: { status: 'approved' } },
{ $sort: { createdAt: -1 } },
{ $limit: 10 }
]
}
},
// Add author's full profile
{
$lookup: {
from: 'users',
localField: 'author.userId',
foreignField: '_id',
as: 'authorProfile',
pipeline: [
{
$project: {
username: 1,
'profile.displayName': 1,
'profile.bio': 1,
'profile.profilePicture': 1,
'activity.stats': 1
}
}
]
}
}
);
}
// Projection stage
if (Object.keys(projection).length > 0) {
pipeline.push({ $project: projection });
}
// Sort stage
if (Object.keys(sort).length > 0) {
pipeline.push({ $sort: sort });
}
// Pagination
if (skip > 0) {
pipeline.push({ $skip: skip });
}
if (limit > 0) {
pipeline.push({ $limit: limit });
}
// Execute aggregation
const results = await this.db.collection(collection).aggregate(pipeline).toArray();
return {
success: true,
results: results,
count: results.length,
pipeline: pipeline
};
} catch (error) {
console.error('Error executing advanced query:', error);
return {
success: false,
error: error.message,
queryOptions: queryOptions
};
}
}
// Utility methods for document processing and optimization
calculateProfileCompleteness(profileData) {
let score = 0;
const maxScore = 100;
// Basic information (40 points)
if (profileData.firstName) score += 10;
if (profileData.lastName) score += 10;
if (profileData.bio) score += 10;
if (profileData.profilePicture) score += 10;
// Professional information (30 points)
if (profileData.company) score += 10;
if (profileData.jobTitle) score += 10;
if (profileData.skills && profileData.skills.length > 0) score += 10;
// Contact information (20 points)
if (profileData.phoneNumber) score += 10;
if (profileData.addresses && profileData.addresses.length > 0) score += 10;
// Additional information (10 points)
if (profileData.socialMedia && Object.values(profileData.socialMedia).some(url => url)) score += 10;
return Math.min(score, maxScore);
}
generateSlug(title) {
return title
.toLowerCase()
.replace(/[^a-z0-9\s-]/g, '')
.replace(/\s+/g, '-')
.replace(/-+/g, '-')
.trim('-');
}
generateExcerpt(body, maxLength = 200) {
const text = body.replace(/<[^>]*>/g, '').trim(); // Remove HTML tags
return text.length > maxLength ? text.substring(0, maxLength) + '...' : text;
}
calculateReadingTime(text) {
const wordsPerMinute = 200;
const wordCount = this.calculateWordCount(text);
return Math.ceil(wordCount / wordsPerMinute);
}
calculateWordCount(text) {
const cleanText = text.replace(/<[^>]*>/g, '').trim(); // Remove HTML tags
return cleanText.split(/\s+/).filter(word => word.length > 0).length;
}
extractKeywords(text, maxKeywords = 10) {
// Simple keyword extraction - in production, use NLP libraries
const words = text.toLowerCase().match(/\b\w{4,}\b/g) || [];
const frequency = {};
words.forEach(word => {
frequency[word] = (frequency[word] || 0) + 1;
});
return Object.entries(frequency)
.sort(([, a], [, b]) => b - a)
.slice(0, maxKeywords)
.map(([word]) => word);
}
extractSearchTerms(title, body) {
const titleWords = title.toLowerCase().match(/\b\w{3,}\b/g) || [];
const bodyWords = this.extractKeywords(body, 20);
return [...new Set([...titleWords, ...bodyWords])];
}
parseContentSections(body) {
// Simple section parsing - would be more sophisticated in production
const sections = [];
const lines = body.split('\n');
let order = 0;
lines.forEach(line => {
const trimmed = line.trim();
if (trimmed.startsWith('#')) {
const level = trimmed.match(/^#+/)[0].length;
sections.push({
type: 'heading',
content: trimmed.replace(/^#+\s*/, ''),
level: level,
order: order++
});
} else if (trimmed.startsWith('```')) {
sections.push({
type: 'code',
content: trimmed.replace(/```(\w+)?/, ''),
language: trimmed.match(/```(\w+)/)?.[1] || 'text',
order: order++
});
} else if (trimmed.length > 0) {
sections.push({
type: 'paragraph',
content: trimmed,
order: order++
});
}
});
return sections;
}
analyzeSentiment(text) {
// Placeholder sentiment analysis - use proper NLP library in production
const positiveWords = ['good', 'great', 'excellent', 'amazing', 'wonderful', 'fantastic'];
const negativeWords = ['bad', 'terrible', 'awful', 'horrible', 'disappointing'];
const words = text.toLowerCase().split(/\s+/);
let score = 0;
words.forEach(word => {
if (positiveWords.includes(word)) score += 0.1;
if (negativeWords.includes(word)) score -= 0.1;
});
return {
score: Math.max(-1, Math.min(1, score)),
magnitude: Math.abs(score),
language: 'en'
};
}
calculateReadabilityScore(text) {
// Simple readability calculation - use proper libraries in production
const sentences = text.split(/[.!?]+/).filter(s => s.trim().length > 0);
const words = text.split(/\s+/);
const avgWordsPerSentence = words.length / sentences.length;
// Simple scoring based on average sentence length
if (avgWordsPerSentence < 15) return 90;
if (avgWordsPerSentence < 20) return 70;
if (avgWordsPerSentence < 25) return 50;
return 30;
}
assessComplexity(text) {
const wordCount = this.calculateWordCount(text);
const readabilityScore = this.calculateReadabilityScore(text);
if (wordCount < 500 && readabilityScore > 70) return 'simple';
if (wordCount < 2000 && readabilityScore > 50) return 'moderate';
return 'complex';
}
async updateUserStatistics(userId) {
// Update user statistics after profile changes
await this.db.collection('users').updateOne(
{ _id: new ObjectId(userId) },
{
$set: {
'activity.updatedAt': new Date()
}
}
);
}
async updateAuthorStatistics(authorId, action) {
const updates = {};
if (action === 'post_created') {
updates['$inc'] = {
'activity.stats.totalPosts': 1
};
}
updates['$set'] = {
'activity.updatedAt': new Date(),
'activity.lastActiveAt': new Date()
};
await this.db.collection('users').updateOne(
{ _id: new ObjectId(authorId) },
updates
);
}
async updateUserRecentPosts(userId, postId, postDocument) {
await this.db.collection('users').updateOne(
{ _id: new ObjectId(userId) },
{
$push: {
'content.recentPosts': {
$each: [
{
postId: postId,
title: postDocument.content.title,
createdAt: postDocument.publication.createdAt,
status: postDocument.publication.status,
viewCount: 0,
likeCount: 0
}
],
$slice: -10 // Keep only the 10 most recent posts
}
}
}
);
}
async setupOptimizedIndexes() {
console.log('Setting up optimized indexes for document collections...');
try {
// Apply indexes from schema definitions
for (const [collectionName, schema] of this.documentSchemas.entries()) {
const collection = this.db.collection(collectionName);
for (const indexStrategy of schema.indexStrategies) {
await collection.createIndex(indexStrategy.fields, {
background: true,
unique: indexStrategy.unique || false,
sparse: indexStrategy.sparse || false,
partialFilterExpression: indexStrategy.partialFilterExpression
});
}
}
console.log('Optimized indexes created successfully');
} catch (error) {
console.error('Error setting up optimized indexes:', error);
throw error;
}
}
async applySchemaValidation() {
console.log('Applying schema validation rules...');
try {
// Apply validation for users collection
await this.db.createCollection('users', {
validator: {
$jsonSchema: {
bsonType: 'object',
required: ['username', 'email'],
properties: {
username: {
bsonType: 'string',
pattern: '^[a-zA-Z0-9_]{3,30}$',
description: 'Username must be 3-30 characters with only letters, numbers, and underscores'
},
email: {
bsonType: 'string',
pattern: '^[^\\s@]+@[^\\s@]+\\.[^\\s@]+$',
description: 'Valid email address required'
},
'profile.yearsOfExperience': {
bsonType: 'int',
minimum: 0,
maximum: 70,
description: 'Years of experience must be between 0 and 70'
}
}
}
},
validationLevel: this.config.validationLevel,
validationAction: this.config.strictValidation ? 'error' : 'warn'
});
// Apply validation for posts collection
await this.db.createCollection('posts', {
validator: {
$jsonSchema: {
bsonType: 'object',
required: ['content.title', 'author.userId'],
properties: {
'content.title': {
bsonType: 'string',
minLength: 1,
maxLength: 500,
description: 'Post title is required and must be 1-500 characters'
},
'content.contentType': {
bsonType: 'string',
'enum': ['article', 'tutorial', 'review', 'announcement', 'news'],
description: 'Content type must be one of the predefined values'
},
'publication.status': {
bsonType: 'string',
'enum': ['draft', 'review', 'published', 'archived', 'deleted'],
description: 'Publication status must be one of the predefined values'
}
}
}
},
validationLevel: this.config.validationLevel,
validationAction: this.config.strictValidation ? 'error' : 'warn'
});
console.log('Schema validation rules applied successfully');
} catch (error) {
// Collections might already exist, which is fine
if (!error.message.includes('already exists')) {
console.error('Error applying schema validation:', error);
throw error;
}
}
}
}
// Benefits of MongoDB Advanced Data Modeling:
// - Flexible document structures that adapt to application requirements
// - Embedded relationships for optimal read performance and data locality
// - Denormalized data patterns for reduced join operations and improved query speed
// - Hierarchical data modeling with natural document nesting capabilities
// - Schema evolution support without complex migration procedures
// - Optimized indexing strategies for diverse query patterns
// - Rich data types including arrays, objects, and geospatial data
// - Query pattern optimization through strategic embedding and referencing
// - SQL-compatible operations through QueryLeaf integration
// - Production-ready data modeling patterns for scalable applications
module.exports = {
AdvancedDataModelingManager
};
Understanding MongoDB Document Architecture
Advanced Schema Design and Relationship Optimization Patterns
Implement sophisticated data modeling workflows for enterprise MongoDB applications:
// Enterprise-grade data modeling with advanced relationship management capabilities
class EnterpriseDataModelingOrchestrator extends AdvancedDataModelingManager {
constructor(mongoUri, enterpriseConfig) {
super(mongoUri, enterpriseConfig);
this.enterpriseConfig = {
...enterpriseConfig,
enableAdvancedRelationships: true,
enableDataGovernance: true,
enablePerformanceOptimization: true,
enableComplianceValidation: true,
enableSchemaEvolution: true
};
this.setupEnterpriseCapabilities();
this.initializeDataGovernance();
this.setupAdvancedRelationshipManagement();
}
async implementAdvancedDataStrategy() {
console.log('Implementing enterprise data modeling strategy...');
const dataStrategy = {
// Multi-tier data organization
dataTiers: {
operationalData: {
embedding: 'aggressive',
caching: 'memory',
indexing: 'comprehensive',
validation: 'strict'
},
analyticalData: {
embedding: 'conservative',
caching: 'disk',
indexing: 'selective',
validation: 'moderate'
},
archivalData: {
embedding: 'minimal',
caching: 'none',
indexing: 'basic',
validation: 'basic'
}
},
// Advanced relationship management
relationshipManagement: {
dynamicReferencing: true,
cascadingOperations: true,
relationshipIndexing: true,
crossCollectionValidation: true
}
};
return await this.deployDataStrategy(dataStrategy);
}
async setupAdvancedDataGovernance() {
console.log('Setting up enterprise data governance...');
const governanceCapabilities = {
// Data quality management
dataQuality: {
validationRules: true,
dataCleansingPipelines: true,
qualityMonitoring: true,
anomalyDetection: true
},
// Compliance and auditing
compliance: {
dataLineage: true,
auditTrails: true,
privacyControls: true,
retentionPolicies: true
},
// Schema governance
schemaGovernance: {
versionControl: true,
changeApproval: true,
backwardCompatibility: true,
migrationAutomation: true
}
};
return await this.deployDataGovernance(governanceCapabilities);
}
}
SQL-Style Data Modeling with QueryLeaf
QueryLeaf provides familiar SQL syntax for MongoDB data modeling and schema operations:
-- QueryLeaf advanced data modeling operations with SQL-familiar syntax for MongoDB
-- Create comprehensive user profile schema with embedded relationships
CREATE DOCUMENT_SCHEMA user_profiles AS (
-- Core identification
user_id VARCHAR(24) PRIMARY KEY,
username VARCHAR(255) UNIQUE NOT NULL,
email VARCHAR(255) UNIQUE NOT NULL,
-- Embedded personal information
profile OBJECT(
first_name VARCHAR(100),
last_name VARCHAR(100),
display_name VARCHAR(200),
bio TEXT,
date_of_birth DATE,
phone_number VARCHAR(20),
-- Professional information (embedded object)
professional OBJECT(
company VARCHAR(255),
job_title VARCHAR(255),
years_experience INTEGER CHECK(years_experience >= 0 AND years_experience <= 70),
education_level VARCHAR(100),
skills ARRAY[VARCHAR(100)],
languages ARRAY[OBJECT(
language VARCHAR(50),
proficiency VARCHAR(20) CHECK(proficiency IN ('beginner', 'intermediate', 'advanced', 'native'))
)]
),
-- Social media links (embedded object)
social_media OBJECT(
facebook VARCHAR(255),
twitter VARCHAR(255),
linkedin VARCHAR(255),
instagram VARCHAR(255),
github VARCHAR(255),
website VARCHAR(255)
),
-- Profile media (embedded object)
profile_picture OBJECT(
url VARCHAR(500),
thumbnail_url VARCHAR(500),
uploaded_at TIMESTAMP,
file_size INTEGER,
dimensions OBJECT(
width INTEGER,
height INTEGER
)
)
),
-- Contact information (embedded array)
contact OBJECT(
addresses ARRAY[OBJECT(
type VARCHAR(20) CHECK(type IN ('home', 'work', 'billing', 'shipping')),
address_line_1 VARCHAR(255),
address_line_2 VARCHAR(255),
city VARCHAR(100),
state VARCHAR(100),
postal_code VARCHAR(20),
country VARCHAR(100),
is_primary BOOLEAN DEFAULT false,
coordinates OBJECT(
latitude DECIMAL(10, 7),
longitude DECIMAL(10, 7)
)
)],
phone_numbers ARRAY[OBJECT(
type VARCHAR(20) CHECK(type IN ('mobile', 'home', 'work')),
number VARCHAR(20),
country_code VARCHAR(5),
is_primary BOOLEAN DEFAULT false,
is_verified BOOLEAN DEFAULT false
)],
email_addresses ARRAY[OBJECT(
email VARCHAR(255),
type VARCHAR(20) CHECK(type IN ('primary', 'work', 'personal')),
is_verified BOOLEAN DEFAULT false,
is_primary BOOLEAN DEFAULT false
)]
),
-- User settings (embedded object)
settings OBJECT(
privacy OBJECT(
profile_visibility VARCHAR(20) CHECK(profile_visibility IN ('public', 'private', 'friends')) DEFAULT 'public',
email_visible BOOLEAN DEFAULT false,
phone_visible BOOLEAN DEFAULT false,
searchable BOOLEAN DEFAULT true
),
notifications OBJECT(
email OBJECT(
posts BOOLEAN DEFAULT true,
comments BOOLEAN DEFAULT true,
mentions BOOLEAN DEFAULT true,
messages BOOLEAN DEFAULT true,
newsletter BOOLEAN DEFAULT false,
marketing BOOLEAN DEFAULT false
),
push OBJECT(
posts BOOLEAN DEFAULT true,
comments BOOLEAN DEFAULT true,
mentions BOOLEAN DEFAULT true,
messages BOOLEAN DEFAULT true
),
sms OBJECT(
security BOOLEAN DEFAULT true,
important BOOLEAN DEFAULT false
)
),
interface OBJECT(
theme VARCHAR(20) CHECK(theme IN ('light', 'dark', 'auto')) DEFAULT 'light',
language VARCHAR(10) DEFAULT 'en',
timezone VARCHAR(50) DEFAULT 'UTC',
date_format VARCHAR(20) DEFAULT 'MM/DD/YYYY',
currency VARCHAR(3) DEFAULT 'USD'
)
),
-- Activity tracking (embedded object)
activity OBJECT(
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
last_login_at TIMESTAMP,
last_active_at TIMESTAMP,
status VARCHAR(20) CHECK(status IN ('active', 'inactive', 'suspended', 'deleted')) DEFAULT 'active',
email_verified_at TIMESTAMP,
phone_verified_at TIMESTAMP,
-- Denormalized statistics for performance
stats OBJECT(
total_posts INTEGER DEFAULT 0,
published_posts INTEGER DEFAULT 0,
total_comments INTEGER DEFAULT 0,
total_likes INTEGER DEFAULT 0,
total_views INTEGER DEFAULT 0,
followers_count INTEGER DEFAULT 0,
following_count INTEGER DEFAULT 0,
engagement_rate DECIMAL(5,2) DEFAULT 0.0,
average_post_views DECIMAL(10,2) DEFAULT 0.0,
profile_completeness DECIMAL(5,2) DEFAULT 0.0
),
-- Recent activities (embedded array with limited size)
recent_activities ARRAY[OBJECT(
type VARCHAR(50),
timestamp TIMESTAMP,
details OBJECT,
ip_address VARCHAR(45),
user_agent VARCHAR(500)
)] -- Limited to last 50 activities
),
-- Content relationships (selective embedding/referencing)
content OBJECT(
-- Recent posts embedded for performance
recent_posts ARRAY[OBJECT(
post_id VARCHAR(24),
title VARCHAR(500),
created_at TIMESTAMP,
status VARCHAR(20),
view_count INTEGER,
like_count INTEGER
)] -- Limited to last 10 posts
-- Large collections referenced
favorite_post_ids ARRAY[VARCHAR(24)],
-- Bookmarks with metadata
bookmarks ARRAY[OBJECT(
content_id VARCHAR(24),
content_type VARCHAR(20) CHECK(content_type IN ('post', 'comment', 'user')),
bookmarked_at TIMESTAMP,
tags ARRAY[VARCHAR(50)],
notes TEXT
)]
),
-- Social relationships (hybrid approach)
social OBJECT(
-- Following relationships (embedded for moderate size)
following ARRAY[OBJECT(
user_id VARCHAR(24),
username VARCHAR(255),
followed_at TIMESTAMP,
relationship_type VARCHAR(20) CHECK(relationship_type IN ('friend', 'colleague', 'interest'))
)],
-- Large follower lists referenced
follower_ids ARRAY[VARCHAR(24)],
-- Group memberships
groups ARRAY[OBJECT(
group_id VARCHAR(24),
group_name VARCHAR(255),
role VARCHAR(20) CHECK(role IN ('member', 'moderator', 'admin')),
joined_at TIMESTAMP
)]
),
-- Flexible metadata for extensibility
metadata OBJECT(
custom_fields OBJECT,
tags ARRAY[VARCHAR(50)],
categories ARRAY[VARCHAR(50)],
source VARCHAR(100),
referrer VARCHAR(255)
),
-- Indexes for optimal performance
INDEX idx_username (username),
INDEX idx_email (email),
INDEX idx_status_last_active (activity.status, activity.last_active_at DESC),
INDEX idx_skills (profile.professional.skills),
INDEX idx_location (contact.addresses.city, contact.addresses.state),
-- Text search index
INDEX idx_text_search ON (
username TEXT,
profile.display_name TEXT,
profile.bio TEXT,
profile.professional.skills TEXT
),
-- Compound indexes for common query patterns
INDEX idx_visibility_stats (settings.privacy.profile_visibility, activity.stats.total_posts DESC),
INDEX idx_company_role (profile.professional.company, profile.professional.job_title)
);
-- Advanced post schema with flexible content structure
CREATE DOCUMENT_SCHEMA content_posts AS (
-- Core identification
post_id VARCHAR(24) PRIMARY KEY,
slug VARCHAR(500) UNIQUE NOT NULL,
-- Author information (denormalized for performance)
author OBJECT(
user_id VARCHAR(24) NOT NULL,
username VARCHAR(255) NOT NULL,
display_name VARCHAR(200),
profile_picture VARCHAR(500),
total_posts INTEGER,
follower_count INTEGER,
verified BOOLEAN DEFAULT false
),
-- Flexible content structure
content OBJECT(
title VARCHAR(500) NOT NULL,
subtitle VARCHAR(500),
excerpt TEXT,
body TEXT NOT NULL,
content_type VARCHAR(20) CHECK(content_type IN ('article', 'tutorial', 'review', 'announcement', 'news')) DEFAULT 'article',
-- Rich media content
media ARRAY[OBJECT(
type VARCHAR(20) CHECK(type IN ('image', 'video', 'audio', 'embed')),
url VARCHAR(1000),
thumbnail_url VARCHAR(1000),
caption TEXT,
alt_text TEXT,
dimensions OBJECT(
width INTEGER,
height INTEGER
),
file_size INTEGER,
mime_type VARCHAR(100),
duration INTEGER, -- For video/audio
uploaded_at TIMESTAMP
)],
-- Structured content sections
sections ARRAY[OBJECT(
type VARCHAR(20) CHECK(type IN ('paragraph', 'heading', 'list', 'code', 'quote')),
content TEXT,
level INTEGER, -- For headings
language VARCHAR(20), -- For code blocks
order_index INTEGER
)],
-- SEO and metadata
seo OBJECT(
meta_title VARCHAR(500),
meta_description TEXT,
keywords ARRAY[VARCHAR(100)],
canonical_url VARCHAR(1000),
open_graph_image VARCHAR(1000),
structured_data OBJECT
),
-- Content analysis
formatting OBJECT(
reading_time INTEGER, -- Minutes
word_count INTEGER,
language VARCHAR(10) DEFAULT 'en',
rtl_direction BOOLEAN DEFAULT false
)
),
-- Publication management
publication OBJECT(
status VARCHAR(20) CHECK(status IN ('draft', 'review', 'published', 'archived', 'deleted')) DEFAULT 'draft',
visibility VARCHAR(20) CHECK(visibility IN ('public', 'private', 'unlisted', 'password_protected')) DEFAULT 'public',
password VARCHAR(255),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
published_at TIMESTAMP,
scheduled_publish_at TIMESTAMP,
-- Revision tracking (limited to recent changes)
revisions ARRAY[OBJECT(
version INTEGER,
changed_at TIMESTAMP,
changed_by VARCHAR(24),
change_type VARCHAR(20) CHECK(change_type IN ('content', 'metadata', 'status')),
changes_summary TEXT,
previous_title VARCHAR(500),
previous_content TEXT
)] -- Limited to last 10 revisions
allow_comments BOOLEAN DEFAULT true,
allow_sharing BOOLEAN DEFAULT true,
allow_indexing BOOLEAN DEFAULT true,
require_approval BOOLEAN DEFAULT false
),
-- Categorization and tagging
taxonomy OBJECT(
categories ARRAY[OBJECT(
category_id VARCHAR(24),
name VARCHAR(255),
slug VARCHAR(255),
level INTEGER,
parent_category VARCHAR(255)
)],
tags ARRAY[OBJECT(
tag VARCHAR(100),
relevance_score DECIMAL(3,2) DEFAULT 1.0,
added_by VARCHAR(24),
added_at TIMESTAMP
)],
custom_fields OBJECT(
difficulty VARCHAR(20), -- For tutorials
estimated_time INTEGER, -- For how-to content
target_audience VARCHAR(100),
prerequisites ARRAY[VARCHAR(100)]
)
),
-- Engagement metrics (denormalized for performance)
engagement OBJECT(
views OBJECT(
total INTEGER DEFAULT 0,
unique INTEGER DEFAULT 0,
today INTEGER DEFAULT 0,
this_week INTEGER DEFAULT 0,
this_month INTEGER DEFAULT 0,
sources OBJECT(
direct INTEGER DEFAULT 0,
social INTEGER DEFAULT 0,
search INTEGER DEFAULT 0,
referral INTEGER DEFAULT 0
)
),
interactions OBJECT(
likes INTEGER DEFAULT 0,
dislikes INTEGER DEFAULT 0,
shares INTEGER DEFAULT 0,
bookmarks INTEGER DEFAULT 0,
comments OBJECT(
total INTEGER DEFAULT 0,
approved INTEGER DEFAULT 0,
pending INTEGER DEFAULT 0,
spam INTEGER DEFAULT 0
)
),
metrics OBJECT(
engagement_rate DECIMAL(5,2) DEFAULT 0.0,
average_time_on_page INTEGER DEFAULT 0, -- Seconds
bounce_rate DECIMAL(5,2) DEFAULT 0.0,
social_shares INTEGER DEFAULT 0
),
-- Top comments embedded for quick access
top_comments ARRAY[OBJECT(
comment_id VARCHAR(24),
content TEXT,
author OBJECT(
user_id VARCHAR(24),
username VARCHAR(255),
profile_picture VARCHAR(500)
),
created_at TIMESTAMP,
like_count INTEGER,
is_highlighted BOOLEAN DEFAULT false
)] -- Limited to top 5 comments
),
-- Comment management (hybrid approach)
comments OBJECT(
-- Recent comments embedded
recent ARRAY[OBJECT(
comment_id VARCHAR(24),
parent_comment_id VARCHAR(24),
content TEXT,
author OBJECT(
user_id VARCHAR(24),
username VARCHAR(255),
display_name VARCHAR(200),
profile_picture VARCHAR(500)
),
created_at TIMESTAMP,
updated_at TIMESTAMP,
status VARCHAR(20) CHECK(status IN ('approved', 'pending', 'spam', 'deleted')) DEFAULT 'approved',
like_count INTEGER DEFAULT 0,
reply_count INTEGER DEFAULT 0,
is_edited BOOLEAN DEFAULT false,
is_pinned BOOLEAN DEFAULT false,
flags ARRAY[VARCHAR(50)],
moderation_status VARCHAR(20)
)] -- Limited to last 20 comments
statistics OBJECT(
total_comments INTEGER DEFAULT 0,
approved_comments INTEGER DEFAULT 0,
pending_comments INTEGER DEFAULT 0,
last_comment_at TIMESTAMP
)
),
-- Performance optimization
performance OBJECT(
last_cached TIMESTAMP,
cache_version VARCHAR(10),
search_terms ARRAY[VARCHAR(100)],
search_boost DECIMAL(3,2) DEFAULT 1.0,
sentiment OBJECT(
score DECIMAL(3,2), -- -1 to 1
magnitude DECIMAL(3,2),
language VARCHAR(10)
),
readability_score INTEGER,
complexity VARCHAR(20) CHECK(complexity IN ('simple', 'moderate', 'complex'))
),
-- Flexible metadata
metadata OBJECT(
custom_fields OBJECT,
source VARCHAR(50) DEFAULT 'web',
imported_from VARCHAR(100),
external_ids OBJECT,
experiments ARRAY[OBJECT(
experiment_id VARCHAR(50),
variant VARCHAR(50),
start_date DATE,
end_date DATE
)]
),
-- Optimized indexes for content queries
INDEX idx_slug (slug),
INDEX idx_author_published (author.user_id, publication.published_at DESC),
INDEX idx_status_published (publication.status, publication.published_at DESC),
INDEX idx_categories (taxonomy.categories.name),
INDEX idx_tags (taxonomy.tags.tag),
INDEX idx_engagement (engagement.views.total DESC, publication.published_at DESC),
-- Text search index for content
INDEX idx_content_search ON (
content.title TEXT,
content.body TEXT,
taxonomy.tags.tag TEXT,
taxonomy.categories.name TEXT
),
-- Compound indexes for complex queries
INDEX idx_visibility_engagement (publication.visibility, engagement.views.total DESC),
INDEX idx_type_published (content.content_type, publication.published_at DESC),
INDEX idx_author_stats (author.user_id, engagement.interactions.likes DESC)
);
-- Advanced data modeling analysis and optimization queries
WITH document_structure_analysis AS (
SELECT
collection_name,
COUNT(*) as total_documents,
-- Document size analysis
AVG(BSON_SIZE(document)) as avg_document_size_bytes,
MAX(BSON_SIZE(document)) as max_document_size_bytes,
MIN(BSON_SIZE(document)) as min_document_size_bytes,
-- Embedded array analysis
AVG(ARRAY_LENGTH(profile.professional.skills)) as avg_skills_count,
AVG(ARRAY_LENGTH(contact.addresses)) as avg_addresses_count,
AVG(ARRAY_LENGTH(social.following)) as avg_following_count,
-- Nested object complexity
AVG(OBJECT_DEPTH(profile)) as avg_profile_depth,
AVG(OBJECT_DEPTH(settings)) as avg_settings_depth,
AVG(OBJECT_DEPTH(activity)) as avg_activity_depth,
-- Data completeness analysis
COUNT(*) FILTER (WHERE profile.first_name IS NOT NULL) as profiles_with_first_name,
COUNT(*) FILTER (WHERE profile.bio IS NOT NULL) as profiles_with_bio,
COUNT(*) FILTER (WHERE profile.professional.company IS NOT NULL) as profiles_with_company,
COUNT(*) FILTER (WHERE contact.addresses IS NOT NULL AND ARRAY_LENGTH(contact.addresses) > 0) as profiles_with_address,
-- Activity patterns
AVG(activity.stats.total_posts) as avg_posts_per_user,
AVG(activity.stats.profile_completeness) as avg_profile_completeness,
-- Relationship analysis
AVG(ARRAY_LENGTH(content.favorite_post_ids)) as avg_favorites_per_user,
AVG(ARRAY_LENGTH(social.follower_ids)) as avg_followers_per_user
FROM USER_PROFILES
GROUP BY collection_name
),
performance_optimization_analysis AS (
SELECT
dsa.*,
-- Document size categorization
CASE
WHEN dsa.avg_document_size_bytes < 16384 THEN 'optimal_size' -- < 16KB
WHEN dsa.avg_document_size_bytes < 65536 THEN 'good_size' -- < 64KB
WHEN dsa.avg_document_size_bytes < 262144 THEN 'large_size' -- < 256KB
ELSE 'very_large_size' -- >= 256KB
END as document_size_category,
-- Embedding effectiveness
CASE
WHEN dsa.avg_skills_count > 20 THEN 'consider_referencing_skills'
WHEN dsa.avg_following_count > 1000 THEN 'consider_referencing_following'
WHEN dsa.avg_addresses_count > 5 THEN 'consider_referencing_addresses'
ELSE 'embedding_appropriate'
END as embedding_recommendation,
-- Data completeness scoring
ROUND(
(dsa.profiles_with_first_name * 100.0 / dsa.total_documents +
dsa.profiles_with_bio * 100.0 / dsa.total_documents +
dsa.profiles_with_company * 100.0 / dsa.total_documents +
dsa.profiles_with_address * 100.0 / dsa.total_documents) / 4,
2
) as overall_data_completeness_percent,
-- Performance indicators
CASE
WHEN dsa.avg_profile_depth > 4 THEN 'consider_flattening_structure'
WHEN dsa.max_document_size_bytes > 1048576 THEN 'critical_size_optimization_needed' -- > 1MB
WHEN dsa.avg_followers_per_user > 10000 THEN 'implement_follower_pagination'
ELSE 'structure_optimized'
END as structure_optimization_recommendation,
-- Index strategy recommendations
ARRAY[
CASE WHEN dsa.profiles_with_company * 100.0 / dsa.total_documents > 60
THEN 'Add index on profile.professional.company' END,
CASE WHEN dsa.avg_skills_count > 3
THEN 'Optimize skills array indexing' END,
CASE WHEN dsa.profiles_with_address * 100.0 / dsa.total_documents > 70
THEN 'Add geospatial index for addresses' END,
CASE WHEN dsa.avg_posts_per_user > 50
THEN 'Consider post relationship optimization' END
]::TEXT[] as indexing_recommendations
FROM document_structure_analysis dsa
),
content_modeling_analysis AS (
SELECT
'content_posts' as collection_name,
COUNT(*) as total_posts,
-- Content structure analysis
AVG(BSON_SIZE(content)) as avg_content_size_bytes,
AVG(content.formatting.word_count) as avg_word_count,
AVG(content.formatting.reading_time) as avg_reading_time_minutes,
AVG(ARRAY_LENGTH(content.media)) as avg_media_items,
-- Taxonomy analysis
AVG(ARRAY_LENGTH(taxonomy.categories)) as avg_categories_per_post,
AVG(ARRAY_LENGTH(taxonomy.tags)) as avg_tags_per_post,
-- Engagement patterns
AVG(engagement.views.total) as avg_total_views,
AVG(engagement.interactions.likes) as avg_likes,
AVG(engagement.interactions.comments.total) as avg_comments,
-- Comment embedding analysis
AVG(ARRAY_LENGTH(comments.recent)) as avg_embedded_comments,
MAX(ARRAY_LENGTH(comments.recent)) as max_embedded_comments,
-- Content type distribution
COUNT(*) FILTER (WHERE content.content_type = 'article') as article_count,
COUNT(*) FILTER (WHERE content.content_type = 'tutorial') as tutorial_count,
COUNT(*) FILTER (WHERE content.content_type = 'review') as review_count,
-- Publication patterns
COUNT(*) FILTER (WHERE publication.status = 'published') as published_posts,
COUNT(*) FILTER (WHERE publication.status = 'draft') as draft_posts,
-- Performance metrics
AVG(performance.readability_score) as avg_readability_score,
COUNT(*) FILTER (WHERE performance.complexity = 'simple') as simple_content,
COUNT(*) FILTER (WHERE performance.complexity = 'moderate') as moderate_content,
COUNT(*) FILTER (WHERE performance.complexity = 'complex') as complex_content
FROM CONTENT_POSTS
WHERE publication.created_at >= CURRENT_TIMESTAMP - INTERVAL '30 days'
)
SELECT
poa.collection_name,
poa.total_documents,
poa.document_size_category,
-- Size metrics
ROUND(poa.avg_document_size_bytes / 1024.0, 2) as avg_size_kb,
ROUND(poa.max_document_size_bytes / 1024.0, 2) as max_size_kb,
-- Structure analysis
ROUND(poa.avg_profile_depth, 1) as avg_nesting_depth,
poa.embedding_recommendation,
poa.structure_optimization_recommendation,
-- Data quality
ROUND(poa.overall_data_completeness_percent, 1) as data_completeness_percent,
ROUND(poa.avg_profile_completeness, 1) as avg_profile_completeness,
-- Relationship metrics
ROUND(poa.avg_skills_count, 1) as avg_skills_per_user,
ROUND(poa.avg_following_count, 1) as avg_following_per_user,
ROUND(poa.avg_followers_per_user, 1) as avg_followers_per_user,
-- Performance recommendations
ARRAY_REMOVE(poa.indexing_recommendations, NULL) as optimization_recommendations,
-- Data modeling assessment
CASE
WHEN poa.document_size_category = 'very_large_size' THEN 'critical_optimization_needed'
WHEN poa.embedding_recommendation != 'embedding_appropriate' THEN 'relationship_optimization_needed'
WHEN poa.overall_data_completeness_percent < 60 THEN 'data_quality_improvement_needed'
ELSE 'data_model_optimized'
END as overall_assessment,
-- Specific action items
ARRAY[
CASE WHEN poa.avg_document_size_bytes > 262144
THEN 'Split large documents or reference large arrays' END,
CASE WHEN poa.overall_data_completeness_percent < 50
THEN 'Implement data validation and user onboarding improvements' END,
CASE WHEN poa.avg_followers_per_user > 5000
THEN 'Implement follower pagination and lazy loading' END,
CASE WHEN poa.max_document_size_bytes > 1048576
THEN 'URGENT: Address oversized documents immediately' END
]::TEXT[] as action_items,
-- Performance impact
CASE
WHEN poa.document_size_category IN ('large_size', 'very_large_size') THEN 'high_performance_impact'
WHEN poa.embedding_recommendation != 'embedding_appropriate' THEN 'medium_performance_impact'
ELSE 'low_performance_impact'
END as performance_impact
FROM performance_optimization_analysis poa
UNION ALL
-- Content analysis results
SELECT
cma.collection_name,
cma.total_posts as total_documents,
CASE
WHEN cma.avg_content_size_bytes < 32768 THEN 'optimal_size'
WHEN cma.avg_content_size_bytes < 131072 THEN 'good_size'
WHEN cma.avg_content_size_bytes < 524288 THEN 'large_size'
ELSE 'very_large_size'
END as document_size_category,
ROUND(cma.avg_content_size_bytes / 1024.0, 2) as avg_size_kb,
0 as max_size_kb, -- Placeholder for union compatibility
0 as avg_nesting_depth, -- Placeholder
CASE
WHEN cma.avg_media_items > 10 THEN 'consider_referencing_media'
WHEN cma.max_embedded_comments > 50 THEN 'optimize_comment_embedding'
ELSE 'embedding_appropriate'
END as embedding_recommendation,
CASE
WHEN cma.avg_content_size_bytes > 524288 THEN 'split_large_content'
WHEN cma.avg_embedded_comments > 25 THEN 'implement_comment_pagination'
ELSE 'structure_optimized'
END as structure_optimization_recommendation,
ROUND((cma.published_posts * 100.0 / cma.total_posts), 1) as data_completeness_percent,
ROUND(cma.avg_readability_score, 1) as avg_profile_completeness,
ROUND(cma.avg_categories_per_post, 1) as avg_skills_per_user,
ROUND(cma.avg_tags_per_post, 1) as avg_following_per_user,
ROUND(cma.avg_total_views, 0) as avg_followers_per_user,
ARRAY[
CASE WHEN cma.avg_word_count > 3000 THEN 'Consider content length optimization' END,
CASE WHEN cma.avg_media_items > 5 THEN 'Optimize media storage and delivery' END,
CASE WHEN cma.complex_content > cma.total_posts * 0.3 THEN 'Improve content readability' END
]::TEXT[] as optimization_recommendations,
CASE
WHEN cma.avg_content_size_bytes > 524288 THEN 'critical_optimization_needed'
WHEN cma.avg_embedded_comments > 25 THEN 'relationship_optimization_needed'
ELSE 'data_model_optimized'
END as overall_assessment,
ARRAY[
CASE WHEN cma.avg_content_size_bytes > 262144 THEN 'Optimize content storage and caching' END,
CASE WHEN cma.max_embedded_comments > 50 THEN 'Implement comment pagination' END
]::TEXT[] as action_items,
CASE
WHEN cma.avg_content_size_bytes > 262144 THEN 'high_performance_impact'
ELSE 'low_performance_impact'
END as performance_impact
FROM content_modeling_analysis cma
ORDER BY performance_impact DESC, total_documents DESC;
-- QueryLeaf provides comprehensive MongoDB data modeling capabilities:
-- 1. Flexible document schema design with embedded and referenced relationships
-- 2. Advanced validation rules and constraints for data integrity
-- 3. Optimized indexing strategies for diverse query patterns
-- 4. Performance-focused embedding and referencing decisions
-- 5. Schema evolution support with backward compatibility
-- 6. Data quality analysis and optimization recommendations
-- 7. SQL-familiar syntax for complex MongoDB data operations
-- 8. Enterprise-grade data governance and compliance features
-- 9. Automated performance optimization and monitoring
-- 10. Production-ready data modeling patterns for scalable applications
Best Practices for Production Data Modeling
Document Design Strategy and Performance Optimization
Essential principles for effective MongoDB data modeling in production environments:
- Embedding vs. Referencing Strategy: Design optimal data relationships based on access patterns, update frequency, and document size constraints
- Schema Evolution Planning: Implement flexible schemas that can evolve with application requirements while maintaining backward compatibility
- Performance-First Design: Optimize document structures for common query patterns and minimize the need for complex aggregations
- Data Integrity Management: Establish validation rules, referential integrity patterns, and data quality monitoring procedures
- Indexing Strategy: Design comprehensive indexing strategies that support diverse query patterns while minimizing storage overhead
- Scalability Considerations: Plan for growth patterns and design document structures that scale efficiently with data volume
Enterprise Data Governance
Implement comprehensive data governance for enterprise-scale applications:
- Data Quality Framework: Establish automated data validation, cleansing pipelines, and quality monitoring systems
- Schema Governance: Implement version control, change approval processes, and automated migration procedures for schema evolution
- Compliance Integration: Ensure data modeling patterns meet regulatory requirements and industry standards
- Performance Monitoring: Monitor query performance, document size growth, and relationship efficiency continuously
- Data Lifecycle Management: Design retention policies, archival strategies, and data purging procedures
- Documentation Standards: Maintain comprehensive documentation for schemas, relationships, and optimization decisions
Conclusion
MongoDB data modeling provides comprehensive document design capabilities that enable sophisticated relationship management, flexible schema evolution, and performance-optimized data structures through embedded documents, selective referencing, and intelligent denormalization strategies. The native document model and rich data types ensure that applications can represent complex data relationships naturally while maintaining optimal query performance.
Key MongoDB Data Modeling benefits include:
- Flexible Document Structures: Rich document model with native support for arrays, embedded objects, and hierarchical data organization
- Optimized Relationships: Strategic embedding and referencing patterns that balance performance, consistency, and maintainability
- Schema Evolution: Dynamic schema capabilities that adapt to changing requirements without complex migration procedures
- Performance Optimization: Document design patterns that minimize query complexity and maximize read/write efficiency
- Data Integrity: Comprehensive validation rules, constraints, and referential integrity patterns for production data quality
- SQL Accessibility: Familiar SQL-style data modeling operations through QueryLeaf for accessible document design
Whether you're designing user management systems, content platforms, e-commerce applications, or analytical systems, MongoDB data modeling with QueryLeaf's familiar SQL interface provides the foundation for sophisticated, scalable document-oriented applications.
QueryLeaf Integration: QueryLeaf automatically optimizes MongoDB data modeling operations while providing SQL-familiar syntax for schema design, relationship management, and validation rules. Advanced document structures, embedding strategies, and performance optimization are seamlessly handled through familiar SQL constructs, making sophisticated data modeling accessible to SQL-oriented development teams.
The combination of MongoDB's flexible document capabilities with SQL-style modeling operations makes it an ideal platform for applications requiring both complex data relationships and familiar database design patterns, ensuring your data architecture can evolve efficiently while maintaining performance and consistency as application complexity and data volume grow.