A comprehensive collection of recipes, guides, and solutions for MongoDB - from basics to advanced topics including Atlas, Vector Search, LLM integration, and scaling.
This cookbook contains practical recipes for:
- CRUD operations and query patterns
- Performance optimization and indexing
- Aggregation framework
- Replication and high availability
- Sharding and horizontal scaling
- Schema design best practices
- Security and encryption (including CSFLE)
- MongoDB Atlas cloud deployment
- Vector Search for AI applications
- LLM integration and RAG systems
- Troubleshooting common problems
- Backup, recovery, and monitoring
- Installation Guide - Install MongoDB on various platforms
- Connection Guide - Connect from shell and programming languages
- Create Operations - Insert documents
- Read Operations - Query and find documents
- Update Operations - Modify existing documents
- Delete Operations - Remove documents
- Indexes - Create and manage indexes
- Query Optimization - Optimize query performance
- Aggregation Basics - Pipeline operations and patterns
- Replica Sets - High availability and failover
- Client-Side Field Level Encryption (CSFLE) - Encrypt sensitive data
- Common Problems - Solutions to frequent issues
- Getting Started with Atlas - Cloud deployment and management
- Atlas Vector Search - Semantic search with embeddings
- MongoDB with LLMs - RAG, conversation storage, prompt management
- Scaling MongoDB - Horizontal and vertical scaling strategies
Basic installation and connection
Create, Read, Update, Delete operations with examples in multiple languages
Index types, query optimization, performance tuning
Pipeline stages, operators, real-world examples
Data modeling, embedded vs referenced documents, design patterns
Replica sets, failover, read preferences, write concerns
Horizontal scaling, shard keys, zone sharding
Common problems and solutions, debugging techniques
Authentication, authorization, encryption, CSFLE
Backup strategies, point-in-time recovery, disaster recovery
Performance metrics, profiling, alerts
Cloud deployment, features, configuration
Semantic search, embeddings, similarity search for AI applications
RAG systems, conversation storage, prompt management, caching
Vertical scaling, horizontal scaling, sharding, global distribution
All recipes include practical code examples in multiple languages:
- JavaScript/Node.js - Most common for modern applications
- Python - Popular for data science and backend
- mongosh - MongoDB Shell for interactive use
- Java, PHP, Go - Where applicable
- Product catalogs
- Shopping carts
- Order management
- Inventory tracking
- Recommendation systems
- Articles and blogs
- Media storage with GridFS
- Full-text search
- Content versioning
- Time-series data
- Event logging
- Real-time dashboards
- Aggregation pipelines
- Vector embeddings storage
- Semantic search
- RAG (Retrieval Augmented Generation)
- Conversation history
- Training data management
- Multi-tenancy
- User management
- Session storage
- API rate limiting
- Sensor data collection
- Time-series analysis
- Device management
- Real-time monitoring
Build AI-powered semantic search applications:
- Store vector embeddings
- Similarity search
- Recommendation engines
- RAG for LLMs
- Multi-modal search
Protect sensitive data with end-to-end encryption:
- Encrypt before sending to database
- Query encrypted fields
- Multiple KMS provider support
- Compliance (HIPAA, GDPR)
Serverless backend:
- Triggers (database, scheduled, authentication)
- Functions
- GraphQL API
- Data API (HTTP endpoints)
Full-text search capabilities:
- Fuzzy matching
- Autocomplete
- Faceted search
- Synonyms
- Custom analyzers
Throughout this cookbook, you'll find best practices for:
- Schema Design: Embed vs reference, denormalization
- Indexing: When and how to create indexes
- Query Optimization: Efficient query patterns
- Security: Authentication, encryption, least privilege
- High Availability: Replica sets, failover
- Scalability: Sharding strategies, connection pooling
- Monitoring: Key metrics to track
- Backup: Regular backups, testing recovery
- Performance: Caching, batching, optimization
- Cost Optimization: Right-sizing, query efficiency
- Slow queries → Index optimization
- Connection issues → Network and authentication
- Out of memory → Query optimization, indexes
- Replication lag → Oplog sizing, network
- Duplicate keys → Unique indexes, upserts
- Large documents → Schema redesign, GridFS
- High costs → Query optimization, caching
- Data loss → Replication, backups, write concerns
- Security breaches → Authentication, encryption, auditing
- Scaling limits → Sharding, read replicas
This cookbook is designed to help MongoDB developers solve real-world problems. Each recipe focuses on practical solutions with working code examples.
- MongoDB
- Express.js
- React
- Node.js
- MongoDB
- FastAPI/Django
- Python
- MongoDB
- Spring Boot
- Java
This cookbook covers MongoDB 6.0+ features with compatibility notes for earlier versions where applicable. Atlas-specific features require MongoDB Atlas.
# Install MongoDB
# See recipes/01-getting-started/installation.md
# Start MongoDB
mongod
# Connect with mongosh
mongosh
# Create database and insert document
use myapp
db.users.insertOne({ name: "John Doe", email: "john@example.com" })
# Query
db.users.findOne({ name: "John Doe" })This cookbook is provided as-is for educational and reference purposes.
Happy coding with MongoDB! 🍃