What is MongoDB?
MongoDB is a document-oriented NoSQL database that uses JSON-like documents with optional schemas. It provides high performance, high availability, and easy scalability with features like automatic sharding and replica sets.
100K+
Operations/sec
16MB
Max document size
BSON
Document format
Flexible
Schema design
CAP Theorem & MongoDB
Consistency
MongoDB provides tunable consistency through read and write concerns
Options: majority, local, linearizable read concerns
Availability
Replica sets provide automatic failover and high availability
Features: Primary-secondary, automatic elections
Partition Tolerance
Sharding enables horizontal scaling across network partitions
Strategy: CP or AP depending on configuration
Core Concepts
Document Model
JSON-like documents with dynamic schemas for flexible data storage
Key Features
- BSON format
- Nested objects
- Arrays
- Dynamic schema
- Rich data types
Example
{ "_id": ObjectId(), "name": "John", "address": { "city": "NYC", "zip": "10001" }, "hobbies": ["reading", "gaming"] }🧮 MongoDB Performance Calculator
Database Configuration
Performance Metrics
Operations/sec9,000
Memory Usage712 MB
Consistency LevelStrong Consistency
Based on read/write concern settings
DurabilityHigh
Journaling enabled
Data Modeling Patterns
Embedded Documents
Store related data together within a single document
Implementation
Nest objects or arrays within parent document✅ Advantages
- Single read operation
- Atomic updates
- Better performance
- Natural data grouping
⚠️ Challenges
- Document size limits
- Data duplication
- Update complexity
- Query flexibility
Aggregation Pipeline Examples
Sales Analysis
Group sales by category and calculate totals
Aggregation Pipeline Example
[
{ $match: { date: { $gte: '2024-01-01' } } },
{ $group: { _id: '$category', total: { $sum: '$amount' } } },
{ $sort: { total: -1 } }
]User Analytics
Calculate average session duration by user type
[
{ $unwind: '$sessions' },
{ $group: { _id: '$userType', avgDuration: { $avg: '$sessions.duration' } } }
]Geographic Analysis
Find nearby locations using geospatial queries
[
{ $geoNear: { near: [lng, lat], distanceField: 'distance' } },
{ $match: { distance: { $lt: 1000 } } }
]Text Search
Full-text search with relevance scoring
[
{ $match: { $text: { $search: 'mongodb database' } } },
{ $addFields: { score: { $meta: 'textScore' } } },
{ $sort: { score: -1 } }
]🏢 Real-world Implementations
Facebook: User Profiles
• 2.8+ billion user profiles
• Flexible schema for varied data
• Embedded documents for preferences
• Sharding by user ID hash
Pattern: Document model for complex user data, horizontal sharding
Adobe: Content Management
• Creative Cloud asset storage
• Polymorphic documents for different asset types
• GridFS for large files
• Full-text search across metadata
Pattern: Polymorphic schema, GridFS for large files, text indexing
eBay: Product Catalog
• 1.3+ billion product listings
• Varying product attributes
• Geographic sharding by seller location
• Aggregation for category analytics
Pattern: Flexible schema for products, geo-sharding, aggregation pipelines
IoT Analytics Platform
• Time-series sensor data
• Bucket pattern for efficiency
• Capped collections for recent data
• Aggregation for real-time analytics
Pattern: Bucket pattern, capped collections, time-series optimization
💡 Key Takeaways
- • Schema Design: Choose embedding vs. referencing based on access patterns
- • Indexing Strategy: Create indexes aligned with your query patterns
- • Aggregation Power: Use aggregation pipelines for complex analytics
- • Sharding Strategy: Plan shard keys for even data distribution
- • Document Size: Keep documents under 16MB, optimize for common queries
- • Consistency Tuning: Balance consistency vs. performance with read/write concerns
📝 MongoDB Mastery Quiz
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