NoSQL Databases

Introduction

NoSQL (Not Only SQL) databases provide a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases. They are designed to handle large volumes of unstructured data and provide flexible schema design.

Key Characteristics:

Schema-less design
Horizontal scalability
Eventually consistent
BASE properties
Distributed architecture
High availability

Types of NoSQL

Document Stores

Store data in document format (JSON, BSON, etc.):

// MongoDB document example
{
    "_id": "123",
    "username": "john_doe",
    "profile": {
        "name": "John Doe",
        "age": 30,
        "interests": ["coding", "music"]
    },
    "posts": [
        {
            "title": "NoSQL Introduction",
            "likes": 42
        }
    ]
}

Key-Value Stores

Simple key-value pair storage:

# Redis commands example
SET user:123 "John Doe"
SET user:123:email "john@example.com"
EXPIRE user:session:123 3600
HSET user:123:profile name "John Doe" age "30"

Wide-Column Stores

Two-dimensional key-value storage:

-- Cassandra table example
CREATE TABLE users (
    user_id uuid,
    username text,
    email text,
    created_at timestamp,
    PRIMARY KEY (user_id, created_at)
) WITH CLUSTERING ORDER BY (created_at DESC);

Graph Databases

Store data in nodes and edges:

// Neo4j query example
CREATE (john:Person {name: 'John'})
CREATE (jane:Person {name: 'Jane'})
CREATE (john)-[:FOLLOWS]->(jane)
RETURN john, jane;

Use Cases

Document Stores

Ideal for:

Content management systems
Catalogs and product data
User profiles and preferences
Real-time analytics

Key-Value Stores

Ideal for:

Caching
Session management
Real-time recommendations
Shopping carts

Wide-Column Stores

Ideal for:

Time-series data
Weather data
IoT applications
Large-scale data analytics

Graph Databases

Ideal for:

Social networks
Recommendation engines
Fraud detection
Network topology

Popular Implementations

Document Stores

Key-Value Stores

Redis - In-memory data structure store
DynamoDB - AWS's managed NoSQL
Riak - Distributed key-value store

Wide-Column Stores

Cassandra - Highly scalable columnar store
HBase - Hadoop database
ScyllaDB - High-performance alternative

Scaling & Performance

Horizontal Scaling

Scaling Strategies:

Sharding
Replication
Load balancing
Data partitioning

Performance Considerations

Key Factors:

Data model design
Query patterns
Consistency requirements
Hardware resources

Best Practices

Design Guidelines

Best Practices:

Design for query patterns
Plan for scaling
Consider data consistency needs
Implement proper backup strategies
Monitor performance
Handle failure scenarios

Security Considerations

Security Measures:

Authentication
Authorization
Encryption at rest
Network security
Audit logging