Database Overview

Databases are fundamental to modern applications, storing and managing data efficiently. Choosing the right database depends on your data model, scalability needs, and query patterns.

Database Categories

Relational Databases (SQL)

Structured data with predefined schemas, supporting complex queries and transactions.

Characteristics:

• ACID transactions (Atomic, Consistent, Isolated, Durable)
• Structured Query Language (SQL)
• Fixed schema with data types and constraints
• Strong consistency

Best For:

• Financial transactions
• User authentication and profiles
• Inventory management
• Systems requiring complex relationships

NoSQL Databases

Flexible schema design optimized for specific use cases.

NoSQL Types:

• Document: JSON-like documents (MongoDB, CouchDB)
• Key-Value: Simple key-value pairs (Redis, DynamoDB)
• Column-Family: Wide column stores (Cassandra, HBase)
• Graph: Relationship-focused (Neo4j, Amazon Neptune)

Best For:

• Rapid prototyping and evolving schemas
• Real-time big data applications
• Content management systems
• Social networks and recommendation engines

Database Selection Guide

Database	Type	When to Use	Scalability	Consistency
PostgreSQL	Relational	Complex queries, transactions	Vertical scaling first, then read replicas	Strong
MySQL	Relational	Web applications, CMS	Read replicas, sharding	Strong
SQLite	Relational	Embedded, mobile, small apps	Single file only	Strong
MongoDB	Document	Flexible schema, rapid development	Horizontal sharding	Eventual
Redis	Key-Value	Caching, sessions, real-time	Horizontal clustering	Tunable
Cassandra	Column	High write throughput, time-series	Horizontal scaling	Tunable

Data Modeling

Relational Modeling

Normalization: Organize data to minimize redundancy.

Normalization Levels:

• 1NF: Atomic values, no repeating groups
• 2NF: No partial dependencies
• 3NF: No transitive dependencies
• BCNF: Boyce-Codd normal form

When to Normalize:

• Data integrity is critical
• Write-heavy workloads
• Complex relationships

When to Denormalize:

• Read-heavy workloads
• Performance optimization
• Simpler queries

Document Modeling

Embedding vs Referencing:

Embed: Store related data in single document

{
  "user": {
    "name": "John",
    "address": { "street": "123 Main" }
  }
}

Use When: Data always accessed together, one-to-one relationships

Reference: Store separate documents with IDs

{
  "order": {
    "user_id": "user123",
    "items": ["item1", "item2"]
  }
}

Use When: Data accessed independently, one-to-many relationships

Scaling Strategies

Vertical Scaling (Scale Up)

Add more resources (CPU, RAM, storage) to a single server.

Pros:

• Simple to implement
• No application changes needed
• Strong consistency maintained

Cons:

• Hardware limits
• Single point of failure
• Expensive at scale

Horizontal Scaling (Scale Out)

Add more servers to distribute load.

Read Replicas:

• Master handles writes
• Replicas handle reads
• Eventual consistency

Sharding:

• Data partitioned across multiple servers
• Each shard handles subset of data
• Requires sharding key

Pros:

• Theoretically unlimited scaling
• Better fault tolerance
• Cost-effective

Cons:

• Complex to implement
• Distributed transactions challenging
• Cross-shard queries difficult

Performance Optimization

Indexing

Data structures that speed up data retrieval.

Index Types:

• B-Tree: Default for most databases, good for range queries
• Hash: Fast for exact matches
• Full-text: Text search capabilities
• Composite: Multiple columns

When to Index:

• Frequently queried columns
• Columns in WHERE, JOIN, ORDER BY clauses
• Foreign keys

When NOT to Index:

• Columns frequently updated (slow down writes)
• Tables with few rows
• Low-selectivity columns (many duplicates)

Query Optimization

Best Practices:

• Use EXPLAIN/EXPLAIN ANALYZE to understand query plans
• Avoid SELECT *, only fetch needed columns
• Use LIMIT for large result sets
• Batch operations instead of loops
• Optimize JOINs by indexing join columns

Caching

Application-Level Caching:

• Cache frequent queries in Redis
• Invalidate cache on data changes
• Consider cache expiration policies

Database-Level Caching:

• Query cache (MySQL)
• Buffer pool (PostgreSQL)
• Connection pooling

Database Backup Strategies

Backup Types

Type	Description	Recovery Time	Storage
Full Backup	Complete database backup	Fastest	Most
Incremental	Changes since last backup	Medium	Medium
Differential	Changes since last full backup	Medium	Less than incremental

Backup Considerations

• Recovery Point Objective (RPO): Maximum data loss tolerated
• Recovery Time Objective (RTO): Maximum downtime tolerated
• Backup retention: How long to keep backups
• Off-site storage: Store backups in different location

Common Scenarios

Scenario: E-commerce Application

Database: PostgreSQL with Redis cache Why: ACID transactions for orders, Redis for session storage and product caching

Scenario: Real-time Analytics

Database: MongoDB with aggregation pipeline Why: Flexible schema for diverse event data, powerful aggregation for analytics

Scenario: Content Management System

Database: PostgreSQL Why: Strong consistency, complex queries for content relationships

Scenario: Mobile Application Backend

Database: SQLite on device, PostgreSQL server Why: Offline capability with sync to central database

Further Reading

• PostgreSQL - Advanced PostgreSQL features and commands
• PostgreSQL Internals - WAL, MVCC, storage engines
• MySQL - MySQL administration and optimization
• Redis - Redis data structures and patterns
• Redis Patterns - Common Redis use cases
• MongoDB - MongoDB CRUD and aggregation
• MongoDB Aggregation - Advanced data processing
• SQLite - SQLite for embedded applications
• SQLite Optimization - Performance tuning for SQLite