Database Design Principles

Start with Requirements

Understand the data you need to store and how it will be accessed before designing schemas.

Normalize for Integrity

Apply normalization rules to reduce data redundancy and maintain consistency.

Optimize for Access Patterns

Design schemas based on how data will be queried, not just how it’s stored.

Plan for Growth

Consider data volume growth and design for horizontal scaling when needed.

Relational Database Design

Entity-Relationship Modeling

• Entities: Real-world objects (Users, Products, Orders)
• Attributes: Properties of entities (name, email, price)
• Relationships: How entities relate to each other

Normalization Forms

• 1NF: Eliminate repeating groups
• 2NF: Remove partial dependencies
• 3NF: Remove transitive dependencies
• BCNF: Boyce-Codd normal form for complex dependencies

Common Patterns

• User Management: Users, roles, permissions
• E-commerce: Products, categories, orders, payments
• Content Management: Articles, categories, tags, comments
• Social Features: Users, posts, likes, follows

NoSQL Design Considerations

Document Databases (MongoDB)

// Embedded documents for related data
{
  _id: ObjectId("..."),
  user: {
    name: "John Doe",
    email: "john@example.com"
  },
  posts: [
    { title: "Post 1", content: "..." },
    { title: "Post 2", content: "..." }
  ]
}

Key-Value Stores (Redis)

• Simple key-value pairs for caching
• Hashes for structured data
• Sets and sorted sets for unique collections
• Lists for ordered data

Graph Databases (Neo4j)

• Nodes represent entities
• Relationships connect nodes
• Properties on both nodes and relationships
• Cypher query language for traversals

Indexing Strategies

B-Tree Indexes (Default)

• Good for range queries and sorting
• Used in most relational databases
• Balanced tree structure for O(log n) operations

Hash Indexes

• O(1) lookup for exact matches
• Not good for range queries
• Used in key-value stores

Full-Text Search Indexes

• For text search within documents
• Support for fuzzy matching and relevance scoring
• Used in document and search databases

Composite Indexes

• Multiple columns in a single index
• Good for queries filtering on multiple fields
• Order of columns matters for performance

Performance Optimization

Query Analysis

• Use EXPLAIN plans to understand query execution
• Identify slow queries and missing indexes
• Monitor query performance over time

Denormalization Trade-offs

• Sometimes duplicate data for read performance
• Update multiple places when data changes
• Use materialized views for complex aggregations

Partitioning Strategies

• Horizontal Partitioning: Split rows across multiple tables
• Vertical Partitioning: Split columns across multiple tables
• Functional Partitioning: Separate by business domain

Migration Strategies

Schema Evolution

• Add columns with default values
• Use feature flags for gradual rollout
• Plan rollback strategies

Data Migration

• Migrate in small batches to minimize downtime
• Use dual-write strategies during transition
• Validate data integrity after migration

Versioning

• Keep track of schema versions
• Support multiple versions during transition
• Use migration tools (Flyway, Liquibase)

Best Practices

Naming Conventions

• Use consistent naming across tables and columns
• Avoid reserved keywords
• Use singular or plural consistently

Constraints and Validation

• Use database constraints for data integrity
• Validate data at application level too
• Handle constraint violations gracefully

Documentation

• Document schema decisions and rationale
• Keep data dictionary up to date
• Include examples and usage patterns