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name: database-design-patterns description: Database schema design patterns and optimization strategies for relational and NoSQL databases. Use when designing database schemas, optimizing query performance, or implementing data persistence layers at scale.

Database Design Patterns

Expert guidance for designing scalable database schemas, optimizing query performance, and implementing robust data persistence layers across relational and NoSQL databases.

When to Use This Skill

• Designing database schemas for new applications
• Optimizing slow queries and database performance
• Choosing between normalization and denormalization strategies
• Implementing partitioning, sharding, or replication strategies
• Migrating between database technologies (SQL to NoSQL or vice versa)
• Designing for high availability and disaster recovery
• Implementing caching strategies and read replicas
• Scaling databases horizontally or vertically
• Ensuring data consistency in distributed systems

Core Concepts

Data Modeling

Design schemas that reflect business domain, access patterns, and consistency requirements. Balance normalization (data integrity) with denormalization (read performance) based on workload characteristics.

ACID vs. BASE

• ACID (Relational): Atomicity, Consistency, Isolation, Durability - strong guarantees
• BASE (NoSQL): Basically Available, Soft state, Eventually consistent - flexibility

CAP Theorem

Distributed systems choose two of three: Consistency, Availability, Partition Tolerance.

Polyglot Persistence

Use the right database for each use case: PostgreSQL for transactions, MongoDB for documents, Redis for caching, Elasticsearch for search, Cassandra for time-series, Neo4j for graphs.

Quick Reference

Task	Load reference
Core database principles (ACID, BASE, CAP)	skills/database-design-patterns/references/core-principles.md
Schema patterns (normalization, star schema, documents)	skills/database-design-patterns/references/schema-design-patterns.md
Index types and strategies (B-tree, hash, covering)	skills/database-design-patterns/references/indexing-strategies.md
Partitioning and sharding approaches	skills/database-design-patterns/references/partitioning-patterns.md
Replication modes (primary-replica, multi-leader)	skills/database-design-patterns/references/replication-patterns.md
Query optimization and caching	skills/database-design-patterns/references/query-optimization.md

Workflow

Phase 1: Requirements Analysis

1. Identify access patterns (read-heavy vs. write-heavy)
2. Determine consistency requirements (strong vs. eventual)
3. Estimate data volume and growth rate
4. Define SLA requirements (latency, availability)

Phase 2: Schema Design

1. Model entities and relationships
2. Choose normalization level based on workload
3. Design for query patterns, not just storage
4. Consider data distribution strategy (partitioning/sharding)

Phase 3: Performance Optimization

1. Analyze query execution plans (EXPLAIN ANALYZE)
2. Add indexes for frequent queries
3. Implement caching where appropriate
4. Configure connection pooling
5. Monitor and iterate

Phase 4: Scaling Strategy

1. Implement read replicas for read scaling
2. Consider partitioning for large tables (>100M rows)
3. Plan sharding strategy for horizontal scaling
4. Design for high availability with replication

Common Mistakes

Over-normalization: Too many joins slow down reads. Denormalize for read-heavy workloads.

Missing indexes: Analyze query patterns and add indexes for frequent WHERE/JOIN columns.

Wrong index type: Use composite indexes with correct column order (equality first, then range).

Ignoring replication lag: Handle eventual consistency with read-your-writes pattern.

Poor partitioning key: Choose keys that distribute data evenly and align with query patterns.

N+1 queries: Use JOINs or batch loading instead of querying in loops.

Inefficient pagination: Use keyset pagination instead of OFFSET for large datasets.

Connection exhaustion: Implement connection pooling sized for your workload.

Best Practices

1. Model for access patterns - Design schemas around how data will be queried
2. Index strategically - Index frequently queried columns, avoid over-indexing
3. Partition large tables - Use for tables >100M rows or time-series data
4. Replicate for reads - Primary-replica for read scaling, multi-leader for geo-distribution
5. Optimize queries - Analyze execution plans, avoid N+1, use proper pagination
6. Cache hot data - Application-level caching with appropriate TTLs
7. Pool connections - Size connection pools based on workload
8. Monitor continuously - Track query performance, index usage, replication lag
9. Plan for growth - Design for 3x current load
10. Choose consistency wisely - Match consistency level to business requirements

Resources

Books:

• "Designing Data-Intensive Applications" (Kleppmann)
• "High Performance MySQL" (Schwartz)

Sites:

• use-the-index-luke.com
• PostgreSQL documentation
• MongoDB documentation

Tools:

• EXPLAIN ANALYZE
• pg_stat_statements
• Percona Toolkit
• pt-query-digest