DATABASE DESIGN IN SYSTEM DESIGN
โ Database Design defines how data is structured, stored, accessed, and managed to support scalable, reliable, and high-performance systems.
โ A well-designed database is essential for fast queries, consistency, fault tolerance, and long-term maintainability.
โ 1. DATA MODELING
โ Begin with understanding business requirements
โ Identify real-world entities โ convert them into tables/collections
โ Define relationships between entities
โ Common models:
โ Relational Model (SQL) โ structured tables + strict schemas
โ Document Model (NoSQL) โ flexible JSON-like documents
โ Key-Value Stores โ extremely fast lookup
โ Wide Column Stores โ scalable column families (e.g., Cassandra)
โ Graph Databases โ nodes + edges (e.g., Neo4j)
โ 2. SCHEMA DESIGN PRINCIPLES
โ Normalize data to reduce duplicates
โ Use denormalization for high-read systems
โ Choose primary keys that are stable and unique
โ Use indexes carefully to speed up queries
โ SQL Schema Example:
โ Users Table โ user_id (PK), name, email, password
โ Orders Table โ order_id (PK), user_id (FK), amount, status
โ 3. NORMALIZATION
โ Reduces redundancy and inconsistency
โ Normal Forms (1NF โ 3NF) help eliminate duplicate data
โ Good for OLTP systems where consistency matters
But: too much normalization can hurt read performance
โ 4. DENORMALIZATION
โ Combine data into fewer tables
โ Reduces joins โ improves query speed
โ Ideal for analytics, dashboards, and read-heavy systems
โ Example:
โ Storing user_name directly inside orders table for faster queries
โ 5. INDEXING
โ Improves query performance
โ Types of indexes:
โ Primary Index โ on primary key
โ Secondary Index โ for filtering/search
โ Composite Index โ multiple fields
โ Full-Text Index โ search operations
โ Over-indexing slows down writes โ balance required
โ 6. RELATIONSHIPS & CONSTRAINTS
โ One-to-One, One-to-Many, Many-to-Many
โ Foreign key constraints ensure consistency
โ Cascading operations manage dependent data
โ NoSQL platforms may store embedded or referenced relationships
โ 7. SQL VS NOSQL
โ SQL
โ Strong consistency
โ ACID transactions
โ Structured, relational
โ Great for financial & transactional systems
โ NoSQL
โ Flexible schema
โ Horizontal scaling
โ High performance
โ Great for distributed and high-volume systems
โ 8. DATA PARTITIONING (SHARDING)
โ Divide data across multiple servers
โ Techniques:
โ Range Sharding (AโM, NโZ)
โ Hash Sharding (hash(key) โ shard)
โ Geo-Sharding (region-based)
โ Helps scale large databases
โ 9. REPLICATION
โ Copy data across multiple nodes
โ Types:
โ MasterโSlave Replication โ write to master, read from slaves
โ MasterโMaster Replication โ multi-write configurations
โ Quorum-based Replication โ consistency levels adjustable
โ Improves availability and read performance
โ 10. CONSISTENCY MODELS
โ Strong Consistency โ always latest data
โ Eventual Consistency โ replicas sync over time
โ Read-After-Write Consistency โ user sees own updates immediately
โ Used in distributed databases (DynamoDB, Cassandra, MongoDB)
โ 11. TRANSACTIONS & ACID
โ Atomicity
โ Consistency
โ Isolation
โ Durability
โ Guarantees safe & reliable operations in databases
โ NoSQL may use BASE (Basically Available, Soft state, Eventual consistency)
โ 12. DATA SECURITY & BACKUP
โ Encryption at rest & in transit
โ Role-based access
โ Automated backups & point-in-time recovery
โ Disaster recovery via multi-region replicas
โ TIP
โ Database design ensures:
โ High performance
โ Strong consistency or tuneable consistency
โ Efficient read/write operations
โ Scalability through replication & sharding
โ Maintainability, data integrity, and security
A strong database design is the foundation of robust system architecture.
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