How to Choose the Database

In any system built on a Database Management System (DBMS), selecting the right database is a key architectural decision. It influences data consistency, performance, scalability, maintainability and cost. Rather than choosing based on popularity, the decision should match your system's structure, data patterns and growth plans.

Core Considerations for Database Selection

1. Scalability: Decide whether your system needs to scale vertically (adding resources to a single machine) or horizontally (adding more machines). Relational databases usually scale vertically, while NoSQL databases are designed for horizontal scaling.
2. Data Consistency: If strict ACID compliance is essential like in financial systems a relational database is best. If your system can tolerate temporary inconsistencies for better performance or availability, eventual consistency in NoSQL databases may be suitable.
3. Query Patterns and Workload: Understand whether your system will be read-heavy, write-heavy or balanced. If complex joins or reporting are needed, relational databases are ideal. For key-based lookups or document retrieval, NoSQL databases are more efficient.
4. Operational Complexity: Managed databases (e.g., Amazon RDS, Azure SQL) simplify tasks like backups and failover. If your team lacks experience in managing complex infrastructure, prefer fully managed solutions.
5. System Resilience: Check uptime requirements and whether the system needs multi-region support. Important systems may require features like automated replication and failover.
6. Performance Needs: Determine whether your system needs low latency, high throughput or both. Choose a database that matches those performance characteristics from the start.

Types of Databases and When to Use Them

• Relational Databases (e.g., MySQL, PostgreSQL): Best for structured data and strong consistency. Suitable for traditional business systems.
• Document Databases (e.g., MongoDB): Ideal for semi-structured data and flexible schemas. Good for web apps and microservices.
• Columnar Databases (e.g., Redshift, Snowflake): Built for analytics and high-speed read queries.
• Key-Value Stores (e.g., Redis, DynamoDB): Great for simple, high-speed access and caching.
• Time Series Databases (e.g., InfluxDB): Designed for metrics and time-stamped data like logs or sensor readings.

Architecture Patterns That Affect Database Choice

• Polyglot Persistence: Use multiple databases within one system, each for different needs.
• CQRS: Use one database for writes (e.g., SQL) and another optimized for reads (e.g., columnar).
• Event Sourcing: Store state changes as events, useful for audit trails.
• Microservices: Let each service manage its own database, tailored to its domain.

Key Implementation Tips

• Design for Failure: Use backups, replication and disaster recovery plans.
• Plan Schema Evolution: Choose systems that support safe schema changes.
• Prefer Managed Services: If operations are complex, go with cloud-managed databases.
• Ensure Monitoring: Track query speed, errors, replication and performance trends.

A Simple Framework for Choosing

1. Understand the workload: Are your operations transactional or analytical?
2. Know your consistency needs: Is strict correctness required?
3. Plan for scalability: Will your data or users grow fast?
4. Factor in operations: Can your team manage this database?
5. Account for security and compliance: Does it support encryption, access control?
6. Estimate total cost: Consider infrastructure, licenses and staff time.