Database Architecture

Overview

Database architecture decisions strongly influence system scalability, consistency, and failure behavior.

Common Patterns

• Single primary with read replicas
• Sharding by tenant or key range
• Polyglot persistence by workload type
• Event sourcing with append-only logs

flowchart LR
    A[Application] --> P[(Primary DB)]
    P --> R1[(Read Replica 1)]
    P --> R2[(Read Replica 2)]

Production Example

A reporting-heavy SaaS platform routes writes to a primary PostgreSQL node and read traffic to replicas. A cache layer protects frequent dashboard queries.

-- Read request routed to replica
SELECT customer_id, monthly_spend
FROM billing_summary
WHERE month = '2026-08'
ORDER BY monthly_spend DESC
LIMIT 100;

Design Considerations

• Consistency requirement per use case
• Recovery objectives (RPO and RTO)
• Data retention and archival policy
• Migration and schema evolution strategy

Common Pitfalls

• Treating all workloads with one storage model
• Ignoring replication lag in read-after-write flows
• Missing backup restoration drills

Key Takeaways

• Database architecture must match workload patterns.
• Read/write separation improves scale for read-heavy systems.
• Recovery planning is as important as schema design.