Interview › Databases & Caching
How do you scale reads versus writes on a relational database?
Databases & Caching · Basic level
Answer
Reads can be scaled with replicas, caching, materialized views, query optimization, denormalized read models, and analytics offload. Writes are harder because consistency and uniqueness usually concentrate around one writer, so write scaling may require partitioning, sharding, queues, batching, or architecture changes.
Technical explanation
Read scaling is safe only when stale reads are acceptable or read-after-write paths use the primary.
Write scaling is constrained by transactions, constraints, referential integrity, and cross-shard behavior.
Most teams should tune queries and cache reads before redesigning writes.
Hands-on example
Example split:
POST /orders -> primary RDS writer
GET /products -> Redis/Valkey cache
GET /reports -> read replica or warehouse
Heavy post-processing -> queue and worker
This keeps OLTP writes short and moves expensive reads away from the writer.
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