Performance and Scale

Understanding MemoryDB’s architecture (clusters, shards, primaries, replicas, routing, and failover) gives you the blueprint, but a blueprint alone does not guarantee a fast building. Performance under real traffic depends on how you size, distribute, and connect to that architecture. A cluster with generous node counts can still deliver poor latency if keys are unevenly distributed, if clients open thousands of idle connections, or if the application sends one command at a time in a tight loop. This lesson examines the four tuning levers that determine whether a MemoryDB deployment actually performs well: shard sizing for write and total throughput, replica count for read scaling and failover readiness, latency tuning through CloudWatch metrics, and client connection behavior. These levers interact with each other, and the AWS-preferred principle is clear: cluster size alone does not guarantee good performance if traffic patterns and client behavior are poorly designed. A bigger cluster does not automatically mean lower latency, and the nuanced reasons behind that statement are what separate a functional deployment from an optimized one.

Shard sizing and throughput ceilings

Shards are the fundamental unit of horizontal scaling in MemoryDB. Each shard owns a range of hash slotsThe 16,384 fixed-size logical partitions that MemoryDB uses to map every key to exactly one shard through a CRC16 hash function.. One primary node within each shard handles all writes for its portion of the keyspace. Increasing the shard count distributes keys across more primaries, which raises aggregate write throughput and total data capacity in a roughly linear fashion.

However, shard sizing decisions should be driven by working-set size, request rate, and hot-key distribution rather than a simple “add more shards” reflex.

The hot-key problem

When a disproportionate number of requests target keys that hash to the same shard, that single primary becomes a bottleneck regardless of how many other shards sit idle. Think of it like a grocery store with ten checkout lanes where every customer lines up at lane three. The store has capacity, but the experience is terrible for anyone in that lane.

Identifying hot keys requires examining per-shard EngineCPUUtilization and request counts. If one shard consistently shows high CPU while others remain cool, the key distribution is skewed. The remedy is to redesign key naming so traffic spreads more ...