When a high-throughput transactional system like Redis approaches its limits

A McKnight Consulting Group benchmark compared Redis Open Source 8.8.0 and Aerospike on identical four-node AWS hardware using 500 million in-memory records and a uniform 90/10 read-update workload, emphasizing sustained throughput and window-to-window latency stability rather than headline averages alone. At targets from 1.6 million to 2.4 million operations per second, Redis showed increasing throughput variation and fell to a dependable rate of 2.10 million operations per second at the highest target, while Aerospike remained close to each target with reported rate jitter of no more than 0.2%. Redis’s high-percentile latency and its variability also rose sharply at higher load, whereas Aerospike’s reported latency values and fluctuations increased more modestly. The discussion argues that deep-tail latency matters even when it affects only a small fraction of database calls, because multi-call user interactions compound the likelihood of encountering a slow operation. It concludes that capacity planning and service-level monitoring should examine dependable throughput, tail-latency variation across short measurement windows, and application request fan-out, while noting that benchmark results at sub-millisecond precision can be affected by environmental noise and should be interpreted primarily by their overall patterns.

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