How higher core counts expose the limitations of process-per-core database scaling

Rising server CPU core counts, including AMD EPYC processors with up to 256 cores per socket, are increasing pressure on database architectures to use parallel hardware efficiently rather than relying on faster individual cores. The passage distinguishes sharding across machines from process-per-core scaling within a machine, arguing that the latter can tie process counts, routing, monitoring, and rebalancing complexity directly to available cores. It says highly concurrent agentic AI workloads amplify these concerns because they generate many small, overlapping database operations. Aerospike is presented as using a multithreaded, NUMA-aware model that distributes work across cores within a single database process and separates migration activity from client-serving threads, while Redis is characterized as commonly using multiple single-threaded processes to utilize multicore systems. An independent McKnight Consulting Group benchmark cited in the passage found that, on equivalent four-node 32-core clusters, Aerospike achieved 2.39 million operations per second and 0.2% rate jitter versus Redis’s 2.10 million operations per second and 4.4% jitter, while using four total processes instead of 128. The passage concludes that as hardware grows denser, native multicore database designs may reduce the operational overhead associated with process multiplication, though process-based architectures can still scale effectively.

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