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Nobody builds a system intending to make it unoperatable. It happens gradually. A cluster grows. Another service gets added. Multi-tenancy gets introduced to cut costs.
Each decision is reasonable in isolation. Then one night something breaks, and the engineer on call is staring at dashboards they can’t interpret, running commands th...
Most large-scale systems don’t fail because of a single bad decision. They fail because a set of reasonable decisions start interacting in ways no one had to think about early on.
Distributed caches are no different. At small scale, the system is simple, predictable, and easy to reason about. Clients connect to a cluster, data flows where ...
At 3AM on a Tuesday, your Valkey cluster is at 60% capacity but the nodes are pegged at 100% CPU. Memory usage is fine. Query patterns look normal. Profiling shows 40% of CPU time spent in epoll_wait() and TLS state management, not request processing. You discover the problem isn’t with the cache itself, but with the 500,000 TCP connections bein...
The following is a summary and analysis of presentations given at Unlocked Conference. The views expressed by the speakers are their own and do not necessarily reflect the editorial position of their companies.
Scaling advice is easy to collect ...
In this series, we investigate the performance gains for large-scale inference clusters with distributed KV caching, optimized routing, cluster orchestration, and other techniques. This post focuses on offloading the KV cache to remote storage (Valkey + S3) with LMCache and Momento Accelerator.
Momento specializes in hyperscale caching and...