Caching Archives

The Rise of the Internal Cache Platform

How Uber and Mercado Libre scaled distributed caches to billions of ops/sec, and the connection storms, ownership failures, and Valkey decisions that shaped their platforms.

Understanding the NxM Problem in Distributed Caches

Mo holding a tablet surrounded by a bunch of chaotic lines

When N clients each connect to M nodes, connection load grows multiplicatively. Learn how the NxM problem emerges at scale and why connection storms happen during routine operations.

Why Large Cache Systems Need Routing Layers

Illustration of Mo managing traffic from clients to databases

Large cache clusters often hit connection and latency limits before memory or CPU. Learn why routing layers and edge caching emerge at scale.

Reduce TTFT by >50% with LMCache + Momento Accelerator

Mo pointing at Momento Accelerator Architecture

How distributed KV caching with LMCache and Momento Accelerator enables unified access to remote token storage, improving inference efficiency at scale.

Large Objects Ruin the Party – Valkey 9 Tames Them

illustration of Mo carrying a large rock

Tail latencies are where promises break. You can have a system that’s fast 99% of the time, but that 1% is what users remember.

Breakthroughs Are Just Boring Improvements That Pile Up

Engineering wins look dramatic from the outside. From the inside, they’re months of small, unglamorous changes – the kind nobody celebrates until they compound into something that looks impossible.

Cache Rebalancing Was Broken. Here’s How Valkey 9.0 Fixed It

split screen depicting a before and after

Few things make SREs more nervous than rebalancing a cache cluster. You know the feeling. You add a node, trigger a rebalance, and suddenly latency graphs start jumping. It’s a familiar risk of the job, especially when your cache sits between your users and your database. A small configuration mistake here can suddenly unleash a […]