Why Large Payloads Break Caches at Scale

Large payloads in cache systems like Valkey don't necessarily cause failures due to a single oversized item but rather from numerous medium-sized requests that arrive simultaneously and overwhelm the system's event loop. Despite configurations to limit payload sizes, issues such as cumulative payload volume, command shape, and write-path allocation pressure can degrade performance. Valkey 9.0's updates, such as copy avoidance, help alleviate some of these issues by reducing event-loop pressure, yet challenges persist, particularly in scenarios involving complex command shapes like MGETs, which can create inefficiencies in distributed cluster environments. Observability and runtime guardrails are crucial for managing these challenges, as demonstrated by Snap's migration from KeyDB to Valkey, where they implemented CPU throttling and custom handling at the proxy layer to mitigate large-payload problems without altering application code. This approach highlights the importance of embedding guardrails above the engine to manage system pressures dynamically, ensuring operational stability in large-scale deployments.