A production triage and performance engineering guide for Amazon ElastiCache for Valkey: diagnosing shard skew, replication lag, cluster-mode failover, and managed-service boundaries.
A production engineering guide to identifying slow commands, hot keys, big collections, unbounded pipelines, and blocking Lua scripts in Valkey without impacting live traffic.
A production triage workflow for diagnosing Valkey on EC2: separating engine event loop delays from host memory fragmentation, CPU credit exhaustion, eviction storms, and network PPS limits.
Azure applications typically fail first at the edges: Front Door configuration, App Service connection pools, SQL failover groups, Redis cache invalidation, and Service Bus backlog — a reference architecture that makes these failure boundaries explicit.
Terraform review fails when humans rediscover the same constraints in every PR — how OPA, Sentinel, and Checkov encode policy gates that catch public storage buckets, unencrypted databases, and missing tags at plan time.
Redis has eight eviction policies and a maxmemory limit. The policy you pick determines whether your cache degrades safely or silently corrupts your hit rate under load.
Rate limiting fails when the platform enforces one behavior while the product promised another to clients. The technical mechanism matters less than treating rate limits as a documented contract with defined scope, limits, and error semantics.