Why Distributed Caches Fail Under Real Traffic
A distributed cache is often added when database read traffic starts dominating latency. At first, the cache works well: frequently requested data is served from the cache instead of the database, reducing repeated database queries and making response times more predictable.
The failure pattern usually appears when traffic grows or becomes uneven. A hot key expires during a spike, a deployment starts with a cold cache, or a node failure shifts traffic back to the database. Even a small drop in hit rate can multiply backend reads, raise p99 latency, saturate connection pools, and trigger retry storms.
Distributed caches are not unreliable by default. They become separate distributed dependencies with their own consistency, capacity, and failure boundaries. This newsletter examines how caches fail under real traffic and how teams can design them to degrade predictably.
Why distributed caches exist#
A cache reduces read latency by storing frequently requested data in memory. For high-hit-rate workloads, this reduces repeated database reads and frees up more database capacity for writes, transactions, and complex queries.
A cache is not usually the source of truth. It stores selected data from a backing store and must stay fresh enough to meet the workload’s freshness and consistency requirements.
A single cache node works only while the working set and request rate fit within one machine’s memory, CPU, and network limits. Distributed caching extends capacity and throughput by partitioning data across nodes, and it can improve availability or read scaling by replicating selected data.
