--- title: "How to tame the thundering herd problem" url: https://daily.dev/posts/how-to-tame-the-thundering-herd-problem-t1eucwlba source_url: https://redis.io/blog/how-to-tame-the-thundering-herd-problem type: article source: "Redis" published: 2026-05-13T18:21:47.643Z updated: 2026-05-13T18:22:11.181Z tags: ["distributed-systems", "redis"] reading_time: 16 upvotes: 1 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How to tame the thundering herd problem **[Redis](https://daily.dev/sources/redislabs)** · 16 min read · 1 upvotes · 0 comments ## Summary The thundering herd problem occurs when many clients simultaneously request the same resource, overwhelming backends during cache misses, traffic spikes, or coordinated events. Key causes include synchronized cache expiration, lock contention, and auto-scaling cold starts. Solutions using Redis include TTL jitter to stagger expirations, request coalescing via distributed locks, rate limiting with atomic counters, load shedding, and queue-based processing with Redis Streams. Bloom filters help prevent cache penetration. The post also compares Redis Cloud against Amazon ElastiCache and Google Memorystore, noting that both competitors are frozen at Redis 7.2 (Valkey fork) and lack features like Active-Active geo-distribution and native vector search. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Caching in Distributed Systems - Part III](https://daily.dev/posts/caching-in-distributed-systems---part-iii-qcazxcqub) · The Polymathic Engineer · 18 upvotes · 0 comments --- Tags: [#distributed-systems](https://daily.dev/tags/distributed-systems), [#redis](https://daily.dev/tags/redis) [View this post on daily.dev](https://daily.dev/posts/how-to-tame-the-thundering-herd-problem-t1eucwlba)