A Rails Performance Guidebook: from 0 to 1B requests/day
summary
Claims real load handling: the system serves 16.3 billion tickets and a Rails monolith sits on about 400 TB of relational data, with the biggest ticket table at 41 GB and events shards at multiple TBs. Concrete tools and fixes include using write-through caching, database indexes and EXPLAIN, avoiding greedy SELECTs, S3 cold storage archival (85% of data archived), latency error budgets, and targeted migrations/splits that measurably cut hot queries.
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chapters (13)
- 0:00Introduction
- 4:25Rails scaling myth
- 7:57Performance mindset
- 8:41Monitoring and error budgets
- 11:07Database query basics
- 13:13Caching derived data
- 15:39The dangers of misusing caching
- 17:48Cold storage for rare data
- 21:38Product limits for scalability
- 23:00Avoid bloated API payloads
- 24:05Breaking down slow workflows
- 25:32Power user optimizations
- 27:14Trade-offs and perfection
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