Scale, Flow and Microservices
summary
Shows that the same scaling laws from biology and cities apply to software organizations: mammal metabolic rate scales with exponent 0.75, typical companies show 85% scaling (revenue per doubling of staff) while Amazon/AWS shows exponent 1.15 (superlinear growth). Hierarchical, queue-filled organizations impose sublinear limits and ageing; small‑world, loosely coupled teams (Spotify/Valve patterns, two‑pizza teams on platforms) enable superlinear returns. Track the 4 key metrics (deployment frequency, lead time/cycle time, mean time to recovery, change failure rate) as leading indicators of organizational health.
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chapters (17)
- 0:00Intro
- 8:17What do mice and elephants have in common?
- 10:14What do Newsagents and Walmart have in common?
- 10:58What do Hamburg and Leipzig have in common?
- 13:21Complex adaptive systems
- 13:493 simple principles
- 15:45Complex adaptive systems are everywhere
- 16:45Corporate metabolism
- 17:00Why do metabolic rates slow as size increases?
- 22:07Flow of work in organizations
- 24:20Corporate metabolism
- 25:28Identifying the signs of ageing
- 27:53Corporate mortality
- 29:36The alchemist's stone
- 36:13As cities grow...
- 36:43Existence proofs
- 40:13Summary/Outro
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