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Data Evolution
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Classroom Contents
Why Are Distributed Systems So Hard?
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- 1 Introduction
- 2 Agenda
- 3 Storytime
- 4 Data Evolution
- 5 Scaling
- 6 Cloud Computing
- 7 Why Scale Horizontally
- 8 What Does It Mean To Run A Distributed System
- 9 A Node On Distributed Computing
- 10 Summary
- 11 Shared Nothing Architecture
- 12 Unreliable Message Delivery
- 13 Why Are We Fenced Off
- 14 Building Observability
- 15 What We Can Know
- 16 The Cap Theorem
- 17 C
- 18 Replication Lag
- 19 Consistency is a Spectrum
- 20 Availability is Not Binary
- 21 Partition Tolerance
- 22 Hardware
- 23 Hardware Failure
- 24 Cables
- 25 Sharks
- 26 Kevlar
- 27 Network Partitions
- 28 Resource Isolation
- 29 Process Suspension
- 30 Network Glitch
- 31 People do bad things
- 32 Why does this matter
- 33 Practical reality
- 34 The correctness result
- 35 Mitigation strategies
- 36 Consensus Algorithms
- 37 The Woods Theorem
- 38 Building Mental Models
- 39 Incident Analysis
- 40 Blameless Discussions
- 41 Mental Models
- 42 Human Failure
- 43 Alert Fatigue
- 44 User Mindsets
- 45 Designing Systems for Humans
- 46 HugOps
