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Distributed Systems & Reliability

Course Contents

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Welcome to Hard Mode The Fallacies of Distributed Computing Failure Models: Crash, Omission, Timing Network Partitions & Split Brain Heartbeats & Failure Detection Gray Failures Checkpoint: Why Distribution Is Hard

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The Clock-Sync Problem Physical vs Logical Time Lamport Timestamps Vector Clocks Ordering in Practice Checkpoint: Time & Order

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The Consensus Problem Quorum Intuition, Revisited Leader Election Raft, Visually Paxos, the Intuition Gossip Protocols Distributed Locks & Leases ZooKeeper & etcd in Practice Codelab: Leader Election with etcd Checkpoint: Consensus & Coordination

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The Conflict Problem CRDTs Operational Transformation Conflict Resolution Strategies Offline-First Sync Checkpoint: Collaborative & Replicated State

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Timeout Hierarchies Retries, Budgets & Retry Storms Circuit Breakers Bulkheads Backpressure Load Shedding & Graceful Degradation Tail Latency: Living at p99 Cascading Failures Chaos Engineering Codelab: Run a Chaos Experiment Checkpoint: Resilience & Overload Control

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SLIs, SLOs & Error Budgets Alerting Philosophy Incident Response & Postmortems Disaster Recovery: RTO & RPO Multi-Region Patterns Cell-Based Architecture & Deployment Stamps Data Integrity: Backups That Restore Shadow Traffic & Dark Launches Checkpoint: The Reliability Discipline

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Facebook, 2021: The BGP Self-Lockout AWS Kinesis, 2020: The Thread-Limit Cascade us-east-1: The Region the Internet Leans On Anatomy of a Cascading Failure How to Read a Postmortem Checkpoint: Learning from Real Outages Capstone Pass 3: The Marketplace Survives

Cascading Failures

See how failure climbs a service graph — why a death spiral will not stop on its own, how resilience patterns break the chain, and why recovery can cascade too.

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