Foundations of Distributed Systems

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reading · we frame, you read MIT or the canonical taught · we author, no canonical fits ↺ spirals back to earlier lessons

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Complete Theory of Computation first.

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reading · 30 min + ~221 min canonical

What a Distributed System Is, and Why It's Hard

You already operate distributed systems — you just may not have called them that. The moment your application talks to a database on another machine, a Redis cache, a background-j…

primary MIT 6.5840 Distributed Systems (formerly 6.824)

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reading · 30 min + ~221 min canonical L1

Failure Models: Crash-Stop, Crash-Recovery, and Byzantine

To build a system that tolerates failure, you first have to say precisely what kinds of failure you're tolerating — because "the machine broke" hides wildly different scenarios, a…

primary MIT 6.5840 Distributed Systems (formerly 6.824)

after L1 locked

reading · 35 min + ~221 min canonical L1

Time, Clocks, and the Ordering of Events

Here's a question that sounds trivial and isn't: in a distributed system, did event A happen before event B? On one machine, easy — check the clock, or the order they executed. Ac…

primary MIT 6.5840 Distributed Systems (formerly 6.824)

after L2 locked

reading · 35 min + ~221 min canonical L1

Impossibility Results: FLP and the CAP Theorem

The theory-of-computation course taught you that some problems are provably impossible to solve at all. Distributed systems has its own impossibility results, and they're just as …

primary MIT 6.5840 Distributed Systems (formerly 6.824)

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reading · 30 min + ~221 min canonical L1

Distributed Snapshots and Consistency in Practice

This lesson closes the foundations course by turning theory into practice. First, a beautiful algorithm: how do you capture a consistent snapshot of an entire distributed system —…

primary MIT 6.5840 Distributed Systems (formerly 6.824)

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exercise reading · 15 min L1

Exercises: Easy (Warm-up)

- [ ] Implement Lamport logical clocks in Ruby — Increment on send, max+1 on receive - [ ] Implement vector clocks in Ruby — Array of counters, merge on receive - [ ] Diagram the …

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exercise reading · 30 min L1

Exercises: Medium (Core)

- [ ] MIT 6.5840 Lab 1: MapReduce — Build a MapReduce framework: coordinator distributes map and reduce tasks to workers, handles worker failures - [ ] MIT 6.5840 Lab 2: Key/Value…

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exercise reading · 45 min L1

Exercises: Hard (Stretch)

- [ ] Prove the FLP impossibility result (written) — One faulty process prevents deterministic consensus - [ ] Implement a gossip protocol in Ruby — Epidemic-style dissemination, …

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exercise reading · 60 min L1

Mini-Project

- [ ] Build a distributed key-value store in Ruby — 3 server processes communicating via TCP, quorum reads/writes (W=2, R=2 of N=3), handle network partitions, demonstrate consist…

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L10

project reading · 300 min L1

Project: Logical Clock Visualizer

Build a Ruby program simulating 3-5 distributed processes exchanging messages. Implement both Lamport timestamps and vector clocks. Output a space-time diagram (ASCII art) showing…

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L11

project reading · 300 min L1

Project: Gossip Protocol Simulator

Implement epidemic-style gossip dissemination in Ruby. N nodes, each holding a value. One node gets an update, gossips to random peers each round. Measure how many rounds until al…

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L12

project reading · 600 min L1

Capstone: Distributed Key-Value Store with Raft

- [ ] Milestone 1: Node GenServer with state (term, log, commit index) - [ ] Milestone 2: Leader election with randomized timeouts - [ ] Milestone 3: Log replication — AppendEntri…

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L13

assessment reading · 56 min L1

Self-Assessment: Foundations of Distributed Systems

- [ ] What is the CAP theorem? Give an example of a CP system and an AP system. - [ ] What are Lamport clocks and vector clocks? What problem do they solve? - [ ] Explain the happ…

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13 lessons. Read in order; spiral back when you need to. By the end you'll have used the core ideas twice — once on the abstract, once on something you'll meet at work next week.