Distributed Systems

Five lessons covering how systems work when they span multiple machines.

Replication (keeping copies of data in sync for availability and performance), consistency models (what guarantees you get when reading replicated data), consensus (how nodes agree on a value — Paxos and Raft), partitioning (splitting data across nodes for horizontal scale), and distributed transactions (maintaining ACID across multiple services).

These are the hardest problems in computing. A single machine is predictable: operations complete or they don't. In a distributed system, messages are delayed, nodes crash, networks partition, and clocks disagree. Understanding these failure modes is what separates systems that work from systems that lose data.

Every topic builds on the foundations: networking (how nodes communicate), databases (what's being replicated and partitioned), cryptography (how trust is established), and algorithms (the protocols that solve these problems).