Distributed Systems Simulation — CAP, Consensus and Replication

Every system starts as a monolith. One server, one database, one codebase. It's simple, fast to build, and easy to debug. For most early-stage products, this is exactly right.

But traffic grows. The single machine starts sweating. CPU pegs at 90%. Response times creep up. You add more RAM, upgrade the CPU — this is vertical scaling, and it works until it doesn't. Every machine has a ceiling.

Next, a harder failure: the entire system goes down because one component failed. A bad deploy, a disk failure, a runaway query — and every user sees an error page. This is the single point of failure problem.

Distributed systems exist to solve both problems. Instead of one machine doing everything, you split the work across many machines. Traffic is spread across multiple servers via a load balancer. Data is replicated across multiple databases. No single failure takes down the whole system.

But this introduces a whole new class of problems: the machines need to agree on things, communicate over a network that can fail, and coordinate without a single brain. Everything else in this module is about solving those new problems.