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Open SystemDecoder on a larger screen to build systems, run simulations, and inject chaos.
Daily Challenge
SystemArena
5
Questions
Think Deep
1×
Daily Shot
5 sharp system design questions. One attempt daily — streaks, XP & leaderboard.
What's waiting for you on desktop
Live Simulations
Watch latency spike, queues fill, and nodes fail in real time. Every slider change is instant.
Visual Architecture Canvas
Drag nodes, draw edges, and build any distributed system topology from scratch.
Chaos Engineering
Kill servers, introduce packet loss, throttle CPUs — and watch your system react.
Real-time Insights
Throughput, p99 latency, error rates — all charted live as your simulation runs.
40+
Concepts
<1s
Feedback
∞
Replays
"The best way to understand a distributed system
is to break it."
System Evolution
Why one machine is never enough — and what happens when you split work across many.
Architecture Diagram
Monolith
Client
Single Server
All code + DB
One failure → everything down
Distributed
Client
Load Balancer
API A
replica
API B
replica
One node fails → others continue
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.
Tradeoffs
Engineering Edge Cases
Splitting a monolith too early is expensive. You inherit distributed complexity before you need it.
Distributed systems require discipline: stateless services, explicit failure handling, and careful data ownership.
What comes next
Distributed systems communicate over a network. Networks are unreliable. That's where the first problem appears.