Interactive System Design Education

From Monolith to Microservices: Master the Art of Scaling

Learn why systems fail and how to fix them. Interactive diagrams, real-world case studies, and structured roadmaps.

Start with the Foundation

The Foundation

The OSI Model of System Design — 5 layers every architect must master

Cloud

Broker

Layer 1

Requirements & Scope

The "Why"

Before writing a single line of code, define what you're building and for whom. Estimate traffic, storage, and bandwidth to right-size your architecture from day one.

Daily Active Users

10.0K

1K100M

Estimated Storage

19.5 MB

~2 KB per user (profile + metadata)

Bandwidth Required

28.9 KB/s

~6 req/s at 5 KB/req

Layer 2

Data Modeling

The "What"

Choose the right data store for your access patterns. Relational databases for structured, transactional data. NoSQL for flexible schemas and massive scale.

SQL ExamplesPostgreSQL, MySQL, CockroachDB

NoSQL ExamplesMongoDB, Cassandra, DynamoDB

Layer 3

High-Level Design

The "Blueprint"

Sketch the major components and how they connect. Every production system follows a similar pattern: clients → load balancer → app servers → cache → database.

Click any component to highlight its role in the request flow

Layer 4

Deep Dive

The "Critical Components"

Go beneath the surface. Understand the algorithms and trade-offs that separate junior engineers from senior architects.

Consistent hashing maps both servers and data keys onto a virtual ring (hash space). When a server is added or removed, only K/n keys need to be remapped (where K = total keys, n = servers), instead of nearly all keys with naive modulo hashing. This is critical for distributed caches like Memcached and databases like DynamoDB.

Adding a 4th cache node? Only ~25% of keys move, not 75%.

In a distributed system, you can only guarantee two of three properties: Consistency (all nodes see the same data), Availability (every request gets a response), and Partition tolerance (system works despite network failures). Since network partitions are inevitable, you must choose between CP (consistent but may be unavailable) or AP (available but may be stale).

Banking systems choose CP. Social media feeds choose AP.

One node (the leader) accepts all writes and replicates changes to follower nodes. Followers serve read requests, distributing read load. If the leader fails, a follower is promoted via leader election. This pattern is used by PostgreSQL, MySQL, and MongoDB replica sets.

1 leader handles writes, 3 followers handle 90% of reads.

Layer 5

Scale & Bottlenecks

The "Failure Mode"

Systems don't fail gracefully by default. Plan for failure: redundant servers, automatic failover, and graceful degradation under load.

The Playground

Apply what you learned. Each case study includes an interactive challenge.

Easy

Single Service

URL Shortener

TinyURL

Design a service that converts long URLs into short, shareable links. Focus on hashing, redirection, and basic storage.

The Whiteboard — drag & drop architecture

Open Whiteboard

Medium

Distributed State

Chat Messaging

Build a real-time messaging system handling millions of concurrent connections with delivery guarantees.

The Delivery Guarantee

Sender

Receiver

At-most-once: fast but messages can be lost during network failures.

🔒 Pass the Concept Check to unlock

Hard

Extreme Scale & Concurrency

Ticket Booking

BookMyShow

Prevent double-booking when thousands of users click 'Buy' simultaneously. Master distributed locking.

The Race Condition Simulator

Tickets left: 10 / 10

Overbooked: 0

Without or , concurrent requests can oversell inventory.