Specialization
System Design
System design is where everything else in this program — databases, caching, distributed systems, cloud infrastructure — comes together into a single skill: designing a system that works correctly under real-world scale and failure, and being able to explain and defend those design decisions out loud. This is also, deliberately, the most heavily interviewed skill at every major tech company, so this module is built around both the engineering substance and the framework for communicating it under interview conditions.
Topic Map
Everything in this module, at a glance
In Depth
Every topic, explained — with real-world industry context
Scalability Fundamentals
Scalability is the question "what happens when this gets 10x or 100x more traffic?" — the starting point of every system design discussion.
Basics
- Vertical scaling (bigger machine) vs. horizontal scaling (more machines)
- Load balancers — distributing requests across servers
- Stateless vs. stateful services & why statelessness scales more easily
- Back-of-the-envelope capacity estimation
Intermediate
- Layer 4 (transport) vs. Layer 7 (application) load balancing
- Horizontal partitioning & sharding a dataset
- Auto-scaling based on real traffic patterns
- Identifying the actual bottleneck before scaling blindly
Advanced
- Consistent hashing — scaling a sharded/cached system without a full reshuffle
- Designing for graceful degradation under extreme load
- Multi-region scaling & the trade-offs it introduces
- Estimating cost, not just capacity, at scale
Real-World Industry Use Case
Scalability is explicitly assessed in system design interviews by whether a design survives a 10x or 100x increase in load — it is the single question every interviewer eventually asks, no matter what system you're designing.
Caching & Performance at Scale
Caching is the highest-leverage performance tool in system design — but every cache introduces the hard problem of knowing when data has gone stale.
Basics
- What to cache, and what should never be cached
- Cache-aside — the most common caching pattern
- In-memory caches (Redis/Memcached) as a shared layer
- CDN & edge caching for static content
Intermediate
- Write-through vs. write-behind caching strategies
- Cache invalidation strategies — the genuinely hard part
- Cache eviction policies (LRU, LFU, TTL)
- Multi-layer caching (browser → CDN → application → database)
Advanced
- Cache stampede & thundering herd problems under load
- Designing cache consistency across multiple regions
- Deciding when caching introduces dangerous staleness for a specific feature
- Measuring cache hit ratio & tuning for it
Real-World Industry Use Case
Knowing cache-aside, write-through, and write-behind — and specifically when caching helps versus when it introduces dangerous staleness — is called out as one of the ten core technical concepts every system design interview evaluates in 2026.
Data & Messaging at Scale
At scale, the question is never just "SQL or NoSQL" — it's which specific engine fits this exact workload, and how services communicate without being tightly coupled together.
Basics
- Choosing relational (PostgreSQL/MySQL) vs. non-relational (MongoDB/Cassandra/DynamoDB) for a given problem
- Synchronous vs. asynchronous communication between services
- What a message queue solves that a direct API call doesn't
- Basic pub/sub — one event, multiple consumers
Intermediate
- Kafka — high-throughput event streaming
- SQS — simple, managed job queues
- Database replication for read scaling & durability
- Designing an API contract between two independent services
Advanced
- Event-driven architecture across an entire system
- CQRS — separating read and write models for scale
- Exactly-once vs. at-least-once message delivery guarantees
- Designing a system that tolerates a downstream service being fully down
Real-World Industry Use Case
The real 2026 system design question isn't "which database" in the abstract — it's knowing when to reach for Kafka (high-throughput event streaming) versus SQS (simple job queues) versus Pub/Sub (fan-out to multiple consumers) for a specific piece of a system.
Reliability & Consistency
A system that's fast but wrong, or fast but falls over the moment one server dies, isn't actually a good design — reliability and consistency are what make a system trustworthy.
Basics
- The CAP theorem, applied to real design decisions (not just theory)
- Replication — keeping copies of data for durability & availability
- Single points of failure & how to remove them
- Health checks & automatic failover
Intermediate
- Consistency models — strong vs. eventual consistency, and choosing deliberately
- Designing for partial failure — one component down, not the whole system
- Idempotency — designing operations that are safe to retry
- Circuit breakers — stopping a failing dependency from taking down everything
Advanced
- Distributed consensus (Raft/Paxos) at a conceptual level
- Designing rollback & recovery paths for a failed deployment
- Monitoring, on-call burden & operational maturity as design inputs
- Trading off consistency for availability deliberately, and explaining why
Real-World Industry Use Case
In 2026, interviewers explicitly grade whether you reason about failure modes, monitoring, and rollback paths — a candidate who finishes a 45-minute design interview without ever mentioning what happens when something fails is leaving real points on the table.
High-Level Design (HLD)
High-level design is the skill of taking a vague prompt — "design Twitter" — and producing a coherent architecture: what components exist, how data flows, and what trade-offs were made and why.
Basics
- The HLD framework — clarify requirements, estimate scale, design components
- Drawing a clean architecture/block diagram
- Identifying the 2-3 components that actually matter for a given prompt
- Functional vs. non-functional requirements
Intermediate
- Capacity estimation — traffic, storage & bandwidth math
- Choosing components (load balancer, cache, queue, database) for a specific system
- Communicating trade-offs out loud, not just silently choosing one
- Designing for the stated scale, not an imagined infinite scale
Advanced
- Designing for 10x/100x growth from day one without over-engineering
- Incorporating cost as an explicit design constraint
- Presenting a design under interview time pressure, structured & confident
- Handling a senior-level follow-up: "now the traffic just 10x'd — what changes?"
Real-World Industry Use Case
This is exactly the skill tested by prompts like "Design Instagram," "Design a rate limiter," or "Design a batch inference API for a GPU cluster" — open-ended by design, specifically to see how you reason under ambiguity, not to test memorized answers.
Low-Level Design (LLD) & Design Patterns
Where HLD is the system-wide architecture, LLD is the class-level design — the object-oriented structure, interfaces, and patterns that make the implementation itself maintainable.
Basics
- Core OOP principles — encapsulation, inheritance, polymorphism, abstraction
- SOLID principles, explained with real code examples
- Class diagrams — modeling entities & their relationships
- Interfaces & designing for extension
Intermediate
- Common design patterns — Factory, Singleton, Strategy, Observer
- Designing a clean API contract between classes/modules
- Composition over inheritance in practice
- Refactoring a tangled class structure into something testable
Advanced
- Designing a full LLD for a real system (e.g. a parking lot, an elevator system)
- Applying design patterns judiciously — recognizing over-engineering
- Balancing flexibility against simplicity in a real codebase
- LLD interview practice — designing classes live, under time pressure
Real-World Industry Use Case
LLD interviews — "design a class structure for a parking lot" or "design an elevator system" — specifically test whether you can turn a real-world problem into clean, extensible object-oriented code, which is precisely the daily skill behind writing maintainable production software.
Real-World Case Studies
Everything in this module comes together by actually designing real, well-known systems end to end — the single best way to build genuine system design intuition.
Foundational Case Studies
- Design a URL shortener
- Design a rate limiter
- Design a key-value store
- Design a notification system
Intermediate Case Studies
- Design a social media feed (Instagram/Twitter-style)
- Design a chat/messaging system
- Design a ride-sharing dispatch system
- Design a distributed job scheduler
Advanced Case Studies
- Design a video streaming platform (Netflix/YouTube-style)
- Design a search engine's indexing pipeline
- Design a payment processing system
- Design a batch inference API for a GPU cluster
Real-World Industry Use Case
Working through named, realistic prompts like these — not abstract theory — is exactly how every major system design interview guide in 2026 recommends preparing, because it forces you to actually apply scalability, caching, data, and reliability decisions together instead of in isolation.
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