Most Frequently Asked Problems - Common Lld Interview Problems | OneNoughtOne

0/246

System Design (LLD)Common LLD Interview Problems

Common LLD Interview Problems

LevelAdvanced

Duration120 mins

TopicCommon LLD Interview Problems

1 / 4

Most Frequently Asked Problems

The LLD Interview Canon

Every experienced interviewer knows the phenomenon: certain Low-Level Design problems appear again and again across companies, from startups to FAANG giants. These problems have earned their place through a unique combination of characteristics—they're rich enough to reveal deep design thinking, constrained enough to complete in 45-60 minutes, and familiar enough that candidates can engage meaningfully without extensive domain knowledge.

Understanding why these problems dominate interviews is just as valuable as practicing the problems themselves. When you grasp the underlying structure of a popular LLD problem, you can recognize its patterns in unfamiliar variations, adapt your solutions on the fly, and demonstrate the kind of principled thinking that distinguishes senior engineers from those who merely memorize solutions.

What You Will Master

By the end of this page, you will have a comprehensive map of the most frequently asked LLD problems, understand exactly what skills each problem tests, and recognize the underlying patterns that make these problems interview favorites. This knowledge transforms random practice into strategic preparation.

Why Certain Problems Dominate LLD Interviews

Not all design problems are created equal. The problems that appear repeatedly in interviews share specific characteristics that make them ideal for assessing engineering talent within strict time constraints. Understanding these characteristics helps you predict which problems are likely to appear and why interviewers favor them.

The selection criteria for top LLD interview problems:

Characteristics of Premier LLD Problems

•Universal Domain Familiarity — The best LLD problems involve systems everyone encounters: parking lots, libraries, elevators, games. Candidates don't waste time learning domain rules; they can focus on design.
•Rich but Bounded Complexity — Ideal problems have enough moving parts to reveal depth (multiple entities, relationships, state transitions) without becoming intractable in 45 minutes.
•Natural Extension Points — Great problems invite 'what if' questions: What if we add VIP parking? What if multiple players join? These extensions test adaptability without changing the core problem.
•Clear OOP Application — The problem naturally decomposes into objects with clear responsibilities, allowing interviewers to assess encapsulation, inheritance, and composition decisions.
•Pattern Opportunities — Strong LLD problems create organic opportunities to apply design patterns (Factory for object creation, Strategy for algorithms, Observer for notifications, State for lifecycle management).
•Testable Design Decisions — Every major choice (data structure, relationship type, interface design) can be questioned and defended, revealing the candidate's reasoning process.

The Meta-Skill

When you encounter an unfamiliar LLD problem in an interview, ask yourself: 'What universal domain does this map to? What bounded complexity does it present? Where are the natural extension points?' This meta-analysis helps you navigate novel problems using familiar patterns.

Tier-1 Classics: The Essential Seven

These seven problems form the absolute foundation of LLD interview preparation. They appear with remarkable consistency across companies, industries, and experience levels. Mastering these thoroughly should be your first priority—completing them equips you with patterns that transfer to virtually every other LLD problem.

The essential seven are not arbitrary—they were selected by the industry through natural evolution. Each tests a distinct combination of skills, and together they provide comprehensive coverage of LLD competencies.

The Tier-1 LLD Problem Classics
Problem	Core Skills Tested	Key Design Patterns	Interview Frequency
Parking Lot System	Entity modeling, relationship design, pricing strategies, capacity management	Factory, Strategy, Singleton	Very High (90%+ of LLD interviews include or reference this)
Library Management System	Resource tracking, borrowing/lending workflows, fine calculation, notifications	Observer, State, Repository	Very High
Elevator System	State management, scheduling algorithms, multi-elevator coordination	State, Strategy, Command	Very High
Chess/Tic-Tac-Toe	Game state, move validation, turn management, win detection	Strategy, Command, Memento	High
Hotel Booking System	Reservation management, availability tracking, pricing tiers	Factory, Template Method, State	High
Ride-Sharing Service (Uber/Lyft)	Matching algorithms, location tracking, trip lifecycle, payments	Strategy, Observer, State	High
ATM System	Transaction management, state transitions, security, hardware interfaces	State, Chain of Responsibility, Command	High

Why these seven specifically?

Each problem in the essential seven tests a different primary skill cluster while reinforcing common foundations:

Parking Lot is often the first LLD problem candidates encounter. Its genius lies in apparent simplicity masking rich design decisions: How do you model vehicle types? Spot allocation? Pricing strategies? Multi-floor layouts?
Library System emphasizes lifecycle management and temporal constraints—loans have due dates, reservations expire, fines accumulate. It naturally introduces state machines and observer patterns.
Elevator System is the quintessential algorithm-meets-OOP problem. You must model physical constraints (elevators can't teleport), optimize for user experience, and handle concurrent requests gracefully.
Chess tests rule complexity and validation logic. Each piece has unique movement patterns, and you must detect checkmate, stalemate, and special moves (castling, en passant) while keeping the design clean.
Hotel Booking introduces double-booking prevention, date-range queries, and pricing tiers—classic resource scheduling problems with real-world concurrency implications.
Ride-Sharing combines geospatial concerns, matching algorithms, and complex state machines (ride requested → driver assigned → in progress → completed). It's a modern classic that tests contemporary system design sensibilities.
ATM System emphasizes security, transaction atomicity, and hardware abstraction. It forces you to think about error handling, retry logic, and the boundary between software and physical devices.

Depth Over Breadth

Knowing all seven superficially is worse than knowing three deeply. If you can design a Parking Lot System from first principles—explaining every decision, defending alternatives, and extending gracefully—you'll outperform candidates who've skimmed a dozen problems. Quality of understanding trumps quantity of exposure.

Tier-2 Frequently Asked: The Expanding Repertoire

Once you've mastered the Tier-1 classics, expanding to Tier-2 problems builds pattern recognition breadth and prepares you for less predictable interviews. These problems appear regularly but not universally—expect to see one or two in a typical interview loop, especially at companies that prefer novel problems.

Tier-2 LLD Problems: Expanding Your Repertoire
Problem	Core Focus	Pattern Opportunities	Why It's Important
Online Shopping System (Amazon)	Cart management, checkout flow, inventory, payments	Observer, Strategy, Factory	Tests e-commerce domain, common in retail tech
Food Delivery System (DoorDash/Uber Eats)	Order routing, restaurant management, driver assignment	Strategy, Observer, State	Modern gig economy design challenges
Movie Ticket Booking (BookMyShow)	Seat selection, show scheduling, theater management	Singleton, Factory, Strategy	Real-time seat locking, concurrency handling
Vending Machine	State transitions, inventory, payment handling	State, Strategy, Command	Finite state machine masterclass
Snake and Ladder Game	Game board, dice mechanics, turn management	Strategy, Observer, Singleton	Simple rules, rich state tracking
Car Rental System	Vehicle fleet, reservation, maintenance tracking	Factory, State, Template Method	Inventory lifecycle management
Airline Booking System	Seat inventory, multi-leg flights, ancillary services	Factory, Strategy, Composite	Complex reservation hierarchies
Stack Overflow / Q&A Platform	Question/answer modeling, voting, reputation	Observer, Strategy, Decorator	Social/gamification elements
Social Media Feed (Twitter/Facebook)	Post modeling, timeline generation, notifications	Observer, Strategy, Iterator	Large-scale content distribution
Splitwise (Expense Sharing)	Expense tracking, debt simplification, settlements	Strategy, Observer	Graph-based debt optimization

Strategic insights for Tier-2 preparation:

Tier-2 problems often combine elements from Tier-1. A Food Delivery System merges concepts from Ride-Sharing (matching, routing) with Restaurant/Hotel Booking (availability, scheduling). If you've deeply understood Tier-1, you can synthesize solutions for Tier-2 on the fly.

Domain-specific considerations:

E-commerce problems (Amazon, Food Delivery) emphasize cart state, checkout atomicity, and inventory management.
Booking problems (Movie Tickets, Airlines) focus on seat locking, time-based availability, and concurrent reservation handling.
Game problems (Vending Machine, Snake & Ladder) are state machine showcases—every state transition must be explicit and valid.
Social platforms (Stack Overflow, Twitter) test event-driven architectures, notification systems, and large-scale data modeling.

The Synthesis Skill

Senior candidates distinguish themselves by recognizing Tier-2 problems as compositions of Tier-1 patterns. When asked about a Food Delivery System, they immediately see the Ride-Sharing matching logic, the Restaurant Booking availability model, and the Order Management state machine—and design accordingly.

Company-Specific Favorites and Emerging Trends

While core LLD problems are universal, companies often favor problems that align with their domain. Understanding these preferences helps you tailor preparation for specific targets. Additionally, emerging technology trends create new problem categories that didn't exist five years ago.

Domain-Aligned Problems by Company Type

•Fintech (Stripe, Square, PayPal): • Payment Gateway System • Transaction Ledger • Subscription Billing • Fraud Detection Rules Engine
•Ride-Sharing (Uber, Lyft, Grab): • Ride Matching System • Dynamic Pricing Engine • Driver Dispatch Optimization • Real-time Location Tracking
•E-Commerce (Amazon, Shopify, eBay): • Shopping Cart System • Inventory Management • Order Fulfillment • Product Catalog with Variants
•Streaming (Netflix, Spotify, YouTube): • Content Recommendation Engine • Playlist Management • Watch History Tracker • Content Licensing Rules

Emerging Problem Categories

•API Design Problems: • Rate Limiter • API Gateway • Request Throttling • Circuit Breaker Pattern
•Event-Driven Systems: • Event Sourcing System • Message Queue Manager • Notification Service • Audit Trail System
•Collaboration Tools: • Real-time Document Editor • Calendar Scheduling • Task Management System • Team Chat Application
•IoT & Hardware: • Smart Home Controller • Sensor Data Aggregator • Device Registry • Firmware Update Manager

Preparing for company-specific problems:

Research the company's domain — If interviewing at a payments company, practice Payment Gateway, Ledger, and Subscription systems thoroughly.
Study their engineering blog — Companies like Uber, Netflix, and Airbnb publish detailed design articles. These reveal the problems they find important and the patterns they favor.
Think like their engineers — A payments company cares deeply about transaction atomicity and idempotency. A streaming company prioritizes personalization and caching. Let domain priorities guide your emphasis.
Prepare extensions relevant to their scale — If interviewing at a company handling millions of users, prepare to discuss how your design handles concurrency, horizontal scaling, and eventual consistency.

The Trend Advantage

Candidates who understand emerging problem categories (Rate Limiter, Circuit Breaker, Event Sourcing) demonstrate awareness of modern system design. These problems often appear as extensions to classic problems or as standalone questions at senior levels.

What Each Problem Actually Tests: The Skill Matrix

Different LLD problems emphasize different skills. Understanding this mapping helps you identify weaknesses and target practice. Rather than practicing randomly, use this matrix to ensure comprehensive skill coverage.

Skill Coverage Matrix for Top LLD Problems
Skill Area	Primary Problems	What Interviewers Evaluate
Entity Identification	Parking Lot, Library, Hotel Booking	Can you identify the right nouns to model? Do you distinguish entities from attributes?
Relationship Modeling	All problems, especially Chess, Library	Do you understand composition vs aggregation? Can you articulate cardinalities?
State Machine Design	Elevator, ATM, Vending Machine, Ride-Sharing	Can you enumerate states, define transitions, and handle invalid state transitions gracefully?
Algorithm Integration	Elevator (scheduling), Ride-Sharing (matching), Chess (move validation)	Can you integrate non-trivial algorithms into clean OO designs?
Concurrency Awareness	Movie Tickets (seat locking), Parking Lot (spot allocation), Hotel Booking	Do you recognize race conditions? Can you discuss locking strategies?
Extensibility Design	All problems	Can you design for future requirements without over-engineering?
Pattern Application	All problems	Do you apply patterns purposefully? Can you justify pattern choices?
Edge Case Handling	ATM (insufficient funds), Chess (checkmate), Parking (full lot)	Do you anticipate failure scenarios? Is your design robust?

Using the skill matrix for targeted practice:

Identify your weakest skill areas and prioritize problems that emphasize them:

Weak at state machines? Focus on Elevator, ATM, and Vending Machine. Practice drawing state diagrams before coding.
Struggle with relationship modeling? Spend extra time on Library and Chess. Diagram every association, aggregation, and composition.
Concurrency feels mysterious? Work through Movie Ticket Booking with a focus on seat-locking strategies. Understand optimistic vs pessimistic locking.
Patterns feel forced? Revisit Parking Lot with an explicit pattern-identification exercise. For each pattern you apply, articulate why alternatives are inferior.

The Interviewer's Perspective

Interviewers don't just check if you complete the design—they observe how you arrive at decisions. A candidate who considers three approaches and articulates trade-offs demonstrates more skill than one who jumps to the 'right' answer without exploration. Design is about reasoning, not recipes.

Difficulty Progression: From Foundations to Mastery

LLD problems vary in complexity, and understanding this progression helps you calibrate expectations and build skills systematically. Attempting advanced problems before solidifying fundamentals leads to frustration and superficial learning.

Difficulty Tiers and Recommended Progression

•Foundation Tier (Start Here) • Parking Lot System — The canonical starter problem. Clean entity boundaries, straightforward relationships. • Tic-Tac-Toe — Simple game logic, minimal state management. Perfect for practicing move validation patterns. • Vending Machine — Finite states, clear transitions. Excellent State pattern practice.
•Intermediate Tier (Build Complexity) • Library Management — Adds temporal logic (due dates, fines). Introduces Observer pattern naturally. • Snake and Ladder — Game mechanics plus randomness. Practice board modeling and turn management. • Movie Ticket Booking — Real-time constraints, seat locking. Introduces concurrency considerations.
•Advanced Tier (Full Complexity) • Chess — Complex rule validation, special moves, check/checkmate detection. Tests thorough domain modeling. • Elevator System — Multi-elevator coordination, scheduling algorithms. Tests algorithm-OO integration. • Ride-Sharing Service — Geospatial concerns, matching, complex state machine. Tests modern system sensibilities.
•Expert Tier (Extension & Integration) • Multi-Tenant SaaS Platform — Isolation, tenant management, resource quotas. Tests architectural thinking. • Real-Time Collaborative Editor — Conflict resolution, operational transforms. Tests distributed systems at LLD level. • Trading Platform — Order matching, position tracking, risk management. Tests domain complexity and precision.

Recommended practice timeline:

Week 1-2: Master Foundation Tier. Implement each problem from scratch. Practice explaining decisions aloud.

Week 3-4: Complete Intermediate Tier. Focus on patterns and extensions. For each problem, identify three potential extensions and design for at least one.

Week 5-6: Tackle Advanced Tier. Time yourself—aim for a complete design in 40-50 minutes, leaving time for refinement.

Week 7+: Attempt Expert Tier selectively. These are overkill for most interviews but excellent for Staff+ preparation.

The 80/20 rule applies: 80% of interview success comes from thoroughly mastering the Foundation and Intermediate tiers. Don't rush to advanced problems before your fundamentals are rock-solid.

Quality Metrics for Each Tier

Foundation: Can you design it in 25 minutes with clean diagrams? Intermediate: Can you add two extensions without restructuring? Advanced: Can you defend every decision against alternatives? Expert: Can you discuss production deployment considerations?

Anti-Patterns in Problem Selection

Candidates often make strategic mistakes in how they select and practice LLD problems. Recognizing these anti-patterns helps you avoid common pitfalls and optimize your preparation time.

Anti-Patterns to Avoid

•Collecting Without Practicing — Reading 30 problem solutions without implementing any. Knowledge without practice doesn't transfer to interviews.
•Always Starting Fresh — Never revisiting problems you've solved. Returning to problems after 2-3 weeks solidifies patterns.
•Memorizing Solutions — Learning the 'standard' design by rote instead of understanding why each decision was made.
•Ignoring Extensions — Stopping as soon as core requirements are met. Interviewers probe extensions to assess adaptability.
•Skipping Diagrams — Coding directly without UML/whiteboard practice. Interviews are visual—diagram fluency matters.
•Solo Practice Only — Never practicing with a mock interviewer. Real interviews involve dialogue, not monologue.

Optimal Practice Patterns

•Implement Then Compare — Design independently first, then compare with reference solutions. Note gaps in your thinking.
•Spaced Repetition — Return to previously solved problems after a gap. Can you still design it cleanly? What did you forget?
•First-Principles Reasoning — For each decision, articulate alternatives and trade-offs. 'I chose X because Y, not Z because...'
•Extension Challenges — After completing core design, challenge yourself with two extensions without major restructuring.
•Diagram Everything — Practice drawing class diagrams, sequence diagrams, state diagrams. Time yourself.
•Mock Interviews — Practice with peers or services. Get feedback on communication, not just correctness.

The Memorization Trap

The most dangerous anti-pattern is memorizing solutions. An experienced interviewer can tell instantly—you can't defend decisions you don't understand, and you crumble when the problem varies slightly from your memorized version. Understanding always beats memorization.

Summary: Navigating the LLD Problem Landscape

You now have a strategic map of the LLD interview problem landscape. Let's consolidate the essential insights:

Key Takeaways

•The Essential Seven dominate — Parking Lot, Library, Elevator, Chess, Hotel Booking, Ride-Sharing, and ATM appear in the vast majority of LLD interviews. Master these first.
•Problems share characteristics — Universal domains, bounded complexity, natural extension points, and pattern opportunities make certain problems interview favorites.
•Tier-2 problems synthesize Tier-1 patterns — Food Delivery combines Ride-Sharing and Restaurant systems. Recognizing these compositions accelerates learning.
•Company domains influence selection — Fintech companies favor payment problems; streaming companies favor content systems. Research your target.
•Each problem tests specific skills — Use the skill matrix to identify weaknesses and target practice accordingly.
•Difficulty progression matters — Build from Foundation through Intermediate before attempting Advanced. Don't skip steps.
•Avoid anti-patterns — Implement independently, use spaced repetition, practice extensions, and conduct mock interviews.

What's next:

Now that you know which problems to practice, the next page explores how to categorize them by pattern. You'll learn to see problems not as isolated challenges but as instances of recurring problem archetypes—enabling you to recognize and solve novel problems by mapping them to familiar categories.

Page Complete

You've mapped the LLD interview problem landscape. You understand the Essential Seven, Tier-2 expansions, company-specific variations, skill mappings, difficulty progressions, and preparation anti-patterns. This strategic foundation ensures your practice time yields maximum returns.