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