Table of Contents

  • Basic
    1. Object-Oriented Programming (OOP) Fundamentals
    2. Class Design and Relationships
    3. Design Patterns (Foundational)
    4. Basic UML Diagrams
    5. Error Handling and Exception Design
    6. Coding Principles
    7. Code Modularity
    8. Data Structures
    9. Testing Fundamentals
  • Advanced
    1. Advanced Design Patterns
    2. SOLID Principles
    3. Clean Code Practices
    4. Advanced UML Diagrams
    5. Concurrency and Parallelism
    6. Microservices and API Design
    7. Data-Intensive Design
    8. Advanced Testing 9.Performance Considerations
    9. Event-Driven Architecture
    10. Domain-Driven Design (DDD)
    11. Security and Resilience in Design
  • Interview Questions

Basic

Low-level design (LLD) is the process of detailing the components and modules of a system, creating a blueprint that focuses on the implementation aspects. It is concerned with specifying the internal structure and interactions of individual modules and their components.

Object-Oriented Programming (OOP) Fundamentals

OOP is a programming paradigm based on the concept of “objects”, which bundle data (attributes) and methods (functions) together. OOP enables better organization, reusability, and scalability in programming.

  1. Classes and Objects:

    • Class: A blueprint for creating objects, defining their attributes and behaviors.
    • Object: An instance of a class containing real values instead of variables.

  2. Inheritance:

    • A mechanism where a new class (subclass) inherits properties and methods from an existing class (superclass). It promotes code reuse.
    • Example: A Dog class can inherit from a Animal class.

  3. Encapsulation:

    • Encapsulation refers to the bundling of data and the methods that operate on the data into a single unit (class). It also restricts direct access to some of the object’s components (through access modifiers like private, protected).
    • This is done using getters and setters to access private fields.

  4. Polymorphism:

    • Polymorphism means “many shapes” and refers to the ability of different classes to provide different implementations of the same method.
    • It can be achieved through method overriding (runtime polymorphism) and method overloading (compile-time polymorphism).

  5. Abstraction:

    • Abstraction hides the complexity by exposing only essential features of an object. It can be achieved through abstract classes or interfaces, allowing the user to focus on high-level operations.

Class Design and Relationships

Class design involves creating the structure and behavior of classes, ensuring that classes are well-organized and efficient.

  1. Cohesion:

    • Cohesion refers to how closely related and focused the responsibilities of a class or module are. High cohesion means the class has a clear, single responsibility, leading to better maintainability and readability.

  2. Coupling:

    • Coupling refers to the degree of dependence between classes. Low coupling means fewer dependencies between classes, which makes the system more flexible and easier to change.
    • Ideally, classes should be loosely coupled.

  3. Relationships:

    • Aggregation: A “has-a” relationship, where one object contains another, but the contained object can exist independently of the container.
    • Composition: A stronger form of aggregation where the contained object cannot exist without the container. It’s a “part-of” relationship.
    • Association: A relationship where objects can interact with each other, typically by using each other’s methods.
    • Dependency: A weaker relationship, where one object relies on another for certain functionality.

Design Patterns (Foundational)

Design patterns are standard solutions to common design problems.

  1. Singleton:

    • Ensures a class has only one instance and provides a global point of access to it. Often used for resources like database connections or configuration settings.

  2. Factory Method:

    • Defines an interface for creating objects, but allows subclasses to alter the type of objects that will be created. It decouples object creation from its usage.

  3. Strategy:

    • Defines a family of algorithms, encapsulates each one, and makes them interchangeable. This allows the algorithm to be selected at runtime.

  4. Observer:

    • Defines a dependency between objects so that when one object changes state, all its dependent objects are notified and updated automatically.

  5. Adapter:

    • Allows incompatible interfaces to work together. The adapter pattern acts as a bridge between two incompatible interfaces.

Basic UML Diagrams

Unified Modeling Language (UML) is a standardized way to visualize the design of a system.

  1. Class Diagrams:

    • Represent the static structure of a system, showing the system’s classes, their attributes, methods, and relationships.

  2. Sequence Diagrams:

    • Illustrate how objects interact in a particular sequence of events. They focus on the order of messages exchanged between objects.

  3. Component Diagrams:

    • Show the components or modules of a system and their interactions. They provide a high-level view of system architecture.

Error Handling and Exception Design

Designing robust systems requires handling errors gracefully.

  1. Exception Hierarchies:

    • Organizing exceptions into a hierarchy allows for better management and classification of errors. For example, IOException could be a subclass of Exception.

  2. Graceful Error Handling Mechanisms:

    • Ensures that an application does not crash due to errors and can recover or provide useful feedback to users. Using try-catch blocks and handling exceptions at appropriate levels in the system is crucial.

Coding Principles

These principles help maintain clean, readable, and maintainable code.

  1. DRY (Don’t Repeat Yourself):

    • Avoid duplication by ensuring that every piece of knowledge or logic has a single, unambiguous representation.

  2. KISS (Keep It Simple, Stupid):

    • Focus on simplicity. The simpler a solution is, the easier it is to maintain and extend.

  3. YAGNI (You Aren’t Gonna Need It):

    • Avoid adding functionality until it’s necessary. This principle helps prevent overengineering and unnecessary complexity.

Code Modularity

Designing software with modular components improves reusability, testability, and maintainability.

  1. Designing Modules or Components for Reusability and Testability:
    • Each module should have a single responsibility, be easily reusable across the system, and be easy to test in isolation (using unit tests).

Data Structures

Data structures are used to store and organize data efficiently.

  1. Array:

    • A collection of elements, identified by index or key. Fixed in size.

  2. Linked List:

    • A linear data structure where elements (nodes) are linked using pointers. It can grow dynamically.

  3. Stack:

    • A collection where elements are added and removed in a Last In First Out (LIFO) order.

  4. Queue:

    • A collection where elements are added and removed in a First In First Out (FIFO) order.

  5. Hash Table:

    • A data structure that stores key-value pairs. It allows for efficient lookups, insertions, and deletions based on keys.

  6. Basic Tree Structures (Binary Tree, Binary Search Tree):

    • Binary Tree: A tree data structure where each node has at most two children.
    • Binary Search Tree: A binary tree where nodes are arranged such that for each node, its left child is smaller and its right child is larger.

Testing Fundamentals

Testing is crucial to ensure the correctness and reliability of the system.

  1. Unit Testing:

    • Involves testing individual units or components of the software in isolation to ensure that each part functions as expected.

  2. Mocking and Stubbing:

    • Mocking: Creating fake objects that simulate the behavior of real objects for testing purposes.
    • Stubbing: Providing predefined responses to function calls during testing.

  3. Integration Testing Basics:

    • Focuses on testing the interactions between different components or systems to ensure that they work together as expected.

Advanced

Low-level design refers to the detailed design of individual components, functions, and modules within a system. Advanced topics delve into the strategies for creating more efficient and maintainable designs.

Advanced Design Patterns

Design patterns are standard solutions to common problems in software design. Here’s an overview of the ones you listed:

  1. Builder Pattern

    • Separates the construction of a complex object from its representation, allowing for different representations to be created using the same construction process.
    • Useful for constructing objects with many parameters or optional configurations.

  2. Prototype Pattern

    • Used to create new objects by copying an existing object, often known as the prototype.
    • Helps in creating objects with complex states without knowing their exact class.

  3. Mediator Pattern

    • Defines an object that controls how a set of objects interact, preventing direct communication between them.
    • Used to reduce dependencies between objects and to centralize communication logic.

  4. Chain of Responsibility Pattern

    • Allows a chain of objects to handle a request, passing it along the chain until one object processes it.
    • Useful for situations where multiple handlers might need to process a request, like in event processing.

  5. Command Pattern

    • Encapsulates a request as an object, allowing for parameterization of clients with queues, requests, and operations.
    • Often used in undo/redo systems or event handling.

  6. Visitor Pattern

    • Lets you add further operations to objects without having to modify the objects themselves.
    • Used to separate an algorithm from the objects it operates on, ideal for operations on complex object structures.

  7. Decorator Pattern

    • Attaches additional responsibilities to an object dynamically.
    • Provides a flexible alternative to subclassing for extending functionality.

SOLID Principles

SOLID is a set of principles that guide software design to make systems more understandable, flexible, and maintainable:

  1. Single Responsibility Principle (SRP)

    • A class should have only one reason to change, meaning it should have one job or responsibility.

  2. Open/Closed Principle (OCP)

    • Software entities (classes, modules, functions) should be open for extension but closed for modification.
    • New functionality should be added through extension, not by modifying existing code.

  3. Liskov Substitution Principle (LSP)

    • Objects of a superclass should be replaceable with objects of a subclass without affecting the correctness of the program.
    • Subtypes must behave in a way that doesn’t alter the expected behavior of the base class.

  4. Interface Segregation Principle (ISP)

    • A client should not be forced to implement interfaces it doesn’t use.
    • Split larger interfaces into smaller ones so that clients only implement what they need.

  5. Dependency Inversion Principle (DIP)

    • High-level modules should not depend on low-level modules. Both should depend on abstractions.
    • Abstractions should not depend on details; details should depend on abstractions.

Clean Code Practices

Clean code is code that is easy to read, maintain, and understand. Some important practices are:

  • Writing maintainable, readable, and scalable code: Code should be easy to understand and adapt to changing requirements. It’s about clarity over cleverness.
  • Refactoring legacy code: Continuously improving and simplifying existing code, without changing its behavior, to keep it maintainable.
  • Effective naming conventions: Descriptive, clear, and consistent names for variables, functions, and classes that convey their purpose.

Advanced UML Diagrams

UML (Unified Modeling Language) diagrams help visualize system design and architecture. Advanced UML diagrams include:

  1. State Diagrams

    • Illustrate the states an object can be in and the transitions between these states.
    • Useful in modeling finite state machines or complex lifecycle processes.

  2. Activity Diagrams

    • Represent workflows of activities and actions, often used to show the flow of control or data in a process.
    • Useful for modeling business processes or algorithms.

  3. Deployment Diagrams

    • Show the physical deployment of artifacts (like code and database) on hardware nodes.
    • Helpful in understanding the distribution of software in a system.

Concurrency and Parallelism

Concurrency involves managing multiple tasks at once, while parallelism is executing tasks simultaneously. Key topics include:

  • Thread safety and synchronization: Ensures that shared data is protected from race conditions and inconsistent states when accessed by multiple threads.
  • Deadlock detection and prevention: Identifying and avoiding situations where threads are stuck waiting on each other.
  • Asynchronous programming: Writing programs that can perform tasks in the background without blocking the main thread.
  • Design patterns for concurrency: Patterns like Producer-Consumer and Thread Pool manage concurrency by structuring how tasks are processed.

Microservices and API Design

Microservices involve designing applications as a collection of loosely coupled services. Key considerations are:

  • API versioning and design best practices: Ensuring backward compatibility while evolving APIs.
  • Contract-first design: Designing the API contract (e.g., Swagger/OpenAPI) before implementing the API.
  • API security: Authenticating and authorizing users or services using techniques like OAuth.
  • Fault tolerance patterns:
    • Circuit Breaker: Prevents system overload by detecting failures.
    • Retry: Retries a failed operation a set number of times.
    • Bulkhead: Isolates failure to prevent it from affecting the whole system.

Data-Intensive Design

Designing systems that deal with large volumes of data. Key considerations are:

  • Advanced data structures: Structures like AVL trees, Red-Black Trees, Tries, and Graphs help in optimizing data retrieval and storage.
  • Caching mechanisms: Techniques like LRU cache reduce repeated access to slow resources like databases.
  • Memory optimization techniques: Efficient use of memory through data structure design, object pooling, and garbage collection optimization.

Advanced Testing

Advanced testing techniques ensure high-quality and reliable software:

  • Test-driven development (TDD): Writing tests before code to guide development and ensure correctness.
  • Behavior-driven development (BDD): Extending TDD by writing tests in a more natural language format, describing behaviors.
  • Integration and end-to-end testing: Testing the interaction of multiple components and the entire system from the user’s perspective.

Performance Considerations

Performance is critical for high-traffic systems:

  • Big-O complexity analysis: Understanding the time and space complexity of algorithms to choose efficient solutions.
  • Profiling and optimization: Identifying and removing performance bottlenecks in your code.
  • Database query optimization: Writing efficient SQL queries and designing efficient database schemas.

Event-Driven Architecture

Event-driven architecture (EDA) revolves around systems reacting to events or changes in state:

  • Event sourcing: Storing all changes to application state as a sequence of events.
  • CQRS: Separates the command (write) and query (read) sides of a system, allowing for more scalable solutions.
  • Messaging systems: Technologies like RabbitMQ or Kafka facilitate the decoupling of system components by enabling asynchronous communication.

Domain-Driven Design (DDD)

DDD focuses on modeling systems based on the domain they are solving problems for:

  • Entities, Value Objects, Aggregates: Core building blocks of DDD, where entities have identities, value objects have no identity, and aggregates are collections of related entities.
  • Repositories: Abstraction for accessing and persisting aggregates.
  • Bounded Contexts: Defining clear boundaries around a specific part of the domain, ensuring teams work with a consistent model.
  • Ubiquitous language: A common language shared by both developers and business experts to describe the system and its domain.

Security and Resilience in Design

Security and resilience are key to creating robust systems:

  • Secure coding practices: Writing code to protect against vulnerabilities such as SQL injection, XSS, etc.
  • Threat modeling and mitigation: Identifying potential threats and designing the system to mitigate those risks.
  • Resilient system design: Patterns like fallbacks, circuit breakers, and retry mechanisms to ensure a system continues to function even when some components fail.

Interview Questions

Basic-Level Design Questions

These questions are intended to test a candidate’s understanding of basic design principles, object-oriented design, and familiarity with common patterns and algorithms.

  1. Design a parking lot system:

    • Design classes for parking spots, vehicles, and the parking lot.
    • What types of vehicles should your system support?
    • How would you handle parking spot availability?

  2. Design a library management system:

    • Create a system for borrowing and returning books.
    • How will you manage book inventory?
    • How would you handle overdue books?

  3. Design a basic ATM system:

    • Design classes for account management, transactions, and receipts.
    • How would you implement security (PIN, transaction limits)?
    • How would you handle different account types (checking, savings)?

  4. Design a simple file system:

    • What classes would be involved in a file system?
    • How would you manage files and directories?
    • How would you handle file permissions?

  5. Design a simple email system:

    • What classes would you need for email management?
    • How would you structure the system to send, receive, and delete emails?
    • How would you implement spam filters?

  6. Design a simple messaging app (chat system):

    • Design classes for sending, receiving, and storing messages.
    • How would you implement message encryption?
    • How would you handle user authentication and message delivery?

Intermediate-Level Design Questions

These questions often involve more complex systems and integration of multiple components or subsystems. Candidates should also consider scalability, reliability, and performance.

  1. Design an online bookstore system:

    • How would you handle inventory management, order processing, and payment systems?
    • What database schemas would you consider for book information, users, and orders?
    • How would you handle discounts, promotions, and recommendations?

  2. Design a URL shortening service (like bit.ly):

    • How would you generate unique shortened URLs?
    • How would you store mappings between original and shortened URLs?
    • How would you ensure the system is scalable to handle billions of URLs?

  3. Design a movie ticket booking system:

    • How would you design the system to manage theaters, showtimes, and reservations?
    • How would you handle overbookings and seat availability?
    • How would you manage user preferences, payments, and cancellations?

  4. Design a notification system (e.g., push notifications, emails):

    • How would you handle different types of notifications (emails, SMS, in-app)?
    • How would you ensure the system can handle millions of notifications per day?
    • How would you prioritize or throttle notifications?

  5. Design a social media platform (e.g., Twitter):

    • How would you design the architecture to support following, posting, and likes?
    • How would you scale the system to handle millions of users and posts?
    • How would you design a feed system with personalized recommendations?

  6. Design an e-commerce shopping cart system:

    • How would you design cart items, user sessions, and payment processing?
    • How would you implement pricing calculations, discounts, and tax calculations?
    • How would you manage cart persistence (e.g., between sessions)?

Advanced-Level Design Questions

These questions are for senior positions and require candidates to design highly scalable, fault-tolerant, and performant systems with a strong focus on trade-offs, edge cases, and real-world constraints.

  1. Design a distributed cache (e.g., Redis or Memcached):

    • How would you design a distributed caching system to store key-value pairs?
    • How would you ensure consistency and handle cache invalidation?
    • How would you implement replication and sharding in the cache?

  2. Design a real-time collaborative document editing system (e.g., Google Docs):

    • How would you handle concurrent edits from multiple users in real time?
    • How would you design a conflict resolution mechanism?
    • How would you implement offline support and eventual consistency?

  3. Design a large-scale file storage system (e.g., Dropbox, Google Drive):

    • How would you handle file uploads, downloads, and synchronization across devices?
    • How would you ensure fault tolerance and data redundancy?
    • How would you design a system to manage access control, versioning, and sharing?

  4. Design a recommendation system (e.g., for an e-commerce platform or a movie app):

    • How would you build a recommendation engine based on user preferences and behaviors?
    • What algorithms would you use (collaborative filtering, content-based filtering)?
    • How would you handle cold starts, scalability, and personalization?

  5. Design a real-time stock trading platform:

    • How would you design a system that supports buying and selling of stocks in real time?
    • How would you handle trading volumes, low latency, and transaction consistency?
    • How would you ensure market data updates and provide order matching?

  6. Design a large-scale event logging and monitoring system:

    • How would you design a system to handle billions of events and logs per day?
    • How would you ensure the system is fault-tolerant and scalable?
    • How would you handle querying, indexing, and storing log data for quick access?

General Design and Best Practices

  1. Explain the SOLID principles:

    • How do each of the SOLID principles help improve the design and maintainability of a system?

  2. Discuss the trade-offs between different types of databases (SQL vs. NoSQL):

    • How would you decide whether to use a relational database or a NoSQL database in your system?

  3. How do you ensure the scalability and reliability of your system?

    • What strategies would you use to scale a system horizontally or vertically?
    • How do you handle failover and fault tolerance?

  4. Design a multi-tiered application:

    • How would you separate the concerns of your application into multiple layers (e.g., presentation, business logic, data access)?
    • What kind of communication would occur between the layers, and how would you ensure loose coupling?

  5. Explain how you would perform load testing and capacity planning for your system.

Behavioral Design Questions

These assess the candidate’s thought process and approach to designing systems, especially when under constraints.

  1. What steps would you take if you were asked to design a system that must be highly available and fault-tolerant?
  2. How do you ensure that your system design is flexible and can accommodate future changes?
  3. How would you approach debugging and optimizing a low-level design?
  4. Can you describe a situation where you had to make a difficult design trade-off? What was your reasoning?

Tips for Preparing

  • Practice UML diagrams (Class, Sequence, Use Case, etc.) to represent your design.
  • Focus on designing systems that are modular, scalable, and maintainable.
  • Be ready to discuss performance, complexity, and edge cases during the design.
  • Communicate clearly and break down the design problem step by step.


Basic-Level LLD Questions

  1. Design a ticket reservation system:

    • What classes and data structures would you need?
    • How would you handle seat availability, cancellations, and booking limits?
    • What would happen if a user tries to book the same seat simultaneously?

  2. Design a basic cash register system:

    • How would you implement classes for cash registers, transactions, and receipts?
    • How would you handle discount and tax calculations?
    • How would you implement inventory management for products?

  3. Design an online voting system:

    • How would you store and validate votes?
    • How would you ensure that multiple users cannot vote more than once?
    • How would you handle the results calculation and the integrity of voting data?

  4. Design a system for managing a conference schedule:

    • How would you design a system to handle sessions, speakers, and attendees?
    • How would you manage session timings and conflicts?
    • How would you allow attendees to register and track their sessions?

  5. Design a simple calendar application:

    • How would you handle recurring events and reminders?
    • How would you design the system to allow multiple users to add events?
    • How would you manage different time zones for events?

Intermediate-Level LLD Questions

  1. Design a payment gateway system:

    • How would you handle multiple types of payment methods (credit card, PayPal, etc.)?
    • How would you implement fraud detection and security?
    • How would you design the system to handle successful, pending, or failed transactions?

  2. Design a ride-sharing application (like Uber or Lyft):

    • How would you handle the matching of drivers with riders?
    • How would you design the system to track real-time location updates?
    • How would you handle surge pricing based on demand?

  3. Design an inventory management system:

    • How would you manage stock levels, suppliers, and product catalog?
    • How would you design it to handle inventory across multiple warehouses?
    • How would you handle product return and exchange?

  4. Design a logging and monitoring system (e.g., for server logs):

    • How would you store logs for querying and reporting?
    • How would you handle log levels (e.g., info, warn, error)?
    • How would you ensure logs are processed and stored at scale in real-time?

  5. Design a file compression tool (like ZIP or TAR):

    • How would you design the algorithm to compress and decompress files?
    • How would you handle large file sizes and compression ratios?
    • How would you optimize the performance of compression and decompression?

  6. Design a URL shortener service (e.g., bit.ly):

    • How would you generate a unique short URL for a long URL?
    • How would you store the mapping of short URLs to long URLs?
    • How would you ensure scalability as the service grows?

Advanced-Level LLD Questions

  1. Design a distributed message queue system (like Kafka or RabbitMQ):

    • How would you design the architecture for high throughput and low latency?
    • How would you handle message persistence, ordering, and delivery guarantees?
    • How would you handle message acknowledgment, retries, and failure handling?

  2. Design a multi-user online game system (like Chess, Checkers, etc.):

    • How would you manage users, game states, and moves?
    • How would you handle real-time communication between players (web sockets, polling)?
    • How would you design for scaling with millions of simultaneous players?

  3. Design a global search engine (like Google):

    • How would you handle crawling and indexing the web?
    • How would you design the ranking algorithm (PageRank, etc.)?
    • How would you scale the search engine to handle billions of queries per day?

  4. Design a distributed file system (like HDFS or GFS):

    • How would you handle file storage, redundancy, and replication across multiple nodes?
    • How would you design the system to ensure fault tolerance in case of server failures?
    • How would you manage file access, permissions, and metadata?

  5. Design a social networking platform (like Facebook or Instagram):

    • How would you design a user feed, including posts, likes, and comments?
    • How would you handle friend relationships and privacy settings?
    • How would you manage scalability as the user base grows?

  6. Design a real-time chat application (like Slack or WhatsApp):

    • How would you manage user connections, message delivery, and presence status?
    • How would you design it to scale with millions of users and messages?
    • How would you implement message encryption and privacy?

  7. Design a distributed caching system (e.g., for large-scale web applications):

    • How would you ensure that the cache can scale horizontally and handle high loads?
    • How would you manage cache eviction policies (LRU, LFU)?
    • How would you design a system to ensure consistency between the cache and the underlying database?

Edge Cases and Performance-Oriented LLD Questions

  1. Design a rate-limiting system (e.g., for API calls or user actions):

    • How would you design the system to limit requests per user or IP address?
    • What algorithms would you use (Token Bucket, Leaky Bucket)?
    • How would you handle burst traffic and ensure fair rate-limiting?

  2. Design a system to detect duplicate images (e.g., in an image hosting platform):

    • How would you compare images to detect similarity?
    • What hashing or fingerprinting algorithms would you use (e.g., perceptual hash)?
    • How would you scale this for millions of images?

  3. Design a system for calculating distance between locations (like Google Maps):

    • How would you calculate the shortest path between two locations?
    • How would you handle real-time traffic data in the system?
    • How would you manage scalability if the system needs to serve millions of requests?

  4. Design a logging and alerting system that can handle millions of events per second:

    • How would you ensure real-time ingestion and storage of log data?
    • How would you process logs for alert generation and anomaly detection?
    • How would you design the system to scale and handle both high throughput and low latency?

Behavioral and Problem-Solving LLD Questions

  1. Design a system that must be both highly available and partition-tolerant (using CAP Theorem):

    • How would you make trade-offs between consistency, availability, and partition tolerance?
    • How would you design for eventual consistency in this system?

  2. How would you design a system with the following constraints:

    • High throughput and low latency.
    • Fault tolerance and high availability.
    • Real-time data processing.
    • How would you balance these requirements in your design?

  3. Design a system where multiple components need to collaborate, but you cannot modify all the components:

    • How would you implement communication between these components?
    • How would you ensure that each component can evolve independently?

  4. How would you design a system that needs to ensure data consistency across multiple data stores (e.g., SQL and NoSQL)?

    • What patterns would you apply (e.g., event sourcing, CQRS)?
    • How would you handle conflicts and ensure the system remains consistent?

System Design Trade-offs and Optimizations

  1. How would you optimize the performance of a system under heavy load (e.g., millions of requests per second)?

    • How would you handle bottlenecks in databases, CPU, memory, or network?
    • How would you scale the system horizontally and vertically?

  2. Explain how you would handle data migrations when updating a system:

    • How would you ensure minimal downtime?
    • How would you design the system to be backward compatible during the migration?

  3. Design a system that requires high fault tolerance but also needs to perform background tasks at regular intervals (e.g., scheduled jobs):

    • How would you ensure that tasks complete even if part of the system fails?
    • How would you schedule tasks without blocking critical system processes?

Advanced Algorithms and Data Structures Questions

  1. Design an autocomplete feature using a trie data structure:

    • How would you implement the search and insert operations efficiently?
    • How would you scale the autocomplete service to handle large dictionaries?

  2. Design a distributed rate limiter for a global API:

    • How would you ensure fair distribution of requests and prevent abuse?
    • How would you handle distributed state in the rate limiter?