System design interview questions are the true test of an engineer’s ability to think beyond the code and understand the components of the business, which makes it run smoothly. Whether in technical, electrical, or mechanical domains, system design engineers ensure that every component of the enterprise works smoothly to meet performance, safety, scalability, and efficiency standards.
System design interviews assess if the candidate can reason through vague problems, trade-offs in designs, and design for scalability, resilience, and maintainability. Top companies like Google, Meta, etc, evaluate how candidates think about architecture, trade-offs, and performance. As McGee1 highlighted, over 60% of IT leaders keep system design expertise in-house because it directly impacts innovation and business success.
In this article, we have curated 50 frequently asked system design interview questions & answers. With real-world examples, clear frameworks, and actionable strategies, you will gain the confidence to excel in system design interviews.
Key Takeaways
- To succeed in system design interview you need to understand in depth High-Level Design, topics like system architecture, scalability, and availability, and Low-Level Design, such as code structure, design patterns, and database setup.
- You will explore techniques like load balancing, caching, database replication, and fault tolerance to design systems that scale well and stay reliable.
- You will get familiar with today’s design methods such as microservices, event-driven architecture, containerization, and API gateways.
- You will understand how to create secure authentication and authorization systems, and build strong disaster recovery plans to keep systems safe and resilient.
Top 50 System Design Interview Questions
System design is about thinking like an architect and building systems that scale, stay reliable under pressure, and handle real-world challenges. Today’s tech companies expect engineers to justify every decision and deliver solutions that are robust, efficient, and maintainable.
Here is a set of 50 system design interview questions from basic to advanced level.
Top Low-Level System Design Interview Questions
Low level design (LLD) focuses on the internal workings of a system, how each component, class, or function is structured and interacts. This section covers the most important Low-level system design interview questions.
These LLD questions helps you master topics like design patterns, class diagrams, data flow, and scalability at the code level.
Q1. What is Low-Level System Design in software development?
Low-level system design (LLD) in software development is the process of defining how individual components of a system will work at the code level.
It focuses on the detailed design and logic behind every feature and function, ensuring that the system built from the High-level design (HLD) is efficient, maintainable, and reliable.
Q2. How does database indexing optimize query performance?
Databasе indеxing improvеs quеry еfficiеncy by allowing fastеr lookups without scanning еntirе tablеs.
There are many benefits of database indexing.
- Database indexing accеlеratеs SELECT quеriеs and JOIN opеrations
- It rеducеs disk I/O and rеsponsе timе
- It supports rangе quеriеs and filtеring еfficiеntly
Tradе-Off: Indеxеs incrеasе storagе usagе and can slow down writе opеrations (INSERT/UPDATE/DELETE).
Q3. What are the key considerations in designing a relational database schema?
An еffеctivе rеlational databasе schеma dеsign balancеs data intеgrity, pеrformancе, and scalability. Its kеy considеrations arе as follows:
- Normalization: Rеmovе rеdundancy whilе maintaining pеrformancе.
- Constraints: Usе kеys and rеlationships for data consistеncy.
- Indеxеs: Optimizе for frеquеnt quеriеs.
- Rеlationships: Propеrly dеfinе onе-to-onе, onе-to-many, and many-to-many.
- Data Typеs: Choosе optimal fiеld typеs to rеducе spacе.
- Scalability: Plan for sharding or partitioning if nееdеd.
Q4. Why is concurrency control important in multi-threaded systems?
Concurrency control ensures data integrity and correctness when multiple threads access shared resources. Without proper control, issues like race conditions and deadlocks can occur, leading to unpredictable results.
Q5. What are UML Behavioral Diagrams?
UML behavioral diagrams describe how system components interact dynamically. These diagrams help visualize logic flow and runtime behavior, making them essential in LLD.
Types of UML Behavioral Diagrams
There are four main types of UML Behavioral Diagrams:
- Use Case Diagrams: Show system-user interactions
- Sequence Diagrams: Depict message flow over time
- Activity Diagrams: Represent workflows or processes
- State Machine Diagrams: Show state transitions.
Q6. How do you model a sequence diagram for a user login process?
To model a user login in a sequence diagram, let’s consider the objects involved.
Objects Involved: User, Login Controller, Authentication Service, and Database.
To model a sequence diagram for a user login process, the flow goes like this:
- The user enters credentials and sends them to the login controller
- The login controller forwards the credentials to the authentication service
- The authentication service checks the credentials in the database
- The database responds with a success or failure
- The authentication service returns the result to the login controller, which then responds to the user.
Q7. How would you model behavior of a system using a state diagram?
A state diagram shows how an object or system moves through different states in response to events. To model behavior of a system using a state diagram, follow the steps below:
First, identify the key states your object can be in. For example, in a payment system, the states could be pending, processing, completed, or failed.
Next, define the transitions between these states based on events:
- Payment approved, moves it from processing to completed
- Payment failed, moves it from processing to failed
Q8. What factors influence the choice of data structures in LLD?
The factors influencing the choice of data structures in LLD are as follows.
- Access Patterns: Read-heavy, hash maps, write-heavy, and queues/lists.
- Time Complexity: Need for fast lookups, use Hash tables.
- Space Efficiency: Arrays or Bitsets for low memory.
- Concurrency: Use thread-safe structures.
- Scalability: Distributed hash tables for large systems.
Example: Graphs for social networks, Tries for autocomplete, Queues for message systems.
Q9. What are the benefits of normalization?
Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related ones and defining relationships between them using keys. The benefits of normalization are as follows:
- Eliminates redundancy and anomalies
- Improves data consistency and storage efficiency
- Simplifies schema maintenance
- Clarifies relationships between entities
However, over-normalization can degrade performance; a balance with denormalization is key for scalable systems.
Q10. How do you design an efficient logging and monitoring system?
Designing an efficient logging and monitoring system for a complex application requires both structured logging and real-time observability.
To design an efficient logging system, you can follow the points below:
- Define a log format (JSON/structured text) to keep logs consistent and easy to parse
- Set log levels (DEBUG, INFO, WARN, ERROR, FATAL) so you can filter important messages quickly
- Ensure centralized log collection (e.g., ELK Stack, Splunk, Fluentd) to access all logs in one place
- Add correlation IDs to trace requests across microservices for easier debugging
To design an efficient monitoring system, you can implement the points below:
- Collect system metrics (CPU, memory, request latency) to keep an eye on application health
- Use time-series databases (Prometheus, InfluxDB) for storing and analyzing metrics over time
- Implement dashboards (Grafana, Kibana) for visual insights into system performance
Q11. What are Design patterns?
Design patterns are proven solutions to recurring design challenges. These patterns form the foundation of robust, extensible system design.
The benefits of design patterns are as follows.
- Promote reusability and consistency
- Simplify maintenance and scalability
- Provide a shared language among developers
Examples: Singleton, Factory, Observer, Strategy, Builder
Q12. Why are Design patterns important in software development?
Design patterns are essential in software development because they standardize problem-solving approaches across projects. These patterns are the building blocks of modular, adaptive software architecture. The importance of design patterns in software development are:
- Design patterns improve maintainability and clarity
- Reduce code duplication
- They facilitate collaboration via a common design vocabulary
- Enable flexibility and scalability
Q13. Explain the Singleton Design Pattern with use cases.
The Singleton design pattern ensures that a class has only one instance throughout the application and provides a global access point to that instance. It is useful when exactly one object is needed to coordinate actions across the system.
The use cases of the singleton design pattern are.
- Configuration Managers
- Database Connection Pools
- Logging Services
Note: Overuse can introduce unwanted global state, making testing more challenging.
Q14. What is the Observer Design Pattern?
The observer design pattern defines a one-to-many relationship between objects, so that when one object (the subject) changes its state, all dependent objects (observers) are notified automatically. It is especially useful in event-driven systems where multiple components need to react to changes without tightly coupling them.
Q15. What are the pros and cons of the Factory Design Pattern?
The factory design pattern provides a centralized way to create objects without exposing the creation logic to the client. It is particularly useful when the system needs to create different types of related objects dynamically, making the code more flexible and easier to maintain.
Pros
The benefits of the factory design pattern are:
- Encapsulation of object creation: Client code doesn’t need to know the concrete class being instantiated.
- Loose coupling: Changes in object creation don’t affect client code.
- Scalability: Adding new object types is simple, just extend the factory.
- Consistency: Ensures all objects of a certain type are created in a standardized way.
Cons
Few disadvantages of the factory design pattern are.
- Complexity: Introduces an extra layer of abstraction, which can be overkill for small projects.
- Maintenance overhead: Large factories with many object types can become hard to manage.
- Limited flexibility: If object creation requires complex configuration, simple factories may not be enough.
Q16. What is the Strategy Design Pattern in Java?
The strategy design pattern is a behavioral pattern that lets you define a family of algorithms, encapsulate each one, and make them interchangeable at runtime. Instead of hardcoding multiple behaviors with lots of if-else statements, you can switch strategies dynamically depending on the context. This makes your code flexible, modular, and easier to maintain. In Java, the pattern typically involves:
- A strategy interface defining a common method for all algorithms
- Concrete strategy classes that implement the interface with different behaviors
- A context class that holds a reference to a strategy and uses it to perform operations
Q17. How would you design logging for a distributed system?
In distributed systems, logging must ensure centralization and traceability. The key elements to design logging for a distributed system are:
- Structured logs: Include service name, request ID, and timestamp
- Centralized aggregation: ELK Stack, Fluentd
- Tracing: Use correlation IDs or tools like Jaeger, Zipkin
- Performance metrics: Capture latency and error rates
Goal: Enable quick debugging and bottleneck detection across services.
Q18. How does database replication improve reliability and performance?
Database replication means creating and maintaining multiple copies of the same database across different servers. This improves reliability because if one database goes down, another replica can immediately take over, ensuring continuous service.
It also boosts performance by distributing read queries across replicas, reducing the load on the primary database. Replication can be synchronous (real-time updates) or asynchronous (slightly delayed but faster).
Systems like MySQL, PostgreSQL, and MongoDB support these methods. By ensuring high availability, faster reads, and disaster recovery readiness, replication plays a vital role in building fault-tolerant systems at scale.
Q19. How to handle versioning and backward compatibility in LLD?
In LLD, you can handle versioning by creating versioned APIs like /v1/users and /v2/users and gradually deprecating old ones. For databases, use migration tools to update schemas safely. Keep backward compatibility with optional fields and feature toggles. This way, new updates don’t break existing users or systems.
Q20. How to design a secure authentication and authorization system?
To design a secure system, start with strong authentication. You can use token-based methods like OAuth 2.0 or JWTs. They store passwords securely with hashing and salting, and implement multi-factor authentication (MFA).
For authorization, apply Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC) to define who can access what.
In distributed systems, use Identity Providers like Keycloak or Auth0 and secure token exchanges between services.
Always enforce TLS/SSL, log access attempts, monitor anomalies, and regularly audit the system to ensure security and compliance while protecting sensitive data.
Top High-Level System Design Interview Questions
High-level design (HLD) gives a view of a system and shows how its main components work together. It acts like a roadmap that helps teams build systems that are scalable, modular, and easy to maintain. HLD focuses on meeting both business goals and technical needs while keeping the design simple and clear.
This section covers the most important high-level system design interview questions. It includes topics such as system architecture, scalability, data flow, APIs, caching, load balancing, and more.
Q21. What are the key components of a High-Level Design (HLD)?
High-lеvеl systеm dеsign (HLD) is thе stagе in softwarе dеsign whеrе you plan thе ovеrall structurе of a systеm. It shows how diffеrеnt componеnts such as databasеs, sеrvеrs, APIs, and usеr intеrfacеs work togеthеr.
Q22. What strategies ensure high availability in a distributed system?
High availability means keeping the system online even during failures. To ensure high availability in a distributed system, an individual should follow the strategies below.
- Use redundant servers, load balancers, and database replication
- Add failover mechanisms so backups take over automatically
- Deploy in multiple regions or zones to avoid downtime and use monitoring tools for quick issue detection
Q23. How do you ensure observability in a distributed system?
Observability means understanding what’s happening inside your system using logs, metrics, and traces. In distributed systems, where many services interact, observability helps track how data moves and where issues arise.
You implement structured logging for clarity, metrics collection for performance insights, and distributed tracing (using tools like Jaeger or Zipkin) to follow requests across services. Dashboards built with Grafana or Prometheus visualize the system’s health in real time.
Observability allows teams to detect problems early, debug faster, and maintain smooth performance even in complex architectures.
Q24. How do you ensure high availability in an HLD?
High availability means keeping your system up and running even if something fails. To achieve this, you replicate important components like servers and databases so there’s always a backup. Load balancers help distribute user requests across multiple servers, preventing overload.
You can also deploy your system in different regions to avoid complete outages. Add an automatic failover mechanism to switch to backup systems instantly. Finally, set up monitoring and alerts so issues are detected quickly.
Together, these strategies ensure users experience little to no downtime, even during hardware or network failures.
Q25. What is load balancing, and why is it important in HLD?
Load balancing helps manage traffic by dividing user requests across multiple servers. Imagine thousands of users trying to access a website without load balancing, one server might crash from overload. Techniques like Round Robin, Least Connections, and Weighted Distribution help share traffic efficiently.
Advanced systems use geo-load balancing, which sends users to the closest server for faster response times. This not only improves performance but also ensures high availability, since no single server becomes a failure point.
Tools like Nginx, HAProxy, and AWS ELB are commonly used for load balancing in modern architectures.
Q26. What are the key considerations for designing a scalable system in HLD?
A scalable system can handle growth smoothly, whether it’s more users, data, or requests. The best approach is horizontal scaling, adding more servers instead of just upgrading one. Use partitioning or sharding to split large databases, and add caching tools like Redis to reduce database load.
Asynchronous processing with message queues like Kafka helps handle background tasks efficiently. To handle heavy reads, add replica databases, and use CDNs to deliver static content faster. Designing for scalability means planning for growth without breaking performance or reliability as traffic increases.
Q27. How do you handle security concerns in HLD?
Security should be built into the system design from day one, not added later. Start with strong authentication and authorization using methods like OAuth 2.0, JWT, or role-based access control. Always encrypt sensitive data both in transit (with HTTPS/TLS) and at rest (using AES or KMS).
Protect APIs with rate limiting and input validation to prevent attacks like SQL injection or XSS. Keep all software and dependencies updated to patch vulnerabilities.
Finally, follow zero trust principles, never assume any user or service is safe by default, and always verify access.
Q28. What is caching in HLD, and why is it important?
Caching is like saving shortcuts for frequently used data so your system does not have to fetch it repeatedly. It stores data temporarily in fast-access memory, such as redis or memcached. Types of caching include client-side, server-side, and CDN caching.
The benefits are huge, it speeds up responses, reduces pressure on databases, and improves overall user experience. Caching is especially useful for data that does not change often, like product lists or profile information.
In short, caching makes systems faster, more efficient, and more scalable.
Q29. What are the steps involved in designing an API in HLD?
The steps involved in designing an API in HLD are:
- Understanding what data or services you want to expose
- Define your resources (like users or orders) and create endpoints (e.g., /users/{id})
- Use proper HTTP methods, GET, POST, PUT, and DELETE, for different operations
- Choose clear request and response formats, usually JSON
- Add authentication (like OAuth 2.0 or API keys) and implement error handling with standard status codes
- Plan for versioning (like /v1, /v2) to support updates without breaking older clients
- Finally, document everything using tools like Swagger or OpenAPI for easy integration
Q30. How do you ensure data consistency across distributed systems?
In distributed systems, data lives across multiple servers, which makes consistency tricky. To handle this, you can use models like Strong, Eventual, or Causal consistency depending on your system’s needs. Use techniques such as two-phase commits for atomic operations and idempotent operations to safely retry failed tasks.
When conflicts happen, apply policies like “last write wins” or use CRDTs for automatic resolution. Remember, there’s always a trade-off between availability and consistency (as per the CAP theorem), so choose based on your system’s priority, speed, or accuracy.
Q31. What are the benefits of system design?
System design provides a blueprint to build software that is scalable, reliable, and maintainable. It ensures high performance, efficient resource use, and fault tolerance while integrating security from the start.
A well-designed system reduces errors, simplifies updates, and supports growth, turning ideas into robust, cost-effective, and user-ready products.
Q32. What is fault tolerance, and why is it important in HLD?
Fault tolerance means your system keeps working even when parts of it fail. It is achieved through redundancy, replication, and graceful degradation. For example, if one microservice crashes, others continue functioning. Using circuit breakers and failure isolation prevents one issue from affecting the entire system.
The goal is simple – avoid downtime and maintain reliability. Fault-tolerant systems build trust with users and ensure that critical features remain available even during partial failures or outages.
Q33. How do you design for disaster recovery in HLD?
Disaster recovery ensures your system can bounce back quickly after major failures like data loss or outages. Always keep automated backups and test them regularly. Use geo-replication to store data in multiple regions, so if one fails, another takes over.
Add failover mechanisms to switch traffic automatically to backup servers. Define clear RPO (how much data loss is acceptable) and RTO (how fast you can recover). Regularly conduct DR drills to test your plan. A solid recovery strategy keeps your business running smoothly during unexpected events.
Q34. What is Event-Driven Architecture (EDA) in HLD?
Event-driven architecture (EDA) is a design where different parts of a system communicate through events, not direct calls. When something happens like a user placing an order, it creates an event that’s sent to an event bus (like Kafka or RabbitMQ).
Other services that care about that event act on it independently. This approach makes systems loosely coupled, scalable, and resilient. It is widely used in real-time systems, such as e-commerce order processing, IoT platforms, and analytics pipelines, where instant response and flexibility are key.
Q35. What role does fault tolerance play in HLD?
Fault tolerance means your system keeps working even when some parts fail. In HLD, this is critical for reliability and user trust. The goal is to design systems that degrade gracefully instead of completely breaking down. To achieve fault tolerance:
- Redundancy: Keep backup servers or data replicas
- Isolation: Separate components so one failure doesn’t affect others
- Replication: Store multiple data copies across regions
- Circuit breakers: Stop cascading failures in microservices
In short, fault tolerance ensures that even during hardware, network, or service failures, users experience minimal or no downtime.
Q36. How do you design for disaster recovery in HLD?
Disaster recovery (DR) focuses on getting your system back up quickly after a major failure like a data center crash. In HLD, it’s about planning for “what if everything goes wrong.” To design for disaster recovery in HLD, we should follow the strategies below:
- Regular backups of data and configurations
- Geo-replication across multiple regions or data centers
- Failover systems that automatically switch to standby servers.
- Defined RPO (Recovery Point Objective) and RTO (Recovery Time Objective) to measure recovery goals
- Regular DR drills to test readiness
Aim: The main aim is to minimize data loss and downtime when unexpected disasters strike.
Q37. What is Event-Driven Architecture (EDA) in HLD?
Event-driven architecture (EDA) is a design where components communicate using events, not direct calls. Instead of constantly checking for updates, systems react when something happens.
The benefits of event-driven architecture (EDA) in HLD are:
- High scalability and flexibility
- Loose coupling between services
- Great for real-time systems like IoT, analytics, or payment processing
Q38. What are the key considerations for designing a logging and monitoring system in HLD?
A good logging and monitoring system keeps your application healthy and helps you fix problems quickly.
For Logging
The key considerations for logging are discussed below:
- Use structured logs (JSON format)
- Include log levels like INFO, WARN, ERROR
- Add trace IDs to track requests across services
- Store logs centrally using ELK stack or Splunk
For Monitoring
The key considerations for monitoring in HLD are:
- Track metrics like CPU usage, latency, and error rates
- Use tools like Prometheus and Grafana for dashboards
- Set up alerts for unusual spikes
Q39. What are design considerations for real-time systems?
Real-time systems require quick responses, often within milliseconds. Designing them involves choosing low-latency components and ensuring predictable performance. Use in-memory databases like Redis for fast data access, and prefer asynchronous communication using queues or event streams for efficiency.
Proper load balancing and horizontal scaling ensure that the system can handle high traffic smoothly. Monitoring tools track delays, helping developers detect and fix bottlenecks quickly.
For critical systems like stock trading or IoT, minimizing delays through optimized algorithms, network tuning, and efficient caching is essential. The goal is simple, deliver accurate results instantly and consistently.
Q40. What are the principles of RESTful API design in HLD?
RESTful API design focuses on creating clean, consistent, and easy-to-use APIs. Its key principles include:
- Use correct HTTP methods – GET (read), POST (create), PUT (update), DELETE (remove)
- Define clear resource URIs like /users/{id}
- Keep APIs stateless, meaning no session stored on the server
- Use standard HTTP status codes (200, 404, 500)
- Support JSON/XML responses through content negotiation
- Always version your APIs – e.g., /api/v1/
Good RESTful APIs are predictable, scalable, and simple for developers to use and maintain.
Q41. What is a message broker in HLD?
A message broker in high-level design (HLD) is a system component that helps different services or applications communicate with each other asynchronously.
Instead of services talking to each other directly, they send messages through the broker. The broker then receives, stores, and forwards these messages to the right destination.
Q42. How does a CDN provide high availability and low latency?
A Content Delivery Network (CDN) improves both speed and reliability by bringing content closer to users.
Benefits of CDN
The benefits of a content delivery network (CDN) are:
- Faster load times
- Reduced bandwidth usage
- Better user experience
CDNs like Cloudflare, Akamai, or AWS CloudFront make global applications perform smoothly everywhere.
Q43. What are the considerations for designing a fault-tolerant network infrastructure in HLD?<H3>
A fault-tolerant network keeps running smoothly even if part of it fails. Designing one means preparing for problems in advance. The considerations for designing a fault-tolerant network infrastructure in HLD are:
Redundancy: Use backup network paths and servers
Isolation: Separate critical systems to reduce failure impact
Dynamic routing (BGP, OSPF): Reroute traffic automatically
Load balancing: Evenly share network load
Security layers: Firewalls and intrusion detection systems
Disaster recovery: Keep hot or warm backup sites ready
It ensures that even during outages, the system remains stable and users stay connected.
Q44. What role does containerization play in HLD?
Containerization means packaging your app and its dependencies into a single, portable unit called a container (e.g., docker). The benefits of containerization are as follows:
- Consistency: Runs the same in dev, test, and production
- Scalability: Easily scale using orchestration tools like Kubernetes
- Efficiency: Uses fewer resources than virtual machines
- Isolation: Keeps each microservice independent
- Resiliencye: A crash in one container does not affect others
Q45. What is the difference between horizontal and vertical scaling in HLD?
In system design, scaling means increasing a system’s capacity to handle more load.
- Vertical scaling involves upgrading existing hardware such as adding more CPU or RAM to a single machine to improve performance. It is simple but limited, as hardware upgrades can only go so far.
- Horizontal scaling, on the other hand, adds more machines or servers to distribute the load. This approach is preferred for cloud-based and large-scale applications because it increases flexibility, fault tolerance, and redundancy.
In short, vertical scaling is about getting stronger, while horizontal scaling is about getting wider to handle growing traffic smoothly.
Q46. How do you design a database for a large-scale application?
Designing a database for a large-scale system starts with understanding data access patterns and usage. For high performance, you might normalize data to remove redundancy, then selectively denormalize for faster reads. Sharding and partitioning help distribute data across multiple servers to manage huge datasets efficiently.
Using indexes improves query speed, while replication ensures availability and fault tolerance. It is also important to choose between SQL and NoSQL based on requirements, SQL for consistency, NoSQL for scalability.
Finally, regular backups, caching, and load balancing complete a reliable, large-scale database design that grows with user demand.
Q47. What is the role of a reverse proxy in system design?
A reverse proxy acts as a middleman between users and backend servers. When a user sends a request, it first goes to the proxy, which forwards it to the right server and then sends the response back to the user. This setup improves security, since backend servers remain hidden, and also enhances performance through caching and compression.
Reverse proxies can also help with load balancing, directing traffic evenly across multiple servers. Common tools like Nginx and HAProxy are widely used to manage traffic efficiently, reduce server overload, and provide a smoother user experience even during peak times.
Q48. How do microservices improve scalability and maintainability in HLD?
Microservices break a large application into smaller, independent services that handle specific functions. Each microservice can be developed, deployed, and scaled separately, which makes the system more flexible and easier to manage.
For example, if one service faces high traffic, you can scale it without affecting others. This architecture also improves fault isolation; a failure in one microservice doesn’t bring down the entire system.
Additionally, teams can use different tech stacks for different services, increasing productivity and innovation. Overall, microservices enhance both scalability and maintainability, making large systems more resilient and easier to evolve over time.
Q49. What is the role of API gateways in HLD?
An API gateway serves as the single entry point for all client requests in a microservices-based system. Instead of calling multiple services directly, users send requests to the gateway, which routes them to the correct microservice. This setup simplifies communication, improves security, and allows centralized management of features like authentication, rate limiting, and logging.
API gateways also reduce latency by handling load balancing and response aggregation efficiently. Tools like Kong, AWS API Gateway, and Apigee are commonly used for this purpose. Essentially, API gateways act as the traffic controllers of modern distributed systems.
Q50. How do you handle rate limiting and throttling in system design?
Rate limiting and throttling protect systems from overload by controlling how many requests users or clients can make in a given time. In HLD, this ensures fair resource use and prevents abuse, such as API spamming. Common methods include token buckets and leaky bucket algorithms, which track and limit request frequency.
Rate limits can be applied per user, per API key, or per IP address. When a user exceeds the limit, the system responds with a “429 Too Many Requests” error. Proper throttling keeps systems stable, protects resources, and ensures all users get fair access to services.
How You Can Crack the System Design Interview?
System design skills are essential to an organization, as they connect various functions and components of the business to ensure the smooth functioning of backend processes and frontend user interaction. Considered to be the most demanding profile with ML and AI solutions at the center stage. Hence, preparation for a system design interview requires a structured approach.
Aspirants keen to take the role of system design engineers, our System Design masterclass is curated for you in a structured format. In this masterclass, you’ll learn step by step through live sessions about system design frameworks in real world use cases. By the end of this course, you’ll be able to crack the system design interview with confidence and ease.
Conclusion
System Design is a very broad concept, it requires a strong command of system design interview questions. A deep understanding of core engineering concepts such as object-oriented design, design patterns, data structures, algorithms, database optimization, and distributed systems is essential. Along with the strong technical knowledge, strategic problem-solving skills like evaluating trade-offs, handling scalability, and ensuring fault tolerance is required. These questions assess candidates on how much they know about system design.
Run through the listed 50 system design interview questions, covering all the key concepts to get a better understanding of interview patterns in FAANG companies.. The given system design questions & answers prepares you not only for interviews but also give you the architectural mindset needed to build robust, scalable, and maintainable systems.
FAQs: 50 Frequently Asked System Design Questions
Q1. What is the main difference between High-Level Design (HLD) and Low-Level Design (LLD)?
High-Level Design (HLD) focuses on the overall system architecture, outlining the major components like servers, databases, and load balancers and how they interact. It’s the “blueprint” of the system. Low-Level Design (LLD), on the other hand, dives into the details of individual components, defining class diagrams, data structures, and specific algorithms. It’s the “implementation plan” for each part of the blueprint.
Q2. Why is horizontal scaling often preferred over vertical scaling?
Vertical scaling (adding more power, like CPU or RAM, to an existing server) is simpler but has physical limits and can be expensive. Horizontal scaling (adding more servers to distribute the load) is preferred for large-scale applications because it offers better fault tolerance, higher availability, and virtually limitless scalability, making it more flexible and resilient as traffic grows.
Q3. What does “read-heavy” mean?
A “read-heavy” system is one where data is accessed or retrieved frequently, but updated or written rarely. In other words, most operations involve reading data, not changing it.
Q4. Why are design patterns like Singleton and Factory important in system design?
Design patterns are important because they provide proven, reusable solutions to common software development problems. They help create a shared language among developers, improve code maintainability and clarity, reduce redundancy, and make the overall system more flexible and scalable.
Q5. How can you ensure a system remains available even if parts of it fail?
To ensure high availability and fault tolerance, you should use strategies like redundancy (having backup servers and databases), replication (copying data across multiple locations), and failover mechanisms that automatically switch to a backup if a primary component fails. Using load balancers also helps by distributing traffic, preventing any single server from becoming a point of failure.
Reference
- McGee
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