System Design Interview Questions: Most Common Questions and How to Answer Them

System design interviews evaluate your ability to design complex, scalable systems and make architectural decisions. And system design interview questions play a pivotal role here.

In system design interviews, you’ll be asked open-ended questions to design a system or discuss key design concepts. Whether you’re a beginner or an experienced engineer, this guide will equip you with practical knowledge and confidence to tackle any system design question.

This guide will help you prepare by covering:

• A comprehensive list of system design interview questions – from the most common system design questions asked at FAANG companies to advanced scenarios.

• Sample high-level approaches to each question, outlining what interviewers look for.

• Key system design concepts (caching, load balancing, CAP theorem, API gateways, scalability, microservices vs. monolith, etc.) that often come up during discussions.

Entry-level vs. Senior-level Expectations

If you’re a newbie, you might face entry level system design questions that are narrower in scope or theoretical (e.g. explaining components like load balancers or designing a small utility).

Interviewers want to see that you grasp essential system design concepts (caching, APIs, database scaling, etc.) and can apply logical thinking.

As you advance, the questions become more open-ended and complex – you could be asked to design the architecture of a globally distributed system with millions of users.

Senior candidates are expected to discuss deeper trade-offs (consistency vs availability, for example) and consider edge cases in scaling, reliability, and security.

No matter the level, preparation is key. Even experienced engineers can struggle with system design if they haven’t practiced – for instance, designing WhatsApp or YouTube, or explaining the difference between an API Gateway and a Load Balancer, can trip up even seasoned pros​.

The good news is that the more you practice these questions, the more comfortable you’ll become in tackling them.

Next, we’ll break down a wide range of system design interview questions, starting from the basics and building up to advanced challenges.

(Pro Tip: As you go through these questions, always start by clarifying requirements, then outline your high-level architecture, and discuss components in detail. Communicate your assumptions and trade-off decisions – interviewers love to hear your thought process.)

Entry-Level System Design Questions (Beginner-Friendly)

Let’s start with entry-level system design questions – ideal for fresh graduates and junior developers. These questions are typically simpler and more focused, often used by interviewers to test your grasp of fundamental concepts and your ability to design basic systems.

Don’t worry, entry level doesn’t mean unimportant – nailing these basics shows you have a strong foundation for more complex design challenges ahead.

What to Expect for Beginners

You’ll generally encounter two types of problems:

• Conceptual questions – definitions or comparisons of core system design concepts (e.g. “What is a CDN?” or SQL vs NoSQL differences). These assess your theoretical understanding of key technologies.

• Small-scale design scenarios – design a simple system or component (e.g. a URL shortener or a basic chat system). These are less about massive scale and more about correct architecture and covering all requirements.

Let’s look at some examples in each category.

• Fundamental Concept Questions (Basics) Entry-level interviews often include theoretical questions about system design components and principles.

Make sure you can explain these clearly:

1. What is the difference between an API Gateway and a Load Balancer? – (Concept: Networking) An API Gateway is a single entry point that orchestrates and aggregates calls to various services (often in microservices architecture), while a Load Balancer distributes incoming requests across multiple server instances to spread load. Both deal with traffic routing, but in different contexts.

2. Forward Proxy vs. Reverse Proxy – what’s the difference? – (Concept: Networking) A forward proxy sits in front of clients to intercept outbound requests (often for filtering or caching, e.g. a corporate proxy server), whereas a reverse proxy sits in front of servers to intercept inbound client requests, often for load balancing, caching, or security (e.g. Nginx as a reverse proxy).

3. Horizontal Scaling vs. Vertical Scaling – (Concept: Scalability) Horizontal scaling means adding more machines/instances to distribute load, while vertical scaling means adding more power (CPU/RAM) to an existing machine. Horizontal scaling (scale-out) improves fault tolerance and is commonly used in distributed systems; vertical scaling (scale-up) is limited by hardware capacity.

4. Monolithic vs. Microservices Architecture – (Concept: Architecture) Monolithic architecture means a single unified codebase/application for the entire system, whereas microservices break the system into independent services that communicate via APIs. Monoliths are simpler to develop/deploy initially, but microservices offer more scalability and flexibility for large, complex applications.

5. What is Caching and what are caching strategies? – (Concept: Performance) Caching is storing frequently accessed data in a fast storage layer (memory) to speed up reads. Strategies include LRU (Least Recently Used) eviction, write-through vs write-back (when syncing cache with database), and cache invalidation policies. It helps reduce load on databases and improve response times.

6. SQL vs NoSQL databases – how do they differ? – (Concept: Databases) SQL databases are relational (tables with fixed schemas, ACID transactions) – great for structured data and complex queries. NoSQL databases are non-relational (key-value stores, document DBs, wide-column, or graph DBs) – they offer flexible schemas and horizontal scalability for big data and high throughput needs.

7. What is database sharding/partitioning? – (Concept: Databases) Sharding (horizontal partitioning) means splitting a database into smaller chunks (shards) spread across multiple servers to handle more data or load. Each shard holds a subset of the data (e.g. by user ID range). This improves scalability but adds complexity (e.g. figuring out which shard to query). Vertical partitioning is splitting by feature (different tables on different servers). Both are ways to partition data to scale out.

8. What is a CDN and how does it work? – (Concept: Networking) A Content Delivery Network (CDN) is a globally distributed network of servers that caches and delivers static content (images, videos, scripts) to users from a location geographically close to them. This reduces latency and load on your origin servers by serving content from edge servers. It’s helpful for improving website/app load times​.

9. What is a Rate Limiter? – (Concept: Security/Performance) A rate limiter is a mechanism that restricts the number of requests an entity (user or IP) can make to a service in a given time window. This prevents abuse (like brute force or spam) and helps ensure fair usage. Implementations often use token bucket or leaky bucket algorithms to throttle requests after a limit, returning an error (e.g. HTTP 429 Too Many Requests) when exceeded.

10. How does Single Sign-On (SSO) work? – (Concept: Security) SSO allows a user to authenticate once and gain access to multiple related systems or applications without logging in again. It typically involves a central identity provider (IdP) that applications trust. For example, logging in with Google on various sites uses Google as the IdP. The user authenticates with Google, receives a token, and the other application validates that token to log the user in. This involves token exchanges (SAML, OAuth/OIDC) and trust setups between service providers and the identity provider.

11. How does a Message Queue (e.g., Apache Kafka) work? – (Concept: Messaging) A message queue is a system for asynchronous communication between services. Producers publish messages to a queue (or topic in Kafka), and consumers subscribe to retrieve those messages, often at their own pace. Kafka, for instance, is a distributed log-based queue that stores streams of records in categories called topics, which are partitioned and replicated across brokers. It’s designed for high throughput and fault tolerance, enabling decoupled microservices to communicate via the queue. The queue ensures reliable delivery and buffering of messages, and patterns like pub/sub, fan-out, and consumer groups allow flexible processing.

These fundamental questions cover the building blocks of system design.

As a candidate, you should be comfortable explaining each concept in simple terms and discussing where/why it is used.

Having a solid grasp of these will greatly help when you tackle the actual system design problems, since you’ll need to apply them appropriately (e.g. knowing when to use a CDN or how to partition a database in the system you design).

For a complete guide, check the system design tutorial for beginners.

Simple System Design Questions (Beginner Level)

Now onto beginner-friendly design scenarios. These are simpler systems or utilities that you might be asked to design. They typically have limited scope, so you won’t need to handle millions of users or extreme scale, but you should still demonstrate clear thinking in your design.

Here are several real examples:

• Design a URL Shortener (e.g. TinyURL) – A classic entry-level design question. Key points: You need an API to create short URLs and redirect to long URLs. Focus on how to generate unique keys (e.g. base-62 encoding, avoiding collisions), how to store the mappings in a database, and handling high read/write traffic (caching popular links in memory, using a database that can handle lots of small writes). Consider expiration of links and analytics as possible extensions.

• Design Pastebin (Text Storage Service) – Users post text and get a sharable URL to that text (like a simple web-based notepad). Key points: Storing the text (could use a database or even flat files for simplicity), generating unique URLs or slugs for each paste, and possibly an expiration feature. Discuss size limits for posts, and how to scale storage if a lot of data is stored (maybe a distributed file storage or database partitions if needed). Security considerations include making private vs public pastes.

• Design a Parking Lot System – This is often seen as an object-oriented design question, but it can be considered a system design on a smaller scale. Key points: Think about the entities – ParkingLot, Levels, Spots, Vehicles, Tickets. How do you assign a parking spot to incoming vehicles? How to track free/occupied spots? This tests how you model real-world systems in code/design (using OOP, classes, relationships). For system design, you might also consider if there’s a software system to track entries (sensors, gate system, payments).

• Design a Vending Machine – Another common OOD-style design. Key points: Identify objects like VendingMachine, Item, Coin, Inventory, etc. How to select items, handle money/coins, dispense change, and manage inventory. While not a “distributed system,” it tests your ability to design a stateful system with interactions (user input -> dispense item or change state).

• Design a Web Cache (e.g. an LRU Cache) – Implement a caching service. Key points: If it’s an in-memory cache (like designing Redis or a simple version of it), discuss data structures for O(1) get/put (hash map + doubly linked list for LRU eviction). If distributed cache, consider how to keep multiple cache servers in sync or how cache eviction is coordinated. Often the simplest approach is designing an LRU cache algorithm, which is a common low-level design question.

• Design an Authentication Service – Users need to register, login, and obtain some auth token (like session or JWT). Key points: Designing the database for user credentials (and storing passwords securely with hashing/salting), an API for login that returns a token, token verification mechanism for other services, and maybe handling password resets or multi-factor auth. Also consider scalability: using caches to store active sessions/tokens, rate limiting login attempts, etc.

• Design a Distributed Key-Value Store – Essentially, a simple NoSQL database (think along the lines of designing a tiny Redis or DynamoDB). Key points: How to store key-value pairs across multiple nodes. Use consistent hashing to distribute keys to nodes (so the system can scale horizontally). Ensure redundancy (replicate data to handle node failures). Discuss read/write path: a client’s request goes to the appropriate node for that key. How to handle node addition or removal (consistent hashing minimizes data movement ). This is a more advanced concept for an “easy” list, but interviewers might only expect a high-level discussion at entry level.

• Design a Job Scheduler (Cron system) – A service that schedules and executes tasks at given times (like cron jobs). Key points: You’ll need a way to store scheduled jobs (maybe in a database with a timestamp or a priority queue sorted by next execution time). A worker or dispatcher continuously checks for due tasks and executes them (or sends them to workers). Consider how to ensure reliability – what if a job fails or a worker crashes? You might introduce the concept of acknowledging task completion, retrying failed tasks, and distributed locks if multiple scheduler instances run (to avoid duplicate execution).

• Design a Notification Service – (Sometimes asked as part of another design, but could be standalone.) Key points: The system accepts events and sends notifications to users (via email, SMS, push, etc.). You’d discuss components like a message queue (to buffer notification tasks), worker services that read from the queue and call email/SMS APIs, and how to scale it (multiple workers, retry logic for failures, perhaps a way to track user preferences or rate-limit notifications per user).

Those are plenty of beginner-level scenarios. For each of these, focus on correctness and clarity of design rather than extreme scalability. Show that you can break the problem into components (e.g. for URL shortener: service layer, database, caching layer) and handle core use cases. It’s also good to mention future considerations like “if this needs to scale to millions of users, I would add X” – it shows foresight, even if not required at entry level.

Summary of Entry-Level Questions: Below is a table summarizing these entry-level system design questions, categorized by their key focus and type of system:

Use these as a checklist – can you explain each concept and outline a design for each scenario?

If so, you’re off to a great start for junior-level system design interviews.

Now, let’s ramp up the difficulty and explore the kinds of system design questions you’d get as a mid-level engineer.

Intermediate System Design Questions (For Mid-Level Engineers)

Intermediate-level system design questions are where things get really interesting. These are typically the meat of system design interviews for candidates with some experience (or even strong new grads at top companies). Here you’ll be asked to design well-known applications or services that millions of users might use. The problems are broader in scope than the beginner ones, often requiring integration of multiple components and an understanding of scalable architecture.

What’s expected at intermediate level: Interviewers will look for a structured approach (requirements -> high-level design -> detailed design -> considerations). You should identify and discuss major components like clients, servers, databases, caches, load balancers, etc. Scalability is usually a focus: think about high traffic, large data volumes, and how to handle growth. Also consider aspects like consistency, availability, fault tolerance, and security for each design. The tone can still be conversational and assume the interviewer is collaborating with you, but you need to drive the solution.

Let’s compile a list of real-world system design interview questions commonly asked at this level, categorized by domains like social networks, content streaming, e-commerce, etc. Each bullet will include a short description of the key challenges to address:

• Design Instagram – A social photo-sharing app. Key points: designing the system to store and serve images, a feed of posts for users, handling user relationships (followers/following), and hashtags/search. Discuss how to store media (maybe a blob store or CDN for images), how to design the news feed (pull vs push updates, maybe a feed generation service), and scaling to millions of users (partitioning user data, caching popular posts). Instagram needs to handle a high read-to-write ratio (lots of people viewing feeds vs posting).

• Design Tinder – A dating app focusing on matchmaking. Key points: user profile storage, the swipe mechanism (match suggestions), and real-time notifications for matches/messages. Unique challenges include implementing the matching algorithm (finding nearby users, mutual swipes), and scaling real-time updates (possibly using WebSockets or push notifications when two people match or message each other). Also, discuss how to handle a large user base filtered by location/preferences.

• Design WhatsApp – A messaging app with real-time chat. Key points: support private one-to-one chats and group chats, with considerations for message delivery (store-and-forward model), offline messaging, end-to-end encryption (security). You should mention using messaging protocols (maybe XMPP or a custom protocol), how to ensure messages are delivered in order and reliably (acknowledgments, retries), and scaling the chat service (sharding users by region or user ID, using message queues for buffering). Don’t forget features like showing online status, media sharing (images/voice), etc., at least briefly.

• Design Facebook News Feed – A core feature of Facebook (or any social network) where a user sees posts from friends. Key points: user social graph (friend connections), generating a feed sorted by relevance (ranking algorithm), and real-time updates (seeing new posts, likes, comments appear). At scale, storing feeds for millions of users is challenging – you might discuss an approach like pre-computing feeds versus computing on the fly, or a hybrid. Also, consider how to design the backend to handle heavy read traffic (lots of people scrolling) and write traffic (posting, liking). Caching is important (each user’s feed can be cached or segmented), and maybe a fan-out service to deliver new posts into followers’ feeds.

• Design Twitter – Similar to Facebook feed, but Twitter (a microblogging platform) has the concept of “followers” and tweets (short posts). Key points: The fan-out problem: when a user with millions of followers tweets, how do those followers get the tweet in their timeline? You might mention strategies like Fan-out on write (push tweet to all followers’ timelines at post time) vs Fan-out on read (compute a user’s timeline when they request it), or using a combination (pre-compute for users with few followers, on-the-fly for those with many). Also discuss tweet storage, home timeline service, search (for hashtags), and handling high throughput (Twitter sees a huge number of tweets per second globally).

• Design Reddit – A discussion forum platform with communities (subreddits). Key points: content aggregation with voting. You need to design creation of posts, commenting threads, and voting (which affects ranking). Each subreddit is like its own feed – how to store and retrieve posts by subreddit, and globally for front page. Consider a database schema that separates posts, comments, votes. Also, Reddit has heavy reads and writes (voting is write-heavy). Caching popular posts and comments, and maybe eventual consistency for vote counts (to avoid bottleneck on every vote updating a central count in real-time). Also, think about moderation tools (content filtering) as part of the system.

• Design Netflix – A video streaming service. Key points: Video content storage and streaming (store videos, transcoding into multiple resolutions, using a CDN to stream efficiently), a catalog service (browsing movies/shows), and a recommendation system (personalized suggestions). Focus on how to serve video: typically, the client will stream chunks of video from a CDN or edge server. Netflix’s backend is known for being microservices-based – user account service, billing service, content library service, etc., all behind an API Gateway. Also mention handling a huge number of concurrent streams and ensuring smooth playback (maybe using buffer on client side, adaptive bitrate streaming). Content delivery at scale will involve heavy use of CDNs and caching of content in regional servers.

• Design YouTube – Similar to Netflix in video streaming, but also a social platform where users upload content. Key points: handling video uploads (which triggers an encoding pipeline to generate multiple quality versions stored in a blob store), a streaming service to deliver videos, and features like likes, comments, subscriptions. Also, YouTube search is a component (finding videos by title/keywords) and a recommendation engine for “Up next” videos. Discuss storing metadata in databases, large-scale storage of video files (possibly millions of videos), and how to scale the serving (again, CDN for distribution). The high-level design for YouTube typically includes clients (web/mobile), an application server, a video processing service (for encoding), a large data storage for videos, a CDN, and database for user data and video metadata .

• Design a Search Engine (e.g. Google) – This is usually considered a hard problem, but interviewers might ask for a simplified version at intermediate level. Key points: You need a web crawler to gather pages, an indexing system that builds an inverted index for keywords -> list of pages, and a query service that given a search term, finds relevant pages from the index and ranks them. You’ll want to mention concepts like web crawling breadth-first, respecting robots.txt, storing the index across many servers (perhaps partition by keyword alphabet range), and ranking algorithms (like Google’s PageRank or simpler TF-IDF scoring). Also consider the sheer data volume (billions of pages) – storage of the index likely in a distributed file system or NoSQL store, and caching popular queries.

• Design an E-commerce Website (Amazon) – An online store system. Key points: The system needs to handle browsing products, searching, a shopping cart, checkout process, payment, and order management. Break it down: product catalog service (with database of products, categories), search service (to find products by keyword), user service (accounts, addresses, payment info), shopping cart service (temporary cart storage per user), order service (placing orders, order state machine from pending -> shipped, etc.), and payment processing (possibly integrating with third-party payment gateways). You should mention handling high traffic (especially for big sale events), consistency of inventory (to avoid overselling an item), and perhaps how to scale the database (sharding by product ID or splitting read/write loads). Caching product details and pages would be helpful for performance.

• Design Spotify – A music streaming service. Key points: streaming audio (which is easier in size than video, but still needs a CDN or edge servers for efficiency), a large library of songs metadata, playlists management, and real-time aspects like showing what your friends are listening to. A challenge is designing the recommendation or radio features (though you might not need to dive deep into that in an interview). Focus on how a user selects a song and the song is delivered (likely chunks of MP3 via CDN), how to keep track of user playlists (database of playlists, possibly partitioned by user), and how to scale to millions of listeners (maybe replicating popular songs in multiple data centers). Also mention how to handle sudden spikes (e.g. a new album release).

• Design TikTok – A short-video social platform. Key points: It combines aspects of video streaming and social feed. Users upload videos (so an upload service and video storage similar to YouTube), and users consume an endless feed of videos tailored to them (so a recommendation system is central – likely based on user interactions like likes, watch time, etc.). TikTok’s challenge is the rapid content personalization and the sheer volume of videos. You should talk about video CDN for delivery, a service for the feed (maybe that pre-computes the next N videos for a user based on ranking algorithms), and scaling both storage and data processing (maybe a big data pipeline to continuously update recommendations). Also, features like comments, shares, and music overlay can be touched on.

• Design Shopify – An e-commerce platform that allows individual merchants to create online stores. Key points: It’s like building a multi-tenant e-commerce system. Each merchant has their own storefront (with product listings, cart, checkout). Consider designing a core platform that handles product catalogs, inventory, orders, and payments for many stores (tenants). You might have a single database with a tenant ID separating data, or separate databases per store for isolation – discuss trade-offs. Also, think about providing customizable storefronts (maybe templating engine for the UI, but that might be beyond scope). The system should be robust to handle many shops, each of which could have spikes (e.g. if one store has a big sale).

• Design Airbnb – An online marketplace for rentals (homes, apartments). Key points: Very similar to e-commerce in structure: you have listings (with descriptions, photos, availability calendar), search and filter by location/date, a booking process, and a payment system. One challenge is search with geolocation – finding rentals in an area for given dates, which involves searching by location range and availability (this might involve a specialized data store or geospatial index). Also, a calendar system to manage booking dates (to avoid double-booking the same property). Discuss how to keep listings data consistent and how to handle heavy search traffic (caching popular city searches, etc.). Messaging between host and guest is another component (communication system).

• Design Autocomplete for Search – Not a full system but a component: how to implement search suggestions as you type (like Google’s autocomplete). Key points: You need efficient prefix queries. Typically, you’d use a trie (prefix tree) or an inverted index of prefixes to possible completions. At scale, store this in memory or a fast database. If designing for a large scale (like across the web or a large site), you’d pre-compute suggestions for common prefixes. You also rank suggestions by popularity (based on past searches or frequency of terms). For interview, mention using a trie and maybe how to update it as new terms appear. It’s also good to discuss how to handle user input in real-time (each keystroke triggers a query to this service).

• Design a Rate Limiter – We mentioned this as a concept question, but it can also be framed as a design problem: implement a service or component that rate-limits actions (like API calls). Key points: You could design this as a library within each service or a distributed service that other services call to check if a given user/IP is allowed. One approach: use a token bucket algorithm for each key (user or IP). For distributed, maybe a centralized store (like Redis) to keep counters of requests per window. Ensuring accuracy in distributed environment is tricky – you might use atomic operations in Redis or a database to increment counts. Discuss sliding window vs fixed window counters (and issues like synchronization and performance).

• Design a Distributed Message Queue (Kafka) – Similarly, earlier we discussed what a message queue is; as a design problem, you could be asked to outline a system like Kafka. Key points: Producers push messages to a queue/topic, consumers read them. Messages need to be stored durably (possibly on disk) and replicated across brokers for fault tolerance. Partition the queue for scalability (multiple brokers can handle different partitions). Consumers might form groups to share the workload (each message goes to one consumer in the group). Talk about delivery semantics (at least once vs at most once vs exactly once – Kafka by default is at least once). Also mention ordering guarantees (within a partition, messages are ordered). Essentially, this is designing a high-throughput, fault-tolerant distributed log.

• Design a Flight Booking System – Book flights from airlines. Key points: Searching flights (by date, route, etc.), booking seats on a flight, and ensuring consistency of seat inventory. The tricky part is preventing two people from booking the last seat at the same time – you may need locking or a transactions system. One strategy is to use optimistic locking on seat inventory or a two-phase commit (hold the seat, then confirm booking). Also, integrate with payment. The system likely has a central database of flights and reservations. At scale, think about partitioning data by airline or route. Also consider queries like “search all flights from X to Y on date D” – possibly using an index on origin/destination and date.

• Design an Online Code Editor – Like a simplified version of CoderPad or the coding window in LeetCode, or even Google Docs but for code. Key points: Real-time collaborative editing (multiple people editing the same file – so some concurrency control algorithm like Operational Transformation or CRDT might come in if truly collaborative). If it’s just an online IDE for a single user, focus on how to run code securely on a server (sandboxing user code execution, possibly using containers or VMs), returning the output. For multi-user, discuss how to share the document state (maybe via WebSockets to broadcast changes). Also saving the code (persistence), and supporting multiple languages (compilation/execution environment).

• Design a Stock Exchange System – This involves processing financial trades (buy/sell orders) with low latency. Key points: An order matching engine at its core – it maintains an order book for each stock (buy and sell orders sorted by price and time) and matches compatible orders. You need to handle a large volume of orders quickly (within milliseconds). Data consistency and atomicity are crucial (matching an order means updating the order book, maybe partially filling orders, etc.). Also consider connectivity: multiple brokerages connecting, maybe via APIs, to send orders. The system should be extremely reliable and scalable (stock exchanges handle millions of orders daily). Likely use an in-memory data structure for order books for speed, with persistence logs for durability. This is a complex one often for senior candidates, but if you mention key aspects at intermediate, that’s impressive.

• Design an Analytics Platform (Metrics & Logging) – A system to collect, process, and display analytics (like user events, logs, metrics). Key points: This is like designing a mini version of Splunk or Elastic Stack or any telemetry system. Components: data ingestion (perhaps a firehose of events coming in, use a queue or streaming ingestion pipeline), data storage (could be time-series DB for metrics, or document store for logs), processing (maybe streaming processors to aggregate or filter data), and a query interface or dashboard for users to get insights. Discuss how to handle high write throughput (lots of events), possibly by partitioning by time or key. Also, storing data efficiently (cold vs hot storage) and making recent data quickly queryable. Real-time vs batch processing could be mentioned (e.g. using Spark or Flink vs a simpler aggregator).

• Design a Notification Service – We touched on this earlier in entry-level but it can also be expanded at intermediate level, especially if integrated into a larger system. Key points: A robust notification system that can send different types of notifications (emails, SMS, push notifications). Use a message queue to decouple the trigger from the sending. A Notification Service would have a database of user contact info and preferences, workers for each channel (email gateway, SMS gateway, push service), and perhaps a scheduler for delayed notifications. At scale, ensure you can send millions of messages (use third-party services or multiple servers). Also consider tracking delivery (did the email send succeed or bounce? etc.) and retry logic for failures.

• Design a Payment System – For example, a payment gateway or digital wallet. Key points: Processing payments securely, possibly between buyers and sellers or peer-to-peer. You’d have to integrate with external payment processors (credit card networks, banks) if designing a gateway like Stripe, or handle internal balances if a wallet. Ensure atomic transactions – money should either move or not, no half-states. Discuss storing transaction records, handling concurrency (two people shouldn’t spend the same money twice, etc.), idempotency (if a network call times out and repeats, ensure you don’t double-charge). Security and compliance (encryption, PCI compliance if dealing with cards) are important to mention. This can get very deep, but at intermediate level focus on high-level flow: user initiates payment -> service validates and processes -> updates balances or calls external API -> returns result, and how to scale (e.g. partition by region or user ID, etc., for the database).

That’s a huge list of system design questions, but these have all been asked in one form or another in real interviews (across FAANG and other companies). You might not get exactly these, but something similar in scope. For instance, designing “a social network” could imply Facebook, Twitter, Instagram – the fundamentals overlap (profiles, posts, feed, etc.). Likewise, designing “a streaming service” could be phrased as Netflix or YouTube or “a video platform.” So understanding the core components of each domain is key.

At this level, it’s also crucial to discuss bottlenecks and trade-offs in your designs. For example, if you choose a SQL database for storing user data, mention how you’d scale it (master-slave replication for reads, or sharding when writes grow).

If you use a cache, mention cache invalidation or what happens on cache misses. Think about failure modes: “What if this component goes down? How do we ensure the system still works (maybe in degraded mode)?”

Interviewers often push on one area deeply, so be prepared to dive into, say, “How exactly would the feed generation work for 1 million users?” or “How would you design the database schema for the orders system?”

To summarize this section, here’s a table listing the intermediate-level questions and the main themes for each:

That’s quite a spectrum! By practicing these, you’ll build a toolkit of patterns and components that you can mix and match for almost any system design question. It’s also useful to note that many interview questions combine elements of these (for example, designing “Uber” is like a combination of a real-time location service + a dispatch system + a payment system; it would be advanced, but you can break it down using simpler parts we covered).

Before moving to the advanced section, keep in mind these general tips for intermediate designs: Always clarify the scope (do we need real-time updates? how many users are we targeting?), state your assumptions (X requests per second, Y TB of data, etc.), and mention future growth. Use analogies or comparisons (“this part is similar to how Twitter handles fan-out”) if it helps.

And don’t forget about bottlenecks: if one database might not handle the load, propose sharding or adding a cache, etc.

The interviewer wants to see that you’re thinking about scalability and reliability constantly at this level.

Advanced System Design Questions (Expert Level)

Now for the advanced system design questions – these are the tough ones that might be given to senior engineers or those interviewing for specialized roles. These problems typically involve massive scale, high complexity, or niche architectures that require a deep understanding of distributed systems. If you can handle these, you’re likely well prepared for anything a system design interview throws at you.

At the advanced level, you are expected to consider all aspects: scalability, consistency, partition tolerance, availability, fault tolerance, security, and even maintainability of the system. The questions often involve designing systems that handle unique challenges (like global consistency, real-time collaboration, or heavy computation).

Let’s list some advanced-level design scenarios, along with what makes them challenging:

• Design a Location-Based Service (e.g. Yelp) – A service for local business search and reviews. Key points: Supports queries like “find restaurants near me”. Requires storing geolocation data (latitude/longitude for businesses) and enabling geo-spatial queries efficiently. Likely uses spatial indexes (like an R-tree or geohash indexing in a database). Also, handling user reviews and ratings (which is straightforward CRUD, but at scale). Another challenge: sorting by “best match” which could involve popularity and distance. Consider data updates (businesses opening/closing). For scale, partition data by region.

• Design Uber (Ride-Sharing Service) – Hailed as one of the most challenging designs because it involves real-time matching of drivers and riders on a global scale. Key points: Real-time location tracking of many drivers, dispatching the nearest driver to a rider, dynamic pricing, and a complex state (ride lifecycle). The system likely needs a real-time pub-sub mechanism (to broadcast rider requests to drivers or vice versa). Also uses geospatial indexing to find nearby drivers quickly. Handling payments after rides, and scaling to millions of rides per day across cities, with high reliability (people depend on it to commute!). Discuss splitting by region (maybe each city has a dispatch service cluster) and a global coordination for some data. Also mention route optimization (possibly using external APIs or map services for ETA calculation).

• Design a Food Delivery App (DoorDash) – Similar to ride-sharing but with three parties: customer, delivery driver, and restaurants. Key points: Order placement, sending order to the restaurant, dispatching a driver, and live tracking of delivery. Challenges include routing (grouping multiple deliveries, optimizing driver routes), handling peak loads (e.g. meal times can spike orders in a region), and integrating with restaurant systems. It’s like a combination of e-commerce order system + real-time delivery (Uber-like) system. Ensure reliability in communication (you don’t want to lose an order). Also, inventory might be simpler (menu items), but timing is crucial (food should arrive fresh, etc. – though that’s more physical).

• Design Google Docs (Collaborative Document Editing) – Real-time collaborative editing for text documents. Key points: Multiple users editing the same document simultaneously. This requires a concurrency control algorithm like Operational Transformation (OT) or Conflict-free Replicated Data Type (CRDT) to ensure all users see the same final document regardless of edit order. You have to handle offline edits (if a user disconnects and reconnects) and merging changes. Also, need a server infrastructure that relays changes to all clients (maybe via WebSocket). At scale, consider many documents and many concurrent rooms of editors. Partition by document (each doc’s edits handled by one server maybe). This is heavy on complex algorithms and consistency. Also consider how to store the document revisions (versioning, so you can undo or track changes).

• Design Google Maps – A mapping and navigation system. Key points: There are multiple features: rendering maps (tiles), search for places, directions calculation, and live traffic updates. For map rendering, one approach is to use pre-rendered map tiles (images) for various zoom levels and segments of the map; these can be served via CDN. For searching places, need geospatial search on an index of place names (similar to Yelp design). For directions, you need to compute shortest paths on a huge graph of roads – algorithms like Dijkstra or A* (with heuristics) come into play, often precomputing routes or using distributed algorithms for speed. Live traffic might feed into route recommendations (that could involve real-time data ingestion from many users or sensors). The scale is enormous: storing the entire world’s map data, plus user data. Partitioning data by region is a must. Also, results need to be fast; likely a lot of precomputation and caching is used (like pre-compute travel times between important nodes, etc.).

  High-level design for Google Maps system: This diagram illustrates components like the user interface, a load balancer, and backend services for location search, route finding, and mapping. A distributed search system handles queries (like finding nearby places), a graph processing service computes routes, and a specialized database (Graph DB) stores the map graph. Live data (like traffic) flows in through a pub-sub system (Kafka), updating the routing recommendations. Google Maps demonstrates an advanced system requiring geospatial indexing, graph algorithms, and real-time data integration.

• Design Zoom (Video Conferencing) – Real-time video and audio communication for multiple participants. Key points: Ultra-low latency streaming of audio/video, typically via UDP protocols (and using techniques for NAT traversal for peer-to-peer when possible, or via relay servers). Handling group calls (maybe a mesh network or routing through a server that mixes streams). Need to manage varying bandwidth (adaptive bitrate), and keep things in sync. Also, screen sharing, chat, etc., as additional features. Scalability: likely each meeting is handled by a specific server (or cluster) that mixes streams, and multiple servers across regions for all the meetings. This is complex due to real-time constraints; ensuring < 200ms latency is challenging, as is scaling to large meetings (100+ participants).

• Design a File Storage/Sharing Service (Dropbox) – Cloud file storage that syncs across devices. Key points: Storing user files reliably (with backups or replication). Handling concurrent edits to the same file (file versioning, last-write-wins or keep multiple versions). Sync client on devices that detects changes and uploads/downloads differences. Might use techniques like block storage – splitting files into chunks so only changed chunks are synced. Also, deduplication if many users store the same file (optional). Consider how to scale storage (possibly storing files in blob storage like S3, and metadata in a database). Also, sharing links with others – generating links with permissions. The system must be highly available and never lose data, so replication (e.g. 3 copies of each file in different data centers).

• Design a Ticket Booking System (e.g. Movie tickets, or any event booking like BookMyShow) – Similar to flight booking in concurrency challenges but perhaps simpler in search. Key points: Show available seats for a venue and allow booking. The hard part: concurrent seat selection. Many users might try to book the same seat around the same time when a popular show opens. Solutions: have a reservation hold system (lock a seat for a short time once a user selects it, then confirm booking on payment). Use database transactions or a distributed lock for each seat. If the scale is large (say a stadium booking), partition by sections or something. But usually, the number of seats isn’t huge (a few hundred), so a single database can handle it, just need proper locking logic. Also, after booking, send tickets (maybe via email/QR code generation). The system should also prevent overselling seats and handle payments.

• Design a Distributed Web Crawler – A system that crawls websites and indexes content (part of a search engine, basically). Key points: We covered some aspects in search engine design, but specifically crawling has challenges: politeness (don’t hit a site too frequently), prioritization (which pages to crawl first), distribution (assign different sites to different crawler machines), and infinite content traps (like calendars that generate new pages forever). You might use a URL frontier (a queue) that feeds many crawler workers. Use hashing on URLs to assign them to specific workers (ensuring the same site’s URLs go to the same worker maybe, for politeness). Need to store fetched pages and note which URLs were seen to avoid repeats. This system must be very efficient to crawl billions of pages regularly.

• Design a Code Deployment System – Essentially, a CI/CD pipeline that takes code from developers and deploys it to servers reliably. Key points: Continuous Integration (running tests on new commits), and Continuous Deployment (rolling out updates to production). Discuss components like a build server (to compile/test code when new commits are pushed), an artifact repository (store build outputs like Docker images or binaries), and a deployment orchestrator (that takes an artifact and deploys to target servers/containers). Consider deployment strategies (blue-green, canary releases) to avoid downtime. Also, rollback mechanism if a deployment fails. The challenge is coordinating across many servers/services. Tools like Jenkins, Kubernetes, etc., might be mentioned as analogies.

• Design a Cloud Object Storage (Amazon S3) – A service to store and retrieve files (objects) at massive scale. Key points: S3 stores hundreds of billions of objects, so the design uses partitioning: objects are identified by keys, which are hashed to determine which storage node (or cluster) holds them. The system must be highly durable (11 nines durability in S3) which means multi-site replication, and using checksums to detect corruption. For availability, it’s often accessible from multiple data centers. It’s essentially a distributed file system accessible via an API. Discuss how to handle metadata (e.g. directory structure simulated via key prefixes, though S3 is flat namespace internally). Also, consider consistent hashing for distributing objects, and what happens when a node fails (data re-replication).

• Design a Distributed Locking Service – For example, something like Chubby (by Google) or Zookeeper. This provides coordination between distributed systems by offering locks or consensus. Key points: This touches on consensus algorithms (like Paxos or Raft) to ensure only one client holds a lock at a time across a cluster. You’d typically design a service with multiple nodes (for fault tolerance) that elect a leader (using consensus) and that leader coordinates locks. Clients can request a lock on a resource, if available they get it, if not they wait. Also consider lock timeouts (to avoid deadlocks if a client dies holding a lock). The system must handle node failures gracefully – using replication and consensus to avoid a single point of failure. Essentially, it’s like designing a tiny specialized database that safely agrees on who holds what lock. This is definitely advanced because it involves understanding of distributed consensus.

As you can see, advanced questions often incorporate multiple complex components and require knowledge of specialized algorithms or approaches (real-time collaboration, geo-indexing, consensus protocols, etc.).

When answering these, it’s completely fine (even expected) to break the problem down: “This is a big problem, let’s tackle one aspect at a time.”

For instance, for Uber, you might first design the ride request matching for one city, then talk about scaling it out and adding other features like surge pricing. Showing that you can handle complexity by dividing it into sub-problems is key at this level.

Also, use the fundamentals in advanced answers: Many advanced systems are just combinations of simpler ones. Google Maps combines search + maps + real-time data, for example. So refer back to approaches you know. If you discussed caching earlier, apply it here too (“We’d definitely use caching for map tile images via a CDN to handle the load”).

Another tip: for these advanced designs, mention known technologies or papers if relevant. E.g., “For distributed locking, we could use an approach like the Chubby lock service by Google which uses Paxos for leader election .” or “Our search index could be stored in something like Bigtable (a distributed storage system) to allow it to scale.” This isn’t required, but if you happen to be familiar, it shows strong depth. Just be ready to explain it in simple terms if asked.

To wrap up this section, here’s a summary table of the advanced questions and their domains:

Mastering these advanced scenarios will make you comfortable in any system design discussion.

Remember, even if you’re not interviewing for a “senior” position, showing awareness of advanced topics can set you apart. It demonstrates a big-picture understanding. However, ensure you can explain them clearly – don’t just drop buzzwords without explanation.

Clarity is just as important as complexity.

Common System Design Interview Questions (FAANG and More)

Let’s get into detail of some of the most common system design interview questions you might encounter. These are popular at top tech companies like Google, Meta (Facebook), Amazon, etc. For each, we’ll outline the scenario and key considerations, along with links to resources or full solutions where available.

1. Design a Social Media Platform (e.g. Twitter, Instagram)

One of the most frequently asked questions is to design a scalable social media platform – for example, “How would you design Instagram or Twitter?” This type of question tests your ability to handle large-scale reads/writes, news feed generation, and social features.

Approach: Outline how users would post content and how followers would receive updates. For instance, describe using databases to store user profiles and posts, cache frequently viewed feeds, and possibly a news feed service to aggregate posts. Discuss scalability: a distributed architecture that can handle millions of users (horizontal scaling with multiple servers, and load balancers to distribute traffic). Mention using a Content Delivery Network (CDN) for serving images/videos and strategies for data partitioning (sharding user data by user ID or geography). Don’t forget consistency vs. availability trade-offs – e.g., slightly stale feeds might be acceptable for higher availability.

Watch the tutorial to design Instagram.