Faang-Level System Design Interview Questions and Solutions

Ace your FAANG system design interview with top questions, expert solutions, and proven strategies.

In this complete guide, we cover frequently asked FAANG-level system design interview questions and provide structured solutions with key considerations.

You'll learn best practices to tackle these open-ended questions and understand common pitfalls (and how to avoid them) to impress your interviewers.

Understanding FAANG System Design Interviews

FAANG companies (Facebook, Amazon, Apple, Netflix, Google) use system design interviews to assess your ability to design complex, large-scale systems that are scalable, reliable, and maintainable.

Interviewers want to see how you approach a vague problem, clarify requirements, make trade-offs, and design a system that meets the goals.

Key aspects interviewers evaluate include:

• Scalability: Can your design handle millions of users or requests? (Horizontal vs. vertical scaling, sharding, load balancing, etc.)

• Reliability & Availability: Does your system avoid single points of failure and remain available 24/7?

• Efficiency: Are you using appropriate data storage, caching, and processing to meet performance needs?

• Maintainability & Evolution: Is the design modular and clear, so it can be extended or improved over time?

Tip: Treat the interview as a collaborative discussion. Ask clarifying questions, think out loud, and justify your decisions. This shows your communication skills and systematic thinking.

Common FAANG-Level System Design Interview Questions

FAANG interviews often ask candidates to "Design X" – where X is a well-known system or feature. In fact, one analysis found that the same set of questions appear frequently (from hundreds of real interview experiences on Glassdoor).

Here are some of the most common system design questions you should be ready for:

• Design a Scalable Social Media Service (e.g., Twitter, Facebook): Design a platform where users can post content (tweets, posts) and others see updates in a feed. Focus: handling huge volumes of data and read-heavy workloads with low latency feeds (What are the most common system design interview questions at FAANG companies?). Consider the fan-out of posts to followers, news feed generation, and storing billions of user interactions.

• Design a Messaging or Chat Application (e.g., WhatsApp, Facebook Messenger): Users send real-time messages (one-to-one or group chat). Focus: real-time message delivery, maintaining long-lived connections or push notifications, and handling spikes in usage. Ensure message ordering, delivery guarantees, and offline message storage.

• Design a File Storage/Sharing Service (e.g., Dropbox, Google Drive): Users upload, download, and share files. Focus: a distributed file system that stores files reliably with replication, handles concurrency (versioning or locking), and provides fast access from anywhere. Consider metadata storage, chunking large files, and data consistency. Find out a complete solution for designing Dropbox.

• Design a Video Streaming Platform (e.g., Netflix, YouTube): Stream video content to millions of users. Focus: video encoding/transcoding, content delivery network (CDN) for global distribution, and buffering/caching for smooth playback. Handle high bandwidth, streaming protocols, and library management (recommendations, search). Find out a complete solution for designing video streaming platform.

• Design an E-Commerce System (e.g., Amazon): An online store with product listings, shopping cart, orders, and payments. Focus: a highly available web service that supports viewing products, user reviews, search, inventory management, and payment processing. Ensure ACID properties for orders (or appropriate trade-offs), and scalability for traffic spikes (Black Friday). Find out a complete solution for designing an e-commerce system.

• Design a Location-Based Service (e.g., Uber ride-hailing): Connect users and providers based on location (e.g., rider and nearest drivers). Focus: real-time location tracking, querying nearby entities with low latency, and dispatching requests. Consider geospatial indexing, updating positions frequently, and handling dynamic demand.

• Design a Search Engine or Autocomplete Feature: Users type queries and get relevant results or suggestions. Focus: efficient data indexing (in-memory indices, inverted indexes), ranking algorithms, and fast lookup for suggestions. Handle large-scale web crawling (if full search engine) and frequent updates to the index.

• Design a Rate Limiter for an API: Protect a service by limiting how many requests a client can make. Focus: a mechanism (token bucket, leaky bucket algorithms) to track usage per API key or IP and throttle excessive requests. Ensure the solution is distributed (works across multiple servers) and fast to check on each request.

• Design a Social Graph and Recommendation System: Manage relationships (friend connections or follower graph) and recommend new connections or content. Focus: graph data storage and traversal, possibly using graph databases or efficient relational queries (What are the most common system design interview questions at FAANG companies?). Consider algorithms for friend recommendations or content personalization using the graph and user behavior.

These questions cover a broad range of systems, but they share common themes.

Next, we'll look at how to approach these design problems in a structured way, and then delve into a few example questions with solution outlines to illustrate best practices.

How to Approach System Design Questions (Structured Solutions)

Tackling system design questions requires a structured approach.

Rather than jumping straight into drawing boxes and lines, follow these steps to ensure you cover all important aspects:

1. Clarify Requirements and Scope: Begin by gathering requirements – ask what features are in scope and the goals of the system. Determine functional requirements (what the system should do) and non-functional requirements (scale, performance, availability, etc.). This step is crucial: many candidates fail by skipping it. For example, if designing a chat app, clarify if it needs group chat, message persistence, expected user load, latency requirements, etc.

2. Outline a High-Level Architecture: Identify the core components and sketch a high-level design. This usually includes clients (web/mobile), servers or service layers, databases, caches, load balancers, etc. At this stage, also think about data flow: how data moves through your system.

3. Dive Deeper into Key Components: Pick the most critical parts of the system and flesh out details. This might include the database schema and type (SQL vs NoSQL), caching strategy, communication protocols (HTTP, WebSocket, gRPC), and specific algorithms or data structures. Address how different components interact. For instance, if you're designing a feed, detail how posts are stored and retrieved, and if a fan-out service is used to push updates to followers.

4. Consider Scalability and Reliability: Now evaluate your design against massive scale or failures. How will it scale when user or data volume grows 10x or 100x? Discuss strategies like horizontal scaling (adding more servers) vs vertical scaling (stronger servers) and techniques like sharding or partitioning data. Incorporate load balancers to distribute traffic and ensure redundancy for high availability. Identify any single points of failure and suggest backups or failover mechanisms. If the system requires global availability, consider multi-data-center or multi-region designs.

5. Discuss Trade-offs and Choices: Every design decision has alternatives. Explain why you chose a certain database (SQL vs NoSQL) or why you decided to cache data in memory, etc. Mention the trade-offs – e.g., consistency vs availability (CAP theorem) if relevant, or simplicity vs complexity. Interviewers appreciate when you acknowledge trade-offs and justify your choices.

6. Summarize and Evolve: Finally, recap how your design meets the requirements. Check if you've covered the goals and ask if the interviewer has any specific areas to drill into. Be prepared for follow-up questions like "How would this system handle 10x more traffic?" or "What if we also need feature Y?" and adapt your design accordingly. Showing an ability to evolve the design based on feedback is key.

By following a clear, step-by-step process like the above, you demonstrate a methodical approach Next, let's apply this approach to a few example questions and outline how to design those systems.

Example System Design Questions and Solution Outlines

Below, we walk through several popular FAANG-level system design questions and outline how to approach each. Each example includes key considerations, best practices, and potential pitfalls to watch out for.

Example 1: Design a URL Shortener (e.g., TinyURL or bit.ly)

Scenario: Design a service that takes a long URL and generates a short URL alias. When someone hits the short link, the service redirects them to the original long URL. This is a common interview question to test your ability to design a simple but high-scale service.

Requirements to Clarify:

• What is the expected traffic? (e.g., read-heavy service with vastly more redirections than new URL creations – potentially 100:1 ratio.)
• Should links expire or be customizable by users?
• Any analytics needed (click counts, etc.)?

Key Components and Design:

• API Service: Provides endpoints to create a short URL and to redirect a short URL to the original. This can be a set of web servers behind a load balancer to handle many concurrent requests.

• Database for URL Mapping: Use a database to store the mapping from the short code to the original URL. A NoSQL key-value store (like DynamoDB or Cassandra) is a good choice for large scale, as it can distribute data and handle high throughput. The key is the short code, the value is the long URL (and perhaps metadata). This storage must be highly available – if it's down, redirections fail ([How to Design a URL Shortener Service (System Design Interview Guide).

• Short URL Generation: To create a unique short code for each URL, consider strategies:

  • Simple ID generator: Use an auto-incrementing ID and encode it in base62 (0-9, a-z, A-Z) to form the short string. However, a single sequence can be a bottleneck.

  • Distributed ID generation: Use techniques like Twitter Snowflake (which generates unique IDs in a distributed system) or multiple ID generators to avoid a single point of failure.

  • Hashing: Hash the original URL to generate a code, but beware of collisions – you'd need a way to handle duplicates if two URLs hash to same code.

• Caching: To improve performance, you can cache popular mappings in memory (using Redis or an in-memory cache) so that frequent redirects don't always hit the database. Since reads (redirects) are far more frequent, this significantly reduces latency.

• Redirection: The service should quickly lookup the code in the database (or cache) and return an HTTP redirect to the original URL. Keep the redirect handling lightweight.

• Scalability Considerations: Partition the database by short code range or use consistent hashing so that many database servers can share the load as the number of links grows (How to Design a URL Shortener Service (System Design Interview Guide)). Also, plan for horizontal scaling of the stateless API servers behind a load balancer to handle increasing traffic.

• Additional features: If custom aliases are allowed (user picks the short string) or link expiration is needed, incorporate those into the design (with additional fields in the database, and background jobs to purge expired links).

Best Practices:

• Ensure the generated short IDs are not predictable (to avoid someone guessing valid links) (How to Design a URL Shortener Service (System Design Interview Guide)). Using random or hashed IDs helps.

• Design for a high read-to-write ratio by optimizing lookups (with caching and fast DB reads).

• Plan for fault tolerance: replicate the URL data across data centers so that even if one database node fails, the service still resolves links.

Common Pitfalls (and how to avoid them):

• Pitfall: Using a single database server or a single ID generator – it becomes a scalability and reliability bottleneck. Avoidance: Use distributed systems (multiple ID generators, sharded database) and load balancing.

• Pitfall: Not considering what happens if the same long URL is submitted multiple times. Avoidance: You might check if a URL was seen before and return the existing short link (to save space), but this is optional. If implemented, use a secondary index on the long URL to find existing records.

• Pitfall: Storing the short code in a very small space (e.g., only 6 characters) without planning for what to do when that space exhausts. Avoidance: Plan for increasing the code length or use a 64-bit ID space to have billions of possibilities.

By addressing these points, you can confidently propose a scalable and efficient URL shortener design in your interview.

Check complete solution for designing URL shortening srevice

Example 2: Design a Social Media News Feed (e.g., Twitter or Instagram)

Scenario: Design the core of a social network where users can follow others and see a news feed of recent posts. This question examines your ability to handle massive scale (millions of users, high read volume) and maintain low latency updates.

Requirements to Clarify:

• Volume of users and data: e.g., How many active users? How many posts per second? Is the system read-heavy (many more feed views) compared to writes (new posts)? (Typically yes – e.g., each tweet is read many times by followers).

• What features in feed: just chronological posts, or ranked content, multimedia, etc.? Real-time updates or can slight delays be tolerated?

• Should we support search or hashtags in this design, or just the feed?

High-Level Design:

• Service APIs: You will have endpoints for posting new content and for fetching the user's home timeline (feed). Also possibly an API for a user to follow/unfollow others.

• Application Servers: These handle incoming requests (post or get feed) and coordinate with backend services. They should be stateless and scalable behind load balancers.

• Data Storage:

  • Posts Storage: A database to store each post (tweet). This could be a NoSQL store for flexibility and easy horizontal scaling (since number of posts is huge), or a SQL database if strong consistency is needed. Each post record contains the user ID, timestamp, content, etc.

  • Feed Generation: Two common approaches:

    1. Push Model (pre-compute feeds): On each new post, push it to all followers' feed storage. For example, maintain a sorted list of post IDs for each user’s feed. This is fast to read (just read pre-computed feed) but can be heavy to write (a user with 10M followers triggers 10M insert operations).

    2. Pull Model (on-the-fly): Store posts by user and compute the feed on demand by pulling recent posts from all the people you follow, then merging/sorting them. This reduces write amplification but makes read slower.

    • In practice, a hybrid is used: push to a reasonable extent (for average users), and for celebrities with massive followers maybe use pull or a fan-out service to distribute load asynchronously.

  • Social Graph Storage: A system to store who follows whom (follower graph). Often a separate service or database optimized for graph queries or simple enough with a relational table (user_follows table).

• Caching: Cache the results of feed generation for a short time. For example, when a user requests their feed, store that feed in Redis. If they refresh shortly after, you can serve from cache. This reduces load when users repeatedly check feeds.

• Additional Services: If the feed includes heavy content (images/videos), those might be stored in an object storage (like AWS S3 or a CDN) but referenced in the feed. If ranking (like Facebook's newsfeed algorithm) is needed, a recommendation service might rank posts.

Scalability and Performance:

• Use sharding for the posts database based on user ID (to distribute writes/reads across servers). Ensure that users with many followers are distributed to avoid hotspots.

• Introduce a Fan-out Service or message queue: When a user posts, place the post ID and fan-out tasks into a queue that workers consume to update follower feeds. This decouples the write API from the heavy work of distribution, improving latency for the poster.

• Employ multiple cache layers if needed: e.g., an in-memory cache for the latest N posts of popular users (so their followers can quickly pull new content).

• Consider eventual consistency: In such systems, it's acceptable if a new post appears in a follower's feed after a few seconds delay. Prioritize availability and latency over strict immediate consistency.

Best Practices:

• Back-of-the-envelope calculations: It's often good to estimate expected read/write rates and data sizes. For example, if there are 500 million tweets per day, that's about 5,800 tweets/sec on average – can your design handle that? This shows you understand scale.

• Bottlenecks: Identify and address them. A potential bottleneck is the fan-out process for popular users (one user generating flood of writes). Solution: maybe limit how fast one user can post, or handle their posts differently (e.g., store once and let followers pull).

• CDN for static content: Use CDNs to serve images/videos in posts to reduce load on your servers and keep content delivery fast globally.

Common Pitfalls:

• Pitfall: Not clarifying read vs write load – designing a system that can't handle the disproportionate read load. Always clarify traffic distribution (e.g., ratio of feed views to posts).

• Pitfall: Ignoring timeline freshness. If you push updates lazily, some designs might show stale content for too long. Mitigation: have a strategy for real-time updates, like WebSocket notifications or short polling to fetch new posts.

• Pitfall: Single points of failure. If all feed generation goes through one service, it can bring the system down. Use redundant services and partitioning to avoid this.

Designing a social media feed is complex, but focusing on data modeling, caching, and scalable fan-out mechanisms will demonstrate a strong solution.

Mention real-world techniques (like how Twitter uses fan-out on writes vs. Facebook's approach to rank on read) if you know them, but only if time permits.

Check the complete solution for designing Twitter

Example 3: Design a Chat/Messaging Service (e.g., WhatsApp)

Scenario: Design a real-time chat service where users can send messages to each other (one-to-one and group chats). This question checks your understanding of real-time communication, stateful connections, and delivering messages reliably at scale.

Requirements to Clarify:

• Is it one-to-one chat only, or do we need group chats with many participants?
• Should messages be stored for history? (Most likely yes, for syncing across devices or if offline.)
• What is the target scale (number of active users, messages per second)?
• Any extra features in scope (typing indicators, attachments, read receipts)?

Design Components:

• Persistent Connections: Chat apps often maintain a persistent connection from client to server (e.g., using WebSockets or long polling) to allow instant delivery. When a user is online, their app keeps a connection to the chat server.

• Chat Servers: These are servers that handle the connections and relay messages. They manage user sessions (which user is connected to which server) and handle message fan-out. Multiple chat server instances will be running; an arrangement (like a load balancer or connection broker) is needed to route each user to a server and possibly redistribute load if a server goes down.

• Message Flow: When User A sends a message to User B:

  • The message goes to User A's connected chat server.
  • That server (or a background service) ensures the message is delivered to User B's chat server (if B is online) or stored for later delivery (if B is offline).
  • If it's a group chat, the server will forward the message to all participants (potentially via their respective servers).

• Storage: Use a database to store chat history and offline messages. A NoSQL store (like Cassandra or MongoDB) can store messages with keys like chat_id + timestamp. This allows retrieving conversation history quickly. For reliability, replicate messages across data centers so they aren't lost.

• Delivery Semantics: Ensure at-least-once delivery. The system should retry sending if an acknowledgement from the recipient isn’t received. Usually, an ACK is sent from client to server when a message is delivered (which can also be used for "delivered/read" status).

• Scalability: Partition users across different servers based on user ID or region. For instance, users could be assigned to specific clusters so that messages between two users on the same cluster stay local, and for cross-cluster communication, servers communicate over the network. Use a broker or index service to know which server a user is connected to (if at all).

• Push Notifications: If a user is offline (not connected), the system should send a push notification via Apple/Google push services to their device so they come online and fetch the message.

Key Considerations:

• Real-Time Delivery: Use protocols suited for low-latency delivery (WebSocket, XMPP, etc.). Keep latency low by minimizing hops in message path.

• Ordering: In one chat thread, messages should arrive roughly in send order. If using multiple servers, you might need to tag messages with timestamps or sequence IDs and ensure ordering when displaying.

• Scaling User Connections: Each chat server can only hold so many open connections. Scale horizontally by adding servers. A load balancer or dispatcher can direct new connections to the least-loaded server.

• Group Chats: A group message to 100 people might need to be forwarded to 100 connections. Optimize by perhaps having multicast-like logic (but typically, the server will just loop through recipients). Storing group membership in a fast lookup store (like in memory or a database table) is important for fan-out.

• Security: End-to-end encryption is a major aspect for real chat apps (like WhatsApp) but in an interview, you'd mention it as an aside unless it's the focus, since it doesn't change high-level architecture except that servers may not see plaintext messages.

Best Practices:

• Clarify capacity needs: e.g., "Let's assume 50 million daily active users, each sending 50 messages a day" to ground the discussion in scale.
• Use of Queues: Implement a messaging queue (in-memory or a system like Kafka) between servers for buffering messages, especially for offline users or bursts. This decouples sending from actual delivery.
• Stateless vs Stateful: Chat servers are somewhat stateful (holding connections), but don't keep persistent user data locally. If a server crashes, clients should reconnect and continue. Design so that any server can handle any user connection (with minimal warm-up), which improves fault tolerance.

Common Pitfalls:

• Pitfall: Neglecting offline scenarios. Simply broadcasting messages works when everyone is online, but if a user is offline, they might miss messages. Solution: store undelivered messages and deliver when the user comes back. Use push notifications as a wake-up.

• Pitfall: Not asking about group chat. Group chat significantly increases complexity (n-body problem of message fan-out). Always clarify this upfront so you design appropriately.

• Pitfall: Single server design. If you design as if one server will handle all chats, the interviewer will be concerned. You must partition the load among many servers, so describe how you would divide users or rooms across servers (sharding by user ID, etc.).

• Pitfall: Ignoring data limits. Chat history can be huge. If you don't mention how to store or limit history (e.g., only last 6 months in fast DB, older in cold storage), it might seem like an oversight.

By demonstrating a design that handles real-time messaging, persistent storage, and scaling out to millions of users, you'll show you can build a WhatsApp-like system under FAANG-level constraints.

Example 4: Design a Ride-Hailing Service (e.g., Uber)

Scenario: Design the system backend for a ride-hailing service like Uber, where users request rides and are matched with nearby drivers. This question involves multiple components interacting in real-time and highlights design for low latency and high reliability.

Requirements to Clarify:

• Is the scope just matching riders and drivers, or also payments, user management, etc.? (Often, focus on the core ride request -> dispatch flow.)
• What is the geographic scale? One city, multiple cities globally? (Impacts how you design for regional traffic.)
• Expected concurrency: How many ride requests per second, how many drivers active, etc.?

Core Components Design:

• User and Driver Clients: Mobile apps that send location updates and ride requests, and receive updates (driver arrival, etc.).

• Gateway & Load Balancing: A gateway service that all client apps connect to for requests. Distribute incoming requests to the appropriate backend services.

• Location Service: Continuously ingest location pings from driver apps (and possibly rider apps). This could be a high-throughput service that updates each driver's location in a database or in-memory data store. Efficiently indexing locations is crucial. A common approach is to use a spatial index (like a grid or geohash) to partition the map into cells, so you can query “drivers near this location” quickly.

• Matching/Dispatch Service: When a rider requests a ride, this service finds the best driver nearby:

  • Query the Location Service for drivers within X radius of pickup.

  • Filter available drivers (not on another trip).

  • Apply some selection logic (closest driver, or other criteria).

  • Send the ride request to that driver (to their app). If they accept, confirm the match; if they decline or timeout, try another driver.

• Ride Management Service: Once a ride is accepted, this service manages the state of the ride (driver en route, ride started, completed). It coordinates updates to both rider and driver clients, likely through push notifications or persistent connections.

• Data Stores:

  • A database for persistent data: rider info, driver info, ride records (for history, receipts, etc.), and possibly current ride state.

  • Often a relational DB is used for rides and user profiles (for consistency in transactions like payments).

  • The live location data might be kept in-memory cache or fast NoSQL store because it’s high-churn data.

• Payment Service: Handles fare calculation and processing payment after ride (this can be mentioned but might be out of scope for the design discussion unless asked).

Scalability and Partitioning:

• Regional Segmentation: It often makes sense to partition the system by geography. For example, have servers/instances of dispatch service for each region (or data center per region) so that load is localized (a request in New York is handled by the US-east servers, etc.). This reduces latency and keeps data (like location DB) smaller per region for faster queries.

• Real-time Considerations: The matching needs to happen quickly. Ensure that the location updates are real-time (within a second) and that the dispatch service can query the latest locations fast. In-memory data structures (like an Quad-tree or KD-tree for spatial indexing) may be used. If using a DB, ensure indexes on coordinates.

• Reliability: Redundancy is critical. If your single dispatch service fails, no rides happen – so have multiple instances and a failover mechanism. Similarly, replicate the location tracking service so that a crash doesn’t lose all active location data (maybe each driver sends to two servers).

• Queue for requests: Use a message queue to buffer ride requests if the system experiences bursts, so they are processed in order without losing any. But be mindful, adding queues can add latency; in a tight loop like dispatching a ride, often direct service calls are fine if everything is highly available.

Best Practices:

• Break down the problem into components: For example, explicitly talk about separate modules like "location tracking", "matching algorithm", "trip state management", "payments". This shows you think in terms of modular responsibilities.

• Trade-offs: Acknowledge trade-offs like choosing the nearest driver vs. slightly farther but more experienced driver (could mention but not too much – mainly focus on system aspects). Also discuss consistency: e.g., what if two riders request at the same time and the same driver looks available to both? You might need locking or a way to mark a driver as tentatively assigned.

• Use external services: In real systems, third-party services like Google Maps API are used for things like ETA calculation, routing, or geocoding addresses. You can mention that non-core functionality (maps, traffic data) can be outsourced to keep the design focused.

Common Pitfalls:

• Pitfall: Trying to do everything in one service. If you don't separate concerns (like one monolithic service doing location tracking + matching + updating rides), it will be less clear and likely not scalable. Better to split into specialized services as described.

• Pitfall: Ignoring data freshness. If your location updates are even 10 seconds old, the matching might dispatch a driver who has moved far away. Emphasize real-time updates and maybe a cutoff (like only consider drivers who updated location in the last X seconds).

• Pitfall: No backup for failures. What if a driver doesn't respond? Your design should mention cycling to another driver or having a timeout. Similarly, if a whole region server crashes, the system should retry those requests on a backup.

• Pitfall: Database bottlenecks for hotspots. If everyone in a city is writing to one location database shard, that's a problem. Mitigation: partition by smaller zones or use a highly scalable data store for locations (in-memory grid).

Designing Uber in 45 minutes is challenging, so focus on the real-time, distributed nature of the problem and how you'd ensure low latency and fault tolerance.

If you clearly explain the interaction between the components (location service -> dispatch -> driver app, etc.) and how it scales, you'll cover the key points.

Check complete solution for design Uber

Recommended Resources for System Design Preparation

Mastering system design interviews takes practice and deep understanding. The following resources are highly recommended for honing your skills and learning from real-world system design scenarios:

• Grokking System Design Fundamentals – Ideal for beginners, this course covers the core concepts and building blocks of system design. You'll learn about key components like caching, databases, load balancers, and more in a digestible way.

• Grokking the System Design Interview – A comprehensive course featuring numerous popular system design interview questions (including many mentioned in this guide). It walks through detailed solutions, diagrams, and trade-off discussions for each problem, which is fantastic for seeing how to structure your own answers.

• Grokking the Advanced System Design Interview – For senior engineers or those aiming to go beyond the basics, this course delves into advanced topics and complex systems. It covers things like designing systems for extreme scale, multi-region deployments, and nuanced design considerations that can set you apart in an interview.

In addition to courses, consider practicing by sketching out designs for different products and getting feedback. Websites and forums (like LeetCode discussion boards, or Reddit's system design communities) can provide insights and additional example questions.