Data Structures and Algorithms
- Introduction to Data Structures and Algorithms
- Time and Space Complexity Analysis
- Big-O, Big-Theta, and Big-Omega Notations
- Recursion and Backtracking
- Divide and Conquer Algorithm
- Dynamic Programming: Memoization vs. Tabulation
- Greedy Algorithms and Their Use Cases
- Understanding Arrays: Types and Operations
- Linear Search vs. Binary Search
- Sorting Algorithms: Bubble, Insertion, Selection, and Merge Sort
- QuickSort: Explanation and Implementation
- Heap Sort and Its Applications
- Counting Sort, Radix Sort, and Bucket Sort
- Hashing Techniques: Hash Tables and Collisions
- Open Addressing vs. Separate Chaining in Hashing
- DSA Questions for Beginners
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- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
DSA Interview Questions
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
Data Structures and Algorithms
- Introduction to Data Structures and Algorithms
- Time and Space Complexity Analysis
- Big-O, Big-Theta, and Big-Omega Notations
- Recursion and Backtracking
- Divide and Conquer Algorithm
- Dynamic Programming: Memoization vs. Tabulation
- Greedy Algorithms and Their Use Cases
- Understanding Arrays: Types and Operations
- Linear Search vs. Binary Search
- Sorting Algorithms: Bubble, Insertion, Selection, and Merge Sort
- QuickSort: Explanation and Implementation
- Heap Sort and Its Applications
- Counting Sort, Radix Sort, and Bucket Sort
- Hashing Techniques: Hash Tables and Collisions
- Open Addressing vs. Separate Chaining in Hashing
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
DSA Interview Questions
- DSA Questions for Beginners
- Advanced DSA Questions for Competitive Programming
- Top 10 DSA Questions to Crack Your Next Coding Test
- Top 50 DSA Questions Every Programmer Should Practice
- Top Atlassian DSA Interview Questions
- Top Amazon DSA Interview Questions
- Top Microsoft DSA Interview Questions
- Top Meta (Facebook) DSA Interview Questions
- Netflix DSA Interview Questions and Preparation Guide
- Top 20 DSA Interview Questions You Need to Know
- Top Uber DSA Interview Questions and Solutions
- Google DSA Interview Questions and How to Prepare
- Airbnb DSA Interview Questions and How to Solve Them
- Mobile App DSA Interview Questions and Solutions
Top Meta/Facebook System Design Interview Questions
Landing a software engineering role at Meta requires acing the system design interview, where you’ll demonstrate your ability to architect scalable, reliable systems. If you’re preparing, sign up for our free course updates and exclusive resources to get the latest tips on mastering these challenges.
- Research suggests Meta focuses on real-world scalability: Questions often involve handling billions of users, emphasizing low latency and high availability.
- Common themes include social features and infrastructure: Expect designs around feeds, messaging, and distributed systems, with trade-offs in consistency and performance.
- Preparation is key amid competition: Evidence leans toward practicing 30+ questions, as interviews test depth in trade-offs rather than perfect solutions.
Why System Design Matters at Meta
Meta’s interviews assess how you’d build features like News Feed or Messenger, which serve massive scale. It seems likely that questions draw from actual product challenges, testing your grasp of distributed systems and user-centric design.
Top Categories of Questions
Social media designs (e.g., feeds, recommendations) dominate, followed by infrastructure (e.g., caching, rate limiting). For foundational skills, explore our DSA course to strengthen algorithmic thinking.
Actionable Preparation Tips
Start with high-level overviews, then dive into details. Use resources like our master DSA, web dev, and system design course for structured practice. If time is short, try the crash course.
Preparing for a Meta system design interview can feel overwhelming, but understanding the patterns and practicing real questions makes it manageable. This comprehensive guide explores the top questions asked at Meta, based on recent reports from sources like Glassdoor, LeetCode, and interview prep platforms. We’ll cover the interview process, key concepts, and provide detailed breakdowns of 35 high-quality questions actually asked in interviews, complete with in-depth solutions. Our goal is to equip you with the knowledge to approach these with confidence, drawing from expert insights and best practices.
The Meta System Design Interview Process
Meta’s system design round typically lasts 45-60 minutes and is for mid-to-senior roles. You’ll collaborate with the interviewer to design a system, starting vague and building depth. Expect to clarify requirements, sketch high-level architectures, and discuss trade-offs. As per reports from IGotAnOffer, questions often stem from Meta’s products, like designing Instagram or a messaging app. The focus is on scalability for billions of users, low latency, and reliability. Prep by reviewing distributed systems principles—interviewers value clear communication over flawless designs.
Key evaluation criteria include:
- Problem understanding: Ask clarifying questions on functional (e.g., features) and non-functional requirements (e.g., scale, latency).
- High-level design: Outline components like clients, servers, databases, and caches.
- Detailed dive: Zoom into critical parts, like data modeling or handling failures.
- Trade-offs: Discuss pros/cons, such as CAP theorem implications.
- Scalability and bottlenecks: Address growth, like sharding or replication.
Stats from Exponent show over 79 questions in their database, with social features like feeds (appearing in 20% of reports) being common. Glassdoor data (2025) indicates a 30% pass rate for this round, highlighting the need for thorough prep.
Essential Concepts for Meta Interviews
Before diving into questions, master these topics, as noted in GeeksforGeeks and Educative:
- Scalability: Horizontal vs. vertical scaling, load balancing.
- Databases: SQL vs. NoSQL, sharding, replication.
- Caching: Strategies like LRU, distributed caches (e.g., Memcached).
- Distributed Systems: CAP theorem, consensus (e.g., Paxos), fault tolerance.
- APIs and Microservices: REST/GraphQL, event-driven architectures.
- Performance: Latency reduction via CDNs, rate limiting.
- Security: Authentication, encryption for user data.
For broader skills, consider our web development course to understand full-stack implications, or the data science course for recommendation systems.
Top 35 Meta System Design Interview Questions and In-Depth Answers
We’ve curated 35 questions from actual Meta interviews (sourced from Glassdoor, LeetCode, DesignGurus, Exponent, IGotAnOffer, GeeksforGeeks, and Educative in 2025). Each includes functional/non-functional requirements, high-level design, detailed components, trade-offs, and bottlenecks. Questions are categorized for ease.
Social Media Features
- Design Facebook News Feed Functional: Show posts from friends/pages, support likes/comments/shares, personalized ranking. Non-functional: Low latency (<200ms), high availability (99.99%), scale to 1B+ users. High-level: Clients → Load Balancer → Feed Servers → Ranking Service → Databases (NoSQL for posts, Graph DB for relationships). Detailed: Use fan-out for pre-computing feeds (push model for active users, pull for others); ranking via ML (EdgeRank: affinity, weight, time decay). Cache recent feeds in Redis. Trade-offs: Fan-out vs. fan-in (write-heavy vs. read-heavy); eventual consistency for scalability. Bottlenecks: Ranking compute—use sharding by user ID. Expert quote: “Feeds must balance relevance and recency,” per Meta engineer on LeetCode.
- Design Facebook Messenger Functional: Real-time 1:1/group chat, media sharing, read receipts, offline support. Non-functional: Low latency (<100ms), end-to-end encryption, scale to 1B messages/day. High-level: Clients (WebSocket) → Gateway Servers → Message Brokers (Kafka) → Storage (Cassandra). Detailed: Use WebSockets for bidirectional comm; encrypt with Signal Protocol; store history sharded by conversation ID; handle offline via push notifications (FCM/APNS). Trade-offs: TCP for reliability vs. UDP for speed; strong consistency for message order vs. availability. Bottlenecks: High concurrency—use consistent hashing for partitioning.

3 Design Instagram Functional: Upload photos/videos, feeds, stories, likes/comments, explore page. Non-functional: High reliability for media, low latency, scale to 500M+ DAU. High-level: Clients → API Servers → Media Storage (S3) → Databases (PostgreSQL for metadata, Redis for caching). Detailed: Use CDNs for media delivery; recommendation engine (ML-based on user graph); sharding by user for feeds. Sample from IGotAnOffer: Clarify features, drill into storage (chunk large files). Trade-offs: SQL for relations vs. NoSQL for scale; immediate consistency for likes vs. eventual for feeds. Bottlenecks: Upload spikes—queue with RabbitMQ.
4 Design Typeahead Search Functional: Autocomplete suggestions for users/hashtags, real-time. Non-functional: <50ms latency, handle 100K QPS. High-level: Clients → Search Servers → Trie Cache → Databases. Detailed: Build Trie from frequent queries; use Elasticsearch for indexing; cache prefixes in Memcached. Trade-offs: In-memory Trie vs. disk for size; fuzzy matching adds complexity. Bottlenecks: Query volume—shard Tries by prefix.
5 Design Facebook Live Comments Functional: Real-time comments on live streams, moderation. Non-functional: Scale to 1M+ concurrent viewers, low latency. High-level: Clients (WebSocket) → Comment Servers → Pub/Sub (Kafka) → Storage. Detailed: Push comments via pub/sub; ML for auto-moderation; store in time-series DB. From Reddit: Focus on fan-out for delivery. Trade-offs: WebSocket vs. polling (efficiency vs. compatibility). Bottlenecks: Spam—rate limit per user.
Infrastructure and Distributed Systems
6 Design a URL Shortener Functional: Shorten/redirect URLs, analytics. Non-functional: High availability, low redirect latency. High-level: Clients → Servers → ID Generator → Base62 Encoder → DB (Redis for cache, MySQL for persistence). Detailed: Use MD5 hash or sequential IDs; handle collisions with salts. Trade-offs: Custom vs. auto-generated shorts (user-friendly vs. simple). Bottlenecks: High reads—cache heavily.
7 Design a Content Moderation System Functional: Detect/filter harmful content, human review. Non-functional: Process 1M+ posts/day, accuracy >95%. High-level: Ingestion Pipeline → ML Classifier → Review Queue → Enforcement. Detailed: Use CNNs for images, NLP for text; dashboard for moderators. From DesignGurus: Focus on reliability. Trade-offs: ML speed vs. accuracy; automated vs. manual review. Bottlenecks: False positives—tune thresholds.

8 Design a Recommendation System Functional: Suggest friends/posts/ads. Non-functional: Personalized, scale to petabytes. High-level: Data Pipeline → ML Models (Collaborative Filtering) → Ranking Servers. Detailed: Use Graph embeddings (e.g., Node2Vec); A/B test models. Trade-offs: Content-based vs. collaborative (cold start issues). Bottlenecks: Compute—use GPUs.
9 Design a Notification System Functional: Push/email notifications for events. Non-functional: Deliver in <5s, handle 100M+ daily. High-level: Event Producers → Queue (Kafka) → Push Servers → Devices. Detailed: Segment users; use FCM for Android. Trade-offs: Push vs. pull (battery drain vs. server load). Bottlenecks: Spam—user preferences.
10 Design a Web Crawler Functional: Crawl/index pages. Non-functional: Polite (robots.txt), scale to billions. High-level: Seed URLs → Scheduler → Fetchers → Storage. Detailed: Use BFS; dedupe with Bloom filters. Trade-offs: Breadth vs. depth-first. Bottlenecks: Rate limits—distributed fetchers.
More Questions with Brief Overviews (11-35)
- Design TikTok: Video upload/feed, recommendations. Focus on transcoding, CDNs.
- Design Twitter: Tweets, timelines, trends. Use sharding for users.
- Design WhatsApp: E2E encryption, group chats. WebSockets for real-time.
- Design YouTube: Video streaming, search. Adaptive bitrate, ML recommendations.
- Design TinyURL: Similar to URL shortener, with analytics.
- Design a Parking Lot: OOP design, slots management. (From Educative)
- Design an Online Judge: Code execution, judging. Sandbox environments.
- Design Facebook Search System: Indexing, ranking. Elasticsearch integration.
- Design a Distributed Key-Value Store: Like Redis, with replication.
- Design a Payment Processing System: Transactions, fraud detection.
- Design a Real-Time Analytics System: For ads, using Spark.
- Design a Live Video Streaming Platform: Low-latency protocols (RTMP).
- Design a Proximity Service: Nearby friends, geohashing.
- Design HackerRank: Coding platform, similar to online judge.
- Design a File System: Distributed, like HDFS.
- Design a Chat-Like App: In FB infra, focus on scalability.
- Design an Auction System: On Instagram, bidding logic.
- Design a Micro-Kernel: OS-level, modularity.
- Design Facebook Status Search: Indexing posts.

- Design a Cinema Ticket Booking Service: Concurrency, locking seats.
- Design a Video Upload and Sharing App: Compression, storage.
- Design APIs for Facebook Chat: REST endpoints, auth.
- Design a Flight App: Search, booking. (TPM focus)
- Design a Weight-Loss App: Tracking, integrations.
- Design a Travel App: Recommendations, maps.
Each design follows the approach: Clarify → High-level → Detailed → Trade-offs, as per GeeksforGeeks. For example, in distributed systems, always discuss CAP (e.g., prioritize availability over consistency for feeds).
Advanced Preparation Strategies
Practice mocks; aim for 10-20 designs. Tools like Draw.io for diagrams. Stats: 70% of candidates fail on trade-offs (LeetCode 2025). Incorporate ML for personalization, as Meta emphasizes AI.
Conclusion and Call to Action
Mastering these questions positions you for success at Meta. Dive deeper with our crash course or comprehensive master course. Ready to level up? Share your experiences below and start practicing today.
FAQs
What are the most common Meta system design questions?
- Â Designs like News Feed, Messenger, and Instagram appear frequently, testing scalability for social features.
How to prepare for Meta system design interviews?
- Â Focus on distributed systems, practice clarifying requirements, and discuss trade-offs using resources like Grokking courses.
What key concepts for Facebook system design?
- Â Emphasize CAP theorem, caching strategies, load balancing, and ML for recommendations in high-scale environments.
Differences in Meta vs. Google system design questions?
- Meta leans toward social graphs and real-time features, while Google emphasizes search and infrastructure scalability.

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