Top 5 System Design Interview Questions for 2026

Before diving into questions, internalize this framework. Use it for every answer:

1. Clarify Requirements (2–3 min): Functional requirements (what it does) and non-functional requirements (scale, latency, availability). Ask: "How many DAU? Read-heavy or write-heavy? Any specific latency SLAs?"
2. Capacity Estimation (2–3 min): Estimate QPS, storage needs, and bandwidth. This grounds your architecture in real numbers and immediately impresses interviewers.
3. High-Level Design (10–15 min): Draw the core components — clients, load balancers, API servers, databases, caches, queues. Explain data flow.
4. Deep Dive (10–15 min): Pick the hardest 2–3 components and go deep. Database schema, sharding strategy, caching approach, failure handling.

Question 1: Design a URL Shortener (like bit.ly)

Frequently asked at: Amazon, Google, Adobe, Atlassian

Requirements Clarification

• Functional: Shorten a long URL, redirect short URL to original, optional custom aliases, expiration support
• Non-functional: 100M URLs created/day, 10B redirects/day, read:write ratio ~100:1, latency <100ms for redirects

Core Architecture

• URL Generation: Use Base62 encoding (a-z, A-Z, 0-9) on an auto-incremented ID or MD5 hash. A 7-character Base62 string gives 62^7 = 3.5 trillion unique URLs.
• Database: NoSQL (DynamoDB or Cassandra) for the short→long URL mapping. High read QPS with simple key-value lookups = perfect NoSQL fit.
• Cache: Redis with LRU eviction. Cache the top 20% of URLs that receive 80% of traffic. Cache hit rate should exceed 95% for redirects.
• Redirect: Return HTTP 301 (permanent, client caches) or 302 (temporary, always hits server). Use 302 if you need analytics; 301 for pure performance.

Key Trade-offs to Discuss

Hash collision handling (add random salt and rehash), custom alias conflicts (check DB before accepting), global uniqueness across distributed ID generators (use Twitter Snowflake or UUID v4).

Question 2: Design a Real-Time Chat System (like WhatsApp)

Frequently asked at: Meta, Microsoft, Slack, Uber

Requirements Clarification

• Functional: 1-1 messaging, group chats (up to 500 members), message delivery receipts, online presence
• Non-functional: 50M DAU, <500ms message delivery, messages stored for 5 years, 99.99% availability

Core Architecture

• WebSocket Servers: Persistent bidirectional connections between client and server. Each WebSocket server handles ~50K connections. A connection service maps user_id → server_id.
• Message Queue (Kafka): Decouple message sending from delivery. Producer (sender's server) → Kafka topic → Consumer (recipient's server). Ensures no message loss even if recipient is temporarily offline.
• Database: Cassandra for messages (optimized for write-heavy, time-series append workloads). Partition key: conversation_id; sort key: timestamp. Enables efficient "load last 50 messages" queries.
• Presence Service: Redis pub/sub for online/offline status. Each client sends a heartbeat every 30 seconds. TTL-based expiration marks users offline automatically.

Key Trade-offs to Discuss

Fan-out for group messages (write to each member's inbox vs. pull model), end-to-end encryption (Signal Protocol), message ordering guarantees (Lamport timestamps).

Question 3: Design a Social Media Feed (like Twitter/X)

Frequently asked at: Twitter, Meta, LinkedIn, Snap

Requirements Clarification

• Functional: Post tweets, follow users, view personalized home timeline, like and retweet
• Non-functional: 300M DAU, 100K tweets/second peak write, 1M timeline reads/second, feed must load in <200ms

Core Architecture — Push vs Pull

• Pull Model: On feed load, query all followed users' tweets, merge and sort by time. Simple but slow — O(n) queries per feed load where n = number of followees.
• Push Model (Fan-out on Write): When a user tweets, push the tweet ID to all followers' pre-built feed caches immediately. Feed reads are O(1). Problem: celebrities with 50M followers cause fan-out storms.
• Hybrid (What Twitter uses): Push for regular users (<10K followers), pull for celebrities. Merge both at read time. Caps fan-out cost while keeping reads fast.

Feed Ranking

Chronological is easy but engagement-weighted ranking requires an ML scoring pipeline: candidate generation → feature extraction → ranking model → serve top N. Mention this shows depth even if you don't design it fully.

Question 4: Design an API Rate Limiter

Frequently asked at: Stripe, Cloudflare, AWS, any API-heavy company

Requirements Clarification

• Functional: Limit requests per user/IP per time window, return 429 Too Many Requests when exceeded, different limits per API endpoint
• Non-functional: Distributed across multiple servers, <5ms overhead per request, accurate to within 0.1%

Algorithms

• Token Bucket: Each user gets a bucket of N tokens refilled at R tokens/second. Each request consumes 1 token. Allows bursting up to N. Simple and widely used (AWS API Gateway).
• Sliding Window Counter: Track request counts in the current and previous time window. Weighted interpolation gives a smooth rolling count. More accurate than fixed windows, slightly more complex.
• Fixed Window Counter: Simple counter reset every minute. Weakness: allows 2x burst at window boundaries. Easiest to implement.

Distributed Implementation

Use Redis with atomic Lua scripts or INCR + EXPIRE to ensure thread-safe counter updates across all rate limiter servers. Redis cluster handles ~1M ops/second, sufficient for most production workloads.

Question 5: Design a Notification System

Frequently asked at: Amazon, Uber, Airbnb, Flipkart

Requirements Clarification

• Functional: Send push notifications, SMS, and email; support scheduled and event-triggered notifications; user preference management
• Non-functional: 10M notifications/day, delivery within 30 seconds of trigger, at-least-once delivery guarantee

Core Architecture

• Event Producers: Any service (Order Service, Payment Service) publishes events to Kafka topics.
• Notification Service: Consumes events, applies user preferences (do-not-disturb hours, opted-out channels), enriches with user data, and routes to channel-specific queues.
• Channel Workers: Separate services for Push (APNs/FCM), SMS (Twilio/SNS), and Email (SendGrid/SES). Each has its own retry logic and dead-letter queue.
• Idempotency: Each notification has a unique ID. Workers check a Redis set before sending to prevent duplicates on retry.

Question 6: Design a Ride-Sharing Service (like Uber)

Frequently asked at: Uber, Lyft, Grab, Ola

The Core Challenge: Location Matching

• Geohashing: Encode GPS coordinates into a string where proximity = shared prefix. Store driver locations in Redis with geohash as key. Find nearby drivers: query matching geohash prefixes.
• Driver Location Updates: Each driver sends location every 4 seconds. At 1M active drivers, that's 250K writes/second to the location store — use Redis Sorted Sets with geospatial commands (GEOADD, GEORADIUS).
• Matching Algorithm: ETA-based matching (not just distance). A driver 2km away in traffic may be slower than one 3km away on a highway.
• Trip State Machine: Requested → Driver Accepted → Driver Arriving → Trip Started → Trip Ended → Payment Processing. Use a distributed state machine backed by a transactional database.

Question 7: Design YouTube/Video Streaming

Frequently asked at: Google (YouTube), Netflix, Twitch, Disney+

Key Components

• Upload Pipeline: Raw video → Object Storage (S3) → Transcoding Queue (Kafka) → Transcoding Workers (convert to 360p/480p/720p/1080p/4K using FFmpeg) → CDN distribution
• Adaptive Bitrate Streaming (ABR): Serve different quality segments based on client bandwidth. HLS or MPEG-DASH protocol breaks video into 2-10 second chunks. Player selects quality per chunk.
• CDN Strategy: Cache popular videos at edge nodes closest to viewers. Long-tail videos (low views) served from origin. Cache-hit ratio for top 1% of videos exceeds 99%.
• Metadata Database: MySQL for video metadata (title, description, tags) with read replicas. Elasticsearch for video search. Cassandra for view counts (write-heavy, eventual consistency acceptable).

Question 8: Design a Distributed Cache (like Redis)

Frequently asked at: Amazon, Microsoft, senior infrastructure roles

Core Design Decisions

• Cache Topology: Write-through (write to cache + DB simultaneously), Write-back (write cache first, async DB flush), Look-aside (app checks cache, on miss reads DB and populates cache).
• Eviction Policies: LRU (Least Recently Used) for general-purpose workloads. LFU (Least Frequently Used) for access-pattern-stable workloads. TTL-based for time-sensitive data.
• Consistency: Cache invalidation is the hardest problem. Event-driven invalidation (DB publishes change events → cache deletes stale key) is more reliable than TTL alone.
• Distributed Sharding: Consistent hashing ensures minimal re-mapping when nodes join/leave. Virtual nodes (vnodes) improve load distribution.

Question 9: Design a Search Autocomplete System

Frequently asked at: Google, Amazon, LinkedIn, Twitter

Core Architecture

• Trie Data Structure: Every prefix maps to top-K completions by search frequency. Traversal is O(prefix_length). Space-optimized with compressed tries (Patricia trees).
• Real-time Updates: Batch update the trie every 1–2 hours from aggregated search logs. Real-time updates for trending queries using a separate hot-prefix cache.
• Serving Layer: Pre-built tries sharded by first character. Each shard fits in memory (~1GB). Sub-millisecond lookup with consistent hashing across trie servers.

Question 10: Design a Payment Processing System

Frequently asked at: Stripe, PayPal, Razorpay, Paytm, FAANG fintech roles

Non-Negotiable Requirements

• Exactly-Once Processing: A payment must never be charged twice or zero times. Use idempotency keys (client-generated UUID per transaction). Server checks if key was already processed before executing.
• ACID Transactions: Debit and credit must happen atomically. Use a relational database (PostgreSQL) with transaction isolation level SERIALIZABLE for payment records.
• Reconciliation: Periodic batch job compares internal records with bank/payment gateway statements. Discrepancies trigger alerts for manual review.
• Saga Pattern for Distributed Transactions: When a payment spans multiple services (inventory, shipping, loyalty points), use the Saga pattern with compensating transactions on failure instead of a distributed 2PC lock.

Common System Design Mistakes to Avoid

• Jumping to architecture without clarifying requirements: Always spend the first 2–3 minutes asking questions. Interviewers intentionally leave requirements vague.
• Single point of failure: Every component you draw must have a redundancy plan. "What happens if this database goes down?" should always have an answer.
• Ignoring the network: Don't assume database calls are free. Mention network latency, serialization overhead, and connection pooling where relevant.
• Over-engineering early: Start simple, then scale. "Initially, a single PostgreSQL instance handles this. As we grow to 10M users, we'd add read replicas, then consider sharding."
• Not discussing trade-offs: Every architectural decision has trade-offs. Strong consistency vs availability. SQL vs NoSQL. Push vs pull. State them explicitly.

How to Practice System Design Effectively

Reading about system design is not the same as practicing it under interview conditions. You need to articulate your thinking out loud, respond to follow-up questions in real time, and adapt when the interviewer changes requirements mid-session. MockExperts' AI System Design Interview mode lets you practice these 10 questions with an interactive whiteboard, follow-up questions from an AI interviewer, and structured feedback on your communication and architectural decisions — available 24/7 without scheduling friction.

What are the most commonly asked system design interview questions in 2026?

The five most frequently asked system design questions at FAANG and top-tier companies in 2026 are: designing a distributed Rate Limiter (token bucket vs sliding window), a Global Chat Application (WebSocket architecture, message ordering), a URL Shortener (base62 encoding, read-heavy optimisation), a Distributed Cache (consistent hashing, eviction policies), and a Notification Service (fan-out, priority queues, delivery guarantees).

How should I structure my system design interview answers?

Follow a 4-step framework: (1) Clarify requirements and estimate scale (QPS, storage, latency SLAs), (2) Design the high-level architecture with core components, (3) Deep-dive into 2-3 critical components showing trade-offs (SQL vs NoSQL, push vs pull, consistency vs availability), (4) Discuss bottlenecks and scaling strategies. Always communicate your reasoning — interviewers value your thought process over the final diagram.