What is the best way to practice mock system design interviews?

The best way to practice mock system design interviews is to use a structured platform like System Design Lab. It provides real interview questions (design URL shortener, design Twitter, design Netflix), an interactive diagram builder, an AI interviewer that asks follow-up questions, and detailed feedback on your architecture, scalability decisions, and trade-offs — exactly like a real FAANG interview.

How do I prepare for a system design interview?

To prepare for a system design interview: (1) Learn core concepts like distributed systems, caching, databases, sharding, and message queues. (2) Practice drawing architecture diagrams under time pressure. (3) Take knowledge quizzes to test your understanding. (4) Do mock system design interviews with AI feedback to get comfortable explaining your decisions. (5) Review community solutions to see how others approach the same problems. System Design Lab covers all five steps in one platform.

What system design interview questions should I practice?

Common system design interview questions include: Design a URL shortener, Design Twitter/social media feed, Design Netflix/video streaming, Design a distributed cache, Design a rate limiter, Design a notification system, Design a ride-sharing app like Uber. System Design Lab offers a growing bank of real interview problems — Twitter, YouTube, URL shortener, Uber and more — covering the major categories asked at FAANG and top-tier tech companies.

Is there a free mock system design interview tool?

Yes — System Design Lab offers a free 7-day trial with access to 3 mock system design interview problems, all learning modules, all quizzes, and community solutions. No credit card required. Premium (₹999 for 90 days) unlocks unlimited problems and AI interviewer access.

How does System Design Lab's AI interview feedback work?

After you submit your system design, the AI evaluates your written explanation and architecture diagram together. It scores you on completeness, scalability, fault tolerance, and clarity, then gives you specific, actionable feedback — similar to what a senior engineer would say after your interview. You can also chat with an AI interviewer in real-time during your attempt.

System Design Lab›System Design Questions›Design Reddit

Design Reddit

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Question Overview

Design a Reddit-style discussion platform built around communities, posts, nested comments, and voting. The core challenges are counting hundreds of millions of votes a day without hot rows, ranking posts by hot and top, and loading deep comment trees quickly.…

Requirements

Create and subscribe to subreddits, and submit link, text, or image posts to a subreddit
Comment on posts and reply to any comment, forming arbitrarily deep, collapsible comment trees
Upvote or downvote posts and comments with one changeable vote per user per item, never double counted
Subreddit and home feeds sorted by hot, new, or top over a day, week, or all time
Feed and post pages under 200 ms p99; reads stay available even if vote processing is degraded
Scores may lag by seconds, but users always see their own votes immediately (read-your-writes)

Back-of-the-envelope numbers

Page views: 100M DAU × 30 pages/day = 3B/day ÷ 86,400 s ≈ 35K QPS average, ~105K QPS at 3× peak
Votes: 200M/day ÷ 86,400 s ≈ 2.3K votes/s average, ~7K/s at 3× peak; a viral post at 6K votes/min is 100 increments/s on one counter
Content writes: 5M posts + 50M comments = 55M/day ÷ 86,400 s ≈ 640 writes/s average, ~1.9K/s at 3× peak
Content storage: 5M × 2 KB + 50M × 500 B = 10 GB + 25 GB = 35 GB/day ≈ 12.8 TB/year, excluding media in object storage
Vote records: 200M/day × ~30 B (user, item, direction, time) ≈ 6 GB/day ≈ 2.2 TB/year, kept to dedupe and allow vote changes
Listing cache: 100K subreddits × 4 sort orders × top 1,000 post IDs × 8 B ≈ 3.2 GB of IDs, a few times that with Redis overhead

Key components

Post and comment stores: sharded SQL keyed by post_id, each comment holding parent_id so a post's whole tree loads in one query
Vote service: upserts (user_id, item_id, direction) keyed by user and item and emits only the score delta, making repeat votes idempotent
Vote aggregator: consumes deltas from Kafka and applies batched increments per item every few seconds, avoiding hot-row contention on viral posts
Hot ranking: Reddit's open-sourced hot formula adds log10 of net score to creation time ÷ 45,000 s, so 10× the votes equals 12.5 hours of recency
Listing service: per-subreddit Redis sorted sets for hot, new, and top, updated only when an item's score changes
Home feed: fan-in on read by merging the cached hot lists of a user's subscriptions, since fan-out to 30M subscribers is impractical
Comment ranking: best sort by the Wilson score lower bound, with deep or low-scored branches truncated behind load-more links

Common mistakes

Running UPDATE posts SET score = score + 1 per vote, serializing thousands of writers on one hot row during a viral spike
Storing only a counter with no per-user vote records, so double votes and up-to-down switches (a −2 delta) cannot be handled
Fanning each new post out to every subscriber's feed, which means 30M writes for a single post in the largest subreddit
Fetching comment trees level by level with one query per parent, turning a 5,000-comment thread into thousands of round trips
Recomputing every post's hot score on a timer; with a creation-time term, a post's score changes only when votes arrive
Promising exact, strongly consistent vote counts, which forces synchronous coordination the product does not actually need

Likely follow-ups

How would you detect and discount coordinated vote manipulation by bot accounts?
How would you render a post with 100K comments without loading the entire tree?
How would you serve top of all time for a subreddit with ten years of posts?
How would you handle deleting a comment that still has replies beneath it?
How would you shard data when one subreddit is orders of magnitude larger than the rest?
How would you personalize the home feed beyond the user's subscribed subreddits?

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