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.

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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 Web Search Engine

Design Web Search Engine

HardSearchsearchrankingindexingdistributedcaching🔵 Google

Question Overview

Design a web-scale search engine that answers free-text queries over tens of billions of pages in under half a second. The hard parts are building and sharding an inverted index, fanning each query out to a thousand shards, ranking in stages, and keeping results fresh.…

Requirements

Return the top 10 results with title, URL, and a query-specific snippet for any free-text query
Support phrase queries, pagination, spelling correction, and language or region filters
Rank by textual relevance, link-based page authority, and learned signals such as click feedback
Reindex changed pages within a day and make breaking news searchable within minutes
p99 under 500 ms; return partial results rather than errors when shards lag or fail
Suppress spam and near-duplicate pages so results stay diverse and trustworthy

Back-of-the-envelope numbers

Queries: 5B/day ÷ 86,400 s ≈ 58K QPS average, ~175K QPS at 3× peak
Index size: 20B docs × ~1,000 tokens = 20 trillion postings × ~1.5 bytes compressed ≈ 30 TB per full index copy
Sharding: 30 TB ÷ 1,000 document-partitioned shards ≈ 30 GB per shard, small enough to keep hot posting lists in memory
Fan-out: with ~50% of queries answered from the result cache, 87.5K QPS × 1,000 shards ≈ 87.5M shard lookups/s at peak
Leaf servers: 87.5M lookups/s ÷ ~2K lookups/s per server ≈ 44K servers, i.e. about 44 replicas of every shard
Document store for snippets: 20B × 10 KB ≈ 200 TB of text, ≈ 50 TB after ~4× compression
Indexing throughput: 1B reindexed docs/day ÷ 86,400 s ≈ 11.6K documents/s through parsing, dedup, and index building

Key components

Indexing pipeline: parse HTML, extract text and anchor text, detect language, drop near-duplicates with SimHash, then tokenize and build per-shard postings
Inverted index partitioned by document: each shard maps term → posting list of doc IDs, term frequencies, and positions, delta-encoded and compressed
Static quality scores such as PageRank computed offline over the link graph; postings ordered by quality so shards can terminate early
Query serving tree: the root parses and rewrites the query, fans out through aggregators to every shard, merges each shard's top-k, then fetches snippets
Multi-stage ranking: BM25 plus static score picks ~1,000 candidates per shard, then a learned-to-rank model reorders the merged top few hundred
Freshness tiers: a large base index rebuilt in batches plus a small real-time index for new and changed pages, merged at query time
Result cache keyed by normalized query and locale, plus hedged requests and per-shard deadlines to cut tail latency

Common mistakes

Sharding the index by term, which creates hot shards for common words and forces cross-shard intersection for multi-word queries
Running the expensive ranking model on every matching document instead of a cheap-to-expensive multi-stage funnel
Waiting for all 1,000 shards on every query so the slowest one sets latency, instead of using deadlines, hedging, and partial results
Rebuilding the whole index to achieve freshness instead of layering a small incremental index over the base index
Computing PageRank or other link signals at query time rather than offline as a static per-document score
Ignoring near-duplicate detection, which wastes index space and fills the results page with copies of the same content

Likely follow-ups

How would you compute PageRank over a 20B-page link graph and refresh it as the graph changes?
How would you make a breaking news article searchable within 60 seconds of publication?
How would you defend ranking against link farms and keyword-stuffed spam pages?
How would you add personalization or location signals without destroying the result cache hit rate?
How would you measure whether a ranking change actually improved result quality?
How would you roll out a new index build without a latency or relevance regression?

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