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 30+ curated problems covering all 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.

Database Sharding & Partitioning | Learn

Why Partition at All?

A single database server has hard ceilings: finite CPU, RAM, disk I/O, and connection slots. Once your working set no longer fits in memory, or writes saturate a single disk, or you exhaust connections, vertical scaling (a bigger box) stops being economical. Partitioning splits one large dataset into smaller pieces so each piece fits comfortably on its own node.

Partitioning vs Sharding vs Replication

Partitioning — the general idea of splitting data into subsets called partitions (or shards).
Sharding — horizontal partitioning across separate machines. Each shard is an independent database holding a disjoint subset of rows.
Replication — keeping copies of the same data on multiple nodes (for availability / read scaling). Orthogonal to sharding — production systems do both: each shard is itself replicated.

Vertical vs Horizontal Partitioning

Vertical partitioning splits columns — e.g. move rarely-used bio and avatar_blob columns into a separate table so the hot columns stay compact.
Horizontal partitioning (sharding) splits rows — e.g. users 1–300 on one node, 301–600 on another. This is what we mean by sharding for scale.