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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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 LeetCode (Online Judge)

Design LeetCode (Online Judge)

MediumDistributed Systemsqueuesecuritysandboxingrankingscheduling

Question Overview

Design an online judge where users submit code in many languages and get a verdict against hidden test cases. The hard parts are running untrusted code safely in sandboxes, scaling a judge fleet for contest spikes, and keeping a live leaderboard accurate.…

Requirements

Browse and search a catalog of problems by difficulty and tag, with statements and sample tests
Run code on sample tests or submit against hidden tests in 15+ languages with per-language limits
Return verdict, runtime, and memory, and keep a searchable submission history per user
Timed contests with a live leaderboard ranked by problems solved, then by penalty time
Untrusted code is fully isolated: no network, no host access, and bounded CPU, memory, processes, and disk
Verdicts within 5 s p95 normally and 30 s at contest peak; exactly one final verdict per submission

Back-of-the-envelope numbers

Submissions: 10M/day ÷ 86,400 s ≈ 116/s average, ~350/s at 3× daily peak
Judge CPU: 116/s × 2 CPU-s ≈ 230 cores busy on average, ~700 cores at 3× peak, before sandbox startup overhead
Contest burst: 100K participants × 10 runs and submits = 1M jobs ÷ 5,400 s ≈ 185/s average, ~550/s at 3× in the opening minutes
Contest CPU: 550/s × 2 CPU-s ≈ 1,100 extra cores, which must be provisioned before the contest starts, not reactively
Submission storage: 10M/day × ~2.2 KB (code plus metadata) ≈ 22 GB/day ≈ 8 TB/year
Test data: 3,000 problems × 50 tests × ~200 KB ≈ 30 GB, small enough to cache on every judge worker's local disk
Leaderboard: 100K participants × ~100 B ≈ 10 MB per contest, a single Redis sorted set

Key components

Submission API: stores the code and a QUEUED row, enqueues a job, and returns a submission_id; clients poll or receive the verdict over WebSocket
Durable job queue (Kafka or SQS) with separate contest and practice queues so practice traffic never starves a live contest
Judge workers: pull jobs under a lease, compile once, run each test in a sandbox, and write the verdict idempotently keyed by submission_id
Sandbox: gVisor or Firecracker microVMs, or containers hardened with seccomp, cgroup limits, no network, a read-only root, and per-test CPU timeouts
Warm pools: pre-started sandboxes per language so submissions do not pay container or VM startup time on the critical path
Test data store: versioned test sets in object storage, cached on worker disks, plus custom checkers for problems with multiple valid outputs
Leaderboard: a Redis sorted set per contest scored by solved count then penalty, persisted to the database and frozen near the end

Common mistakes

Executing submissions synchronously inside the web request, tying up API servers and collapsing the moment a contest starts
Using a default Docker container as the sandbox, sharing the host kernel with no seccomp profile or network isolation
Measuring runtime by wall-clock time on oversubscribed hosts, so the same code gets different verdicts from run to run
Acknowledging the queue message before judging finishes, so a crashed worker silently loses the submission
Autoscaling only after the contest starts, when new VMs take minutes to boot and the queue is already backed up
Computing the leaderboard with an ORDER BY over all participants on every refresh instead of maintaining it incrementally

Likely follow-ups

How would you stop a submission that forks thousands of processes or tries to fill the disk?
How would you keep runtime measurements fair across different worker hardware?
How would you rejudge every submission for a problem after fixing a wrong test case?
How would you support interactive problems where the program converses with a judge process?
How would you detect plagiarism between contest submissions?
What happens if an entire availability zone of judge workers fails mid-contest?

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