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.

Scaling Writes: Four Strategies | Learn

The Write Scaling Challenge

Reads are easy to distribute — spin up replicas and route SELECT traffic to them. Writes are harder because they change state. Every write must update indexes, potentially invalidate cached values, be durably persisted, and be consistently replicated. A single primary node eventually hits a ceiling on the write throughput it can sustain. Once you've exhausted vertical scaling, the only path forward is distributing writes.

Strategy 1: Sharding

Split the database into independent partitions called shards, each stored on a separate server. A shard key determines which shard a given record lives on. Each shard handles a subset of write traffic, so total write throughput scales linearly with the number of shards.

Use when: Write load is the confirmed bottleneck, a clear high-cardinality shard key exists, cross-shard JOINs are not required, and per-record transactional guarantees are not needed across shards.
Hotspots — the primary failure mode: If the shard key has low cardinality or if writes concentrate on one value (e.g., a popular user), all load flows to one shard while others sit idle. The result is worse than no sharding at all.

Strategy 2: Async Writes with Message Queues

Instead of writing directly to the database, write requests are published to a message queue (RabbitMQ, SQS, Kafka). Worker processes drain the queue and write to the database at a controlled, sustainable rate. This converts a spiky write workload into a smooth, leveled stream, protecting the database from traffic spikes.

Strategy 3: CQRS + Event Sourcing

Command Query Responsibility Segregation separates the write path from the read path. The write model accepts commands and appends events to an event log (fast, simple appends). The read model maintains one or more denormalized, query-optimized projections built by consuming events asynchronously. The two models are synchronized eventually, not immediately.

Strategy 4: LSM-Tree Databases

Cassandra, ScyllaDB, and RocksDB use Log-Structured Merge-Trees instead of B-Trees. Writes land in an in-memory buffer (memtable) and a Write-Ahead Log for durability. The memtable is periodically flushed to disk as an immutable SSTable. Background compaction merges SSTables. This architecture makes writes extremely fast (sequential memory writes, no random disk seeks) at the cost of increased read complexity.