Bookmark: System Design Interview: An Insider’s Guide by Alex Xu
A structured guide to approaching system design interview questions using real-world case studies and a repeatable framework—from scaling basics to designing complex systems.
Key Ideas / Takeaways
Alex Introduces a 4-step framework to tackle system design questions:
- Understand the problem and establish the scope
- Propose a high-level design and get buy-in from the interviewer
- Dive deep into chosen components
- Wrap up with optimizations, bottlenecks, and improvements
I found this really useful for demystifying the process and giving some structure to help tackle this kind of interview. The repetition helps to reinforce the process.
The first chapter Scale from Zero to Millions covers: vertical/horizontal scaling, load balancers, database replication, caching, CDN, stateless vs stateful architecture, decoupling via queues, and sharding.
Other design examples include: rate limiter, consistent hashing, key-value store, unique ID generator, URL shortener, web crawler, notification system, news feed, chat system, search autocomplete, YouTube, Google Drive.
What Stuck With Me
- The framework is a powerful tool—I want to internalize the 4-step approach and apply it systematically.
- The scaling chapter, especially the discussion around load balancers and replication, feels foundational even if the examples are somewhat dated.
- Some examples—like explaining Amazon S3 basics—feel unnecessary now, especially for an experienced engineering audience.
- I will come back to this book as needed for future work or job interviews - autocomplete, chat systems and notification system all come up regularly in software design.
Applications / Relevance
- As a learning exercise, I'd like to **implement a load-balancing pattern in Django, e.g. round-robin or sticky sessions.
- Planning to revisit this bookmark when tackling distributed features or app architectural decisions.
- The general patterns (e.g., caching, sharding, queues) remain relevant and can inform future projects like large-scale data ingest or feature expansion.
Lingering Questions
- How is a load balancer implemented? I've never needed this in practice but would probably choose an off the shelf solution.
- How do you choose between load balancer strategies: simple round-robin, least-connections, or sticky sessions?
- How do newer trends—like Kubernetes —map back to these patterns?