In a cafe in the San Francisco Bay Area, I heard a conversation between two engineers with my own ears: "Yesterday, after attending Google's system design round and being asked how to design a distributed file storage system, I suddenly got stuck..." Such scenes are not uncommon in the North American tech circle. As someone who has been through it all, I am well aware that the ability of system design is indeed a key threshold for promotion to senior engineer.
I remember when I participated in the system design interview for the first time, my palms were all sweaty. The interviewer asked me to design a real-time chat system. I piled up all the components I knew at once: WebSocket, Redis, Kafka... As a result, I was left speechless when asked. Later, when I worked as a technical interviewer at Facebook, I came to understand where the problem lay - what we need is not the accumulation of technical terms, but the systematic thinking for solving problems.
My Indian colleague Ravi has an excellent learning method: Every Saturday morning, he would pull me to practice the system design in front of the whiteboard. We will randomly select a common application (such as a shared bike system), and then spend two hours designing it from scratch. At the beginning, we often got stuck in the quagmire of technical details and forgot to consider the actual business scenarios. After three months of persistence, we finally figured out a set of methodologies: first, clarify the boundaries of requirements, then define the key interfaces, and only then consider the technical selection.
Sarah, a senior engineer at Amazon, once told me a secret: She requires the team to do "system dissection" exercises every quarter. For instance, during last year's Double Eleven, they disassembled Taobao's shopping cart system and analyzed how it dealt with the challenge of achieving millions of QPS in an instant. This practical analysis is more useful than reading ten theoretical books because you can see the engineering trade-offs in real scenarios.
In an internal training session at LinkedIn, the mentor particularly emphasized the ability to "tell stories". When designing a recommendation system, one cannot merely say "Use the collaborative filtering algorithm", but should explain: "Considering the cold start problem of new users, we will adopt content-based recommendations in the initial stage and switch to the deep learning model after accumulating sufficient data..." Such an expression can impress the interviewer.
I suggest that everyone who wants to improve their system design ability should establish their own "case library". One can start with something simple, such as designing a short URL service first, and then gradually challenge more complex systems. After each design, ask yourself three questions: Where is the bottleneck? How to expand? How to downgrade when there is a fault? Persistent practice will eventually lead to a qualitative leap.
Release time:2025-04-23
