Finally got it successfully Google SDE Offer. After going through the latest round of Google’s recruitment process, one very clear feeling is: Google’s SDE interviews are really different from those a few years ago. If you still use the "old Google interview experience" knowledge to prepare, such as only brushing LeetCode and only memorizing System Design templates, then there will easily be a significant gap in the real interview. The following is a systematic review of the latest changes and key points of the Google SDE interview based on this complete process.
Interview overview
Google's OA is still two algorithm questions in 90 minutes, but the questions are no longer simple template algorithms, but have added business constraints and status restrictions, emphasizing the candidate's understanding of the algorithm itself, not just being able to write code.
Sharing real questions
Constrained shortest path problem
Given a directed graph, each node has a weight, find the shortest path from the starting point to the end point, and the sum of the weights of all nodes on the path cannot exceed the specified threshold.
This question appears to be Dijkstra, but applying the template directly will make an error, because the state not only includes nodes and distances, but also needs to include "consumed node weights" into the state space. Depending on the input size, one can choose a 2D Dijkstra, pruning optimization, or state compression strategy. What Google focuses on is whether you can engineer transformations based on classic algorithms to meet real-world constraints, not just how to use algorithms.
System Design
Distributed file storage system design
Designing a distributed file storage system requires covering the complete data storage and reading process, while taking into account the high availability and performance of the system. It is necessary to design a multi-copy strategy, data backup and recovery mechanism, and ensure the fault tolerance of the system when a node fails.
First, the components need to be split appropriately, and the Metadata Server and Storage Node are separated, which are responsible for metadata management and actual data storage respectively. The file storage and reading process should be efficient, and a multi-copy strategy should be designed to ensure data reliability. A trade-off should be made between synchronous or asynchronous replication according to needs to take into account consistency and availability. In the event of node failure, a fault-tolerant mechanism needs to be implemented, including fault detection, data rebalancing and automatic recovery, to ensure continuous system availability. Also, performance is optimized through load balancing, caching and partitioning strategies to keep the system stable under high throughput and low latency. The interviewer focuses on whether you can make reasonable engineering trade-offs within the classic system design framework, rather than just copying theories or templates.
Real-time message push system
Another system design is a high-concurrency real-time messaging system, and the inspection points are very engineering:
- Large-scale concurrent online users
- How to ensure that messages are not lost, not out of order, and are as real-time as possible
- Whether to choose Kafka / RabbitMQ / Pub-Sub
- How to design message persistence, ACK, retry, and failure cover
This type of question is very close to real business scenarios within Google, and it is basically impossible to cope with the follow-up questions by just memorizing the "System Design template".
Technical aspects: In addition to algorithms, language and underlying understanding are bonus points
Google's technical aspects don't stop at writing code, but also go deep into the underlying mechanisms of the language you use:
- Python:
- Why GIL exists
- Applicable scenarios for multi-threading and multi-processing
- How to choose between CPU intensive vs IO intensive
- Java:
- JVM memory model
- Basic principles of garbage collection (GC)
- Differences in Common GC Algorithms
- Under what circumstances may OOM occur?
The key here is not "memorizing the definition", but whether you truly understand the impact of these mechanisms in real projects.
BQ noodles
Behavioral Interview is still the core examination link, but the style has changed: the interviewer is no longer satisfied with applying the STAR template, but pays more attention to your decision-making ability in complex and uncertain environments, while emphasizing technical judgment, communication skills and sense of responsibility for results.
Common directions include:
- What is the most complex technical problem you have ever encountered? How did you break it down?
- How do you drive decisions when solutions are controversial?
- How to collaborate with colleagues with completely different backgrounds and experiences?
Why more and more candidates choose Programhelp Google interview assistance?
Judging from the Google interview trends in recent years, it is difficult to cover all risk points simply by answering questions. This is also the core reason why more and more Google candidates choose Programhelp at the critical stage: Google OA provides real-time remote assistance, full coverage of high-frequency question types, and real-time reminders of key status design and boundary conditions. System Design provides in-depth training based on real questioning logic. The technical aspect specifically strengthens the test points of Python/Java high-frequency underlying mechanisms and answers from an engineering perspective. BQ customized polishing starts from real projects. All coaching and assistance are personally participated by engineers with real backgrounds in major manufacturers. They are not templates or robot processes.
