Staff Software Engineer, AI/ML GenAI, Google Workspace

Google

Let's simulate a coding interview scenario. I'd like you to solve a problem and articulate your thought process as you go. Imagine you're given an array of integers. Your task is to write a function that finds the length of the longest strictly increasing subsequence in that array. A strictly increasing subsequence is a sequence of numbers from the array such that each number is greater than the previous one, and their original order in the array is maintained. For example, in the array [1, 3, 2, 4, 5], one possible increasing subsequence is [1, 2, 4, 5], and the longest increasing subsequence is [1, 2, 4, 5], which has a length of 4. Another possible increasing subsequence is [1, 3, 4, 5] which has length of 4. Your function should return 4 in this case. Another example is [10, 9, 2, 5, 3, 7, 101, 18]. The longest increasing subsequence is [2, 3, 7, 18], which has a length of 4. Your function should return 4. Can you describe your approach and then implement the function? Consider edge cases, like an empty array or an array with only one element. How would your approach handle those? What is the time and space complexity of your solution?

Google

Let's design a rate limiter. This is a crucial component in many systems to prevent abuse and ensure fair usage. Your rate limiter should meet these requirements: Functionality: It should limit the number of requests a user can make within a specific time window. For example, allow a user to make 10 requests per minute. Scalability: The rate limiter needs to handle a large number of users and requests concurrently. Imagine millions of users accessing the system. Accuracy: It should accurately track and enforce the rate limits. A small degree of error is acceptable, but significant deviations are not. Low Latency: The rate limiter must not introduce significant delays in request processing. The overhead should be minimal. Fault Tolerance: The system should continue to function correctly even if some components fail. It should be resilient to outages. Cost-Effectiveness: The solution should be cost-effective in terms of resources used (e.g., memory, CPU, network bandwidth). Consider these scenarios and constraints: Users are identified by a unique ID. The time window is configurable (e.g., seconds, minutes, hours). The rate limit is also configurable per user or group of users. You can use any data structures and algorithms you deem appropriate. Assume you have access to a distributed cache (e.g., Redis, Memcached). Walk me through your design. Discuss different approaches, their trade-offs, and how you would address the requirements. Specifically, consider the following: Data structures for storing request counts. Algorithms for incrementing and checking request counts. Handling concurrency and race conditions. Strategies for distributing the rate limiter across multiple servers. How to handle exceeding the rate limit (e.g., returning an error code). Monitoring and alerting. For example, if a user with ID user123 tries to make 11 requests within a minute when their limit is 10, the rate limiter should reject the 11th request. How would your system achieve this efficiently and reliably at scale?

Google

Where do you see yourself in 5 years?