Systems Engineer, Site Reliability Engineering

Google

Given an unsorted array of integers, write a function to sort the array in ascending order using the merge sort algorithm. Explanation of Merge Sort:** Briefly explain the merge sort algorithm, highlighting its divide-and-conquer approach. Implementation:** Provide a step-by-step implementation of the merge sort algorithm. This includes: A mergeSort function that recursively divides the array into smaller subarrays. A merge function that merges two sorted subarrays into a single sorted array. Example:** Input array: [38, 27, 43, 3, 9, 82, 10] Expected output: [3, 9, 10, 27, 38, 43, 82] Efficiency:** Discuss the time and space complexity of merge sort. Why is merge sort considered an efficient sorting algorithm? How does it compare to other sorting algorithms like bubble sort or quicksort in terms of performance (best, average, and worst-case scenarios)? Constraints:** You are not allowed to use built-in sorting functions. The array can contain positive and negative integers. The solution must be implemented in a language of your choice (e.g., Python, Java, C++). Follow-up:** How would you modify the merge sort algorithm to sort the array in descending order? Could you implement an iterative version of merge sort instead of the recursive one? What are the trade-offs?

Google

Let's design a rate limiter for an API. The rate limiter should: Limit the number of requests a user can make to an API within a given time window. Support different rate limits for different users or API endpoints. For example, a free tier user might be limited to 10 requests per minute, while a premium user can make 100 requests per minute. Similarly, some API endpoints might have lower rate limits than others due to resource constraints. Be highly performant to avoid adding significant latency to API requests. Be scalable to handle a large number of users and API requests. Be reliable and prevent accidental overloads that could cause service disruptions. Describe the architecture, algorithms, and data structures you would use to implement such a rate limiter. Consider the following: How would you store the rate limit counters for each user or endpoint? What algorithm would you use to determine whether a request should be allowed or throttled? How would you handle distributed rate limiting across multiple servers? How would you ensure the rate limiter is resilient to failures? As an example, imagine a scenario where a social media platform wants to prevent users from spamming their API with excessive requests to post updates. The platform has millions of users and thousands of API endpoints. Some users pay for premium access with higher request limits. How would you design a robust and efficient rate limiter to handle this scale and these varying requirements?

Google

Where do you see yourself in 5 years?