Senior Software Engineer, D-SDN, Google Global Networking

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. 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?