Recursion is pretty obvious once you see the structure, so I got through the core solution without too much trouble.
Clarify the input structure and edge cases, then propose a recursive depth-first traversal that sums file sizes and recurses into directories. Discuss iterative alternatives and complexity before writing clean code.
Pro tip: Mention that recursion depth could be an issue for very deep file systems and offer an iterative stack-based solution as a follow-up to show you think about production constraints.
Ask about input format, whether sizes are integers, and if the structure can be empty or have missing fields. Confirm expected output type.
Explain that you'll traverse the tree recursively: if the node is a file, return its size; if it's a directory, sum the results of recursively processing each item in its contains list.
State that time complexity is O(n) where n is the total number of nodes, and space complexity is O(d) for recursion depth (or O(n) worst case).
Implement the recursive function in a clean, readable manner, handling edge cases like empty directories and missing fields.
Walk through a small example, then mention iterative DFS/BFS with a stack/queue as an alternative to avoid recursion limits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints and the current implementation to identify bottlenecks. Then propose a combination of algorithmic improvements (e.g., using efficient data structures, parallelization) and system-level optimizations (e.g., caching, I/O reduction), while discussing trade-offs.
Pro tip: Quantify the impact of each optimization with back-of-the-envelope calculations and mention how you would measure performance (e.g., profiling) to validate improvements.
Ask about the file system size, expected growth, hardware limitations, and whether the calculation is batch or real-time. This ensures you optimize for the right scenario.
Analyze the current algorithm and system calls to find CPU, I/O, or memory bottlenecks. Consider using profiling tools to pinpoint hotspots.
Suggest improvements like using a more efficient traversal (e.g., iterative instead of recursive), parallelizing directory scans, or employing a map-reduce approach for distributed file systems.
Discuss caching metadata, reducing system calls (e.g., using bulk stat), leveraging asynchronous I/O, or using in-memory data structures to avoid repeated disk access.
Compare options based on complexity, scalability, and resource usage. Explain how you would benchmark and iterate to ensure the optimization meets performance goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.