Google·Software Engineer·Technical Phone Screen·Senior

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May 2026Remote

Summary

Google SWE coding round, one question the whole time: implement a recursive directory deletion using two provided APIs. Felt manageable but the follow-up discussion on trade-offs is where things got real.

Questions Asked (2)

Given two filesystem APIs, getChildrenPaths(path) and deleteFile(path), implement a deleteDirectory(path) function that removes a directory and all its contents recursively.

Algorithms & Data StructuresTechnical Trade-offsAPI & Integrations

Author's notes

Started with recursive DFS which felt natural.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a recursive depth-first traversal that deletes children before the parent. Discuss iterative alternatives and trade-offs, and analyze time and space complexity.

Pro tip: Mention that recursion depth could cause stack overflow for very deep directories, and offer an iterative solution using an explicit stack as a more robust alternative.

1. Clarify Requirements and Edge Cases

Ask about symlinks, empty directories, non-existent paths, and error handling expectations. Confirm whether the directory itself should be deleted.

2. Design the Recursive Algorithm

Use getChildrenPaths to list contents, recursively call deleteDirectory on each child that is a directory, and deleteFile on files. Finally, delete the now-empty directory.

3. Consider Iterative Alternative

Describe an iterative post-order traversal using an explicit stack to avoid recursion depth limits, and compare trade-offs.

4. Analyze Complexity and Trade-offs

State that time complexity is O(n) where n is total number of files/directories, and space complexity is O(d) for recursion depth or O(n) for iterative stack.

5. Handle Errors and Edge Cases

Discuss error handling: what if a file cannot be deleted? Should the process continue or abort? Consider partial deletion scenarios.

Key Points to Mention

Recursive depth-first traversal: delete children before parent
Use of getChildrenPaths to list contents and deleteFile for files
Stack overflow risk with deep recursion and iterative alternative
Time complexity O(n) and space complexity O(d) or O(n)
Error handling and partial failure scenarios
Symlink handling to avoid infinite loops or unintended deletions

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

What are the trade-offs between recursive DFS, iterative DFS with an explicit stack, and parallel deletion for this problem?

Technical Trade-offsAlgorithms & Data StructuresSystem Design

Author's notes

This part I actually liked.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem context (e.g., tree/graph deletion, memory constraints, performance goals). Then compare the three approaches across dimensions like memory usage, stack overflow risk, ease of implementation, and parallelism overhead. Conclude with a recommendation based on the specific scenario.

Pro tip: Acknowledge that recursive DFS is often simplest but can cause stack overflow; iterative DFS trades simplicity for control and scalability; parallel deletion adds complexity and may not be worth it unless the dataset is massive and the environment supports it. Show you can balance theoretical trade-offs with practical engineering constraints.

1. Clarify the problem and constraints

Ask about the data structure (tree vs. graph), size, depth, memory limits, and whether the deletion is for a single node or a subtree. This sets the context for meaningful trade-offs.

2. Analyze recursive DFS

Discuss its simplicity and elegance, but highlight risks: stack overflow on deep structures, limited control over traversal order, and difficulty in parallelizing due to call stack dependencies.

3. Analyze iterative DFS with explicit stack

Explain that it avoids stack overflow by using heap memory, allows pausing/resuming, and is easier to instrument. Trade-offs: more code complexity, manual stack management, and potential for memory bloat if not careful.

4. Analyze parallel deletion

Discuss potential speedup on large datasets, but note challenges: synchronization overhead, load balancing, race conditions, and the need for thread-safe data structures. Often overkill unless the problem is embarrassingly parallel.

5. Synthesize and recommend

Summarize trade-offs in a table (e.g., memory, speed, complexity, scalability) and give a recommendation based on the clarified constraints. Mention that in practice, iterative DFS is often a safe middle ground.

Key Points to Mention

Stack overflow risk in recursive DFS vs. heap-based explicit stack in iterative DFS
Memory overhead: recursion uses call stack, iterative uses explicit stack (potentially larger but controllable)
Parallel deletion can improve throughput but introduces synchronization and coordination costs
Ease of implementation and maintainability: recursive is simplest, iterative is moderate, parallel is complex
Scalability: iterative and parallel can handle larger datasets, but parallel requires careful design
Applicability: recursive for small/shallow structures, iterative for deep/large, parallel for massive independent subtrees

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.