Snowflake·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Snowflake coding round with a file system design problem. Pretty classic but there are enough edge cases to trip you up if you're not careful about your data structure choices.

Questions Asked (1)

Design a simplified in-memory file system that supports listing files and directories, creating directories recursively, adding content to files, and reading file content.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to use a trie-like structure and I think that was the right call, but I wasted a few minutes second-guessing whether to just use nested hashmaps.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining the core operations, then design a tree-based data structure with nodes representing files and directories. Implement each operation with appropriate algorithms, analyze time and space complexity, and discuss trade-offs and potential optimizations.

Pro tip: Demonstrate awareness of real-world file system challenges like path normalization, concurrency, and efficient directory listing, and mention how your design could scale or be extended.

1. Clarify Requirements and Scope

Ask questions to understand expected operations, constraints (e.g., path format, case sensitivity, max depth), and performance goals. Confirm whether the system needs to support only the listed operations or also others like deletion or moving.

2. Design Data Structures

Propose a tree structure where each node represents a file or directory, storing name, type, content (for files), and children (for directories). Consider using a hash map for children to enable O(1) lookup by name.

3. Implement Core Operations

Detail algorithms for each operation: listing (traverse children), creating directories recursively (split path and create nodes as needed), adding content (find file and append/overwrite), and reading content (find file and return content).

4. Analyze Complexity and Trade-offs

Discuss time and space complexity for each operation, highlighting that path traversal is O(k) where k is path depth, and listing is O(n) for n children. Mention trade-offs between using a tree vs. a flat map with full paths.

5. Discuss Extensions and Optimizations

Suggest potential improvements like caching, concurrency control, or supporting additional operations (delete, move). Mention how the design could scale to larger systems or persistent storage.

Key Points to Mention

Use of a tree data structure with nodes for files and directories, and a map for children to achieve efficient lookups.
Path parsing and normalization to handle absolute/relative paths and recursive directory creation.
Time complexity analysis: O(k) for path traversal, O(1) for adding/reading content if file node is found, O(n) for listing.
Trade-offs between tree-based and flat-map approaches, considering memory overhead and operation efficiency.
Handling edge cases: invalid paths, duplicate names, file vs. directory conflicts, and empty directories.
Potential concurrency issues and how to address them (e.g., locks, read-write locks) in a multi-threaded environment.

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