Tradedesk·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Jun 2026

Summary

Tradedesk software engineer interview had a coding level that got pretty involved, with a file system simulation that kept adding constraints each layer. The capacity management piece was where things got tricky.

Questions Asked (1)

Design a user and file management system with capacity limits. Implement add_user, add_file_by (with capacity enforcement), copy_file with ownership constraints, and update_capacity that evicts the largest files (using lexicographic order as a tiebreaker) when usage exceeds the new capacity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The eviction logic is what got me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design data structures that support efficient operations. Implement each method with careful attention to ownership and capacity rules, and handle eviction by sorting files by size and lexicographic order when needed.

Pro tip: Discuss the trade-offs between different data structures (e.g., hash maps vs. balanced trees) for maintaining files sorted by size, and consider how to optimize eviction to avoid full sorts on every capacity update.

1. Clarify Requirements and Constraints

Ask questions to confirm details like whether file names are unique per user or globally, how capacity is measured (total size or count), and the expected scale of operations.

2. Design Data Structures

Choose structures to store users, files, and their relationships. Consider using a map from user to files, and a global structure to track total usage and files sorted by size for eviction.

3. Implement Core Operations

Write methods for add_user, add_file_by, and copy_file, ensuring ownership constraints are enforced (e.g., only owner can copy). Handle capacity checks in add_file_by.

4. Implement Capacity Update with Eviction

For update_capacity, if new capacity is less than current usage, repeatedly evict the largest file (tiebreak by lexicographic order) until usage is within capacity.

5. Analyze Complexity and Optimize

Discuss time and space complexity of each operation, and propose optimizations like using a heap or balanced BST to efficiently find and remove largest files.

Key Points to Mention

Use appropriate data structures (e.g., hash maps for O(1) user/file lookup, and a sorted structure for eviction).
Enforce ownership constraints: only the owner can copy their file, and copying creates a new file owned by the copier.
Capacity enforcement: check total usage before adding a file; if adding exceeds capacity, either reject or trigger eviction based on requirements.
Eviction policy: when capacity is reduced, evict largest files first; if sizes tie, use lexicographic order of file names (or identifiers) as tiebreaker.
Handle edge cases: adding a file that exactly fills capacity, copying a file when at capacity, updating capacity to a value smaller than the largest file, etc.
Discuss trade-offs: e.g., using a heap for eviction gives O(log n) removal but requires updating heap on file size changes; a sorted list gives O(n) removal but simpler.

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