Netflix·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Netflix coding round, one meaty question about implementing a JSON path query function. Felt like it started simple and then kept growing as they pushed on edge cases and design tradeoffs.

Questions Asked (1)

Implement a JSON path query function that takes a nested JSON object and a dot-separated path string, returning all matching values. Path segments can be literal keys, array indices, or '*' wildcards that match any single key or index at that level. How do you handle consecutive wildcards, missing keys, non-object intermediates, and empty results? Also discuss recursion vs iterative DFS and when to short-circuit.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The wildcard part is where I started to feel the pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a recursive DFS solution that processes path segments one by one, handling wildcards, array indices, and missing keys. Discuss trade-offs between recursion and iterative approaches, and when to short-circuit for efficiency.

Pro tip: Emphasize that wildcards can lead to exponential blow-up, so you should discuss pruning and memoization if the same subproblems repeat. Also, mention that returning all matches requires collecting results, but you can short-circuit if the caller only needs the first match.

1. Clarify requirements and edge cases

Ask about input types, path syntax, wildcard behavior, and expected output format. Confirm handling of consecutive wildcards, missing keys, non-object intermediates, and empty results.

2. Design recursive DFS solution

Process path segments recursively: at each step, if segment is '*', iterate over all keys/indices; if literal, check existence. Handle arrays by treating indices as keys.

3. Address edge cases

For consecutive wildcards, ensure they apply sequentially (e.g., 'a.*.*.b' matches two levels). For missing keys or non-object intermediates, return empty list. For empty results, return empty list.

4. Discuss recursion vs iterative DFS

Recursion is simpler but may hit stack limits; iterative with explicit stack avoids that but is more complex. Choose based on depth and environment.

5. Discuss short-circuiting

If only first match needed, stop early. For all matches, must traverse fully. Mention pruning when a branch cannot yield results.

Key Points to Mention

Handling wildcards: '*' matches any single key or index at that level, including array indices.
Consecutive wildcards: each '*' consumes one path segment, so 'a.*.*.b' requires two levels of wildcard matching.
Missing keys or non-object intermediates: return empty list for that branch; do not throw errors.
Empty results: return empty list, not null or undefined.
Recursion vs iterative DFS: recursion is concise but risks stack overflow; iterative uses explicit stack for control.
Short-circuiting: stop early if only first match needed; otherwise, full traversal required. Prune branches that cannot match remaining path.

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