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Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Snapchat software engineering interview with a pretty gnarly string compression problem. One question, but it had enough layers to keep me busy for the whole session.

Questions Asked (1)

Q1

Given a string, compress it by replacing consecutive repeated substrings with a count and pattern notation, where patterns can be nested recursively. Implement a function that returns the shortest possible encoding, or the original string if no encoding is shorter. Walk through how you detect repeated structure, handle nested encodings, avoid redundant computation, and analyze time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This one hurt a little.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the encoding rules and constraints, then propose a dynamic programming solution that considers all possible splits and checks for repeated substrings. For each substring, compute the shortest encoding by either keeping it as is or compressing it with a count and recursively encoding the pattern. Use memoization to avoid redundant work and compare lengths to decide the final result.

Pro tip: Emphasize that the problem is NP-hard in general but can be solved with DP for reasonable input sizes; mention that you would discuss trade-offs between optimality and performance, and possibly suggest a greedy or heuristic approach if the input is very large.

1. Clarify rules and constraints

Ask about the exact encoding format (e.g., '3[abc]'), whether patterns can be nested, and any constraints on input size or character set. Confirm that the goal is to return the shortest encoding or the original string if no encoding is shorter.

2. Define subproblem and recurrence

Let dp[i][j] be the shortest encoding for substring s[i..j]. For each substring, consider all possible splits into two parts, and also check if the substring can be formed by repeating a smaller pattern. If so, compute the encoding as count + '[' + dp[pattern] + ']' and compare lengths.

3. Detect repeated patterns efficiently

To check if a substring is a repetition of a smaller pattern, use string matching algorithms (e.g., KMP) or precompute the longest border. Alternatively, iterate over possible pattern lengths that divide the substring length and verify repetition.

4. Implement with memoization

Use top-down DP with memoization to compute dp[i][j] for all substrings. For each substring, try all splits and all possible repeated patterns, storing the shortest encoding found. Base case: single character encodes to itself.

5. Analyze complexity and optimize

Time complexity is O(n^3) or O(n^4) depending on pattern detection, and space is O(n^2) for memoization. Discuss potential optimizations like pruning, using suffix automata, or limiting pattern lengths to improve performance.

Key Points to Mention

  • Dynamic programming over substrings with optimal substructure
  • Handling nested encodings by recursively encoding the repeated pattern
  • Using string matching algorithms (KMP, Z-algorithm) to detect repetitions efficiently
  • Memoization to avoid redundant computation of overlapping subproblems
  • Comparing lengths of encoded vs original to decide the final output
  • Time and space complexity analysis and potential trade-offs for large inputs

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