DoorDash·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

DoorDash coding round focused on a string similarity grouping problem that sounds deceptively simple but has real complexity hiding underneath. The swap-based constraint tripped me up more than I expected.

Questions Asked (1)

Given a list of restaurant names, group names that are considered 'similar', where two names are similar if one can be turned into the other by performing at most two single-character position swaps. For a query name, return all matching names from the list. Also discuss the time complexity for n names of length L and how to avoid an O(n^2) comparison blowup.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got the basic idea fast enough.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: define 'similar' as edit distance ≤2 with only swaps, and confirm whether swaps can be at any positions. Then, propose an efficient solution using a hash map to index names by their sorted characters or by a canonical form that groups names within two swaps, and for queries, generate all possible names within two swaps and look them up. Finally, analyze time complexity and discuss trade-offs between precomputation and on-the-fly generation.

Pro tip: Mention that for short strings (e.g., restaurant names), generating all possible strings within two swaps is feasible because the number of combinations is O(L^2), but for longer strings, you might need to cap the length or use a different similarity metric. Also, consider that real-world names may have typos, so this approach handles transpositions but not insertions/deletions.

1. Clarify the problem and constraints

Ask about the definition of 'similar': exactly two swaps or at most two? Can swaps be adjacent? Are names case-sensitive? What is the expected size of n and L? This ensures you solve the right problem.

2. Design a canonical grouping strategy

For each name, generate all strings within two swaps (including itself) and use the lexicographically smallest as a canonical key. Group names by this key in a hash map. This precomputes groups in O(n * L^2) time.

3. Handle queries efficiently

For a query name, generate all strings within two swaps and look up each in the hash map to collect matching names. Alternatively, if queries are frequent, precompute a map from each possible string to its group.

4. Analyze time and space complexity

Preprocessing: O(n * L^2) time and O(n * L^2) space in the worst case. Query: O(L^2) time to generate variants and O(1) per lookup. Avoid O(n^2) by not comparing each pair; instead, use hashing to group.

5. Discuss trade-offs and optimizations

Mention that generating all variants can be memory-heavy for large L; consider limiting L or using a trie for prefix-based pruning. Also, note that this approach only handles swaps, not insertions/deletions, so if those are needed, use edit distance with a threshold.

Key Points to Mention

Definition of similarity: at most two single-character position swaps (transpositions).
Canonical form: generate all strings within two swaps and pick the lexicographically smallest as a key.
Hash map for grouping: map canonical key to list of names, enabling O(1) average lookup.
Time complexity: O(n * L^2) preprocessing, O(L^2) per query, avoiding O(n^2) pairwise comparisons.
Space complexity: O(n * L^2) worst-case due to storing all variants.
Trade-offs: precomputation vs. on-the-fly, memory vs. speed, and limitations (only swaps, not insertions/deletions).

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