Datadog·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Datadog software engineer interview with a data structures question that started simple but had a real follow-up with teeth. The trie optimization is where they actually wanted to see if you knew what you were doing.

Questions Asked (1)

Design a data structure that stores a set of prefix strings, then given a list of input strings, return every string that starts with at least one of those stored prefixes. No duplicates in the output even if multiple prefixes match the same string. Walk through a hash-based approach first, then optimize with a trie, and compare the complexity of both.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I jumped straight to the trie because I thought that's what they wanted to see, and the interviewer pulled me back and asked me to do the naive version first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: we need to store a set of prefixes and efficiently find all input strings that start with any prefix, ensuring no duplicates. Then present a hash-based approach using a set of prefixes and checking each input string against all prefixes, followed by a trie-based optimization that allows efficient prefix matching in O(L) per string. Finally, compare the time and space complexities of both approaches, highlighting the trade-offs.

Pro tip: Mention that in practice, a trie can be combined with a hash set for the output to handle duplicates, and discuss how to handle very large input streams or memory constraints, showing awareness of real-world system design.

1. Clarify requirements and constraints

Ask about the expected size of the prefix set and input strings, whether prefixes can be empty, and if the input list is static or streaming. This ensures the solution fits the context.

2. Hash-based approach

Store prefixes in a hash set. For each input string, check all prefixes to see if any is a prefix of the string. Use a result set to avoid duplicates. Analyze complexity: O(N * P * L) time, where N is number of strings, P is number of prefixes, L is average string length.

3. Trie-based optimization

Build a trie from the prefixes. For each input string, traverse the trie character by character; if a node marks the end of a prefix, the string matches. Add to result set. Complexity: O(N * L) time for traversal, plus O(total prefix length) for trie construction.

4. Compare complexities and trade-offs

Contrast the two: hash approach is simple but slower for many prefixes; trie is faster for matching but uses more memory. Discuss space: hash O(P * L), trie O(total prefix characters). Mention that trie can be optimized with a hash set at nodes or using a ternary search tree.

5. Handle duplicates and edge cases

Ensure no duplicates in output by using a set. Discuss edge cases: empty prefix (matches all), no prefixes, no matches, and very long strings. Also consider if input strings can be processed in a stream.

Key Points to Mention

Hash-based approach: store prefixes in a set, check each string against all prefixes, use a result set to avoid duplicates.
Trie-based approach: build a trie of prefixes, traverse each string until a prefix end is found, add to result set.
Time complexity: hash O(N * P * L) vs trie O(N * L + total prefix length).
Space complexity: hash O(P * L) vs trie O(total prefix characters).
Trade-offs: trie is faster for matching but uses more memory; hash is simpler but slower for many prefixes.
Edge cases: empty prefix, no matches, duplicates, and streaming input.

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