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

Intermediate

Jun 2026

Summary

Meta SWE coding round with a dictionary-focused problem that had two parts. Pretty straightforward on the surface but the prefix matching piece required some thought.

Questions Asked (1)

Write two functions: one that scans a dictionary for records with an exact key match, and another that scans for records whose key starts with a given prefix.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The exact match part was fine, nothing to it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data structure and requirements: is the dictionary a hash map or a sorted structure? For exact match, a hash map gives O(1) average lookup; for prefix match, consider a trie or sorted array with binary search. Then implement both functions, discussing trade-offs in time/space complexity and potential optimizations.

Pro tip: Demonstrate awareness of real-world constraints: for prefix search, a trie may be overkill if the dataset is small; instead, a simple linear scan with early termination or sorting + binary search might be more practical. Also, mention that in production, you'd likely use a database index or a library like Redis for such queries.

1. Clarify requirements and constraints

Ask about the dictionary's implementation (hash map, sorted array, etc.), expected size, frequency of queries, and whether keys are strings. This determines the optimal approach.

2. Design exact match function

For a hash map, simply check if the key exists and return the value; for a sorted array, use binary search. Discuss time complexity: O(1) average for hash map, O(log n) for binary search.

3. Design prefix match function

Consider options: linear scan (O(n)), sorted array + binary search to find range (O(log n + k)), or trie (O(m + k) where m is prefix length). Choose based on constraints and explain trade-offs.

4. Implement and test edge cases

Write clean code for both functions, handling empty dictionary, non-string keys (if applicable), and prefix longer than keys. Test with examples.

5. Discuss optimizations and real-world applications

Mention how these functions might be used in a larger system (e.g., autocomplete, database indexing) and potential improvements like caching or using specialized data structures.

Key Points to Mention

Time and space complexity of each approach (hash map vs. sorted array vs. trie).
Trade-offs between simplicity and performance: linear scan vs. binary search vs. trie.
Handling of edge cases: empty dictionary, no matches, prefix longer than any key.
Assumption about key type: typically strings for prefix matching, but could be other types if serialized.
Real-world considerations: concurrency, memory usage, and whether the dictionary is static or dynamic.
Potential use of built-in functions or libraries (e.g., Python's startswith, or database LIKE queries).

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