Glean·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Glean SWE interview with a search/autocomplete coding problem that looked straightforward but had a memory constraint twist that forced me to think more carefully about the data structure tradeoffs.

Questions Asked (1)

You have a static list of suggestion objects, each with an ngram string, a department ID, and a score. Implement a function that takes a query string, a user's department, and a number k, and returns the top k suggestions where the query is a prefix of the ngram and the department matches, ranked by score descending. The catch: the suggestion list already takes up roughly half of available server memory, so your solution needs to be optimized for fast lookup without blowing memory.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I jumped straight to trie and the interviewer immediately asked about memory.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the constraints and expected query patterns, then propose a memory-efficient data structure like a trie or sorted array with binary search. Emphasize that since the list is static, you can preprocess it into a compact index that supports fast prefix lookups and department filtering without duplicating the data.

Pro tip: Mention that you would measure the actual memory footprint of the index and consider using memory-mapped files or succinct data structures to keep overhead minimal, showing you think about production constraints beyond just algorithmic complexity.

1. Clarify requirements and constraints

Ask about query patterns, update frequency, memory limits, and whether the list can be modified. Confirm that the list is static and that memory is the primary bottleneck.

2. Choose a memory-efficient index

Propose a trie or a sorted array of ngrams with binary search, possibly augmented with department IDs. Discuss trade-offs: tries can be memory-heavy, while sorted arrays with binary search are compact but may require additional structures for department filtering.

3. Optimize for prefix and department filtering

Explain how to quickly find all ngrams with the given prefix, then filter by department. Consider storing department IDs in a separate compact array or using a bitset per department if the number of departments is small.

4. Rank and return top k

Once candidates are found, sort by score descending and take the top k. If k is small, use a min-heap to avoid sorting all matches.

5. Analyze memory and time complexity

Quantify the memory overhead of your index and the time complexity of lookup. Discuss potential further optimizations like compression or caching.

Key Points to Mention

Trie vs. sorted array with binary search: memory and time trade-offs
Department filtering: separate index or bitset to avoid scanning all matches
Top-k selection: min-heap for O(n log k) instead of full sort
Memory overhead: succinct data structures, compression, or memory-mapped files
Static data allows preprocessing and immutable structures
Edge cases: empty query, no matches, k larger than matches

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