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Glean·Software Engineer·Technical Phone Screen·Senior

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Jul 2026Remote

Summary

Glean SWE interview with a simulation-heavy coding problem. Two-part question that starts reasonable and then gets extended mid-interview, which I was not expecting.

Questions Asked (2)

Q1

You have m parallel indexers and a stream of documents, each with an arrival time and a processing duration. Each document has a preferred indexer based on its index mod m. If that indexer is busy, scan forward (wrapping around) and assign to the first available one. If none are free, drop the document. Implement this simulation and return the total documents processed and the indexer that handled the most.

Algorithms & Data StructuresSystem Design
Author's notes

The base assignment logic is fine, just modular arithmetic.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then design a simulation using a min-heap for indexer availability and a queue for incoming documents. Process events in chronological order, assigning each document to its preferred indexer or the next free one, and track processed counts and max load.

Pro tip: Mention that the simulation can be optimized by processing documents in batches when multiple arrive at the same time, and that using a circular scan with a heap ensures O(m) worst-case per document but can be improved with a balanced tree for O(log m).

1. Clarify requirements and edge cases

Ask about input format, tie-breaking, and whether arrival times are sorted. Discuss handling of simultaneous arrivals and indexer availability.

2. Choose data structures

Use a min-heap to track when each indexer becomes free, and a queue for documents sorted by arrival time. For the circular scan, consider a balanced BST or a disjoint-set union structure for efficiency.

3. Simulate event processing

Iterate through documents in arrival order. For each, advance time to its arrival, free any indexers whose completion time <= arrival, then attempt assignment via circular scan.

4. Track metrics and handle drops

Increment processed count when assigned, update per-indexer counts, and increment dropped count if no indexer is free. Keep track of the indexer with the maximum count.

5. Analyze complexity and test

Discuss time and space complexity, and walk through a small example to verify correctness. Consider edge cases like all indexers busy or m=1.

Key Points to Mention

  • Use of a min-heap to efficiently find the next available indexer.
  • Circular scan implementation with modulo arithmetic and handling wrap-around.
  • Time complexity analysis: O(n log m) with heap, or O(n * m) with naive scan.
  • Handling of simultaneous document arrivals and indexer completions.
  • Tracking the most used indexer and total processed count.
  • Edge cases: m=0, no documents, all documents dropped.

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

Q2

Extend the simulation to find the top k indexers by documents processed, and compute what percentage of all documents (including dropped ones) those k indexers handled.

Algorithms & Data StructuresProduct Analytics & Metrics
Author's notes

They dropped this follow-up right after I finished part one, no real pause.

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AI HintsAI Generated

Suggested Approach

First, clarify the data structures and definitions: what constitutes a 'document processed' and a 'dropped document', and whether the simulation already tracks per-indexer counts. Then, outline an algorithm to efficiently find the top k indexers, such as using a min-heap of size k or sorting if the number of indexers is small, and compute the percentage by summing their processed counts and dividing by the total documents (including dropped).

Pro tip: Mention that if the simulation is large-scale or streaming, a heap-based approach is preferred over full sorting to achieve O(n log k) time, and always confirm whether ties in document counts need special handling.

1. Clarify requirements and data

Ask clarifying questions about the simulation's data structures, definitions of 'processed' and 'dropped', and whether the total document count includes dropped ones. Confirm the expected input size to choose an efficient algorithm.

2. Choose algorithm for top k

Decide between sorting all indexers (O(n log n)) or using a min-heap of size k (O(n log k)). For large n and small k, the heap is more efficient; otherwise, sorting may be simpler.

3. Compute total documents and sum of top k

Calculate the total number of documents (including dropped) and sum the processed counts of the top k indexers. Ensure you handle edge cases like k larger than the number of indexers.

4. Calculate percentage and validate

Divide the sum by the total documents to get the percentage. Validate with a small example or sanity check (e.g., percentage should be between 0 and 100).

5. Discuss complexity and edge cases

Analyze time and space complexity, and mention edge cases such as ties, zero total documents, or k=0. Suggest how to extend if the simulation is streaming.

Key Points to Mention

  • Definition of 'documents processed' vs. 'dropped documents' and how they are tracked per indexer.
  • Efficient top-k selection using a min-heap (O(n log k)) vs. sorting (O(n log n)).
  • Total document count must include dropped documents for the percentage calculation.
  • Handling ties in document counts when selecting top k (e.g., arbitrary selection or stable ordering).
  • Edge cases: k > number of indexers, zero total documents, negative or invalid counts.
  • Time and space complexity analysis, and scalability for large simulations.

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