Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Microsoft SWE coding round, one question about processing a stream of timestamped data into time-bucketed windows and returning min/max per window. Pretty focused session, no fluff.

Questions Asked (1)

Given a stream of timestamped data points grouped into fixed-length time windows (e.g. 2-second buckets), write a function that returns a list of (min, max) tuples for each non-empty window, preserving order. Must run in O(n) over total points.

Algorithms & Data Structures

Author's notes

My first instinct was to reach for a sorted structure and I had to talk myself out of it.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the input format and window definition, then propose a single-pass solution that computes the window index for each point and updates the min/max for that window. Use a dictionary or array to store the current min/max per window, and finally output the results in window order.

Pro tip: Mention that you can avoid sorting by using the window index directly, and discuss how to handle large gaps between windows efficiently (e.g., using a dictionary and then sorting keys, or using an array if window indices are bounded).

1. Clarify requirements

Ask about the input format (e.g., list of (timestamp, value) tuples), window size, and whether timestamps are sorted. Confirm that windows are fixed-length and non-overlapping.

2. Choose data structure

Decide between a dictionary (for sparse windows) or an array (if window indices are bounded) to store min and max per window. Consider memory and time trade-offs.

3. Single-pass algorithm

Iterate through each data point, compute its window index (e.g., timestamp // window_size), and update the min and max for that window. Initialize min and max on first encounter.

4. Output in order

If using a dictionary, collect keys and sort them; if using an array, iterate through non-empty entries. Return list of (min, max) tuples in window order.

5. Analyze complexity

Explain that the algorithm runs in O(n) time for processing points, plus O(k log k) for sorting window keys if needed (k = number of non-empty windows). If window indices are bounded, sorting can be avoided, achieving O(n).

Key Points to Mention

Window index calculation: timestamp // window_size (assuming integer timestamps).
Handling empty windows: only include non-empty windows in output.
Time complexity: O(n) for processing points, plus O(k log k) for sorting if using dictionary; O(n) overall if window indices are bounded and array is used.
Space complexity: O(k) for storing min/max per window.
Edge cases: single point, all points in one window, large gaps between windows.
Preserving order: output windows in increasing order of window index.

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