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

IntermediatePending
May 2026

Summary

Google SWE DSA round that went sideways from what I expected. Prepped for hard DP/graph problems, got a medium sliding-window question instead, and didn't spot the optimal approach until the interviewer nudged me toward it.

Questions Asked (1)

Q1

Design a data structure that tracks a stream of values over time and returns a running metric for a time-based sliding window.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Spent way too long confused by the time-based aspect.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: what metric (sum, average, count, etc.), window type (fixed or sliding), and data characteristics (arrival rate, value range). Then propose a design using a deque for O(1) amortized operations, and discuss trade-offs with alternative approaches like circular buffers or balanced trees. Finally, analyze time and space complexity and consider edge cases.

Pro tip: Explicitly discuss how you would handle out-of-order timestamps and late-arriving data, as this is a common real-world complication that interviewers at Google often probe.

1. Clarify Requirements

Ask about the metric (sum, average, max, etc.), window definition (time-based, fixed size, sliding), and data properties (arrival order, value range, update frequency).

2. Choose Data Structures

Propose a deque for O(1) amortized insertion and eviction, or a balanced BST for ordered statistics if needed. Justify your choice based on the metric and constraints.

3. Design Operations

Define methods for adding a value with timestamp, evicting expired values, and querying the current metric. Ensure each operation meets the required time complexity.

4. Analyze Complexity

State the time and space complexity for each operation, and discuss trade-offs between different data structures (e.g., deque vs. heap vs. balanced tree).

5. Handle Edge Cases

Address empty window, out-of-order timestamps, duplicate timestamps, and high-frequency updates. Mention how to adapt the design if needed.

Key Points to Mention

  • Use a deque to maintain values in timestamp order, allowing O(1) amortized addition and eviction.
  • For sum or average, maintain a running total; for max/min, use a monotonic deque or balanced tree.
  • Time-based sliding window requires evicting values older than (current_time - window_size).
  • Discuss trade-offs: deque is simple and efficient for sum/average, but a balanced BST (e.g., TreeMap) supports order statistics for median or percentile.
  • Consider concurrency if the stream is multi-threaded; use locks or lock-free structures.
  • Handle out-of-order data by either rejecting late events, using a buffer, or employing a more complex structure like a segment tree.

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