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

IntermediatePrefer not to say
Jun 2026

Summary

Google SWE coding round with one iterator design problem. Pretty focused session, no fluff.

Questions Asked (1)

Q1

Design an iterator that merges two lists (favorites and photos), yielding favorites first, then photos, while skipping any blocked IDs and ensuring no element is returned more than once.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The example makes it look straightforward but I spent way too long second-guessing whether to use a set for seen IDs or try to be clever with ordering.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints first, then propose an iterator that lazily yields elements from favorites followed by photos, using a seen set to deduplicate and a blocked set to skip. Discuss trade-offs between memory and time, and handle edge cases like empty lists or all elements blocked.

Pro tip: Emphasize lazy evaluation and early termination to handle large or infinite streams efficiently, and mention that using a hash set for seen IDs gives O(1) average lookup, which is crucial for performance.

1. Clarify Requirements

Ask about input types, whether lists can be modified, if blocked IDs are provided as a set, and if duplicates within a single list are possible. Confirm the expected order and behavior when all elements are blocked.

2. Design the Iterator

Propose a class with a constructor that takes favorites, photos, and blocked IDs. Maintain an index for the current list, a flag for which list is active, and a seen set to track yielded IDs.

3. Implement next() and hasNext()

In next(), advance through the current list, skipping blocked or seen IDs, and switch to the next list when exhausted. hasNext() should peek ahead to check if any valid element remains without consuming it.

4. Analyze Complexity

State that each element is processed at most once, so time complexity is O(n) where n is total elements, and space complexity is O(k) for the seen set, where k is the number of unique yielded elements.

5. Discuss Trade-offs and Extensions

Mention alternatives like using a generator or merging on the fly, and how to handle memory constraints if the seen set grows too large. Also consider thread-safety if needed.

Key Points to Mention

  • Lazy evaluation to avoid loading all elements into memory
  • Use of a hash set for O(1) average-time membership checks for blocked and seen IDs
  • Order preservation: favorites first, then photos
  • Deduplication across both lists using a seen set
  • Edge cases: empty lists, all elements blocked, duplicates within a list
  • Time and space complexity analysis

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