This took me a while to wrap my head around.
Use a stack to simulate the recursion of traversing the nested list, pushing iterators for each level. Maintain the invariant that the top of the stack always points to the next integer to return, advancing through empty lists and nested lists lazily. This achieves O(d) space where d is the maximum nesting depth.
Pro tip: Clarify with the interviewer whether the input is a custom NestedInteger interface or a raw nested list (e.g., List<Object>), and handle both cases gracefully. Also, discuss edge cases like empty lists and null values early to show thoroughness.
Ask whether the nested list is represented as a custom NestedInteger class or as a generic List<Object> containing Integers and Lists. Confirm the expected behavior for empty lists and null values.
Use a stack of iterators (or indices) to track the current position at each nesting level. The stack size will be at most the maximum depth d, ensuring O(d) space.
In hasNext(), repeatedly check the top of the stack: if it's an integer, return true; if it's an empty list, pop it; if it's a list, push an iterator for that list and continue. This ensures the next call to next() returns the correct integer.
Call hasNext() first to ensure the iterator is positioned at an integer. Then, retrieve the integer from the top of the stack, advance the iterator, and return the integer.
Explain that time complexity is O(n) total for all elements, and space is O(d). Compare with pre-flattening which uses O(n) space, and highlight the advantage of lazy evaluation for large or infinite structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Recursive version was easy to write, honestly felt like a relief after the stack version.
First, implement the recursive pre-flattening approach by traversing the nested structure and collecting all elements into a flat list, then return an iterator over that list. Next, compare it with the lazy iterator approach (e.g., using a stack) by analyzing time and space complexity, discussing trade-offs in terms of upfront cost, memory usage, and support for infinite or dynamic structures.
Pro tip: Emphasize that the pre-flattening approach is simpler but has O(N) space and time upfront, while the lazy approach is more memory-efficient and can handle infinite sequences, but may have higher per-element overhead. This shows you understand practical engineering trade-offs.
Define the input format (e.g., list of integers and nested lists) and the required iterator methods (e.g., hasNext(), next()). This ensures both implementations adhere to the same contract.
Write a recursive function that traverses the nested structure and appends all elements to a flat list. Then return an iterator over that list, typically using an index pointer.
Time: O(N) to traverse and flatten all elements. Space: O(N) for the flat list, plus O(D) recursion stack where D is the maximum depth. Discuss that this is done upfront.
Describe the lazy approach (e.g., using a stack to simulate recursion) which processes elements on demand. Time: O(1) amortized per next() call, O(N) total. Space: O(D) for the stack, where D is depth, not total elements.
Highlight that pre-flattening is simple but uses O(N) memory and cannot handle infinite structures; lazy is more memory-efficient and supports infinite/large data, but has more complex code and potential per-element overhead.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the iterator's contract and the data structure it traverses, then systematically enumerate edge cases across three dimensions: empty containers, nested empties, and integer boundaries. For each, describe the expected behavior, how your implementation handles it, and how you would test it.
Pro tip: Mention that you would write unit tests for these edge cases before or alongside the implementation, and that you'd verify behavior with the interviewer rather than assume—this shows engineering discipline and avoids misalignment.
Ask or state what the iterator is supposed to do: what data structure it traverses, what 'next' and 'hasNext' should return, and whether it should skip empty nested lists or treat them as elements.
List edge cases in three categories: empty top-level list, deeply nested empty lists (e.g., [[], [[]], []]), and integer boundary values (Integer.MIN_VALUE, Integer.MAX_VALUE, zero, negatives).
For each edge case, state what the correct output should be—e.g., hasNext() returns false immediately for empty lists, nested empties are skipped, and boundary integers are returned without overflow.
Describe how your iterator's logic (e.g., stack-based or recursive) naturally handles these cases, such as checking for empty collections before pushing and using primitive types to avoid boxing issues.
Mention that you would write unit tests covering each edge case, including stress tests with deep nesting, and verify with the interviewer that your assumptions match the requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.