Core Data Structures, Sorting, And Complexity
Asked of: Software Engineer
Last updated

What's being tested
Apple coding screens probe data-structure selection, sorting tradeoffs, and precise time/space complexity reasoning under small implementation constraints. Expect to justify why an array, hash map, heap, stack, queue, or set is the right fit, then code cleanly with edge cases handled.
Patterns & templates
-
Hash map aggregation — use
dict/defaultdict(list)for per-key grouping inO(n)average time; discuss collision and memory tradeoffs. -
Top-k per key — maintain a min-heap of size
kwithheapq;O(n log k)beats full sorting atO(n log n). -
Two pointers on sorted arrays — shrink/search from both ends in
O(n)time; confirm whether sorting costO(n log n)is allowed. -
Sliding window for contiguous substrings/subarrays — expand right, contract left, update counts in
dict; watch duplicate handling and empty inputs. -
Stack validation — bracket matching, monotonic stack, and undo-style parsing;
O(n)time,O(n)worst-case space. -
Python list/dict mechanics —
list.appendamortizedO(1),dictlookup averageO(1), insertion order preserved in Python 3.7+. -
Complexity narration — state variables clearly:
nitems,munique keys,kretained scores; separate algorithmic cost from input parsing.
Common pitfalls
Pitfall: Sorting everything when only top three are needed; use a bounded heap or fixed-size sorted list per student instead.
Pitfall: Claiming
dictoperations are alwaysO(1)without saying average-case; adversarial hashing and resizing are real caveats.
Pitfall: Coding the happy path first and missing empty arrays, duplicate values, single-element inputs, invalid brackets, or tied scores.
Practice these
The practice cards below cover the canonical variants — solve all of them and time yourself.
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