Start by grouping the data structures into families (linear, hash-based, tree-based, heap, graph) to show a structured mental model. For each, state the key invariant first, then derive complexities from that invariant, and finally give a concrete example where it excels and one where it fails. Keep the answer comparative and tie trade-offs back to real-world engineering decisions.
Pro tip: Don't just recite Big-O tables—explain *why* each complexity follows from the structure's invariant, and mention practical constants like cache locality and pointer-chasing overhead, which matter enormously in latency-sensitive trading systems like Optiver's.
Organize the eight structures into logical families: linear (array, linked list, stack, queue), hash-based (hash table), tree-based (BST, heap), and graph. This shows you can chunk information rather than listing facts randomly.
For each structure, first state its defining invariant (e.g., array: contiguous memory; BST: left < node < right; heap: parent ≤ children). Then derive search/insert/delete/iteration complexities from that invariant, noting average vs. worst case where they differ.
For each structure, provide one scenario where it is the ideal choice and one where it breaks down. Make examples concrete and tied to real systems (e.g., arrays for cache-friendly numeric loops, linked lists for frequent middle insertions with known nodes).
Emphasize space complexity, cache behavior, pointer overhead, and worst-case guarantees. For example, hash tables have O(1) average but O(n) worst-case and poor iteration order; balanced BSTs guarantee O(log n) but with higher constants.
Conclude by mapping common engineering needs (fast lookup, ordered iteration, priority access, modeling relationships) to the appropriate structure, showing you can choose the right tool for the job.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.