The sorting part is where I tripped up initially.
Clarify requirements and constraints first, then propose a data structure that supports efficient add, update, and get operations while enabling fast substring search and sorted retrieval. Discuss trade-offs between different approaches (e.g., in-memory indexing vs. database queries) and outline how to handle sorting by priority and creation order.
Pro tip: Mention that substring search can be optimized with a trie or inverted index, but for small-scale or interview settings, a simple linear scan with sorting is acceptable—showing you can balance simplicity and performance.
Ask about expected scale (number of tasks, query frequency), whether updates are frequent, and if the search should be case-sensitive. Confirm the exact sorting rules and tie-breaking.
Decide how to store tasks for efficient add/update/get (e.g., hash map by ID) and how to support substring search (e.g., maintain a list or use a trie). Consider if sorting can be done on the fly or if a sorted structure is needed.
Outline steps: iterate over tasks, filter by substring match on name, collect matches, sort by priority descending and creation order ascending, then limit to max_results. Discuss time complexity.
Address empty results, max_results <= 0, case sensitivity, and potential performance improvements like early termination or indexing. Mention concurrency if relevant.
Walk through a small example to verify correctness. Summarize the approach, trade-offs, and possible extensions (e.g., pagination, fuzzy search).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They pushed hard on the data structure question.
Start by clarifying the requirements and constraints, then propose a data structure that supports efficient sorted retrieval. Explain how you would maintain the sorted order as tasks are added or updated, and analyze the time complexity of each operation including the list_tasks_sorted function.
Pro tip: Mention that if updates are frequent, a balanced BST with an auxiliary index might be better, but if reads dominate, a sorted list with binary search insertion could suffice. Also, discuss the trade-off between maintaining sorted order on write vs. sorting on read.
Ask about the expected frequency of insertions, updates, and queries, and whether the limit is typically small or large. Confirm the sorting criteria: priority descending, then creation order ascending.
Suggest using a balanced binary search tree (e.g., red-black tree) where each node stores task ID, priority, and creation order, with a comparator that orders by priority descending, then creation order ascending. Alternatively, consider a skip list or a sorted array with binary search.
Describe how insertions and updates are handled: for a BST, insert in O(log n) time; for a sorted array, insertion is O(n) due to shifting. If using a heap, note that it doesn't support efficient sorted retrieval beyond the top element.
For the chosen data structure, state the time complexity for insertion, update, deletion, and the list_tasks_sorted operation. For a BST, list_tasks_sorted can be done via in-order traversal taking O(k) where k is the limit, but if the tree is not threaded, it may take O(log n + k) with a stack. For a sorted array, retrieval is O(k) but insertion is O(n).
Compare with other structures like heaps (O(log n) insert, O(k log n) for sorted retrieval), or maintaining a separate sorted list updated on each write. Highlight that the best choice depends on read/write ratio and whether the limit is small.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.