My first instinct was to just lowercase everything on each scan and call it done.
Start by clarifying requirements: case-insensitive substring match on recipe names, return all matches, and consider performance expectations. Then propose a simple linear scan over the hash map's values, but discuss trade-offs like time complexity and potential optimizations such as maintaining an index or using a trie. Finally, outline the implementation with clean code and edge cases.
Pro tip: Mention that while a linear scan is O(n) per query, for a recipe management system the number of recipes is likely small, so it's acceptable; but if search becomes frequent, consider building an inverted index or using a case-insensitive data structure. This shows you balance simplicity with scalability.
Confirm that search should be case-insensitive, match substrings anywhere in the name, and return all matching recipes. Ask about expected data size and query frequency to inform design.
Since the system uses a hash map, iterate over its values and check if the lowercased recipe name contains the lowercased query. Discuss time complexity O(n*m) where n is number of recipes and m is average name length.
If performance is critical, propose alternatives like maintaining a separate index (e.g., inverted index of words) or using a trie for prefix/substring searches, but note added complexity and memory trade-offs.
Write the search function, ensuring it handles empty query (return all or none?), null inputs, and returns an empty list if no matches. Use efficient string operations.
Walk through test cases: exact match, partial match, different cases, no match, and empty query. Verify correctness and discuss potential improvements.
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Went with sort-on-demand first since it's simpler and I didn't want to overcomplicate it.
Start by clarifying the requirements: what fields define 'name' and 'size', expected data volume, read/write ratio, and whether the sort order needs to be stable. Then present a clean implementation that sorts on demand using a comparator, and discuss the trade-offs of maintaining a sorted structure versus sorting on demand, considering performance, memory, and complexity.
Pro tip: Mention that for small to medium datasets, sorting on demand is simpler and less error-prone, but for large datasets with frequent reads and infrequent writes, maintaining a sorted structure (like a balanced BST or sorted array with binary search insertion) can be more efficient. Also, note that Coinbase likely values scalability and clean code, so emphasize choosing the right approach based on data characteristics.
Ask about the definition of 'name' and 'size', expected data volume, frequency of reads vs writes, and whether the list needs to be sorted in-place or can return a new list.
Define the function to accept a sort key (e.g., 'name' or 'size') and an optional order (ascending/descending). Return a list of recipes sorted accordingly.
Use a comparator or key function to sort the list when the function is called. This is simple and works well if the data doesn't change often or the list is small.
Explain that if the recipe list is large and read frequently, you could maintain a sorted data structure (e.g., balanced BST, skip list, or sorted array with binary search insertion) to achieve O(log n) insertion and O(1) or O(log n) retrieval.
Weigh the pros and cons: sorting on demand is simpler, uses less memory, and avoids update overhead; maintaining sorted order gives faster reads but complicates writes and may use more memory. Choose based on access patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.