My first instinct was just a plain hashmap from word to a list of IDs, which works fine for ADD and QUERY.
Start by clarifying requirements and constraints, then propose an inverted index using a hash map from words to sets of sentence IDs, with an auxiliary map from sentence IDs to their words for efficient deletion. Discuss how to return sorted results efficiently, considering whether to sort on query or maintain sorted structures, and analyze time/space complexity for each operation.
Pro tip: Demonstrate awareness of real-world trade-offs: for example, mention that maintaining sorted lists per word can make queries O(1) but deletions O(n), while using unsorted sets and sorting on query is simpler and often sufficient if queries are less frequent. Also, discuss how this design scales with large datasets and potential optimizations like sharding or caching.
Ask about expected scale (number of sentences, words per sentence, query frequency), whether sentence IDs are unique, and if the system needs to handle updates in real-time. Confirm that queries should return sorted IDs and that deletions should remove the sentence from all word mappings.
Propose an inverted index: a hash map mapping each word to a set (or sorted list) of sentence IDs. Also maintain a map from sentence ID to the set of words in that sentence to enable efficient deletion. Discuss the choice between sets and sorted lists based on operation priorities.
For add: tokenize the sentence, and for each word, add the sentence ID to the word's set and update the sentence-to-words map. For delete: retrieve the words for the sentence ID, remove the ID from each word's set, and remove the sentence entry. For query: retrieve the set for the word, sort the IDs, and return.
Discuss time and space complexity for each operation. For example, add is O(L) where L is sentence length; delete is O(W) where W is number of unique words in the sentence; query is O(K log K) where K is number of matching sentences if sorting on query. Compare with maintaining sorted lists for O(1) query but O(K) deletion.
Mention potential improvements like using a trie for prefix searches, sharding the index for distributed systems, caching frequent queries, or using a database with full-text search capabilities. Also discuss concurrency control if multiple clients modify the index.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.