← Confluent Interview Insights
Spent the first few minutes thinking about naive substring matching and then realized they probably wanted something more thought out.
Start by clarifying the requirements: exact phrase matching, case sensitivity, and whether the document list is static or dynamic. Then propose an efficient solution using an inverted index that maps words to their positions in each document, allowing quick phrase verification by checking consecutive positions. Discuss trade-offs between preprocessing time, memory usage, and query speed.
Pro tip: Mention that you would handle tokenization carefully (e.g., punctuation, stemming) and consider using a trie or suffix array for more advanced phrase queries, showing awareness of real-world search systems like those at Confluent.
Ask about case sensitivity, tokenization rules, and whether the document set is static or dynamic. Confirm if the phrase must be an exact match or if variations (e.g., stemming) are allowed.
Propose an inverted index mapping each word to a list of (document ID, position) pairs. Alternatively, for small datasets, a simple scan with string matching could suffice.
Preprocess documents: tokenize, normalize (lowercase, remove punctuation), and record positions. Store the index in a hash map or trie for fast lookup.
For a given phrase, tokenize it, retrieve position lists for each word, and find consecutive positions that match the phrase order. Use intersection algorithms to efficiently find matches.
Discuss time and space complexity: indexing O(total words), query O(k * average postings list length) where k is phrase length. Mention alternatives like suffix arrays for substring search.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.