My first instinct was to just iterate over all stored queries for each log and check word membership, which works but feels bad at scale.
Start by clarifying requirements and constraints, then propose an inverted index mapping each word to the set of query IDs containing it. For each log line, tokenize into words, retrieve candidate query IDs from the index, and intersect those sets to find queries whose every word appears in the log line.
Pro tip: Mention that you would optimize for the common case where log lines are much longer than queries, and that you can use bitsets for fast intersections when the number of queries is large.
Ask about expected scale (number of queries, log lines, words per query/line), latency requirements, and whether queries can be removed or updated. This informs data structure and algorithm choices.
Propose an inverted index: a hash map from word to a set of query IDs that contain that word. Also maintain a list or map of query ID to its set of words for quick lookup and potential verification.
When a query arrives, tokenize it into words, assign a unique query ID, and for each word, add the query ID to the inverted index. Optionally, store the query's word set for later use.
For each log line, tokenize into words, deduplicate words, and collect candidate query IDs by looking up each word in the inverted index. Intersect these candidate sets to find queries where all words are present. Output the matching query IDs (or queries).
Discuss optimizations: using bitsets for fast intersections, caching frequent words, early termination if a query's word count exceeds log line word count, and handling case sensitivity and word boundaries as specified.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.