The naive approach is obvious: for every log-query pair, scan the log for each query word.
Start by clarifying the problem constraints: volume of logs, number of queries, query sizes, and latency requirements. Then propose an inverted index mapping each word to the queries that contain it, and for each log, intersect the query sets of its words to find matching queries. Discuss trade-offs between preprocessing queries versus logs, and how to handle streaming logs efficiently.
Pro tip: Mention that you would preprocess queries into an inverted index and use a bitset or hash set to track candidate queries per log, which is efficient for streaming and scales with the number of distinct words. Also, consider that logs may be processed in batches or with sliding windows, and discuss how to handle updates to queries dynamically.
Ask about the number of logs, queries, average query size, latency requirements, and whether queries are static or dynamic. This determines the appropriate data structures and algorithms.
Build a mapping from each word to the set of queries that contain that word. This allows quick lookup of candidate queries for each log word.
For each log, tokenize into words, retrieve the candidate query sets for each word, and intersect them to find queries where all words are present. Use efficient set intersection (e.g., bitsets or hash sets).
Discuss how to handle high-throughput streams: batch processing, parallelization, and incremental updates. Consider memory usage and whether to index logs instead if queries are few.
Compare with alternative approaches like indexing logs and querying per query, or using a full-text search engine. Discuss when each is preferable based on query/log ratios and update patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.