RIVET: Regex-to-Indexable Keys via Neural Translation for Interactive LIMIT-k Retrieval

Interactive regex search over large text columns needs exact matches under low latency, but indexes help only when a pattern exposes selective fragments. For LIMIT-k queries, the immediate bottleneck is choosing a few indexable keys that reach match-rich candidate buckets before regex verification. RIVET casts this step as constrained regex-to-key generation: a column-specific translator emits short keys, an existing index retrieves candidates, and the native regex engine verifies every output record. Training from repeated regex-key hits moves probability mass toward effective keys, so finite online sampling spends fewer regex checks before collecting the requested matches. Across four large datasets, RIVET reaches 35.8 ms median latency, improves over the strongest baseline REI by 3.4–36×, and reaches up to 1773× speedup over sequential scan. The same mechanism improves TPC-H query plans, preview retrieval under latency budgets, and 80 human-authored OOD regex queries, showing that regex-to-key generation translates into end-to-end system gains.