arXiv subjects

Pascal Ginter

Publications and source records attributed to Pascal Ginter.

2 recordsLinked to original sources

arXiv2026

Teach Your DBMS to LIKE Strings: Fast and General Pattern Matching for Wildcard Joins and Filters

Nowadays, modern applications do more than just store text -- they need to derive meaningful insights from it. To do that, they usually rely on wildcard queries with LIKE predicate to extract patterns. However, modern database management systems (DBMSs) handle these wildcard operations poorly, resorting to nested loops for joins and expensive interpreted evaluation for filters. To address the former, we propose a new join algorithm based on the Aho-Corasick algorithm, which significantly reduces the time complexity. For wildcard filtering, we leverage the code-generation infrastructure to improve performance: we generate specialized code for the LIKE predicate, eliminating the overhead of interpreting the pattern per tuple. Our experimental results show that the new wildcard join algorithm significantly outperforms both baseline DuckDB and Umbra, achieving speedups of up to 30.6x and 114.75x, respectively. The new wildcard filter approach likewise outperforms both baselines, achieving a speedup of 13.3x in a filter-focused stress benchmark. We believe these two techniques will play key roles for high-performance text analytics in modern query engines.

cs.DB↗

arXiv2026

MLSkip: Data Skipping for ML Filters via Lightweight Metadata

Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, they unveil new data management challenges. Concretely, traditional data skipping techniques for integer and string data fail to be applicable to the new filter type. Indeed, there is no known mechanism for pruning non-qualifying row groups, e.g., when reading files from blob storage. In this work, we initiate the study of data skipping techniques for ML filters. We make the case that Parquet's default min-max metadata is enough to enable pruning. To this end, we draw connections to two lines of research: (i) the recently proposed query language for ML models and (ii) neural network verification. Our preliminary results on ReLU architectures show that on tables from TPC-H and TPC-DS, the average pruning effectiveness for filters of selectivity below 0.1% amounts to 27.4%. Finally, inspired by research on spatial joins, we propose an enhanced metadata structure: a size-bounded 2D convex hull that verification tools can make better use of, increasing the pruning effectiveness to 38.31%, while occupying at most 45 bytes per row group and column pair. We observe an end-to-end speedup of 1.07$\times$ over PyTorch in DuckDB.

cs.DB↗