Faster phrase search with shingled bloom filters in Brainstore

Brainstore, a database tailored for handling agent traces, faced challenges with traditional phrase search in large datasets, which often resulted in slow queries due to common terms with rare intersections. The solution involved implementing shingled bloom filters using trigrams instead of unigrams, improving segment elimination by focusing on rare three-word combinations rather than individual common words. This approach significantly enhanced search efficiency, allowing for faster phrase search by pruning irrelevant data more effectively. The improved method was tested on real customer data, reducing the scanned data size from over 100 GB to less than 4 GB, resulting in a 25x increase in efficiency. As Brainstore continues to develop, it aims to further optimize its search capabilities and handle larger datasets, ensuring that agent debugging remains fast and efficient even as data volumes grow.

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