Queries of multi-TB Mass Spectrometry (MS) repositories provide deep insights into biological processes and pose challenging data processing problems. The key bottleneck for running these queries is the number of small random reads. Byte-addressable persistent main memory (PMEM) technologies enable real-time MS search systems by delivering low-latency, high-bandwidth storage. This work presents P-Massive, real-time multi-terabyte scale MS search system. P-Massive takes advantage of PMEM and the underlying nature of its data access patterns to maximize performance. We evaluate P-Massive across various storage hierarchies and project forward over the next decade to understand how MS query systems might evolve. Our evaluation shows that P-Massive offers a cost-effective solution that achieves near-DRAM performance. A single query takes 1.7 seconds in P-Massive, 69× faster than state-of-the-art implementation. In an end-to-end, user-facing application, P-Massive delivers a 90% shorter wait time than the latest MS search tool, returning results within seconds rather than minutes.

Given a database of vectors, a cosine threshold query returns all vectors in the database having cosine similarity to a query vector above a given threshold. These queries arise naturally in many applications, such as document retrieval, image search, and mass spectrometry. The present paper considers the efficient evaluation of such queries, providing novel optimality guarantees and exhibiting good performance on real datasets. We take as a starting point Fagin’s well-known Threshold Algorithm (TA), which can be used to answer cosine threshold queries as follows: an inverted index is first built from the database vectors during pre-processing; at query time, the algorithm traverses the index partially to gather a set of candidate vectors to be later verified against the similarity threshold. However, directly applying TA in its raw form misses significant optimization opportunities. Indeed, we first show that one can take advantage of the fact that the vectors can be assumed to be normalized, to obtain an improved, tight stopping condition for index traversal and to efficiently compute it incrementally. Then we show that one can take advantage of data skewness to obtain better traversal strategies. In particular, we show a novel traversal strategy that exploits a common data skewness condition which holds in multiple domains including mass spectrometry, documents, and image databases. We show that under the skewness assumption, the new traversal strategy has a strong, near-optimal performance guarantee. The techniques developed in the paper are quite general since they can be applied to a large class of similarity functions beyond cosine.