arXiv subjects

Brian Tsan

Publications and source records attributed to Brian Tsan.

8 recordsLinked to original sources

arXiv2026

Filtered Approximate Nearest Neighbor Search in Vector Databases: System Design and Performance Analysis

Retrieval-Augmented Generation (RAG) applications increasingly rely on Filtered Approximate Nearest Neighbor Search (FANNS) to combine semantic retrieval with metadata constraints. While algorithmic innovations for FANNS have been proposed, there remains a lack of understanding regarding how generic filtering strategies perform within Vector Databases. In this work, we systematize the taxonomy of filtering strategies and evaluate their integration into FAISS, Milvus, and pgvector. To provide a robust benchmarking framework, we introduce a new relational dataset, \textit{MoReVec}, consisting of two tables, featuring 768-dimensional text embeddings and a rich schema of metadata attributes. We further propose the \textit{Global-Local Selectivity (GLS)} correlation metric to quantify the relationship between filters and query vectors. Our experiments reveal that algorithmic adaptations within the engine often override raw index performance. Specifically, we find that: (1) \textit{Milvus} achieves superior recall stability through hybrid approximate/exact execution; (2) \textit{pgvector}'s cost-based query optimizer frequently selects suboptimal execution plans, favoring approximate index scans even when exact sequential scans would yield perfect recall at comparable latency; and (3) partition-based indexes (IVFFlat) outperform graph-based indexes (HNSW) for low-selectivity queries. To facilitate this analysis, we extend the widely-used \textit{ANN-Benchmarks} to support filtered vector search and make it available online. Finally, we synthesize our findings into a set of practical guidelines for selecting index types and configuring query optimizers for hybrid search workloads.

cs.DB↗

arXiv2025

Sketched Sum-Product Networks for Joins

Sketches have shown high accuracy in multi-way join cardinality estimation, a critical problem in cost-based query optimization. Accurately estimating the cardinality of a join operation -- analogous to its computational cost -- allows the optimization of query execution costs in relational database systems. However, although sketches have shown high efficacy in query optimization, they are typically constructed specifically for predefined selections in queries that are assumed to be given a priori, hindering their applicability to new queries. As a more general solution, we propose for Sum-Product Networks to dynamically approximate sketches on-the-fly. Sum-Product Networks can decompose and model multivariate distributions, such as relations, as linear combinations of multiple univariate distributions. By representing these univariate distributions as sketches, Sum-Product Networks can combine them element-wise to efficiently approximate the sketch of any query selection. These approximate sketches can then be applied to join cardinality estimation. In particular, we implement the Fast-AGMS and Bound Sketch methods, which have successfully been used in prior work, despite their costly construction. By accurately approximating them instead, our work provides a practical alternative to apply these sketches to query optimization.

cs.DB↗

arXiv2025

PLANSIEVE: Real-time Suboptimal Query Plan Detection Through Incremental Refinements

Cardinality estimation remains a fundamental challenge in query optimization, often resulting in sub-optimal execution plans and degraded performance. While errors in cardinality estimation are inevitable, existing methods for identifying sub-optimal plans -- such as metrics like Q-error, P-error, or L1-error -- are limited to post-execution analysis, requiring complete knowledge of true cardinalities and failing to prevent the execution of sub-optimal plans in real-time. This paper introduces PLANSIEVE, a novel framework that identifies sub-optimal plans during query optimization. PLANSIEVE operates by analyzing the relative order of sub-plans generated by the optimizer based on estimated and true cardinalities. It begins with surrogate cardinalities from any third-party estimator and incrementally refines these surrogates as the system processes more queries. Experimental results on the augmented JOB-LIGHT-SCALE and STATS-CEB-SCALE workloads demonstrate that PLANSIEVE achieves an accuracy of up to 88.7\% in predicting sub-optimal plans.

cs.DB↗

arXiv2024

Spanning Tree-based Query Plan Enumeration

In this work, we define the problem of finding an optimal query plan as finding spanning trees with low costs. This approach empowers the utilization of a series of spanning tree algorithms, thereby enabling systematic exploration of the plan search space over a join graph. Capitalizing on the polynomial time complexity of spanning tree algorithms, we present the Ensemble Spanning Tree Enumeration (ESTE) strategy. ESTE employs two conventional spanning tree algorithms, Prim's and Kruskal's, together to enhance the robustness of the query optimizer. In ESTE, multiple query plans are enumerated exploring different areas of the search space. This positions ESTE as an intermediate strategy between exhaustive and heuristic enumeration strategies. We show that ESTE is more robust in identifying efficient query plans for large queries. In the case of data modifications and workload demand increase, we believe that our approach can be a cheaper alternative to maintain optimizer robustness by integrating additional spanning tree algorithms rather than completely changing the optimizer to another plan enumeration algorithm. The experimental evaluation shows that ESTE achieves better consistency in plan quality and optimization time than existing solutions while identifying similarly optimal plans.

cs.DB↗

arXiv2023

Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering

Researchers produce thousands of scholarly documents containing valuable technical knowledge. The community faces the laborious task of reading these documents to identify, extract, and synthesize information. To automate information gathering, document-level question answering (QA) offers a flexible framework where human-posed questions can be adapted to extract diverse knowledge. Finetuning QA systems requires access to labeled data (tuples of context, question and answer). However, data curation for document QA is uniquely challenging because the context (i.e. answer evidence passage) needs to be retrieved from potentially long, ill-formatted documents. Existing QA datasets sidestep this challenge by providing short, well-defined contexts that are unrealistic in real-world applications. We present a three-stage document QA approach: (1) text extraction from PDF; (2) evidence retrieval from extracted texts to form well-posed contexts; (3) QA to extract knowledge from contexts to return high-quality answers -- extractive, abstractive, or Boolean. Using QASPER for evaluation, our detect-retrieve-comprehend (DRC) system achieves a +7.19 improvement in Answer-F1 over existing baselines while delivering superior context selection. Our results demonstrate that DRC holds tremendous promise as a flexible framework for practical scientific document QA.

cs.CL↗

arXiv2023

Analyzing Query Optimizer Performance in the Presence and Absence of Cardinality Estimates

Most query optimizers rely on cardinality estimates to determine optimal execution plans. While traditional databases such as PostgreSQL, Oracle, and Db2 utilize many types of synopses -- including histograms, samples, and sketches -- recent main-memory databases like DuckDB and Heavy.AI often operate with minimal or no estimates, yet their performance does not necessarily suffer. To the best of our knowledge, no analytical comparison has been conducted between optimizers with and without cardinality estimates to understand their performance characteristics in different settings, such as indexed, non-indexed, and multi-threaded. In this paper, we present a comparative analysis between optimizers that use cardinality estimates and those that do not. We use the Join Order Benchmark (JOB) for our evaluation and true cardinalities as the baseline. Our investigation reveals that cardinality estimates have marginal impact in non-indexed settings. Meanwhile, when indexes are available, inaccurate estimates may lead to sub-optimal physical operators -- even with an optimal join order. Furthermore, the impact of cardinality estimates is less significant in highly-parallel main-memory databases.

cs.DB↗

arXiv2021

Simpli-Squared: A Very Simple Yet Unexpectedly Powerful Join Ordering Algorithm Without Cardinality Estimates

The Join Order Benchmark (JOB) has become the de facto standard to assess the performance of relational database query optimizers due to its complexity and completeness. In order to compute the optimal execution plan -- join order -- existing solutions employ extensive data synopses and correlations -- functional dependencies -- between table attributes. These structures incur significant overhead to design, build, and maintain. In this paper, we present \textit{Simpli}city \textit{Simpli}fied (\textit{Simpli-Squared}), a very simple join ordering algorithm that achieves unexpectedly good results. Simpli-Squared computes the join order without using any statistics or cardinality estimates. It takes as input only the referential integrity constraints declared at schema definition and the number of tuples (size) in the base tables. The join order of a given query is computed by splitting the join graph along the many-to-many joins and sorting the tables based on their size. The tables involved in one-to-many joins are greedily included based on size and the query join graph. The resulting plan can be efficiently generated by a lightweight query rewriting procedure. Experiments on the JOB benchmark in PostgreSQL show that Simpli-Squared achieves runtimes having an increase of only up to 16\% -- and sometimes even a reduction -- compared to four state-of-the-art solutions that are considerably more intricate. Based on these results, we question whether JOB adequately tests query optimizers or if accurate cardinality estimation is such a fundamental requirement for performing well on the JOB benchmark.

cs.DB↗

arXiv2020

DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-Temporal Models

In this paper, we present a cost-based approach for the automatic selection and allocation of a disjoint ensemble of black-box predictors to answer predictive spatio-temporal queries. Our approach is divided into two parts -- offline and online. During the offline part, we preprocess the predictive domain data -- transforming it into a regular grid -- and the black-box models -- computing their spatio-temporal learning function. In the online part, we compute a DJEnsemble plan which minimizes a multivariate cost function based on estimates for the prediction error and the execution cost -- producing a model spatial allocation matrix -- and run the optimal ensemble plan. We conduct a set of extensive experiments that evaluate the DJEnsemble approach and highlight its efficiency. We show that our cost model produces plans with performance close to the actual best plan. When compared against the traditional ensemble approach, DJEnsemble achieves up to $4X$ improvement in execution time and almost $9X$ improvement in prediction accuracy. To the best of our knowledge, this is the first work to solve the problem of optimizing the allocation of black-box models to answer predictive spatio-temporal queries.

cs.AI↗