Modern database systems are increasingly expected to support hybrid queries that combine traditional relational operators with vector-based similarity search enabling applications such as semantic search, contextual recommendations, and multimodal analytics. However, existing benchmarks target either purely relational workloads or isolated similarity search, leaving a gap in evaluating database systems that aim to integrate both. In this paper, we present RVBench , a benchmarking framework for evaluating hybrid relational–vector workloads. RVBench comes with a relational data model that adapts the real-world MediaWiki schema to include vector columns and provides a suite of parameterized SQL templates that interleave similarity search with traditional SQL operations. The framework supports multiple similarity semantics (neighbors based on top-$k$, rank intervals, and sampled ranks) and allows generating hybrid workloads to evaluate database systems on both performance and retrieval quality. We demonstrate RVBench by benchmarking and comparing PostgreSQL and other commercial databases with a reference implementation of the workloads to highlight key trade-offs in query execution strategies and accuracy.

Analytics on modern data analytic and data ware-house systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processing, hardware accelerators like GPUs and code generation techniques. However, optimization of such queries remains a challenge. Existing optimal solutions either cannot be effectively parallelized, or are inefficient while doing a lot of unnecessary work. In this demonstration, we present our system, GPU-QO, which aims to demonstrate query optimization techniques for large analytical queries using GPUs. We first demonstrate Massively Parallel Dynamic Programming (MPDP) - a novel query optimization technique that can run on GPUs to generate optimal plans in a (massively) parallel and efficient manner. We then showcase IDP2-MPDP and UnionDP - two heuristic techniques, again using GPUs, that can even optimize queries containing 1000s of joins. Furthermore, we compare our techniques with current state-of-the-art solutions, and demonstrate how our techniques can reduce optimization time for optimal solutions by nearly two orders of magnitude and produce much better query plans for heuristics (up to 7x).

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, finding the optimal join order becomes a bottleneck in query optimization. Due to the exponential nature of join order optimization, optimizers resort to heuristic solutions after a threshold number of tables. Our objective is two fold: (a) reduce the optimization time for generating optimal plans; and (b) improve the quality of the heuristic solution.

To address the classical selectivity estimation problem for OLAP queries in relational databases, a radically different approach calledPlanBouquetwas recently proposed in[1], wherein the estimation process is completely abandoned and replaced with a calibrated discovery mechanism. The beneficial outcome of this new construction is that provable guarantees on worst-case performance, measured as Maximum Sub-Optimality (MSO), are obtained thereby facilitating robust query processing. ThePlanBouquetformulation suffers, however, from a systemic drawback—the MSO bound is a function of not only the query, but also the optimizer's behavioral profile over the underlying database platform. As a result, there are adverse consequences: (i) the bound value becomes highly variable, depending on the specifics of the current operating environment, and (ii) it becomes infeasible to compute the value without substantial investments in preprocessing overheads. In this paper, we first presentSpillBound, a new query processing algorithm that retains the core strength of thePlanBouquetdiscovery process, but reduces the bound dependency to only the query. It does so by incorporating plan termination and selectivity monitoring mechanisms in the database engine. Specifically,SpillBounddelivers a worst-case multiplicative bound, of$D^2+3D$, where$D$is simply the number of error-prone predicates in the user query. Consequently, the bound value becomes independent of the optimizer and the database platform, and the guarantee can be issued simply by query inspection. We go on to prove thatSpillBoundis within an$O(D)$factor of thebest possibledeterministic selectivity discovery algorithm in its class. We next devise techniques to bridge this quadratic-to-linear MSO gap by introducing the notion ofcontour alignment, a characterization of the nature of plan structures along theboundariesof the selectivity space. Specifically, we propose a variant ofSpillBound, calledAlignedBound, which exploits the alignment property and provides a guarantee in the range$\mathbf {[2D+2,D^2+3D]}$. Finally, a detailed empirical evaluation over the standard decision-support benchmarks indicates that: (i)SpillBoundprovides markedly superior performance w.r.t. MSO as compared toPlanBouquet, and (ii)AlignedBoundprovides additional benefits for query instances that are challenging forSpillBound, often coming close to the ideal of MSO linearity in$D$. From an absolute perspective,AlignedBoundevaluates virtually all the benchmark queries considered in our study with MSO of around10or lesser. Therefore, in an overall sense,SpillBoundandAlignedBoundoffer a substantive step forward in the long-standing quest for robust query processing.

To address the classical selectivity estimation problem in database systems, a radically different query processing technique called PlanBouquet was proposed in 2014. In this approach, the estimation process is completely abandoned and replaced with a calibrated selectivity discovery mechanism. The beneficial outcome is that provable guarantees are obtained on worst-case execution performance, thereby facilitating robust query processing. An improved version of PlanBouquet, called SpillBound (SB), which significantly accelerates the selectivity discovery process, and provides platform-independent performance guarantees, was presented two years ago.

To address the classical selectivity estimation problem in databases, a radically different approach called PlanBouquet was recently proposed in [3], wherein the estimation process is completely abandoned and replaced with a calibrated discovery mechanism. The beneficial outcome of this new construction is that, for the first time, provable guarantees are obtained on worst-case performance, thereby facilitating robust query processing.