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User-defined functions (UDFs) are widely used to enhance the ca- pabilities of DBMSs. However, using UDFs comes with a significant performance penalty because DBMSs treat UDFs as black boxes, which hinders their ability to optimize queries that invoke such UDFs. To mitigate this problem, in this paper we present LAMBDA, a technique and framework for improving DBMSs’ performance in the presence of UDFs. The core idea of LAMBDA is to statically infer properties of UDFs that facilitate UDF processing. Taking one such property as an example, if DBMSs know that a UDF is pure, that is it returns the same result given the same arguments, they can leverage a cache to avoid repetitive UDF invocations that have the same call arguments. We reframe the problem of analyzing UDF properties as a data flow problem. We tackle the data flow problem by building LAMBDA on top of an extensible abstract interpretation framework and de- veloping an analysis model that is tailored for UDFs. Currently, LAMBDA supports inferring four properties from UDFs that are widely used across DBMSs. We evaluate LAMBDA on a benchmark that is derived from production query workloads and UDFs. Our evaluation results show that (1) LAMBDA conservatively and ef- ficiently infers the considered UDF properties, and (2) inferring such properties improves UDF performance, with a time reduction ranging from 10% to 99%. In addition, when applied to 20 produc- tion UDFs, LAMBDA caught five instances in which developers provided incorrect UDF property annotations. We qualitatively compare LAMBDA against Froid, a state-of-the-art framework for improving UDF performance, and explain how LAMBDA can opti- mize UDFs that are not supported by Froid. 

In this demonstration, we will present EVA, an end-to-end AI-Relational database management system. We will demonstrate the capabilities and utility of EVA using three usage scenarios: (1) EVA serves as a backend for an exploratory video analytics interface developed using Streamlit and React, (2) EVA seamlessly integrates with the Python and Data Science ecosystems by allowing users to access EVA in a Python notebook alongside other popular libraries such as Pandas and Matplotlib, and (3) EVA facilitates bulk labeling with Label Studio, a widely-used labeling framework. By optimizing complex vision queries, we illustrate how EVA allows a wide range of application developers to harness the recent advances in computer vision.

 

In recent years, deep learning models have revolutionized computer vision, enabling diverse applications. However, these models are computationally expensive, and leveraging them for video analyt- ics involves low-level imperative programming. To address these efficiency and usability challenges, the database community has de- veloped video database management systems (VDBMSs). However, existing VDBMSs lack extensibility and composability and do not support holistic system optimizations, limiting their practical appli- cation. In response to these issues, we present our vision for EVA, a VDBMS that allows for extensible support of user-defined functions and employs a Cascades-style query optimizer. Additionally, we leverage RAY’s distributed execution to enhance scalability and performance and explore hardware-specific optimizations to facilitate runtime optimizations. We discuss the architecture and design of EVA, our achievements thus far, and our research roadmap. 

State-of-the-art video database management systems (VDBMSs) often use lightweight proxy models to accelerate object retrieval and aggregate queries. The key assumption underlying these systems is that the proxy model is an order of magnitude faster than the heavyweight oracle model. However, recent advances in computer vision have invalidated this assumption. Inference time of recently proposed oracle models is on par with or even lower than the proxy models used in state-of-the-art (SoTA) VDBMSs. This paper presents Seiden, a VDBMS that leverages this radical shift in the runtime gap between the oracle and proxy models. Instead of relying on a proxy model, Seiden directly applies the oracle model over a subset of frames to build a query-agnostic index, and samples additional frames to answer the query using an exploration-exploitation scheme during query processing. By leveraging the temporal continuity of the video and the output of the oracle model on the sampled frames, Seiden delivers faster query processing and better query accuracy than SoTA VDBMSs. Our empirical evaluation shows that Seiden is on average 6.6 x faster than SoTA VDBMSs across diverse queries and datasets.

 

In database-as-a-service platforms, automated ver-ification of query equivalence helps eliminate redundant computation in the form of overlapping sub-queries. Researchers have proposed two pragmatic techniques to tackle this problem. The first approach consists of reducing the queries to algebraic expressions and proving their equivalence using an algebraic theory. The limitations of this technique are threefold. It cannot prove the equivalence of queries with significant differences in the attributes of their relational operators (e.g., predicates in the filter operator). It does not support certain widely-used SQL features (e.g., NULL values). Its verification procedure is computationally intensive. The second approach transforms this problem to a constraint satisfaction problem and leverages a general-purpose solver to determine query equivalence. This technique consists of deriving the symbolic representation of the queries and proving their equivalence by determining the query containment relationship between the symbolic expressions. While the latter approach addresses all the limitations of the former technique, it only proves the equivalence of queries under set semantics (i.e., output tables must not contain duplicate tuples). However, in practice, database applications use bag semantics (i.e., output tables may contain duplicate tuples) In this paper, we introduce a novel symbolic approach for proving query equivalence under bag semantics. We transform the problem of proving query equivalence under bag semantics to that of proving the existence of a bijective, identity map between tuples returned by the queries on all valid inputs. We classify SQL queries into four categories, and propose a set of novel category-specific verification algorithms. We implement this symbolic approach in SPES and demonstrate that it proves the equivalence of a larger set of query pairs (95/232) under bag semantics compared to the SOTA tools based on algebraic (30/232) and symbolic approaches (67/232) under set and bag semantics, respectively. Furthermore, SPES is 3X faster than the symbolic tool that proves equivalence under set semantics. 

We will demonstrate a prototype of sqlcheck, a holistic toolchain for automatically finding and fixing anti-patterns in database appli- cations. The advent of modern database-as-a-service platforms has made it easy for developers to quickly create scalable applications. However, it is still challenging for developers to design performant, maintainable, and accurate applications. This is because develop- ers may unknowingly introduce anti-patterns in the application’s SQL statements. These anti-patterns are design decisions that are intended to solve a problem, but often lead to other problems by violating fundamental design principles. sqlcheck leverages techniques for automatically: (1) detecting anti-patterns with high accuracy, (2) ranking them based on their impact on performance, maintainability, and accuracy of applica- tions, and (3) suggesting alternative queries and changes to the database design to fix these anti-patterns. We will demonstrate that sqlcheck enables developers to create more performant, maintain- able, and accurate applications. We will show the prevalence of these anti-patterns in a large collection of queries and databases collected from open-source repositories. 

Predicate-centric rules for rewriting queries is a key technique in optimizing queries. These include pushing down the predicate below the join and aggregation operators, or optimizing the order of evaluating predicates. However, many of these rules are only applicable when the predicate uses a certain set of columns. For example, to move the predicate below the join operator, the predicate must only use columns from one of the joined tables. By generating a predicate that satisfies these column constraints and preserves the semantics of the original query, the optimizer may leverage additional predicate-centric rules that were not applicable before. Researchers have proposed syntax-driven rewrite rules and machine learning algorithms for inferring such predicates. However, these techniques suffer from two limitations. First, they do not let the optimizer constrain the set of columns that may be used in the learned predicate. Second, machine learning algorithms do not guarantee that the learned predicate preserves semantics. In this paper, we present SIA, a system for learning predicates while being guided by counter-examples and a verification technique, that addresses these limitations. The key idea is to leverage satisfiability modulo theories to generate counter-examples and use them to iteratively learn a valid, optimal predicate. We formalize this problem by proving the key properties of synthesized predicates. We implement our approach in SIA and evaluate its efficacy and efficiency. We demonstrate that it synthesizes a larger set of valid predicates compared to prior approaches. On a collection of 200 queries derived from the TPC-H benchmark, SIA successfully rewrites 114 queries with learned predicates. 66 of these rewritten queries exhibit more than 2X speed up. 

The emergence of database-as-a-service platforms has made deploying database applications easier than before. Now, developers can quickly create scalable applications. However, designing performant, maintainable, and accurate applications is challenging. Developers may unknowingly introduce anti-patterns in the application's SQL statements. These anti-patterns are design decisions that are intended to solve a problem, but often lead to other problems by violating fundamental design principles. In this paper, we present SQLCheck, a holistic toolchain for automatically finding and fixing anti-patterns in database applications. We introduce techniques for automatically (1) detecting anti-patterns with high precision and recall, (2) ranking the anti-patterns based on their impact on performance, maintainability, and accuracy of applications, and (3) suggesting alternative queries and changes to the database design to fix these anti-patterns. We demonstrate the prevalence of these anti-patterns in a large collection of queries and databases collected from open-source repositories. We introduce an anti-pattern detection algorithm that augments query analysis with data analysis. We present a ranking model for characterizing the impact of frequently occurring anti-patterns. We discuss how SQLCheck suggests fixes for high-impact anti-patterns using rule-based query refactoring techniques. Our experiments demonstrate that SQLCheck enables developers to create more performant, maintainable, and accurate applications.