The rapid growth of machine learning capabilities and the adoption of data processing methods using vector embeddings sparked a great interest in creating systems for vector data management. While the predominant approach of vector data management is to use specialized index structures for fast search over the entirety of the vector embeddings, once combined with other (meta)data, the search queries can also become selective on relational attributes - typical for analytical queries. As using vector indexes differs from traditional relational data access, we revisit and analyze alternative access paths for efficient mixed vector-relational search.

Collecting data, extracting value, and combining insights from relational and context-rich sources of many modalities in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators enable declarative and optimizable query specification, they are limited to unsuitable data transformations for capturing or analyzing context. On the other hand, representation learning models can map context-rich data into embeddings, enabling machine-automated context processing but requiring imperative data transformation integration with the analytical query. We present a context-enhanced relational join operator to bridge this dichotomy and introduce an embedding operator composable with relational operators. This approach enables hybrid relational and context-rich vector data processing, with algebraic equivalences compatible with relational algebra and corresponding logical and physical optimizations. We investigate model-operator interaction with vector data processing and study the characteristics of the join operator. We demonstrate the hybrid context-enhanced relational join operators with vector embeddings and evaluate it against a vector database approach. We show step-by-step the impact of logical and physical optimizations, which result in orders of magnitude execution time improvement resulting in tensor join formulation. We also outline the performance tradeoffs and cases of using scan-based processing against vector indexes.

As modern data pipelines continue to collect, produce, and store various data formats, extracting and combining value from traditional and context-rich sources becomes unsuitable for RDBMS. To tap into the dark data, domain experts analyze and extract insights and integrate them into various data repositories. This can involve out-of-DBMS processing with high manual effort and suboptimal performance. While AI systems based on ML models can automate the analysis, they can further generate context-rich answers. Using multiple data sources and models further exacerbates the problem of consolidating and analyzing the data of interest. We envision an analytical engine co-optimized with components that enable context-rich analysis. Firstly, as all the data from different sources is expensive to clean ahead of time, we propose using online data integration via model-assisted similarity operations. Secondly, we aim for a holistic pipeline cost- and rule-based optimization across relational and model-based operators. Thirdly, with increasingly heterogeneous hardware and workloads ranging from relational analytics to generative model inference, we envision a system that adapts to the complex query requirements at runtime. Composing ML-driven insights with established approaches aims to expand decades of research and systems-building effort in making complex functionality and performance effortless for the end user.

As modern data pipelines continue to collect, produce, and store a variety of data formats, extracting and combining value from traditional and context-rich sources such as strings, text, video, audio, and logs becomes a manual process where such formats are unsuitable for RDBMS. To tap into the dark data, domain experts analyze and extract insights and integrate them into the data repositories. This process can involve out-of-DBMS, ad-hoc analysis, and processing resulting in ETL, engineering effort, and suboptimal performance. While AI systems based on ML models can automate the analysis process, they often further generate context-rich answers. Using multiple sources of truth, for either training the models or in the form of knowledge bases, further exacerbates the problem of consolidating the data of interest.We envision an analytical engine co-optimized with components that enable context-rich analysis. Firstly, as the data from different sources or resulting from model answering cannot be cleaned ahead of time, we propose using online data integration via model-assisted similarity operations. Secondly, we aim for a holistic pipeline cost- and rule-based optimization across relational and model-based operators. Thirdly, with increasingly heterogeneous hardware and equally heterogeneous workloads ranging from traditional relational analytics to generative model inference, we envision a system that just-in-time adapts to the complex analytical query requirements. To solve increasingly complex analytical problems, ML offers attractive solutions that must be combined with traditional analytical processing and benefit from decades of database community research to achieve scalability and performance effortless for the end user.

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster response times than the hardware allows for exact query answering. However, existing AQP methods impose steep performance penalties as workload unpredictability increases. Specifically, offline AQP relies on predictable workloads to create samples that match the queries in a priori to query execution, reducing query response times when queries match the expected workload. As soon as workload predictability diminishes, existing online AQP methods create query-specific samples with little reuse across queries, producing significantly smaller gains in response times. As a result, existing approaches cannot fully exploit the benefits of sampling under increased unpredictability. We analyze sample creation and propose LAQy, a framework for building, expanding, and merging samples to adapt to the changes in workload predicates. We show the main parameters that affect the sample creation time and propose lazy sampling to overcome the unpredictability issues that cause fast-but-specialized samples to be query-specific. We evaluate LAQy by implementing it in an in-memory code-generation-based scale-up analytical engine to show the adaptivity and practicality of our framework in a modern system. LAQy speeds up online sampling processing as a function of sample reuse ranging from practically zero to full online sampling time and from 2.5x to 19.3x in a simulated exploratory workload.

As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often considered a second-class citizen. Modern analytical engines optimized for high-bandwidth media and multi-core architectures only exacerbate existing inefficiencies, resulting in prohibitive query-time online sampling and longer preprocessing times in offline AQP systems.