One stone, two birds: A lightweight multidimensional learned index with cardinality support

Innovative learning based structures have recently been proposed to tackle index and cardinality estimation tasks, specifically learned indexes and data driven cardinality estimators. These structures exhibit excellent performance in capturing data distribution, making them promising for integration into AI driven database kernels. However, accurate estimation for corner case queries requires a large number of network parameters, resulting in higher computing resources on expensive GPUs and more storage overhead. Additionally, the separate implementation for CE and learned index result in a redundancy waste by storage of single table distribution twice. These present challenges for designing AI driven database kernels. As in real database scenarios, a compact kernel is necessary to process queries within a limited storage and time budget. Directly integrating these two AI approaches would result in a heavy and complex kernel due to a large number of network parameters and repeated storage of data distribution parameters. Our proposed CardIndex structure effectively killed two birds with one stone. It is a fast multidim learned index that also serves as a lightweight cardinality estimator with parameters scaled at the KB level. Due to its special structure and small parameter size, it can obtain both CDF and PDF information for tuples with an incredibly low latency of 1 to 10 microseconds. For tasks with low selectivity estimation, we did not increase the model's parameters to obtain fine grained point density. Instead, we fully utilized our structure's characteristics and proposed a hybrid estimation algorithm in providing fast and exact results

Bridging Data Management and Machine Learning: Case Studies on Index, Query Optimization, and Data Acquisition

Data management tasks and techniques can be observed in a variety of real world scenarios, including web search, business analysis, traffic scheduling, and advertising, to name a few. While data management as a research area has been studied for decades, recent breakthroughs in Machine Learning (ML) provide new perspectives to define and tackle problems in the area, and at the same time, the wisdom integrated in data management techniques also greatly helps to accelerate the advancement of Machine Learning. In this work, we focus on the intersection area of data management and Machine Learning, and study several important, interesting, and challenging problems. More specifically, our work mainly concentrates on the following three topics: (1) leveraging the ability of ML models in capturing data distribution to design lightweight and data-adaptive indexes and search algorithms to accelerate similarity search over large-scale data; (2) designing robust and trustworthy approaches to improve the reliability of both conventional query optimizer and learned query optimizer, and boost the performance of DBMS; (3) developing data management techniques with statistical guarantees to acquire the most useful training data for ML models with a budget limitation, striving to maximize the accuracy of the model. We conduct detailed theoretical and empirical study for each topic, establishing these fundamental problems as well as developing efficient and effective approaches for the tasks

Bridging Data Management and Machine Learning: Case Studies on Index, Query Optimization, and Data Acquisition

Data management tasks and techniques can be observed in a variety of real world scenarios, including web search, business analysis, traffic scheduling, and advertising, to name a few. While data management as a research area has been studied for decades, recent breakthroughs in Machine Learning (ML) provide new perspectives to define and tackle problems in the area, and at the same time, the wisdom integrated in data management techniques also greatly helps to accelerate the advancement of Machine Learning. In this work, we focus on the intersection area of data management and Machine Learning, and study several important, interesting, and challenging problems. More specifically, our work mainly concentrates on the following three topics: (1) leveraging the ability of ML models in capturing data distribution to design lightweight and data-adaptive indexes and search algorithms to accelerate similarity search over large-scale data; (2) designing robust and trustworthy approaches to improve the reliability of both conventional query optimizer and learned query optimizer, and boost the performance of DBMS; (3) developing data management techniques with statistical guarantees to acquire the most useful training data for ML models with a budget limitation, striving to maximize the accuracy of the model. We conduct detailed theoretical and empirical study for each topic, establishing these fundamental problems as well as developing efficient and effective approaches for the tasks

Towards Scalable OLTP Over Fast Networks

Online Transaction Processing (OLTP) underpins real-time data processing in many mission-critical applications, from banking to e-commerce. These applications typically issue short-duration, latency-sensitive transactions that demand immediate processing. High-volume applications, such as Alibaba's e-commerce platform, achieve peak transaction rates as high as 70 million transactions per second, exceeding the capacity of a single machine. Instead, distributed OLTP database management systems (DBMS) are deployed across multiple powerful machines. Historically, such distributed OLTP DBMSs have been primarily designed to avoid network communication, a paradigm largely unchanged since the 1980s. However, fast networks challenge the conventional belief that network communication is the main bottleneck. In particular, emerging network technologies, like Remote Direct Memory Access (RDMA), radically alter how data can be accessed over a network. RDMA's primitives allow direct access to the memory of a remote machine within an order of magnitude of local memory access. This development invalidates the notion that network communication is the primary bottleneck. Given that traditional distributed database systems have been designed with the premise that the network is slow, they cannot efficiently exploit these fast network primitives, which requires us to reconsider how we design distributed OLTP systems. This thesis focuses on the challenges RDMA presents and its implications on the design of distributed OLTP systems. First, we examine distributed architectures to understand data access patterns and scalability in modern OLTP systems. Drawing on these insights, we advocate a distributed storage engine optimized for high-speed networks. The storage engine serves as the foundation of a database, ensuring efficient data access through three central components: indexes, synchronization primitives, and buffer management (caching). With the introduction of RDMA, the landscape of data access has undergone a significant transformation. This requires a comprehensive redesign of the storage engine components to exploit the potential of RDMA and similar high-speed network technologies. Thus, as the second contribution, we design RDMA-optimized tree-based indexes — especially applicable for disaggregated databases to access remote data efficiently. We then turn our attention to the unique challenges of RDMA. One-sided RDMA, one of the network primitives introduced by RDMA, presents a performance advantage in enabling remote memory access while bypassing the remote CPU and the operating system. This allows the remote CPU to process transactions uninterrupted, with no requirement to be on hand for network communication. However, that way, specialized one-sided RDMA synchronization primitives are required since traditional CPU-driven primitives are bypassed. We found that existing RDMA one-sided synchronization schemes are unscalable or, even worse, fail to synchronize correctly, leading to hard-to-detect data corruption. As our third contribution, we address this issue by offering guidelines to build scalable and correct one-sided RDMA synchronization primitives. Finally, recognizing that maintaining all data in memory becomes economically unattractive, we propose a distributed buffer manager design that efficiently utilizes cost-effective NVMe flash storage. By leveraging low-latency RDMA messages, our buffer manager provides a transparent memory abstraction, accessing the aggregated DRAM and NVMe storage across nodes. Central to our approach is a distributed caching protocol that dynamically caches data. With this approach, our system can outperform RDMA-enabled in-memory distributed databases while managing larger-than-memory datasets efficiently

DB4ML - An In-Memory Database Kernel with Machine Learning Support

In this paper, we revisit the question of how ML algorithms can be best integrated into existing DBMSs to not only avoid expensive data copies to external ML tools but also to comply with regulatory reasons. The key observation is that database transactions already provide an execution model that allows DBMSs to efficiently mimic the execution model of modern parallel ML algorithms. As a main contribution, this paper presents DB4ML, an in-memory database kernel that allows applications to implement user-defined ML algorithms and efficiently run them inside a DBMS. Thereby, the ML algorithms are implemented using a programming model based on the idea of so called iterative transactions. Our experimental evaluation shows that DB4ML can support user-defined ML algorithms inside a DBMS with the efficiency of modern specialized ML engines. In contrast to DB4ML, these engines not only need to transfer data out of the DBMS but also hardcode the ML algorithms and thus are not extensible