Neither traditional disk-based database systems nor modern inmemory database systems are capable of fully exploiting modern servers with multiple NVMe SSDs. LeanStore is a high-performance OLTP storage engine specifically optimized for NVMe SSDs and ...

Vector databases have recently emerged as a hot topic in the field of databases, especially in industry. This is due to the widespread interest in Large Language Models (LLMs), where vector databases provide the relevant context for LLMs to produce more ...

In recent years at Tencent, we have observed that the use of multiple NoSQL databases for storing business data with diverse models has led to increased programming and deployment costs, as well as inefficient maintenance and underutilized resources. In ...

Distributed databases have become indispensable in contemporary computing and data processing, owing to their pivotal role in ensuring high availability and scalability. They effectively cater to the requirements of data management and high-concurrency ...

Modern database systems including IBM Db2 have numerous parameters, "knobs," that require precise configuration to achieve optimal workload performance. Even for experts, manually "tuning" these knobs is a challenging process. We present Db2une, an ...

CXL-based disaggregated memory systems offer options to expand the memory beyond the limits of a single server via cache-coherent memory expansion cards or memory pools. Especially, In-Memory Database Management Systems (IMDBMSs) can benefit from ...

At ByteDance, where we execute over a million Spark jobs and handle 500PB of shuffled data daily, ensuring resource efficiency is paramount for cost savings. However, achieving optimization of resource efficiency in large-scale production environments ...

Chiplet-based CPUs, which combine multiple independent dies on a single package, allow hardware to scale to higher CPU core counts at the cost of more memory heterogeneity and performance variability. This introduces challenges when existing query ...

Existing machine learning (ML) approaches to automatically optimize database management systems (DBMSs) only target a single configuration space at a time (e.g., knobs, query hints, indexes). Simultaneously tuning multiple configuration spaces is ...

In this paper, we propose nsDB, a novel neuro-symbolic database system that integrates neural and symbolic system architectures natively to address the weaknesses of each, providing a strong database capable of data managing, model learning, and complex ...

Distributed in-memory processing frameworks accelerate application runs by caching important datasets in memory. Allocating a suitable cluster configuration for caching these datasets plays a crucial role in achieving minimal cost. We present Agile-ant, ...

Emerging NUMA/CXL-based tiered memory systems with heterogeneous memory devices such as DRAM and NVMM deliver ultrafast speed, large capacity, and data persistence all at once, offering great promise to high-performance in-memory key-value stores. To ...

GPUs offer massive parallelism and high-bandwidth memory access, making them an attractive option for accelerating data analytics in database systems. However, while modern GPUs possess more resources than ever before (e.g., higher DRAM bandwidth), ...

Many IoT applications require the use of multiple deep neural networks (DNNs) to perform various tasks on low-cost edge devices with limited computation resources. However, existing DNN model serving platforms, such as TensorFlow Serving and TorchServe, ...

In-memory key-value cache systems, such as Memcached and Redis, are essential in today's data centers. A key mission of such cache systems is to identify the most valuable data for caching. To achieve this, the current system design keeps track of each ...

A widely used approach to characterize input data in both databases and ML is computing the correlation between attributes. The operation is supported by all major database engines and ML platforms. However, it is an expensive operation as the number of ...

GPU-accelerated database systems have been studied for more than 10 years, ranging from prototyping development to industry products serving in multiple domains of data applications. Existing GPU database research solutions are often focused on specific ...

With the end of Moore's Law, database architects are turning to hardware accelerators to offload computationally intensive tasks from the CPU. In this paper, we show that accelerators can facilitate far more than just computation: they enable algorithms ...

Recently proposed voice query interfaces translate voice input into SQL queries. Unreliable speech recognition on top of the intrinsic challenges of text-to-SQL translation makes it hard to reliably interpret user input. We present MUVE (Multiplots for ...

Data analysts often need to characterize a data stream as a first step to its further processing. Some of the initial insights to be gained include, e.g., the cardinality of the data set and its frequency distribution. Such information is typically ...