[Fall 2023] New Semester Research Meeting
Carnegie Mellon University's renowned Database Group is thrilled to announce the kickoff of its Fall 2023 semester with a highly anticipated meeting on Thursday, August 31st. This gathering promises to be an extraordinary occasion as the members embark on a vainglorious retrospective, celebrating the group's remarkable research achievements over the years, as well as to begin discussions on new projects. As pioneers in the field of database systems and data management, the Database Group at Carnegie Mellon University has consistently... Read More
On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments (Matt Butrovich)
The rise in computer storage and network performance means that disk I/O and network communication are often no longer bottlenecks in database management systems (DBMSs). Instead, the overheads associated with operating system (OS) services (e.g., system calls, thread scheduling, and data movement from kernel- space) limit query processing responsiveness. To avoid these overheads, user-space applications prioritizing performance over simplicity can elide these software layers with a kernel-bypass design. However, extracting benefits from kernel-bypass frameworks is challenging, and the libraries are... Read More
[Summer 2023] Towards a Hardware-software Approach for High-performance Databases (Jignesh Patel)
In various industries, in-database analytics are crucial for decision-making. Yet, the growing amount of data presents challenges as traditional methods become excessively time-consuming and costly. Moore's Law and Dennard scaling, which previously aided data-intensive analytics, are reaching their physical limits. A new approach is needed to handle analytics workloads. The speed gap between processing units and memory creates bottlenecks for analytics workloads (the classic von Neumann bottleneck). Our approach involves using “intelligent” memory that can compute results alongside the stored... Read More
[Spring 2023] 15-721 Final Project Presentations
Carnegie Mellon University is thrilled to announce the upcoming final project presentations for the Advanced Database Systems course for the Spring 2023 semester. This eagerly awaited event showcases the hard work and innovation of the university's talented students as they present their cutting-edge projects to an audience of esteemed guests and fellow students. The presentations promise to deliver captivating insights into the future of database systems. CMU's Advanced Database Systems course, renowned for its comprehensive curriculum and focus on industry-relevant... Read More
Return of the Database Machines? Towards a Hardware-Software Approach for High-performance Databases (Jignesh Patel)
Analytic database applications have an insatiable appetite for higher performance. In the past, a large part of this appetite was met by leveraging the gift of Moore’s Law. However, the slowing down of Moore’s Law now requires a new approach. Fortunately, the hardware landscape is currently undergoing a Cambrian explosion of new architectures. In this talk, I will describe how one class of architecture may provide part of the answer to our search for future high-performance database systems. This architecture... Read More
MS Thesis Defense: High Performance DBMS Design for Intelligent Query Scheduling (Deepayan Patra)
Decades of research in the field of database management systems (DBMSs) have focused on improving system performance with impressive results. Modern analytical databases take advantage of innovative methods such as vectorization and compilation to improve single query performance, use supporting data structures such as indexes or views to reduce data access requirements, and support the execution of multiple queries in parallel while maintaining necessary isolation guarantees. We propose a new line of work with workload and architecture-aware scheduling algorithms to... Read More
MS Thesis Defense: Extendable Rule-Based Action Generation for Self-Driving Database Systems (Mike Xu)
Database management systems (DBMSs) have become more complex to meet increasingly demanding usage. To owners and operators, the need for a self-driving DBMS that can automatically tune and optimize itself without human intervention is apparent now more than ever. Such a self-driving DBMS considers a set of candidate actions to apply to reach a configuration that improves performance for a given workload. Furthermore, the DBMS would continuously adjust the configuration in anticipation of changing workloads and data distributions. Efforts to... Read More
[15-445/645] Fall 2022 Live Call-in Q&A Lecture
For the final lecture in CMU's Introduction to Database Systems (Fall 2022) course, we are allowing anyone to call in with their database questions. The lecture will be livestreamed via Youtube and you will be able to ask your questions to Prof. Andy Pavlo directly. Livestream: https://youtu.be/MxOKUt6LeeU Audience Call-in: https://cmu.zoom.us/j/99783788428?pwd=R2ZSd2x0SUFnRlNIak5TVk5ubmFjQT09 (Must have Zoom account) Read More
[¡Databases! 2022] CrateDB: Distributed SQL Database Built on Top of Lucene (Marios Trivyzas)
CrateDB is a SQL, open-source, distributed database that makes storage and analysis of massive amounts of data simple and efficient. It offers effective data handling due to a high degree of scalability and availability, real-time query performance, and extensible data models. In this talk, we will discuss the fundamental concepts of CrateDB and highlight what makes it unique in comparison with other databases. In particular, we will cover topics such as dynamic schemas, Lucene index, distributed query execution, logical replication... Read More
[¡Databases! 2022] SplinterDB: A Key-Value Store for Modern Storage Devices (Alex Conway)
We built SplinterDB to address two trends. The first is the modern storage hardware offers significantly higher bandwidth and lower latency. The second is that modern applications at VMware and elsewhere store fine-grain data, such as metadata. Current state-of-the-art key-value stores such as RocksDB fail to fully exploit the capabilities of these devices on these types of workloads. SplinterDB is designed around a novel data structure, the mapped Bε-tree, which combines ideas from the theory of external memory hash tables... Read More