News & Events

[Spring 2024] Towards a Systematic Framework for Index Structure Design (Dong Xie)

Feb 29, 2024

Index structures are at the database management systems' core to facilitate efficient data access. Due to the constant changes in application requirements and hardware trends, people are going through exhaustive and painstaking work designing/tailoring new index structures to catch up. In this talk, I will show a vision of a systematic index structure design framework that will allow index designers to focus on data layout design and query algorithms without worrying about support for other practical features (update and concurrency) Read More

[Spring 2024] Beyond SQL: Dataframes in the Database (Devin Petersohn)

Feb 23, 2024

Dataframes are popular tools for interacting with and exploring data, but they are not as well understood nor as deeply studied as databases. Python's pandas. and Apache Spark are two of the most popular dataframes in use by data practitioners, but even these are extremely different from each other in terms of guarantees and user expectations. In this talk, we will explore these differences and take a deep dive into pandas-like dataframes with a theoretical lens, exploring the dataframe data Read More

[Spring 2024] Embedding Database Logic in the Operating System Is Finally a Good Idea (Matt Butrovich)

Feb 19, 2024

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 incompatible Read More

[Winter 2023] Survey and Evaluation of Database Management System Extensibility (Abi Kim)

Dec 6, 2023

Database management system (DBMS) extensibility is a feature which enables users to extend the DBMS with user software. However, the DBMS extensibility environment is fraught with perils, and DBMS developers have to resort to unspecified methods of developing extensions, including copying core DBMS source code and casing between different versions of the DBMS. Extending a DBMS to support new functionality is challenging due to the tight coupling between the system's internal components. This thesis studies and evaluates the design of Read More

[Fall 2023] Viewing Collaborative Editing Through a Databases Lens (Martin Kleppmann)

Sep 14, 2023

Software that allows several users to collaboratively edit a document, such as Google Docs, has traditionally been ignored by the databases community. This is surprising, because managing the edits to a text document, spreadsheet, vector graphics file, etc. is very much a data management problem, albeit with a data model that is very different from that supported by most databases. Collaboration software needs replication, concurrency control, and data layouts for efficient storage and retrieval. In our work on Automerge, a Read More

On Embedding Database Management System Logic in Operating Systems via Restricted Programming Environments (Matt Butrovich)

Aug 18, 2023

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

ML⇄DB Seminar Series — Fall 2023

Aug 3, 2023

Pittsburgh, PA — The Carnegie Mellon University Database Research Group celebrates the grand convergence of data storage and computational mastery with the ML⇄DB Seminar Series (Machine Learning for Databases + Databases for Machine Learning). Each speaker will present the implementation details of their respective systems and examples of the technical challenges they faced when working with real-world customers. CMU-DB's weekly meetings (Mondays @ 4:30pm EST) are available to the public on Zoom. Non-CMU affiliated members of the general public are Read More