News & Events
DB Seminar [Spring 2020] : Zero-Overhead Deterministic C++ Exceptions (“Herb Exceptions”)
In this talk, Rohan Aggarwal will present a new proposal to the C++ standard for zero-overhead exceptions. A fundamental reason why C++ is successful and loved is its adherence to Stroustrup’s zero-overhead principle: You don’t pay for what you don’t use, and if you do use a feature you can’t reasonably code it better by hand. In the C++ language itself, there are only two features that violate the zero-overhead principle, exception handling and RTTI – and, unsurprisingly, these are Read More
DB Seminar [Spring 2020] : Compiling PL/SQL Away
In this talk, Tanuj Nayak will present Compiling PL/SQL Away from CIDR 2020. This paper details a method of overcoming current overheads of PL/SQL interpretation by compiling it to SQL Common Table Expressions (CTE) using the WITH RECURSIVE construct. Read More
DB Seminar [Spring 2020] : sled and rio – modern database engineering with io_uring
sled is an embedded database that takes advantage of modern lock-free indexing and flash-friendly storage. rio is a pure-rust io_uring library unlocking the linux kernel's new asynchronous IO interface. This short talk will cover techniques that have been used to take advantage of modern hardware and kernels while optimizing for long term developer happiness in a complex, correctness-critical Rust codebase. https://fosdem.org/2020/schedule/event/rust_techniques_sled/ Read More
[Spring 2020] Aurosish Mishra (Oracle)
Oracle Database In-Memory - At the heart of multi-model Convergence Aurosish’s team is responsible for all aspects of the data engine for the Oracle Database, ranging from traditional disk-based data accesses and indexing technologies to state-of-the-art distributed in-memory technologies in Oracle's flagship Database In-Memory engine that provides real time analytics at the speed of thought! Currently, they are building Oracle's next-gen cloud-scale distributed data engine that is absolutely available, by exploiting innovative and emerging technologies like Persistent Memory(PMEM) storage, RDMA Read More
[Spring 2020] Carlo Curino (Microsoft)
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, auto-tuning for videoconferencing, intelligent feedback loops in large-scale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few. Meanwhile, as the value of data is increasingly recognized and monetized, concerns about securing valuable data and risks to individual Read More
DB Seminar [Spring 2020] : Time travel with rr, Mozilla’s lightweight record-and-replay debugger
Ever tried reproducing intermittent failures by rerunning a test 100 times, or stared at core dumps where a variable was obviously wrong but you couldn't figure out why? Mozilla's rr records test execution with minimal overhead, and provides you with compact and deterministic execution traces that you can replay forwards and backwards in time. In this talk, we'll focus on how to use rr to simplify your debugging life in practice, briefly describe how it works under the hood and Read More
[Spring 2020] DBMS Project Technical Overview
Matt Butrovich will present a technical overview of CMU's new DBMS project. This is for Advanced Database Systems (15-721) course. Read More
[DB Seminar] Spring 2020 DB Group: OtterTune
In this talk Dana will provide an update on running OtterTune at SocGen. Or, how OtterTune will take over the world. Read More
Master Thesis Talk: Replicated Training in Self-Driving Database Management Systems
Self-driving database management systems (DBMSs) are a new family of DBMSs that can optimize themselves for better performance without human intervention. Self-driving DBMSs use machine learning (ML) models that predict system behaviors and make planning decisions based on the workload the system sees. These ML models are trained using metrics produced by different components running inside the system. Self-driving DBMSs are a challenging environment for these models that require a significant amount of training data that must be representative of Read More