Season 1
Matthias Jasny | P4DB - The Case for In-Network OLTP | #10
Summary: In this episode Matthias Jasny from TU Darmstadt talks about P4DB, a database that uses a programmable switch to accelerate OLTP workloads. The main idea of P4DB is that it implements a transaction processing engine on top of a P4-programmable switch. The switch can thus act as an accelera...
Tobias Ziegler | ScaleStore: A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA | #9
Summary: In this episode Tobias talks about his work on ScaleStore, a distributed storage engine that exploits DRAM caching, NVMe storage, and RDMA networking to achieve high performance, cost-efficiency, and scalability. Using low latency RDMA messages, ScaleStore implements a transparent me...
Chuzhe Tang | Ad Hoc Transactions in Web Applications: The Good, the Bad, and the Ugly | #8
Summary: Many transactions in web applications are constructed ad-hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical code fragments. In this episode, Chuzhe tells us these ad-hoc transactions, database operati...
Michael Abebe | Proteus: Autonomous Adaptive Storage for Mixed Workloads | #7
Summary: Enterprises use distributed database systems to meet the demands of mixed or hybrid transaction/analytical processing (HTAP) workloads that contain both transactional (OLTP) and analytical (OLAP) requests. Distributed HTAP systems typically maintain a complete copy of data in row-oriented ...
Hani Al-Sayeh | Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications | #6
Summary: Distributed in-memory processing frameworks accelerate iterative workloads by caching suitable datasets in memory rather than recomputing them in each iteration. Selecting appropriate datasets to cache as well as allocating a suitable cluster configuration for caching these datasets play a...