Data Management Lab

Abstract:

We propose Multi-Version Partitioned BTrees (MV- PBT) as sole storage and index management structure in key-sorted storage engines like Key/Value-Stores. Secondly, we compare MV-PBT against LSM-Trees. We demonstrate up to 2x better steady throughput over LSM-Trees and several orders of magnitude in over B+-Trees in a YCSB workload. Moreover, MV-PBT exhibits robust time-travel query performance and outperforms LSM-Trees by 20% and B+-Trees by an order of magnitude.

Abstract:

This paper introduces DANSEN, the hardware accelerator component for neoDBMS, a full-stack computational storage system designed to manage on-device execution of database queries/transactions as a Near-Data Processing (NDP)-operation. The proposed system enables Database Management Systems (DBMS) to oload NDP-operations to the storage while maintaining control over data through a native storage interface. DANSEN provides an NDP-engine that enables DBMS to perform both low-level database tasks, such as performing database administration, as well as high-level tasks like executing SQL, on the smart storage device while observing the DBMS concurrency control. Furthermore, DANSEN enables the incorporation of custom accelerators as an NDP-operation, e.g., to perform hardware-accelerated ML inference directly on the stored data. We built the DANSEN storage prototype and interface on an Ultrascale+HBM FPGA and fully integrated it with PostgreSQL. Experimental results demonstrate that the proposed NDP approach outperforms software-only PostgreSQL using a fast of-the-shelf NVMe drive, and signiicantly improves the end-to-end execution time of an aggregation operation by 10.6x. The versatility of the proposed approach is also validated by integrating a compute-intensive data analytics application with multi-row results, outperforming PostgreSQL by 1.5x.