neoDBMS: In-situ Snapshots for Multi-Version DBMS on Native Computational Storage

We study the problem of temporal database indexing, i.e., indexing versions of a database table in an evolving database. With the larger and cheaper memory chips nowadays, we can afford to keep track of all versions of an evolving table in memory. This raises the question of how to index such a table effectively. We depart from the classic indexing approach, where both current (i.e., live) and past (i.e., dead) data versions are indexed in the same data structure, and propose LIT, a hybrid index, which decouples the management of the current and past states of the indexed column. LIT includes optimized indexing modules for dead and live records, which support efficient queries and updates, and gracefully combines them. We experimentally show that LIT is orders of magnitude faster than the state-of-the-art temporal indices. Furthermore, we demonstrate that LIT uses linear space to the number of record indexed versions, making it suitable for main-memory temporal data management.

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Near-data processing in database systems on native computational storage under HTAP workloads

Vinçon

Knödler

Solis-Vasquez

et al. 2022

Proc. VLDB Endow.

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Today's Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-growing data in combination with a mixture of transactional and analytical workloads. While optimizing for aspects such as data freshness and performance isolation, they build on the traditional data-to-code principle and may trigger massive cold data transfers that impair the overall performance and scalability. Firstly, in this paper we show that Near-Data Processing (NDP) naturally fits in the HTAP design space. Secondly, we propose an NDP database architecture, allowing transactionally consistent in-situ executions of analytical operations in HTAP settings. We evaluate the proposed architecture in state-of-the-art key/value-stores and multi-versioned DBMS. In contrast to traditional setups, our approach yields robust, resource- and cost-efficient performance.

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neoDBMS: In-situ Snapshots for Multi-Version DBMS on Native Computational Storage

Cited by 3 publications

References 7 publications

IPEC: Open-Source Design Automation for Inter-Processing Element Communication

IPEC: Open-Source Design Automation for Inter-Processing Element Communication

LIT: Lightning-fast In-memory Temporal Indexing

Near-data processing in database systems on native computational storage under HTAP workloads

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