Deploying Computational Storage for HTAP DBMSs Takes More Than Just Computation Offloading
Authors: 
Kitaek Lee, Insoon Jo, Jaechan Ahn, Hyuk Lee, Hwang Lee, Woong Sul, 
Hyungsoo JungAuthors Info & Claims
Pages 1480 - 1493
Abstract
Hybrid transactional/analytical processing (HTAP) would overload database systems. To alleviate performance interference between transactions and analytics, recent research pursues the potential of in-storage processing (ISP) using commodity computational storage devices (CSDs). However, in-storage query processing faces technical challenges in HTAP environments. Continuously updated data versions pose two hurdles: (1) data items keep changing, and (2) finding visible data versions incurs excessive data access in CSDs. Such access patterns dominate the cost of query processing, which may hinder the active deployment of CSDs.
This paper addresses the core issues by proposing an analytic offload engine (AIDE) that transforms engine-specific query execution logic into vendor-neutral computation through a canonical interface. At the core of AIDE are the canonical representation of vendor-specific data and the separate management of data locators. It enables any CSD to execute vendor-neutral operations on canonical tuples with separate indexes, regardless of host databases. To eliminate excessive data access, we prescreen the indexes before offloading; thus, host-side prescreening can obviate the need for running costly version searching in CSDs and boost analytics. We implemented our prototype for PostgreSQL and MyRocks, demonstrating that AIDE supports efficient ISP for two databases using the same FPGA logic. Evaluation results show that AIDE improves query latency up to 42× on PostgreSQL and 34× on MyRocks.
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Published: 01 February 2023
Published in PVLDB Volume 16, Issue 6
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