Experimental Evaluation of NUMA Effects on Database Management Systems
| Citations: | 2 - 0 self |
BibTeX
@MISC{Kiefer_experimentalevaluation,
author = {Tim Kiefer and Benjamin Schlegel and Wolfgang Lehner and Technische Universität Dresden},
title = {Experimental Evaluation of NUMA Effects on Database Management Systems},
year = {}
}
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Abstract
Abstract: NUMA systems with multiple CPUs and large main memories are common today. Consequently, database management systems (DBMSs) in data centers are deployed onNUMA systems. They serve awide range of database use-cases, single large applications having high performance needs as well as many small applications that are consolidated on one machine to save resources and increase utilization. Database servers often show anatural partitioning in the data that is accessed, e.g., caused by multiple applications accessing only their data. Knowledge about these partitions can be used to allocate adatabase’s memory on the different nodes accordingly: astrategy that increases memory locality and reduces expensive communication between CPUs. In this work, we show that partitioning adatabase’s memory with respect to the data’s access patterns can improve the query performance by as much as 75%. The allocation strategy is enabled by knowledge that is available only inside the DBMS. Additionally, weshow that grouping database worker threads on CPUs, based on their data partitions, improves cache behavior, whichinturn improvesquery performance. We use aself-developed synthetic, low-level benchmark as well as areal database benchmark executed on the MySQL DBMS to verify our hypotheses. We also give anoutlook on how our findings can be used to improve future DBMS performance on NUMA systems. 1
Keyphrases
database management system experimental evaluation numa effect adatabase memory numa system reduces expensive communication multiple cpu onnuma system multiple application high performance need many small application future dbms performance areal database benchmark different node large main memory cache behavior memory locality anatural partitioning mysql dbms database worker thread low-level benchmark improvesquery performance allocation strategy single large application common today data partition database use-cases awide range query performance data center database server data access pattern
