@inbook{fdfd0370bc3d4a81bf38966badb7072c,
title = "MemTest: A novel benchmark for in-memory database",
abstract = "With the rapid development of hardware, a stand-alone computer can employ a memory which has large amounts of volumes. Several industries and research institutions have devoted more resources to develop several in-memory databases, which preload the data into memory for further processing. With the boom of in-memory databases, there emerges requirements to evaluate and compare the performance of these systems impartially and objectively. In this paper, we proposed MemTest, a novel benchmark considering the main characteristics of an in-memory database. This benchmark constructs particular metrics, which cover CPU usage, cache miss, compression ratio, minimal memory space and response time of an in-memory database and are also the core of our benchmark. We design a data model based on inter-bank transaction applications, around which a data generator is devised to support the data distributions of uniform and skew. The MemTest workload includes a set of queries and transactions against the metrics and data model. In the end, we illustrate the efficacy of MemTest through implementations on three different in-memory databases.",
keywords = "Benchmark, Finance, In-memory database, Memory",
author = "Qiangqiang Kang and Cheqing Jin and Zhao Zhang and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.",
year = "2014",
doi = "10.1007/978-3-319-13021-7\_3",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "34--46",
editor = "Jianfeng Zhan and Rui Han and Rui Han and Chuliang Weng",
booktitle = "Big Data Benchmarks, Performance Optimization, and Emerging Hardware - 4th and 5th Workshops, BPOE 2014, Revised Selected Papers",
address = "德国",
}