Proceedings of the Workshop on Testing Database Systems最新文献_第2页

Proceedings of the Workshop on Testing Database Systems 数据库系统测试研讨会论文集

Proceedings of the Workshop on Testing Database Systems Pub Date : 2018-06-15 DOI: 10.1145/3209950

引用次数: 0

Performance of Containerized Database Management Systems 容器化数据库管理系统的性能

Proceedings of the Workshop on Testing Database Systems Pub Date : 2018-06-15 DOI: 10.1145/3209950.3209953

Kim-Thomas Rehmann, E. Folkerts

引用次数: 3

Make Larger Vector Register Sizes New Challenges?: Lessons Learned from the Area of Vectorized Lightweight Compression Algorithms 使更大的矢量寄存器尺寸成为新挑战?:从矢量化轻量级压缩算法领域获得的经验教训

Proceedings of the Workshop on Testing Database Systems Pub Date : 2018-06-15 DOI: 10.1145/3209950.3209957

Dirk Habich, Patrick Damme, A. Ungethüm, Wolfgang Lehner

{"title":"Make Larger Vector Register Sizes New Challenges?: Lessons Learned from the Area of Vectorized Lightweight Compression Algorithms","authors":"Dirk Habich, Patrick Damme, A. Ungethüm, Wolfgang Lehner","doi":"10.1145/3209950.3209957","DOIUrl":"https://doi.org/10.1145/3209950.3209957","url":null,"abstract":"The exploitation of data as well as hardware properties is a core aspect for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, in-memory data processing also benefits from novel processing concepts based on lightweight compressed data. To speed up compression as well as decompression, an active research field deals with the specialization of these algorithms to hardware features such as vectorization using SIMD instructions. Most of the vectorized implementations have been proposed for 128 bit vector registers. However, hardware vendors still increase the vector register sizes, whereby a straightforward transformation to these wider vector sizes is possible in most-cases. Thus, we systematically investigated the impact of different SIMD instruction set extensions with wider vector sizes on the behavior of straightforward transformed implementations. In this paper, we will describe our evaluation methodology and present selective results of our exhaustive evaluation. In particular, we will highlight some challenges and present first approaches to tackle them.","PeriodicalId":436501,"journal":{"name":"Proceedings of the Workshop on Testing Database Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126302433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Adding Velocity to BigBench 增加BigBench的速度

Proceedings of the Workshop on Testing Database Systems Pub Date : 2018-06-15 DOI: 10.1145/3209950.3209956

Todor Ivanov, Patrick Bedué, A. Ghazal, R. Zicari

{"title":"Adding Velocity to BigBench","authors":"Todor Ivanov, Patrick Bedué, A. Ghazal, R. Zicari","doi":"10.1145/3209950.3209956","DOIUrl":"https://doi.org/10.1145/3209950.3209956","url":null,"abstract":"BigBench standardized as TPCx-BB is a popular application benchmark that targets Big Data storage and processing systems. BigBench V2 addresses some of the BigBench limitations by introducing a new simplified data model, semi-structured web logs in JSON file format and new queries mandating late binding. However, it still covers only batch processing workloads and the Big Data velocity characteristic is not addressed. This work extends the BigBench V2 benchmark with a data streaming component that simulates typical statistical and predictive analytics queries in a retail business scenario. Our approach is to preserve the existing BigBench design and introduce a new streaming component that supports two data streaming modes: active and passive. In active mode, the data stream generation and processing happen in parallel, whereas in passive mode, the data stream is pre-generated in advance before the actual stream processing. The stream workload consists of five queries inspired by the existing 30 BigBench queries. To validate the proposed streaming extension, the two streaming modes were implemented and tested using Kafka and Spark Streaming. The experimental results prove the feasibility of our benchmark design. Finally, we outline design challenges and future plans for improving the proposed BigBench extension.","PeriodicalId":436501,"journal":{"name":"Proceedings of the Workshop on Testing Database Systems","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126987453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Fair Benchmarking Considered Difficult: Common Pitfalls In Database Performance Testing 公平基准测试被认为是困难的:数据库性能测试中的常见缺陷

Proceedings of the Workshop on Testing Database Systems Pub Date : 2018-06-15 DOI: 10.1145/3209950.3209955

Mark Raasveldt, Pedro Holanda, Tim Gubner, H. Mühleisen

引用次数: 21

Generating Evolving Property Graphs with Attribute-Aware Preferential Attachment 基于属性感知优先依附的演化属性图生成

Proceedings of the Workshop on Testing Database Systems Pub Date : 2018-06-15 DOI: 10.1145/3209950.3209954

A. Aghasadeghi, Julia Stoyanovich

引用次数: 2