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Characterizing the Scalability and Performance of Analytic Database System 9th

9th International Conference on Security, Privacy, and Anonymity in Computation, Communication and Storage (SpaCCS)

作者： Li, Yishui Li, Hui Chen, Mei Dai, Zhenyu Zhu, Ming Guizhou Univ Guizhou Engn Lab ACMIS Guiyang 550025 Peoples R China Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Peoples R China Chinese Acad Sci Natl Astron Observ Beijing 100016 Peoples R China

ISBN: (纸本)9783319491455;9783319491448

Analytic database is designed for analytical applications which aim to explore the value of massive data. It has been widely used in many areas, from business analytics to scientific data discovery. In order to efficiently processing massive data, analytic database often can be scaled-out to achieve high performance. In this paper, in order to understand the intrinsic performance characteristics of analytic database, we take the popular Greenplum database as the representative analytic database system, and conduct a series of comprehensive performance evaluation over it to characterize its scalability and performance features. According to the experimental results and analysis, we obtained an series of initial insights of the factors which may significantly affect the performance paradigm of Greenplum, which will be helpful for analytic database system users to obtain better analytical performance.

关键词： Analytic database Scalability Data loading parallel query processing Performance evaluation

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Join Decomposition Based on Fragmented Column Indices

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2016年第3期37卷 255-260页

作者： Ivanova, E. Sokolinsky, L. B. Natl Res Univ South Ural State Univ Prospekt Lenina 76 Chelyabinsk 454080 Russia

The paper is devoted to the issue of decomposition of the join relational operator with the aid of distributed column indices. Such decomposition allows one to utilize the modern manycore accelerators (GPU or Intel Xeon Phi) to speed up the query execution for very large databases. Column indices are the new kind of index structures, which exploits "key-value" technics. The paper describes the methods of column index fragmentation based on domain intervals. This technic allows organizing the parallel query processing without exchanges. All column index fragments are stored in main memory in compressed form to conserve space. This approach can be implemented as a coprocessor for relational database systems. The database coprocessor is able to perform resource-intensive operations much more faster than a conventional DBMS.

关键词： Very large databases parallel query processing column indices domain-interval fragmentation

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A New Middleware for Distributed Data processing in CUBRID DBMS 6th

A New Middleware for Distributed Data Processing in CUBRID D...

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6th International Conference on Ambient Systems, Networks and Technologies (ANT) / 5th International Conference on Sustainable Energy Information Technology (SEIT)

作者： Kim, Hyeong-Il Yoon, Min Choi, MunChul Chang, Jae-Woo Chonbuk Natl Univ Jeonju 561756 South Korea

With the sharply increasing amount of data, studies on bigdata processing based on NoSQL have been actively done. However. NoSQL cannot satisfy the ACID properties of database transactions. Therefore, bigdata processing based on RDBMS has been spotlighted. CUBRID Shard stores data in the distributed CUBRID servers by dividing the database. However, CUBRID Shard cannot process a query when data of a user is distributed on the multiple CUBRID servers. Therefore, in this paper we propose a CUBRID based middleware which supports distributed data processing. Through the performance evaluations, we show that our proposed scheme shows better performance than the existing work in terms of query processing time. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (fthp://***/by-nc-nd/4.0/). Peer-review under responsibility of the Conference Program Chairs

关键词： Middleware distributed environment parallel query processing CUBRID

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Algebricks: A Data Model-Agnostic Compiler Backend for Big Data Languages 6

Algebricks: A Data Model-Agnostic Compiler Backend for Big D...

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Conference on 6th ACM Symposium on Cloud Computing (SoCC 2015)

作者： Borkar, Vinayak Bu, Yingyi Carman, E. Preston, Jr. Onose, Nicola Westmann, Till Pirzadeh, Pouria Carey, Michael J. Tsotras, Vassilis J. Univ Calif Irvine Irvine CA USA X15 Software Inc Sunnyvale CA 94086 USA Univ Calif Riverside Riverside CA 92521 USA Oracle Labs Burlington MA USA

ISBN: (纸本)9781450336512

A number of high-level query languages, such as Hive, Pig, Flume, and Jaql, have been developed in recent years to increase analyst productivity when processing and analyzing very large datasets. The implementation of each of these languages includes a complete, data model-dependent query compiler, yet each involves a number of similar optimizations. In this work, we describe a new query compiler architecture that separates language-specific and data model-dependent aspects from a more general query compiler backend that can generate executable data-parallel programs for shared-nothing clusters and can be used to develop multiple languages with different data models. We have built such a data model-agnostic query compiler substrate, called Algebricks, and have used it to implement three different query languages - HiveQL, AQL, and Xquery - to validate the efficacy of this approach. Experiments show that all three query languages benefit from the parallelization and optimization that Algebricks provides and thus have good parallel speedup and scaleup characteristics for large datasets.

关键词： Big Data query Languages parallel query processing

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A New Middleware for Distributed Data processing in CUBRID DBMS

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Procedia Computer Science 2015年 52卷 654-658页

作者： Hyeong-Il Kim Min Yoon MunChul Choi Jae-Woo Chang Chonbuk National University 567 Baekju-daero deokjin-gu 561-756 Jeonju City Korea

With the sharply increasing amount of data, studies on bigdata processing based on NoSQL have been actively done. However, NoSQL cannot satisfy the ACID properties of database transactions. Therefore, bigdata processing based on RDBMS has been spotlighted. CUBRID Shard stores data in the distributed CUBRID servers by dividing the database. However, CUBRID Shard cannot process a query when data of a user is distributed on the multiple CUBRID servers. Therefore, in this paper we propose a CUBRID based middleware which supports distributed data processing. Through the performance evaluations, we show that our proposed scheme shows better performance than the existing work in terms of query processing time.

关键词： Middleware distributed environment parallel query processing CUBRID

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parallel query Evaluation as a Scientific Data Service 16

Parallel Query Evaluation as a Scientific Data Service

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16th IEEE International Conference on Cluster Computing (CLUSTER)

作者： Doug, Bin Byna, Surendra Wu, Kesheng Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9781479955480

Scientific experiments and simulations produce mountains of data in file formats, such as HDF5, NetCDF, and FITS. Often, a relatively small amount of data holds the key to new scientific insight. Locating that critical information in these large files is challenging because existing solutions need significant user involvement in preparing the data, generating indexes, and answering queries. Data management systems that support querying, such as SciDB, require a costly process of loading data from scientific data formats to these systems. The search results also need to be converted back to a format needed by the subsequent data analysis and visualization tools. These steps are time-consuming, tedious, and possibly error-prone. Toward providing efficient data management directly on these scientific file formats, we introduce a framework called Scientific Data Services (SDS). SDS targets to provide efficient data management optimizations as services. In this paper, we introduce the design and implementation of one such service, the parallel querying service. To answer the queries efficiently, we transparently augment user data with bitmap indexes and ordered datasets. We design the querying service to manage these augmented datasets and to redirect queries automatically to bitmap indexes or to ordered datasets based on their availability and the expected query response time. The generation of bitmap indexes and sorted datasets and querying are parallelized to work on large supercomputers. We show that SDS achieves 22X, 55X, and 62X speedups compared to conventional full-scan approach of sifting through data in answering three queries from a plasma physics analysis application.

关键词： Scientific Data Services parallel query processing

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parallel query processing in databases on multicore architectures

Parallel query processing in databases on multicore architec...

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8th International Conference on Algorithms and Architectures for parallel processing

作者： Acker, Ralph Roth, Christian Bayer, Rudolf Transact Software Willy Brandt Allee 2 D-81829 Munich Germany Tech Univ Munich Inst Informat D-85747 Garching Germany

ISBN: (纸本)9783540695004

In this paper we present a novel and complete approach on how to encapsulate parallelism for relational database query execution that strives for maximum resource utilization for both CPU and disk activities. Its simple and robust design is capable of modeling intra- and inter-operator parallelism for one or more parallel queries in a most natural way. In addition, encapsulation guarantees that the bulk of relational operators can remain unmodified, as long as their implementation is thread-safe. We will show, that with this approach, the problem of scheduling parallel tasks is generalized, so that it can be safely entrusted to the underlying operating system (OS) without suffering any performance penalties. On the contrary, relocation of all scheduling decisions from the DBMS to the OS guarantees a centralized and therefore near-optimal resource allocation (depending on the OS's abilities) for the complete system that is hosting the database server as one of its tasks. Moreover, with this proposal, query parallelization is fully transparent on the SQL interface of the database system. Configuration of the system for effective parallel query execution can be adjusted by the DB administrator by setting two descriptive tuning parameters. A prototype implementation has been integrated into the Transbase (R) relational DBMS engine.

关键词： relational DBMS parallel query processing encapsulation intraoperator inter-operator scheduling optimization

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Efficient parallel processing of Analytical Queries on Linked Data 1

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On The Move (OTM) Federated International Conference

作者： Hagedorn, Stefan Sattler, Kai-Uwe Ilmenau Univ Technol Ilmenau Germany

ISBN: (数字)9783642410307

ISBN: (纸本)9783642410291;9783642410307

Linked data has become one of the most successful movements of the Semantic Web community. RDF and SPARQL have been established as de-facto standards for representing and querying linked data and there exists quite a number of RDF stores and SPARQL engines that can be used to work with the data. However, for many types of queries on linked data these stores are not the best choice regarding query execution times. For example, users are interested in analytical tasks such as profiling or finding correlated entities in their datasets. In this paper we argue that currently available RDF stores are not optimal for such scan-intensive tasks. In order to address this issue, we discuss query evaluation techniques for linked data exploiting the features of modern hardware architectures such as big memory and multi-core processors. Particularly, we describe parallelization techniques as part of our CameLOD system. Furthermore, we compare our system with the well-known linked data stores Virtuoso and RDF-3X by running different analytical queries on the DBpedia dataset and show that we can outperform these systems significantly.

关键词： linked data parallel query processing micro benchmark

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Integrating open government data with stratosphere for more transparency

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JOURNAL OF WEB SEMANTICS 2012年 14卷 45-56页

作者： Heise, Arvid Naumann, Felix Hasso Plattner Inst Potsdam Germany

Governments are increasingly publishing their data to enable organizations and citizens to browse and analyze the data. However, the heterogeneity of this Open Government Data hinders meaningful search, analysis, and integration and thus limits the desired transparency. In this article, we present the newly developed data integration operators of the Stratosphere parallel data analysis framework to overcome the heterogeneity. With declaratively specified queries, we demonstrate the integration of well-known government data sources and other large open data sets at technical, structural, and semantic levels. Furthermore, we publish the integrated data on the Web in a form that enables users to discover relationships between persons, government agencies, funds, and companies. The evaluation shows that linking person entities of different data sets results in a good precision of 98.3% and a recall of 95.2%. Moreover, the integration of large data sets scales well on up to eight machines. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Data integration Data cleansing Record linkage Data fusion parallel query processing Map-reduce

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Partial Evaluation for Distributed XPath query processing and Beyond

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2012年第4期37卷 32-32页

作者： Cong, Gao Fan, Wenfei Kementsietsidis, Anastasios Li, Jianzhong Liu, Xianmin Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland IBM TJ Watson Res Ctr Hawthorne NY 10532 USA Harbin Inst Technol Harbin 150001 Peoples R China

This article proposes algorithms for evaluating XPath queries over an XML tree that is partitioned horizontally and vertically, and is distributed across a number of sites. The key idea is based on partial evaluation: it is to send the whole query to each site that partially evaluates the query, in parallel, and sends the results as compact (Boolean) functions to a coordinator that combines these to obtain the result. This approach possesses the following performance guarantees. First, each site is visited at most twice for data-selecting XPath queries, and only once for Boolean XPath queries. Second, the network traffic is determined by the answer to the query, rather than the size of the tree. Third, the total computation is comparable to that of centralized algorithms on the tree stored in a single site, regardless of how the tree is fragmented and distributed. We also present a MapReduce algorithm for evaluating Boolean XPath queries, based on partial evaluation. In addition, we provide algorithms to evaluate XPath queries on very large XML trees, in a centralized setting. We show both analytically and empirically that our techniques are scalable with large trees and complex XPath queries. These results, we believe, illustrate the usefulness and potential of partial evaluation in distributed systems as well as centralized XML stores for evaluating XPath queries and beyond.

关键词： Algorithms Performance Distributed XML documents XPath queries parallel query processing

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