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Knuckles: bringing the database to the data

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2010年第3-4期5卷 214-225页

作者： Alvaro, Peter Ryaboy, Dmitriy V. Agrawal, Divyakant Ask Com 555 12th StSuite 500 Oakland CA 94607 USA

Click-stream data warehousing has emerged as a monumental information management and processing challenge for commercial enterprises. Traditional solutions based on commercial DBMS technology often suffer from poor scalability and large processing latencies. Although click-stream data tends to be collected in a distributed manner to support scaling the servers that host the websites, in general these partitioned click-stream logs are collated and pushed upstream to a centralised database storage repository, creating storage bottlenecks. In this paper, we propose a design of an ad-hoc retrieval system suitable for click-stream data warehouses, in which the data remains distributed and database queries are rewritten to be executed against the distributed data. The query rewrite does not involve any centralised control and is therefore highly scalable. The elimination of centralised control is achieved by supporting a restricted subset of SQL, which is sufficient for most click-stream data analysis. Evaluations conducted using both synthetic and real data establish the viability of this approach.

关键词： distributed databases parallel query processing peer-to-peer computing data partitioning

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Distributed query processing on the grid

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2003年第4期17卷 353-367页

作者： Smith, J Gounaris, A Watson, P Paton, NW Fernandes, AAA Sakellariou, R Newcastle Univ Dept Comp Sci EPSRC Project High Performance Query Proc Grid Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Manchester Dept Comp Sci Manchester M13 9PL Lancs England

Distributed query processing (DQP) has been widely used in data intensive applications where data of relevance to users are stored at multiple locations. This paper argues: (i) that DQP can be important in the Grid, as a means of providing high-level, declarative languages for integrating data access and analysis;and (ii) that the Grid provides resource management facilities that are useful to developers of DQP systems. As well as discussing and illustrating how DQP technologies can be deployed within the Grid, the paper describes Polar*, a prototype implementation of a DQP system running over Globus. Polar* can handle complex data by adopting the ODMG object model and its query language OQL, which supports the invocation of user-defined operations. The Globus components are accessed through the MPICH-G interface rather than in a lower level way. A case study from bioinformatics is used throughout the paper, to show the benefits of the approach.

关键词： grid computing distributed query processing parallel query processing user-defined operation globus MPI ODMG

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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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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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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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Distributed query processing on the grid

Distributed query processing on the grid

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3rd International Workshop on Grid Computing

关键词： grid computing distributed query processing parallel query processing user-defined operation globus MPI ODMG

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Characterizing the Scalability and Performance of Analytic Database System 9th

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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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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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GUFI: Fast, Secure File System Metadata Search for Both Privileged and Unprivileged Users

GUFI: Fast, Secure File System Metadata Search for Both Priv...

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International Conference for High Performance Computing, Networking, Storage and Analysis (HPC)

作者： Manno, Dominic Lee, Jason Challa, Prajwal Zheng, Qing Bonnie, David Grider, Gary Settlemyer, Bradley Los Alamos Natl Lab Los Alamos NM 87545 USA Univ Texas Arlington Arlington TX USA NVIDIA Austin TX USA

ISBN: (纸本)9781665454445

Modern High-Performance Computing (HPC) data centers routinely store massive data sets resulting in millions of directories and billions of files. To efficiently search and sift through these files and directories we present the Grand Unified File Index (GUFI), a novel file system metadata index that enables both privileged and regular users to rapidly locate and characterize data sets of interest. GUFI uses a hierarchical index that preserves file access permissions such that the index can be securely accessed by users while still enabling efficient, advanced analysis of storage system usage by cluster administrators. Compared with the current state-of-the-art indexing for file system metadata, GUFI is able to provide speedups of 1.5x to 230x for queries executed by administrators on a real production file system namespace. Queries executed by users, which typically cannot rely on cluster-wide indexing, see even greater speedups using GUFI.

关键词： parallel query processing file system metadata management file system access control index sharding

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On Efficient Posting List Intersection with Multicore Processors

On Efficient Posting List Intersection with Multicore Proces...

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32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Tatikonda, Shirish Junqueira, Flavio Barla Cambazoglu, B. Plachouras, Vassilis Ohio State Univ Columbus OH 43210 USA

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