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parallel group-by query processing in a cluster architecture

COMPUTER SYSTEMS SCIENCE AND ENGINEERING 2002年第1期17卷 23-39页

作者： Taniar, D Rahayu, JW Monash Univ Sch Business Syst Clayton Vic 3800 Australia La Trobe Univ Dept Comp Sci & Comp Engn Bundoora Vic 3083 Australia

SQL queries in the real world are replete with group-by clauses. These queries are often used for strategic decision making due to the nature of group-by queries where raw information is grouped according to the designated groups and within each group aggregate functions are normally carried out. In strategic decision making, parallelization of these queries becomes necessary to speed up query response time. The trend in parallel computing is now shifted to cluster architectures. In this paper, we propose a technique for parallelization of group-by queries in cluster architectures. The need for a specialized algorithm on cluster architectures is raised due to the fact that most existing work on parallel group-by query processing is based on a shared-nothing architecture, and furthermore, they cannot be efficiently implemented in cluster platforms.

关键词： group-by queries parallel query processing parallel aggregate query processing parallel databases cluster architectures parallel database architectures

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Extensible parallel query processing for exploratory geoscientific data mining

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DATA MINING AND KNOWLEDGE DISCOVERY 2001年第4期5卷 277-304页

作者： Shek, EC Muntz, RR Mesrobian, E HRL Labs LLC Informat Sci Lab Malibu CA 90265 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Disney Online Burbank CA 91521 USA

Exploratory data mining and analysis requires a computing environment which provides facilities for the user-friendly expression and rapid execution of "scientific queries." In this paper, we address research issues in the parallelization of scientific queries containing complex user-defined operations. In a parallel query execution environment, parallelizing a query execution plan involves determining how input data streams to evaluators implementing logical operations can be divided to be processed by clones of the same evaluator in parallel. We introduced the concept of "relevance window" that characterizes data lineage and data partitioning opportunities available for an user-defined evaluator. In addition, we developed a query parallelization framework by extending relational parallel query optimization algorithms to allow the parallelization characteristics of user-defined evaluators to guide the process of query parallelization in an extensible query processing environment. We demonstrated the utility of our system by performing experiments mining cyclonic activity, blocking events, and the upward wave-energy propagation features from several observational and model simulation datasets.

关键词： parallel query processing extensible user-defined operations geoscientific data mining cyclone blocking events upward wave-energy propagation

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Evaluating top-k queries over web-accessible databases

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2004年第2期29卷 319-362页

作者： Marian, A Bruno, N Gravano, L Columbia Univ Dept Comp Sci 1214 Amsterdam Ave New York NY 10027 USA Microsoft Res Redmond WA USA

A query to a web search engine usually consists of a list of keywords, to which the search engine responds with the best or "top" k pages for the query. This top-k query model is prevalent over multimedia collections in general, but also over plain relational data for certain applications. For example, consider a relation with information on available restaurants, including their location, price range for one diner, and overall food rating. A user who queries such a relation might simply specify the user's location and target price range, and expect in return the best 10 restaurants in terms of some combination of proximity to the user, closeness of match to the target price range, and overall food rating. processing top-k queries efficiently is challenging for a number of reasons. One critical such reason is that, in many web applications, the relation attributes might not be available other than through external web-accessible form interfaces, which we will have to query repeatedly for a potentially large set of candidate objects. In this article, we study how to process top-k queries efficiently in this setting, where the attributes for which users specify target values might be handled by external, autonomous sources with a variety of access interfaces. We present a sequential algorithm for processing such queries, but observe that any sequential top-k query processing strategy is bound to require unnecessarily long query processing times, since web accesses exhibit high and variable latency. Fortunately, web sources can be probed in parallel, and each source can typically process concurrent requests, although sources may impose some restrictions on the type and number of probes that they are willing to accept. We adapt our sequential query processing technique and introduce an efficient algorithm that maximizes source-access parallelism to minimize query response time, while satisfying source-access constraints. We evaluate our techniques experimentally using b

关键词： algorithms measurement performance parallel query processing query optimization top-k query processing web databases

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The impact of load balancing to object-oriented query execution scheduling in parallel machine environment

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INFORMATION SCIENCES 2003年第1-4期157卷 33-71页

作者： Taniar, D Leung, CHC Monash Univ Sch Business Syst Clayton Vic 3800 Australia Victoria Univ Technol Sch Commun & Informat Melbourne Vic 8001 Australia

Skewness has been one of the major problems not only in parallel relational database systems, but also in parallel object-oriented database systems. To improve performance of object-oriented query processing, a careful and intelligent skew handling for load balancing must be established. Depending on the parallel machine environment, whether it is a shared-memory or a shared-nothing architecture, load balancing can be achieved through "physical" or "logical" data re-distribution. It is not the aim of this paper to propose or to investigate skew handling methods, but rather to analyze the impact of load balancing to query execution scheduling strategies. Our analysis shows that when load balancing is achieved, "serial" execution scheduling is preferable to "parallel" execution scheduling strategy. In other words, allocating full resources to a sub-query seems to be better than dividing resources to multiple sub-queries. (C) 2003 Elsevier Inc. All rights reserved.

关键词： query access plans execution scheduling serial and parallel execution scheduling inter/intra-operation parallelization parallel query processing parallel object-oriented databases

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Performance of a distributed architecture for query processing on workstation clusters

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2003年第4期19卷 463-478页

作者： Soleimany, C Dandamudi, SP Carleton Univ Sch Comp Sci Ctr Networked Comp Ottawa ON K1S 5B6 Canada

A network of workstations (NOWs) is an attractive alternative to parallel database systems due to the cost advantage. In a typical database, client workstations (nodes) submit queries/transactions and receive responses from the database server. With even recent PC-based client nodes providing traditional workstation-class performance, performance improvements can be obtained by offloading some of the processing typically done on the traditional server node to these powerful client nodes. parallel query processing takes advantage of the idle cycles on the client nodes to process the query. In this paper we introduce a distributed architecture for parallel query processing and study the performance of this architecture in various scenarios. We have implemented the distributed architecture using parallel virtual machine (PVM) on a Pentium-based NOW system. Our results show that the distributed architecture successfully achieves good speedups and scaleups. Furthermore, our results show that the distributed architecture handles both light and heavy workloads in the presence of a background (non-query) load. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： parallel query processing parallel database systems networks of workstations load sharing workstation clusters

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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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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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High performance parallel query processing on a 100 node ATM connected PC cluster

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1999年第1期E82D卷 54-63页

作者： Tamura, T Oguchi, M Kitsuregawa, M Mitsubishi Elect Corp Informat & Commun Syst Dev Ctr Kamakura Kanagawa 2470056 Japan Univ Tokyo Inst Ind Sci Tokyo 1068558 Japan

We developed a PC cluster system which consists of 100 PCs as a test bed for massively parallel query processing. Each PC employs the 200 MHz Pentium Pro CPU and is connected with others through an ATM switch. Because the query processing applications are insensitive to the communication latency and mainly perform integer operations, the ATM connected PC cluster approach can be considered a reasonable solution for high performance database servers with low costs. However, there has been no challenge to construct large scale PC clusters for database applications, as far as the authors know. Though we employed commodity components as much as possible. we developed the DBMS itself, because that was a key component for obtaining high performance in parallel query processing, and there seemed no system which could meet our demand. On each PC node, a server program which acts as a database kernel is running to process the queries in cooperation with other nodes. The kernel was designed to execute pipelined operators and handle voluminous data efficiently, to achieve high performance on complex decision support type queries. We used the standard benchmark, TPC-D, on a 100 GB database to verify the feasibility of our approach, through comparison of our system with commercial parallel systems. As a whole, our system exhibited sufficiently high performance which was competitive with the current TPC-D top records, in spite of not using indices. For some heavy queries in the benchmark, which have high selectivity and joinability, our system performed much better. In addition, we applied transposed file organization to the database for further performance improvement. The transposed file organization vertically partitions the tuples, enabling attribute-by-attribute access to the relations. This resulted in significant performance improvement by reducing the amount of disk I/O and shifting the bottleneck to computation.

关键词： PC cluster parallel relational database parallel query processing TPC-D benchmark

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parallel sort-merge object-oriented collection join algorithms

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COMPUTER SYSTEMS SCIENCE AND ENGINEERING 2002年第3期17卷 145-158页

作者： Taniar, D Rahayu, JW Monash Univ Sch Business Syst Clayton Vic 3800 Australia La Trobe Univ Dept Comp Sci & Comp Engn Bundoora Vic 3083 Australia

One of the differences between relational and object-oriented databases (OODB) is that attributes in OODB can be of a collection type (e.g. sets, lists, arrays, and bags) as well as a simple type (e.g. integer, string). Consequently, explicit join queries in OODB may be based on collection attributes. We call this type of join Collection Join Queries. There are three different kinds of collection join queries, namely: Collection-Equi Join, Collection-Intersect join, and Sub-Collection Join. Basically, a collection-equi join query checks an equality of both collection operands, whereas a collection-intersect join query checks whether there is an intersection between the two join collection attributes. Sub-collection join queries check whether one collection is a sub-collection of the other. In this paper, we present parallel join algorithms for the above three collection join query types based on the sort-merge technique. Sonic of the proposed algorithms employ a nested-loop construct as well. We also outline the complexity of collection merging in the algorithm. parallel join algorithms are normally composed of two stages, data partitioning and local join. For the data partitioning stage in collection-intersect and sub-collection join algorithms, we propose a 'Divide and Partial Broadcast' partitioning. The proposed join algorithms play an important role in parallel object-oriented query processing, due to their superiority over the conventional join methods, which are usually in a form of relational division, and also the inefficiency of original join predicates processing.

关键词： parallel query processing parallel join object-oriented join queries collection types Object-Oriented Databases and sort-merge join

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The information flow based query optimization techniques in parallel object-oriented database

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CHINESE JOURNAL OF ELECTRONICS 2002年第2期11卷 220-225页

作者： Wang, YJ Wang, YJ Lu, XC Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

In parallel database systems, parallelism is utilized to improve the efficiency of query processing. However, parallelism is not equal to high efficiency. Therefore, query optimization techniques should be utilized to improve the efficiency of parallel query processing. In this paper, according to the characteristic of the object-oriented database and its query, based on the semi-join-based parallel query processing algorithm, the information flow based query optimization techniques are proposed, and the results of performance evaluation show that they are efficient and practical.

关键词： parallel object-oriented database parallel query processing query optimization information flow

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