OLAP Query Processing in a Database Cluster

Lima, Alexandre A. B.; Mattoso, Marta; Valduriez, Patrick

doi:10.1007/978-3-540-27866-5_46

Cited by 28 publications

(40 citation statements)

References 7 publications

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“…Others leave the fragmentation technique open [19]. The authors of [62] suggest using adaptive virtual fragmentation [46] to solve this issue. This, however, has not been done yet.…”

Section: Open Problemsmentioning

confidence: 99%

A Survey on XML Fragmentation

Braganholo

Mattoso

2014

SIGMOD Rec.

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Efficient document processing is a must when large volumes of XML data are involved. In such critical scenarios, a well-known solution to this problem is to distribute (map) the data among several processing nodes, and then distribute the processing accordingly, taking advantage of parallelism. This is the approach taken by distributed databases and MapReduce environments. Fragmentation techniques play an important role in these scenarios. They provide a way to "cut" the database into pieces and distribute the pieces over a network. This way, queries can also be "cut" into sub-queries that run in parallel, thus achieving better performance when compared to the centralized environment. However, there is no consensus in the database community as to what an XML fragment is. In fact, several approaches in literature present definitions of XML fragments. In addition to query processing, using XML fragmentation techniques may also be helpful when managing XML documents distributed along the web or clouds. This paper surveys the existing XML fragmentation approaches in literature, comparing their features and highlighting their drawbacks. Our contribution resides in establishing a map of the area.

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“…Others leave the fragmentation technique open [19]. The authors of [62] suggest using adaptive virtual fragmentation [46] to solve this issue. This, however, has not been done yet.…”

Section: Open Problemsmentioning

confidence: 99%

A Survey on XML Fragmentation

Braganholo

Mattoso

2014

SIGMOD Rec.

Self Cite

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“…Because of this, distributed databases have become widely used and important for many applications, which call for more research to find ways to improve their physical database design. Some researchers have proposed schema level tuning [7], table level horizontal partitioning [15] [10]. In this paper, we further detail the extension version for cluster computers and provide performance studies for both single computers and cluster computers.…”

Section: Related Workmentioning

confidence: 99%

Autonomous database partitioning using data mining on single computers and cluster computers

Gruenwald

2012

Proceedings of the 16th International Database Engineering &Amp; Applications Sysmposium on - IDEAS '12

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One of the most important metrics in measuring the performance of a database system is query response time, which is composed of I/O time and CPU time. I/O time is decided by the amount of data read/write from/to disks and how the data is located on disks. CPU time is decided by how the database system performs the query operations. So if we want to reduce the query response time we can reduce either I/O time or CPU time, or both of them. We know retrieving data from disks is much slower than retrieving data from main memory. Hence, one of the common ways to reduce I/O times is clustering data on disks so that queries will access only relevant data. This paper introduces an efficient algorithm, called AutoClust, for automatic database attribute clustering (or also called automatic database vertical partitioning) for single computers as well as cluster computers. It is based on closed item sets mined from queries and their attributes using association rule mining. The paper then presents experimental results comparing the performance of AutoClust with that of a baseline algorithm on both single computers and cluster computers using the TPC-H benchmark running on major commercial database systems. The experiments show that AutoClust has better query costs for both types of computers.

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“…Then it starts processing the range by sequentially submitting as many sub-queries as it takes to the local DBMS. Range sizes are continuously adapted by AVP in order to avoid full table scans (more details in [8]). Such an approach of having many sub-queries per node allied to database replication allows for dynamic load balancing.…”

Section: Pargresmentioning

confidence: 99%

“…Such an approach of having many sub-queries per node allied to database replication allows for dynamic load balancing. ParGRES redistributes the load by reassigning virtual partition ranges between nodes during query execution, as described in [8]. When a node finishes processing its sub-query, it sends messages to other nodes offering help to process their sub-queries.…”

Section: Pargresmentioning

confidence: 99%

See 1 more Smart Citation

High-Performance Query Processing of a Real-World OLAP Database with ParGRES

Paes

Lima

Valduriez

et al. 2008

High Performance Computing for Computational Science - VECPAR 2008

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Abstract. Typical OLAP queries take a long time to be processed so speeding up the execution of each single query is imperative to decision making. ParGRES is an open-source database cluster middleware for high performance OLAP query processing. By exploiting intra-query parallelism on PC clusters, ParGRES has shown excellent performance using the TPC-H benchmark. In this paper, we evaluate ParGRES on a real-world OLAP database. Through adaptive virtual partitioning of the database, ParGRES yields linear and very often super-linear speedup for frequent queries. This shows that ParGRES is a very cost-effective solution for OLAP query processing in real settings

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OLAP Query Processing in a Database Cluster

Cited by 28 publications

References 7 publications

A Survey on XML Fragmentation

A Survey on XML Fragmentation

Autonomous database partitioning using data mining on single computers and cluster computers

High-Performance Query Processing of a Real-World OLAP Database with ParGRES

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