Integrating vertical and horizontal partitioning into automated physical database design

Agrawal, Sanjay; Narasayya, Vivek; Yang, Beverly

doi:10.1145/1007568.1007609

Cited by 288 publications

(171 citation statements)

References 9 publications

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“…Automatic database partitioning have received significant attention from researchers and applied by some database vendors, notably Microsofts SQL Server AutoAdmin [6,3] and IBMs DB2 Database Advisor [17,19]. Many of these works have focused on partitioning (both vertical and horizontally) as an element of physical design for a single-node, along with indexing and materialized views.…”

Section: Effect Of Imbalance Factor and Data Correlationmentioning

confidence: 99%

“…Many of these works have focused on partitioning (both vertical and horizontally) as an element of physical design for a single-node, along with indexing and materialized views. For instance, in [3] a set of physical design alternatives (that includes partitioning) is generated. Then, in order to limit the search space they prune the set of candidates.…”

Section: Effect Of Imbalance Factor and Data Correlationmentioning

confidence: 99%

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Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases

Liroz-Gistau

Akbarinia

Pacitti

et al. 2013

Lecture Notes in Computer Science

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Abstract. Applications with very large databases, where data items are continuously appended, are becoming more and more common. Thus, the development of efficient data partitioning is one of the main requirements to yield good performance. In the case of applications that have complex access patterns, e.g. scientific applications, workload-based partitioning could be exploited. However, existing workload-based approaches, which work in a static way, cannot be applied to very large databases. In this paper, we propose DynPart and DynPartGroup, two dynamic partitioning algorithms for continuously growing databases. These algorithms efficiently adapt the data partitioning to the arrival of new data elements by taking into account the affinity of new data with queries and fragments. In contrast to existing static approaches, our approach offers constant execution time, no matter the size of the database, while obtaining very good partitioning efficiency. We validated our solution through experimentation over real-world data; the results show its effectiveness.

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Section: Effect Of Imbalance Factor and Data Correlationmentioning

confidence: 99%

Section: Effect Of Imbalance Factor and Data Correlationmentioning

confidence: 99%

Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases

Liroz-Gistau

Akbarinia

Pacitti

et al. 2013

Lecture Notes in Computer Science

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show abstract

“…There are methods that do only fragmentation [1,24,26,33,34] and methods that do only allocation of predefined fragments [3,4,7,10,13,20,30]. Some methods also exist that integrate both tasks [9,11,17,19,25,27,29].…”

Section: Related Workmentioning

confidence: 99%

“…Dynamic methods are part of the trend towards fully automatic tuning [31], which has become a popular research direction. Recently, work has appeared aiming at integrating vertical and physical partitioning while also taking other physical design features like indices and materialized views into consideration [1]. Adaptive indexing [2,6] aims to create indices dynamically when the costs can be amortized over a long sequence of read operations, and to drop them if there is a long sequence of write operations that would suffer from having to update both base tables and indices.…”

Section: Related Workmentioning

confidence: 99%

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DYFRAM: dynamic fragmentation and replica management in distributed database systems

Hauglid

Ryeng

Nørvåg

2010

Distrib Parallel Databases

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In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.

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Self-adaptive Statistics Management for Efficient Query Processing

Chen

Dong

et al. 2005

Advances in Web-Age Information Management

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Integrating vertical and horizontal partitioning into automated physical database design

Cited by 288 publications

References 9 publications

Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases

Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases

DYFRAM: dynamic fragmentation and replica management in distributed database systems

Self-adaptive Statistics Management for Efficient Query Processing

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