Performance improvement through table partitioning (comparison of table partitioning in SQL server 2008 (original) (raw)

Database Partitioning: A Review Paper

Data management is much tedious task in growing data environment. Partitioning is the best possible solution which is partially accepted. Partitioning provides availability, maintenance and improvised query performance to the database users. This paper focuses the three key methods of partitioning and helps to reduce the delay in response time. Paper also investigates the composite partition strategies which includes the date, range and hash partitions. The paper shows the encouraging result with partitioning methods and basic composite partition strategies.

A Review on Partitioning Techniques in Database

2014

Data is most important in today's globe as it helps organizations as well as persons to take out information and use it to make various decisions. Data generally stocked in database so that retrieving and maintaining it becomes easy and manageable. All the operations of data handling and maintenance are done using Database Management System. Data management is much monotonous task in growing data environment. Partitioning is possible solution which is partly accepted. Partitioning provides user-friendliness, maintenance and impulsive query performance to the database users. In this paper, brief review of methods of partitioning and helps to reduce the wait in response time. Paper shows the positive result with partitioning methods.

An Active System for Dynamic Vertical Partitioning of Relational Databases

2016

Abstract. Vertical partitioning is a well known technique to improve query response time in relational databases. This consists in dividing a table into a set of fragments of attributes according to the queries run against the table. In dynamic systems the queries tend to change with time, so it is needed a dynamic vertical partitioning technique which adapts the fragments according to the changes in query patterns in order to avoid long query response time. In this paper, we propose an active system for dynamic vertical partitioning of relational databases, called DYVEP (DYnamic VErtical Partitioning). DYVEP uses active rules to vertically fragment and refragment a database without intervention of a database administrator (DBA), maintaining an acceptable query response time even when the query patterns in the database suffer changes. Experiments with the TPC-H benchmark demonstrate efficient query response time.

Automated partitioning design in parallel database systems

2011

In recent years, Massively Parallel Processors (MPPs) have gained ground enabling vast amounts of data processing. In such environments, data is partitioned across multiple compute nodes, which results in dramatic performance improvements during parallel query execution. To evaluate certain relational operators in a query correctly, data sometimes needs to be re-partitioned (i.e., moved) across compute nodes. Since data movement operations are much more expensive than relational operations, it is crucial to design a suitable data partitioning strategy that minimizes the cost of such expensive data transfers. A good partitioning strategy strongly depends on how the parallel system would be used. In this paper we present a partitioning advisor that recommends the best partitioning design for an expected workload. Our tool recommends which tables should be replicated (i.e., copied into every compute node) and which ones should be distributed according to specific column(s) so that the cost of evaluating similar workloads is minimized. In contrast to previous work, our techniques are deeply integrated with the underlying parallel query optimizer, which results in more accurate recommendations in a shorter amount of time. Our experimental evaluation using a real MPP system, Microsoft SQL Server 2008 Parallel Data Warehouse, with both real and synthetic workloads shows the effectiveness of the proposed techniques and the importance of deep integration of the partitioning advisor with the underlying query optimizer.

Efficient Partitioning of Large Databases without Query Statistics

Efficient Partitioning of Large Databases without Query Statistics, 2016

An efficient way of improving the performance of a database management system is distributed processing. Distribution of data involves fragmentation or partitioning, replication, and allocation process. Previous research works provided partitioning based on empirical data about the type and frequency of the queries. These solutions are not suitable at the initial stage of a distributed database as query statistics are not available then. In this paper, I have presented a fragmentation technique, Matrix based Fragmentation (MMF), which can be applied at the initial stage as well as at later stages of distributed databases. Instead of using empirical data, I have developed a matrix, Modified Create, Read, Update and Delete (MCRUD), to partition a large database properly. Allocation of fragments is done simultaneously in my proposed technique. So using MMF, no additional complexity is added for allocating the fragments to the sites of a distributed database as fragmentation is synchronized with allocation. The performance of a DDBMS can be improved significantly by avoiding frequent remote access and high data transfer among the sites. Results show that proposed technique can solve the initial partitioning problem of large distributed databases.

Prediction of Horizontal Data Partitioning Through Query Execution Cost Estimation

ArXiv, 2019

The excessively increased volume of data in modern data management systems demands an improved system performance, frequently provided by data distribution, system scalability and performance optimization techniques. Optimized horizontal data partitioning has a significant influence of distributed data management systems. An optimally partitioned schema found in the early phase of logical database design without loading of real data in the system and its adaptation to changes of business environment are very important for a successful implementation, system scalability and performance improvement. In this paper we present a novel approach for finding an optimal horizontally partitioned schema that manifests a minimal total execution cost of a given database workload. Our approach is based on a formal model that enables abstraction of the predicates in the workload queries, and are subsequently used to define all relational fragments. This approach has predictive features acquired by...

Vertical partitioning in database design

Information Sciences, 1995

\ BS'I'I¢ A("T \Vlwlt ~t transact loll ill a relal ional dat aba.sc sysll,ln is processed, t ransact ion response l illlO is likely domi,mt¢,d by Ill(, disk access time. By partitioning a rclat ioll illlO fr~tglll(,lll,,-;, aCCOl'di~lg to l]w re(lllir(,lllt'nl o[ llallS~l('liOllS, it trall.,4ncl.iOll can avoid accessing the useless data. In this paper, ;Ul al,gorilhm using tlw A" technique, which cat, lind the global optimal parl.itioll quickly, is presenled. Two lolill(qllcqll, iilethods ledllct.ioll o[" tht. t-;t';tl'C']l s[)D.('o ~llld good t!st.illlD.liOll ill'l* also l)l'.p¢~sed t i, illlprov~' lh(' pf?lf(lrlll;-illCO of till, sOltl(]l ])loct,dlll'C.

Vertical partitioning algorithms for database design

ACM Transactions on Database Systems, 1984

This paper addresses the vertical partitioning of a set of logical records or a relation into fragments. The rationale behind vertical partitioning is to produce fragments, groups of attribute columns, that “closely match” the requirements of transactions. Vertical partitioning is ...

Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases

Lecture Notes in Computer Science, 2013

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. ⋆ Work partially funded by the CNPq-INRIA HOSCAR project. Data are appended to the catalog database as new observations are performed and the resulting database size is estimated to reach 100TB very soon. Scientists around the globe can access the database with queries that may contain a considerable number of attributes. The volume of data that such applications hold poses important challenges for data management. In particular, efficient solutions are needed to partition and distribute the data in multiple servers, e.g., in a cluster. An efficient partitioning scheme would try to minimize the number of fragments that are accessed in the execution of a query, thus minimizing the overhead of the distributed execution. Vertical partitioning solutions, such as column-oriented databases [18], may be useful for physical design on each node, but fail to provide an efficient distributed partitioning, in particular for applications with high dimensional queries, where joins would have to be executed by transferring data between nodes. Traditional horizontal partitioning approaches, such as hashing or range-based partitioning, are unable to capture the complex access patterns present in scientific computing applications, especially because these applications usually make use of complicated relations, including mathematical operations, over a big set of columns, and are difficult to be predefined a priori.