OPTIMIZATION IN VERY LARGE DATABASES BY PARTITIONING TABLES
Piotr Bednarczuk
piotr.bednarczuk@wsei.lublin.plUniversity of Economics and Innovation in Lublin, Institute of Computer Science, Lublin, Poland (Poland)
https://orcid.org/0000-0003-1933-7183
Abstract
Very large databases like data warehouse slow down over time. This is usually due to a large daily increase in the data in the individual tables, counted in millions of records per day. How do we make sure our queries do not slow down over time? Table partitioning comes in handy, and, when used correctly, can ensure the smooth operation of very large databases with billions of records, even after several years.
Keywords:
partitioning, data warehouse optimization, billions of records, AdventureWorksDWReferences
Chodkowski A.: Partycjonowanie tabel a wydajność zapytań w SQL Server, seequality.net, 2017, [https://pl.seequality.net/partycjonowanie-tabel-wydajnosc-zapytan-sqlserver/].
Google Scholar
Kumar A., Jitendra Singh Yadav: A Review on Partitioning Techniques in Database International Journal of Computer Science and Mobile Computing 3(5), 2014, 342–347.
Google Scholar
Matalqa S., Mustafa S.: The effect of horizontal database table partitioning on query performance. The International Arab Journal of Information Technology 13(1A), 2016, 184–189.
Google Scholar
Microsoft documentation, Partycjonowanie danych poziomych, pionowych i funkcjonalnych, [https://docs.microsoft.com/pl-pl/azure/architecture/best-practices/data-partitioning].
Google Scholar
Qi W., Song J., Bao Y.: Near-uniform range partition approach for increased partitioning in large database. 2nd IEEE International Conference on Information Management and Engineering – Chengdu, 2010, 101–106, [http://doi.org/10.1109/ICIME.2010.5477529].
DOI: https://doi.org/10.1109/ICIME.2010.5477529
Google Scholar
Song J., Bao Y.: NPA: Increased Partitioning Approach for Massive Data in Real-Time Data Warehouse. 2nd International Conference on Information Technology Convergence and Services – Cebu, 2010, 1–6, [http://doi.org/10.1109/ITCS.2010.5581277].
DOI: https://doi.org/10.1109/ITCS.2010.5581277
Google Scholar
Watson H.: Recent Developments in Data Warehousing. Communications of the Association for Information Systems 8, [http://doi.org/10.17705/1CAIS.00801].
DOI: https://doi.org/10.17705/1CAIS.00801
Google Scholar
Zheng K., Gu D., Fang F., Zhang M., Zheng K., Li Q.: Data storage optimization strategy in distributed column-oriented database by considering spatial adjacency. Cluster Computing 20(4), 2017, 2833–2844, [http://doi.org/10.1007/s10586-017-1081-3].
DOI: https://doi.org/10.1007/s10586-017-1081-3
Google Scholar
Authors
Piotr Bednarczukpiotr.bednarczuk@wsei.lublin.pl
University of Economics and Innovation in Lublin, Institute of Computer Science, Lublin, Poland Poland
https://orcid.org/0000-0003-1933-7183
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