In-Memory Database Systems - A Paradigm Shift

Mohit Kumar Gupta; Vishal Verma; Megha Singh Verma

doi:https://doi.org/10.14445/22315381/IJETT-V6N6P158

Research Article | Open Access | Download PDF

Volume 6 | Number 2 | Year 2013 | Article Id. IJETT-V6N6P158 | DOI : https://doi.org/10.14445/22315381/IJETT-V6N6P158

In-Memory Database Systems - A Paradigm Shift

Mohit Kumar Gupta , Vishal Verma , Megha Singh Verma

Citation :

Mohit Kumar Gupta , Vishal Verma , Megha Singh Verma, "In-Memory Database Systems - A Paradigm Shift," International Journal of Engineering Trends and Technology (IJETT), vol. 6, no. 2, pp. 333-336, 2013. Crossref, https://doi.org/10.14445/22315381/IJETT-V6N6P158

Abstract

In today’s world, organizations like Google, Yahoo, Amazon, Facebook etc. are facing drastic increase in data. This leads to the problem of capturing, storing, managing and analyzing terabytes or petabytes of data, stored in multiple formats, from different internal and external sources. Moreover, new applications scenarios like weather forecasting, trading, artificial intelligence etc. need huge data processing in real time. These requirements exceed the processing capacity of traditional on-disk database management systems to manage this data and to give speedy real time results. Therefore, data management needs new solutions for coping with the challenges of data volumes and processing data in real-time. An in-memory database system (IMDS) is a latest breed of database management system which is becoming answer to above challenges with other supporting technologies. IMDS is capable to process massive data distinctly faster. This paper explores IMDS approach and its associated design issues and challenges. It also investigates some famous commercial and open-source IMDS solutions available in the market.

Keywords

In-Memory Database System (IMDS), Design issues and challenges for IMDS, Commercial and open-source IMDS.

References

[1] IDC Digital Universe Study. (2012) The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. . [Online]. Available: http://idcdocserv.com/1414.
[2] Hector Garcia-Molina, and Kenneth Salem, “Main Memory Database Systems: An Overview”, IEEE Transactions on Knowledge And Data Engineering, Vol. 4, No. 6, pp. 509-516, Dec. 1992.
[3] Main Memory vs. RAM-Disk Databases, McObject LLC. (2003) [Online]. Available: http://www.mcobject.com/130/EmbeddedDatabaseWhitePapers.htm.
[4] Y. Huang, Y. Zhang, X. Ji, Z. Wang, and S. Wang, “A Data Distribution Strategy for Scalable Main-Memory Database”, Advances in Web and Network Technologies, and Information Management, Lecture Notes in Computer Science, Vol. 5731, pp. 13-24, 2009.
[5] A. Gorine, “Building durability into data management for real-time systems”, Boards & Solutions, Sep. 2004.
[6] Oracle TimesTen In-memory database. [Online]. Available: http://www.oracle.com/technetwork/database/database-technologies/timesten/overview/timesten-imdb-086887.html.
[7] IBM SolidDB. [Online]. Available: http://www-01.ibm.com/software/data/soliddb/.
[8] McObject – extremeDB in-memory database systems. [Online]. Available: http://www.mcobject.com/extremedbfamily.shtml.
[9] vFabric SQLFire [Online]. Available: http://www.vmware.com/in/products/vfabric-sqlfire/overview.html.
[10] SAP HANA [Online]. Available: http://www.saphana.com.
[11] SQLite. [Online]. Available: http://www.sqlite.org/mostdeployed.html.
[12] CSQL [Online]. Available: http://csql.sourceforge.net/.
[13] MonetDB [Online]. Available: http://www.monetdb.org/Home.