MySQL Cluster – Evaluation and Tests

  • Websites or web applications, whether they represent shopping systems, on demand services or a social networks, have something in common: data must be stored somewhere and somehow. This job can be achieved by various solutions with very different performance characteristics, e.g. based on simple data files, databases or high performance RAM storage solutions. For todays popular web applications it is important to handle database operations in a minimum amount of time, because they are struggling with a vast increase in visitors and user generated data. Therefore, a major requirement for modern database application is to handle huge data (also called big data) in a short amount of time and to provide high availability for that data. A very popular database application in the open source community is MySQL, which was originally developed by a swedisch company called MySQL AB and is now maintenanced by Oracle. MySQL is shipped in a bundle with the Apache web server and therefore has a large distribution. This database is easily installed, maintained and administrated. By default MySQL is shipped with the MyISAM storage engine, which has good performance on read requests, but a poor one on massive parallel write requests. With appropriate tuning of various database settings, special architecture setups (replication, partitioning, etc.) or other storage engines, MySQL can be turned into a fast database application. For example Wikipedia uses MySQL for their backend data storage. In the lecture Ultra Large Scale Systems and System Engineering teached by Walter Kriha at Media University Stuttgart, the question Can a MySQL database application handle more then 3000 database requests per second? came up some time. Inspired by this issue, I got myself going to find out, if MySQL is able to handle such a amount of requests per second. At that time I also read something about the high availability and scalability solution MySQL Cluster and it was the right time to test the performance of that solution. In this paper I describe how to set up a MySQL database server with the additional MySQL Cluster storage engine ndbcluster and how to configure a database cluster. In addition I execute some database tests on that cluster to proof that its possible the get a throughput of >= 3000 read requests per second with a MySQL database.

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Metadaten
Author:Michael Raith
URN:urn:nbn:de:bsz:900-opus-13949
Document Type:Study Thesis
Language:English
Date of Publication (online):2013/04/04
Publishing Institution:Hochschule der Medien
Release Date:2013/04/04
GND Keyword:JMeter; MySQL; MySQL Cluster; auto sharding; database test; high availability; high performance
Institutes:FB 1: Druck und Medien / Computer Science and Media (Master)
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Access Rights:Frei zugänglich
Licence (German):License LogoEs gilt das UrhG

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