Simplifying Data Migration from Relational Database Management System to Google App Engine Datastore
Cloud computing has been widely introduced because of its ability to increase resource utilization. Furthermore, cloud computing offers resources as services that taken part as one of the next generation computing technologies. Before delivery application
- PDF / 1,785,539 Bytes
- 9 Pages / 439.37 x 666.142 pts Page_size
- 56 Downloads / 210 Views
Abstract Cloud computing has been widely introduced because of its ability to increase resource utilization. Furthermore, cloud computing offers resources as services that taken part as one of the next generation computing technologies. Before delivery application to cloud computing environment, the first step is the migration of the data. Migrating data from relational database management system (RDBMS) to Google App Engine is time-consuming problem. Hence, Google App Engine (GAE) Datastore provides NoSQL data storage with configuration file that contains table schema and CSV or XML file. This study presents the method for simplifying data migration from RDBMS to GAE including blob data migration. The proposed method leverages AppCfg to provide convenience way for data migration. As a result, user has eliminated at least 75 % task effort for data migration.
Keywords Cloud computing Data migration Google App Engine Task queue
Y.-C. Chang (&) Department of Computer Science and Information Engineering, National Taitung Unviersity, Taitung, Taiwan, Republic of China e-mail: [email protected] R.-S. Chang Y. Chen Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan, Republic of China e-mail: [email protected] Y. Chen e-mail: [email protected]
Y.-M. Huang et al. (eds.), Advanced Technologies, Embedded and Multimedia for Human-centric Computing, Lecture Notes in Electrical Engineering 260, DOI: 10.1007/978-94-007-7262-5_101, Springer Science+Business Media Dordrecht 2014
887
888
Y.-C. Chang et al.
Introduction Since cloud computing was introduced, it has been becoming more and more popular every year. Easy to use (setup, maintenance, reliability and scalability) and low cost (pay per use) are the reasons for companies to move their systems into cloud [1–3]. Along with cloud computing growth, data also become larger and more complex every day. The need of database that can handle growth of data is unavoidable. The challenges are not only data growth, data processing and analyzing but also very hard when data become bigger and bigger. Google App Engine (GAE) is a cloud computing provider that offers platform as a service for user to host their web applications in Google’s infrastructures [4]. Moreover, GAE allows developer to write and run an application above Google’s infrastructure which is used by Google [5]. GAE supports distributed data storage that provides NoSql schemaless object which is built on the top of Bigtable called Datastore [6]. Datastore is used for storing common data types like integers, floats, strings, dates and binary data; and for storing binary large objects such as image, audio or video, GAE provides Blobstore. Since data structures between RDBMS and Datastore are not the same, data migration from RDBMS to Datastore seems to be troublesome. Therefore, GAE provides one tool named ‘‘AppCfg’’ that can be used for uploading and downloading either data or application. Before use AppCfg for uploading data, two files sho
Data Loading...
