Information engineering for the practitioner: putting theory into practice
Information engineering for the practitioner: putting theory into practice
Economic incentives for database normalization
Information Processing and Management: an International Journal
Database Management Systems
Cache-Aware Query Routing in a Cluster of Databases
Proceedings of the 17th International Conference on Data Engineering
Quality-Aware and Load-Sensitive Planning of Image Similarity Queries
Proceedings of the 17th International Conference on Data Engineering
Don't Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Application specific data replication for edge services
WWW '03 Proceedings of the 12th international conference on World Wide Web
Ganymed: scalable replication for transactional web applications
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
GlobeDB: autonomic data replication for web applications
WWW '05 Proceedings of the 14th international conference on World Wide Web
Globetp: template-based database replication for scalable web applications
Proceedings of the 16th international conference on World Wide Web
Consistency-preserving caching of dynamic database content
Proceedings of the 16th international conference on World Wide Web
Automatic physical database tuning middleware for web-based applications
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
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Scaling up web applications requires distribution of load across multiple application servers and across multiple database servers. Distributing load across multiple application servers is fairly straightforward; however distributing load (select and UDI queries) across multiple database servers is more complex because of the synchronization requirements for multiple copies of the data. Different techniques have been investigated for data placement across multiple database servers, such as replication, partitioning and de-normalization. In this paper, we describe our architecture that utilizes these data placement techniques for determining the best possible layout of data. Our solution is general, and other data placement techniques can be integrated within our system. Once the data is laid out on the different database servers, our efficient query router routes the queries to the appropriate database server/(s). Our query router maintains multiple connections for a database server so that many queries are executed simultaneously on a database server, thus increasing the utilization of each database server. Our query router also implements a locking mechanism to ensure that the queries on a database server are executed in order. We have implemented our solutions in our system, that we call SIPD (System for Intelligent Placement of Data). Preliminary experimental results illustrate the significant performance benefits achievable by our system.