Many techniques have been proposed to scale web applications. However, the data interdependencies between the database queries and transactions issued by the applications limit their efficiency. We claim that major scalability improvements can be gained by restructuring the web application data into multiple independent data services with exclusive access to their private data store. While this restructuring does not provide performance gains by itself, the implied simplification of each database workload allows a much more efficient use of classical techniques. We illustrate the data denormalization process on three benchmark applications: TPC-W, RUBiS and RUBBoS. We deploy the resulting service-oriented implementation of TPC-W across an 85-node cluster and show that restructuring its data can provide at least an order of magnitude improvement in the maximum sustainable throughput compared to master-slave database replication, while preserving strong consistency and transactional properties.

Abstract—Two of the most common processes in database design community include data normalization and denormalization which play pivotal roles in the underlying performance. Today data warehouse queries comprise a group of aggregations and joining operations. As a result, normalization process does not seem to be an adequate option since several relations must combine to provide answers for queries that involve aggregation. Further, denormalization process engages a wealth of administrative tasks, which include the documentation structure of the denormalization assessments, data validation, and data migration schedule, among others. It is the objective of the present paper to investigate the possibility that, under certain circumstances, the above-mentioned justifications cannot provide justifiable reasons to ignore the effects of denormalization. To date, denormalization techniques have been applied in several database designs one of which is hierarchical denormalization. The findings provide empirical data that show the query response time is remarkably minimized once the schema is deployed by hierarchical denormalization on a large dataset with multi-billion records. It is, thus, recommended that hierarchical denormalization be considered a more preferable method to improve query processing performance. Index Terms—Data warehouse, Normalization, Hierarchical denormalization, Query processing

Abstract- It is currently the norm that relational database designs should be based on a normalized logical data model. The primary objective of this design technique is data integrity and database extendibility. The Third Normal Form is regarded by academicians and practitioners alike to be point at which the database design is most efficient. Unfortunately, even this lower level normalization form has a major drawback with regards to query evaluation. Information retrievals from the database can result in large number of joins which degrades query performance. So you need to sometimes break theoretical rules for real world performance gains. Most existing Conceptual Level RDBMS data models provide a set of constructs that only describes ―what data is used ‖ and does not capture ―how the data is being used‖. The question of ―how data is used ‖ gets embedded in the implementation level details. As a result, every application built on the existing database extracts the same or similar data in different ways. If the functional use of the data is also captured, common query evaluation techniques can be formulated and optimized at the design phase, without affecting the normalized database structure constructed at the Conceptual Design phase. This paper looks at denormalization as an effort to improve the performance in data retrievals made from the database without compromising data integrity. A study on a hierarchical database table shows the performance gain- with respect to response time – using a denormalization technique.