Temporal and versioning model for schema evolution in object-oriented databases

Edelweiss, Nina; Moreira, Álvaro F.

doi:10.1016/j.datak.2004.07.001

Cited by 37 publications

(16 citation statements)

References 7 publications

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“…Much of this work focused on object oriented databases (OODB), including the work by Rundensteiner et al [6] [7] and the temporal versioning research by Grandi et al [8] and Galante et al [9]. There were a number of papers by Zaniolo, Moon, Curino, et al on quantifying and supporting schema evolution in RDBMSes [10] [11] [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Storing Long-Lived Concurrent Schema and Data Versions in Relational Databases

Wall

Angryk

2015

Advances in Intelligent Systems and Computing

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Abstract.Although there is a strong focus on NoSQL databases for cloud computing environments, traditional relational data bases are still an integral part of many computing services in the cloud. Two significant issues in managing a relational database in a cloud environment are handling the inevitable evolution of the database schema and managing changes to system configuration and other data stored in the database as the system evolves over time. Techniques for handling these issues in on-premise databases are much less feasible in cloud computing environments, which demand efficiency, elasticity, and scalability. We propose a versioning system that can be used in relational databases to allow new versions of the database schema and data to be maintained within the same database as the production data. Past research on versioning either handles data versioning but not schema changes, or handles both but is focused on OLAP or XML databases. In this paper, we describe a mechanism for storing concurrent versions of data in an OLTP database. We explore two different implementation alternatives for versioned data storage and evaluate their relative merits given different workloads. We provide a concrete description of how this can be implemented within the InnoDB storage engine, which is the default data store for MySQL databases, and we present a quantitative comparison of the two implementations in InnoDB.

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Section: Introductionmentioning

confidence: 99%

Storing Long-Lived Concurrent Schema and Data Versions in Relational Databases

Wall

Angryk

2015

Advances in Intelligent Systems and Computing

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“…XML schema evolution has been addressed in [27] by means of a guideline-driven approach. Object-oriented schema evolution has been investigated in [18]. In the context of data warehouse X-TIME represent an interesting step toward schema versioning by means of the notion of augmenting schema [19,31].…”

Section: Related Workmentioning

confidence: 99%

Automating database schema evolution in information system upgrades

Curino

Moon

Zaniolo

2009

Proceedings of the 2nd International Workshop on Hot Topics in Software Upgrades

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The complexity, cost, and down-time currently created by the database schema evolution process is the source of incessant problems in the life of information systems and a major stumbling block that prevent graceful upgrades. Furthermore, our studies shows that the serious problems encountered by traditional information systems are now further exacerbated in web information systems and cooperative scientific databases where the frequency of schema changes has increased while tolerance for downtimes has nearly disappeared. The PRISM project seeks to develop the methods and tools that turn this error-prone and time-consuming process into one that is controllable, predictable and avoids down-time. Toward this goal, we have assembled a large testbed of schema evolution histories, and developed a language of Schema Modification Operators (SMO) to express concisely these histories. Using this language, the database administrator can specify new schema changes, and then rely on PRISM to (i) predict the effect of these changes on current applications, (ii) translate old queries and updates to work on the new schema version, (iii) perform data migration, and (iv) generate full documentation of intervened changes. Furthermore, PRISM achieves good usability and scalability by incorporating recent advances on mapping composition and invertibility in the implementation of (ii). The progress in automating schema evolution so achieved provides the enabling technology for other advances, such as light-weight database design methodologies that embrace changes as the regular state of software. While these topics remain largely unexplored, and thus provide rich opportunities for future research, an important area which we have been investigated is that of archival information systems, where PRISM query mapping techniques were used to support flashback and historical queries for database archives under schema evolution.

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“…In other work in time-varying definitions, Galante et al [2005] define a model and language in which schema versions have times of validity. The authors define rules for times of validity and for relationships among versions.…”

Section: Additional Topicsmentioning

confidence: 99%

Online reorganization of databases

Sockut

Iyer

2009

ACM Comput. Surv.

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In practice, any database management system sometimes needs reorganization, that is, a change in some aspect of the logical and/or physical arrangement of a database. In traditional practice, many types of reorganization have required denying access to a database (taking the database offline) during reorganization. Taking a database offline can be unacceptable for a highly available (24-hour) database, for example, a database serving electronic commerce or armed forces, or for a very large database. A solution is to reorganize online (concurrently with usage of the database, incrementally during users' activities, or interpretively). This article is a tutorial and survey on requirements, issues, and strategies for online reorganization. It analyzes the issues and then presents the strategies, which use the issues. The issues, most of which involve design trade-offs, include use of partitions, the locus of control for the process that reorganizes (a background process or users' activities), reorganization by copying to newly allocated storage (as opposed to reorganizing in place), use of differential files, references to data that has moved, performance, and activation of reorganization. The article surveys online strategies in three categories of reorganization. The first category, maintenance, involves restoring the physical arrangement of data instances without changing the database definition. This category includes restoration of clustering, reorganization of an index, rebalancing of parallel or distributed data, garbage collection for persistent storage, and cleaning (reclamation of space) in a logstructured file system. The second category involves changing the physical database definition; topics include construction of indexes, conversion between B + -trees and linear hash files, and redefinition (e.g., splitting) of partitions. The third category involves changing the logical database definition. Some examples are changing a column's data type, changing the inheritance hierarchy of object classes, and changing a relationship from one-to-many to many-to-many. The survey encompasses both research and commercial implementations, and this article points out several open research topics. As highly available or very large databases continue to become more common and more important in the world economy, the importance of online reorganization is likely to continue growing.

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Temporal and versioning model for schema evolution in object-oriented databases

Cited by 37 publications

References 7 publications

Storing Long-Lived Concurrent Schema and Data Versions in Relational Databases

Storing Long-Lived Concurrent Schema and Data Versions in Relational Databases

Automating database schema evolution in information system upgrades

Online reorganization of databases

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