Performance of on-line index construction algorithms

Srinivasan, V.; Carey, Michael J.

doi:10.1007/bfb0032438

Cited by 33 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the global logging PE fails, it becomes impossible to keep consistency between the backup and the primary if all of the update log is not propagated to the backup side. However, since the data structure in both the primary and the backup is in a consistent state and all of the leaf pages of the primary side are fully updated, full recovery is made possible by applying the same type of approach as an online index construction method [23]; for example, by taking the difference between the leaf pages of the primary and the backup and applying the difference information of updated data to the backup side. However, since all of the leaf pages must be scanned, the processing time is increased.…”

Section: Combination Of N-gll+stg and Plmentioning

confidence: 99%

Dependable configurations and recovery for the Fat-Btree parallel directory structure

Miyazaki¹,

Yokota²

2006

Electron. Comm. Jpn. Pt. III

View full text Add to dashboard Cite

SUMMARYIn this paper, we will discuss a highly dependable system configuration method and recovery method for our proposed Fat-Btree structure, which is a directory structure for shared-nothing parallel computers. The goals are to enhance the service of operation during failure, to minimize the probability of data loss, and to enhance availability by combining physiological logging, logical logging, disk mirroring, staggered allocation, etc. Various system configurations formed by the combination of these methods will be quantitatively evaluated in this paper. As a result, it will be shown that a combination of physiological logging and disk mirroring is most appropriate when hardware cost can be ignored, and that a combination of global logical logging and physiological logging is most appropriate when hardware cost is considered.

show abstract

Section: Combination Of N-gll+stg and Plmentioning

confidence: 99%

Dependable configurations and recovery for the Fat-Btree parallel directory structure

Miyazaki¹,

Yokota²

2006

Electron. Comm. Jpn. Pt. III

View full text Add to dashboard Cite

show abstract

“…The recovery log may serve as the "side file." Srinivasan and Carey further divide "side file" online algorithms for index creation according to whether concurrent updates are captured in a list or in an index [39]. (Srinivasan and Carey do not consider capturing updates in the recovery log.…”

Section: Online Index Operationsmentioning

confidence: 99%

Transactional support for adaptive indexing

et al. 2014

View full text Add to dashboard Cite

Adaptive indexing initializes and optimizes indexes incrementally, as a side effect of query processing. The goal is to achieve the benefits of indexes while hiding or minimizing the costs of index creation. However, indexoptimizing side effects seem to turn read-only queries into update transactions that might, for example, create lock contention. This paper studies concurrency control and recovery in the context of adaptive indexing. We show that the design and implementation of adaptive indexing rigorously separates index structures from index contents; this relaxes constraints and requirements during adaptive indexing compared to those of traditional index updates. Our design adapts to the fact that an adaptive index is refined continuously and exploits any concurrency opportunities in a dynamic way. A detailed experimental analysis demonstrates that (a) adaptive indexing maintains its adaptive properties even when running concurrent queries, (b) adaptive indexing can exploit the opportunity for parallelism due to concurrent queries, (c) the number of concurrency conflicts and any concurrency administration overheads follow an adaptive behavior, decreasing as the workload evolves and adapting to the workload needs.

show abstract

“…In the context of online construction of indexes (Section 5.1), Srinivasan [1992] and Srinivasan and Carey [1992b] discuss how the duration of reorganization and the degradation caused by reorganization both affect user performance. Their performance model predicts the loss in the potential transaction processing in the database system.…”

Section: Trade-offs Between Users Andmentioning

confidence: 99%

“…"M" at the end of an algorithm's name means "merge" (two sources), "S" means "sort," and "B" means "basic" (no sorting). Srinivasan [1992] and Srinivasan and Carey [1992b] discuss a simulation model and use it to analyze the performance of their ten algorithms. It is a closed queuing model that predicts the time to construct the index and the loss in the potential transaction processing in the database system.…”

Section: Srinivasan and Careymentioning

confidence: 99%

Online reorganization of databases

Sockut

Iyer

2009

ACM Comput. Surv.

View full text Add to dashboard Cite

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.

show abstract

Performance of on-line index construction algorithms

Cited by 33 publications

References 10 publications

Dependable configurations and recovery for the Fat-Btree parallel directory structure

Dependable configurations and recovery for the Fat-Btree parallel directory structure

Transactional support for adaptive indexing

Online reorganization of databases

Contact Info

Product

Resources

About