Concurrent file conversion between B+-tree and linear hash files
Information Systems
Proceedings of the sixteenth international conference on Very large databases
A parallel algorithm for record clustering
ACM Transactions on Database Systems (TODS)
Dynamic file allocation in disk arrays
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Parallel database systems: the future of high performance database systems
Communications of the ACM
Algorithms for creating indexes for very large tables without quiescing updates
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Parallel database systems: open problems and new issues
Distributed and Parallel Databases - Special issue: Research topics in distributed and parallel databases
Database Reorganization—Principles and Practice
ACM Computing Surveys (CSUR)
Performance Analysis of a Concurrent File Reorganization Algorithm for Record Clustering
IEEE Transactions on Knowledge and Data Engineering
Principles of Transaction-Based On-Line Reorganization
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
On-line tuning of data placement in parallel databases
On-line tuning of data placement in parallel databases
On-line reorganization in object databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Towards self-tuning data placement in parallel database systems
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Safely and Efficiently Updating References During On-line Reorganization
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Online reorganization of databases
ACM Computing Surveys (CSUR)
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Online reorganization in read optimized MMDBS
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Automated partitioning design in parallel database systems
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Scalable and dynamically balanced shared-everything OLTP with physiological partitioning
The VLDB Journal — The International Journal on Very Large Data Bases
Eliminating unscalable communication in transaction processing
The VLDB Journal — The International Journal on Very Large Data Bases
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Whenever data is moved across nodes in the parallel database system, the indexes need to be modified too. Index modification overhead can be quite severe because there can be a large number of indexes on a relation. In this paper, we study two alternatives to index modification, namely OAT (One-At-a-Time page movement) and BULK (bulk page movement). OAT and BULK are two extremes on the spectrum of the granularity of data movement. OAT and BULK differ in two respects: first, OAT uses very little additional disk space (at most one extra page), whereas BULK uses a large amount of disk space. Second, BULK uses sequential prefetch I/O to optimize on the number of I/Os during index modification, while OAT does not. Using an experimental testbed, we show that BULK is an order of magnitude faster than OAT. In terms of the impact on transaction performance during reorganization, BULK and OAT perform differently: when the number of indexes to be modified is either one or two, OAT has a lesser impact on the transaction performance degradation. However, when the number of indexes is greater than two, both techniques have the same impact on transaction performance.