Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Automatic physical database tuning: a relaxation-based approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Database tuning advisor for microsoft SQL server 2005: demo
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
COLT: continuous on-line tuning
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
DB2 design advisor: integrated automatic physical database design
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Automatic SQL tuning in oracle 10g
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Autonomous Management of Soft Indexes
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Self-organizing tuple reconstruction in column-stores
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Self-selecting, self-tuning, incrementally optimized indexes
Proceedings of the 13th International Conference on Extending Database Technology
Benchmarking adaptive indexing
TPCTC'10 Proceedings of the Second TPC technology conference on Performance evaluation, measurement and characterization of complex systems
Merging what's cracked, cracking what's merged: adaptive indexing in main-memory column-stores
Proceedings of the VLDB Endowment
Stochastic database cracking: towards robust adaptive indexing in main-memory column-stores
Proceedings of the VLDB Endowment
Concurrency control for adaptive indexing
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
Proper physical design is a momentous issue for the performance of modern database systems and applications. Nowadays, a growing amount of applications require the execution of dynamic and exploratory workloads with unpredictable characteristics that change over time, e.g., social networks, scientific databases and multimedia databases. In addition, as most modern applications move to the big data era, investing time and resources in building the wrong set of indexes over large collections of data can severely affect performance. Offline, online and adaptive indexing are three distinct approaches to the problem of automating the physical design choices. Offline indexing is best in static environments with stable workloads. Online indexing is best in relatively dynamic environments where the query workload can be monitored. Adaptive indexing is best in fully dynamic environments where no idle time or workload knowledge may be assumed. We observe that these three approaches are complementary, while none of them can satisfy the needs of modern applications in isolation. We envision a new index selection approach, holistic indexing that excels its predecessors by combining the best features of offline, online and adaptive indexing while overcoming their weaknesses. The main goal is the creation of a database kernel that can autonomously create partial indexes which are continuously refined during query processing as in adaptive indexing but at the same time the system continuously detects any opportunity to improve the physical design offline; whenever any idle time occurs it tries to exploit knowledge gathered during query processing to refine existing indexes further or create new ones. We sketch the research space and the new challenges such a direction brings.