• DocumentCode
    2182026
  • Title

    Adaptive indexing for relational keys

  • Author

    Graefe, Goetz ; Kuno, Harumi

  • Author_Institution
    HP Labs. Palo Alto, Palo Alto, CA, USA
  • fYear
    2010
  • fDate
    1-6 March 2010
  • Firstpage
    69
  • Lastpage
    74
  • Abstract
    Adaptive indexing schemes such as database cracking and adaptive merging have been investigated to-date only in the context of range queries. These are typical for non-key columns in relational databases. For complete self-managing indexing, adaptive indexing must also apply to key columns. The present paper proposes a design and offers a first performance evaluation in the context of keys. Adaptive merging for keys also enables further improvements in B-tree indexes. First, partitions can be matched to levels in the memory hierarchy such as a CPU cache and an in-memory buffer pool. Second, adaptive merging in merged B-trees enables automatic master-detail clustering.
  • Keywords
    cache storage; database indexing; relational databases; tree data structures; B-tree index; CPU cache; adaptive indexing; adaptive merging; automatic master-detail clustering; database cracking; in-memory buffer pool; memory hierarchy; relational database; relational keys; self-managing indexing; Algorithm design and analysis; Cost function; Data security; Data structures; Indexes; Indexing; Merging; Relational databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Engineering Workshops (ICDEW), 2010 IEEE 26th International Conference on
  • Conference_Location
    Long Beach, CA
  • Print_ISBN
    978-1-4244-6522-4
  • Electronic_ISBN
    978-1-4244-6521-7
  • Type

    conf

  • DOI
    10.1109/ICDEW.2010.5452743
  • Filename
    5452743