CODS: Evolving data efficiently and scalably in column oriented databases

Ziyang Liu, Sivaramakrishnan Natarajan, Bin He, Hui I. Hsiao, Yi Chen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Database evolution is the process of updating the schema of a database or data warehouse (schema evolution) and evolving the data to the updated schema (data evolution). Database evolution is often necessitated in relational databases due to the changes of data or workload, the suboptimal initial schema design, or the availability of new knowledge of the database. It involves two steps: updating the database schema, and evolving the data to the new schema. Despite the capability of commercial RDBMSs to well optimize query processing, evolving the data during a database evolution through SQL queries is shown to be prohibitively costly. We designed and developed CODS, a platform for efficient data level data evolution in column oriented databases, which evolves the data to the new schema without materializing query results or unnecessary compression/decompression as occurred in traditional query level approaches. CODS ameliorates the efficiency of data evolution by orders of magnitude compared with commercial or open source RDBMSs.

Original languageEnglish (US)
Pages (from-to)1521-1524
Number of pages4
JournalProceedings of the VLDB Endowment
Volume3
Issue number2
DOIs
StatePublished - Sep 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

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