22 Jul, 2009
Column Oriented DBMS
Posted by Bhavin Turakhia
Conventionally we take DBMS for granted as a structured data store that stores data in the form of rows. Infact most application developers can begin visualizing their data as rows in an RDBMS quite naturally.
While RDBMS serve the purposes of OLTP applications well, OLAP / data anlytics type applications have conventionally not been able to obtain the type of performance needed from RDBMses. This is where column oriented DBMS can help.
In the simplest form the difference between a conventional RDBMS and a column oriented database is that the latter stores data in a column form rather than a row form when persisted to disk. Another way to look at this is that the storage in a column oriented DBMS transposes the rows and columns of the storage in a conventional RDBMS.
For eg
| ID | Name | Age |
| 1 | Bhavin | 29 |
| 2 | Roger | 30 |
This would be persisted in a conventional RDBMS as follows -
1,Bhavin,29|2,Roger,30
In a column oriented DBMS this would be persisted as -
1,2|Bhavin,Roger|29,30
It is common knowledge that the slowest piece of a DB query is its disk seek time. While the RDBMS favors queries which require fetching all data of a given row, the latter model favors queries which require aggregates. For instance – count of all users with age >20, or sum of ages of all users, and so on. These type of queries will run much faster on a column oriented DBMS due to lesser seek time required to obtain the data.
OLAP and BI applications mostly consist of data aggregation and would therefore run faster on column oriented databases.
For a list of column-oriented DBMSes refer to http://en.wikipedia.org/wiki/Column-oriented_DBMS
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