Column based Databases
Shweta Agrawal

What is a column based DB?
ID NAME SEX AGE SALARY ADDRRESS PHONE PAN...
1 Sunil Sharma M 40 10,000 ... ... ...
2 Neha Agarwal F 25 12,000 ... ... ...
3 Anant Agarwal M 28 15,000 ... ... ...
4 Vishal Mehta M 30 8,000 ... ... ...
One page of the table storage
1|Shweta Agrawal|M| 1|2|3|4...|Shweta
40|10000...|2|Neha Agrawal|Neha Agrawal|
Agrawal|F|25| Anant Agarwal|Vishal
12000...|3|Anant Mehta...|M|F|M|M...|
Agarwal|M|28| 40|25|28|30...|10000|
15000...|4|Vishal 12000|15000|8000...
Mehta|M|30|8000...
Row based storage Column based storage

Column stores
1|2|3|4|5| Shweta M|F|M|M|M|F| 40|25|28|30| 10000|12000|
6|.... Agrawal|Neha F... 45|20... 15000|8000|
Agrawal| 15000|
Anant 5000...
Agarwal|
Vishal ...
Mehta|
Srinivas
Pathak|
Rubina
Mehta....
1st page of each column store

Query processing on row store
SELECT name, salary FROM employee WHERE age > 40
 Evaluate condition age>40 possibly using an index
on age.
 Get a found­set containing row number/ID of rows
that satisfy above condition.
 Retrieve all rows in the above found­set.
 Send only name, and salary from the rows as result
to client

Query processing on a column
store
SELECT name, salary FROM employee WHERE age > 40
 Evaluate condition age > 40 on column age, using an
index if present
 Get a found­set containing row number/ID of rows that
satisfy above condition
 Retrieve name's from name's column store for all rows in
the found­set
 Retrieve salary's from salary column for all rows in the
found­set
 Associate name with salary by row id/number for final
result

A quick calculation of IO
 Table has 10 columns
 1 million rows.
 Each row is 100 bytes
 30% of employees are above age 40
 Total amount of data read in row based store =
100MB * 0.3 = 30MB
 Total amount of data read in column based
store 100MB * 0.3 * 0.2 (only 2 columns) = 6MB

Why is it important?
 Wide fact tables in data­warehouses
 Analytics queries on data­warehouse tend to
aggregate/analyse a few columns but a large
number of rows.
 Full table scans for analytics queries in row
stores
 Normalization means more joins

An example star schema

Benefits of column based DB
 Low pages read = Less IO = faster queries
 Processes CPU bound instead of IO bound
 Compression
 Page level compression
 Column level compression (lookup tables)
 Natural intra­query parallelism on conditions on
different columns

Row based equivalents
 Index every column?
 Maintenance: updates/insert/deletes
 Storage
 Most importantly: Index is value=>id, column is
id=>value
 Useful for selective queries only

Row based equivalents
 Vertical partitioning?
 Joins (although fast ones)
 Table overhead
 Cannot use horizontal partitioning
 Row based query engine not geared up to make
use of the column based storage.

Summary
 For adhoc analytics queries, column based
storage reduces IO, and makes queries faster
 Column based query engines written ground up
for analytics queries make good use of this
storage.
 Indexing every column, or vertical partioning not
same as column based storage.

References
 Commercial products
 Sybase IQ
 Vertica
 MySQL's InfoBright storage engine
 To know more, read
 http://databasecolumn.vertica.com/