Column Stores

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What are the cost-sensitive major factors in query processing?

– Size of database

– Index or not

– Join

Current hardware configuration

– Cheap storage – allow distributed redundant data store

– Fast CPUs – compression/decompression

– Limited disk bandwidth – reduce I/O

– Increasingly deep memory

hierarchies!

– Disk seeks extremely slow

– Avoid loading data that is not accessed (OLAP, analytics)

- OLAP (On-line Analytical Processing) [1]

is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).

OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).

Data Storage Models

• There are different ways to store tuples.

• We have been assuming the n-ary storage model so far

Refrerences

[1]http://datawarehouse4u.info/OLTP-vs-OLAP.html

[2]http://saphanatutorial.com/column-data-storage-and-row-data-storage-sap-hana/