Ahsan Abdullah 1 Data Warehousing Lecture-8 De-normalization Techniques Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research National University of Computers & Emerging Sciences, Islamabad

Ahsan Abdullah 2 De-normalization Techniques

Ahsan Abdullah 3 Splitting Tables ColAColBColC Table Vertical Split ColAColBColAColC Table_v1Table_v2 ColAColBColC Horizontal split ColAColBColC Table_h1Table_h2

Ahsan Abdullah 4 Splitting Tables: Horizontal splitting… Breaks a table into multiple tables based upon common column values. Example: Campus specific queries. GOAL  Spreading rows for exploiting parallelism.  Grouping data to avoid unnecessary query load in WHERE clause.

Ahsan Abdullah 5 Splitting Tables: Horizontal splitting ADVANTAGE  Enhance security of data.  Organizing tables differently for different queries.  Graceful degradation of database in case of table damage.  Fewer rows result in flatter B-trees and fast data retrieval.

Ahsan Abdullah 6 Splitting Tables: Vertical Splitting  Infrequently accessed columns become extra “baggage” thus degrading performance.  Very useful for rarely accessed large text columns with large headers.  Header size is reduced, allowing more rows per block, thus reducing I/O.  Splitting and distributing into separate files with repeating primary key.  For an end user, the split appears as a single table through a view.

Ahsan Abdullah 7 Pre-joining …  Identify frequent joins and append the tables together in the physical data model.  Generally used for 1:M such as master- detail. RI is assumed to exist.  Additional space is required as the master information is repeated in the new header table.

Ahsan Abdullah 8Pre-Joining… normalized Tx_IDSale_IDItem_IDItem_QtySale_Rs Tx_IDSale_IDItem_IDItem_QtySale_RsSale_dateSale_person denormalized Sale_IDSale_dateSale_person Master Detail 1 M

Ahsan Abdullah 9 Pre-Joining: Typical Scenario Typical of Market basket query Join ALWAYS required Tables could be millions of rows Squeeze Master into Detail Repetition of facts. How much? Detail 3-4 times of master

Ahsan Abdullah 10 Adding Redundant Columns… ColAColB Table_1 ColAColCColD…ColZ Table_2 ColAColB Table_1’ ColAColCColD…ColZ Table_2 ColC

Ahsan Abdullah 11 Adding Redundant Columns… Columns can also be moved, instead of making them redundant. Very similar to pre-joining as discussed earlier. EXAMPLE Frequent referencing of code in one table and corresponding description in another table.  A join is required.  To eliminate the join, a redundant attribute added in the target entity which is functionally independent of the primary key.

Ahsan Abdullah 12 Redundant Columns: Surprise Note that:  Actually increases in storage space, and increase in update overhead.  Keeping the actual table intact and unchanged helps enforce RI constraint.  Age old debate of RI ON or OFF.

Ahsan Abdullah 13 Derived Attributes: Example Age is also a derived attribute, calculated as Current_Date – DoB (calculated periodically). GP (Grade Point) column in the data warehouse data model is included as a derived value. The formula for calculating this field is Grade*Credits. #SID DoB Degree Course Grade Credits Business Data Model #SID DoB Degree Course Grade Credits GP Age DWH Data Model Derived attributes  Calculated once  Used Frequently DoB: Date of Birth