Dwh lecture 07-denormalization
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De-normalization involves combining or modifying tables from a normalized database structure to improve query performance for data warehousing and decision support systems. It works by reducing the number of tables and joins needed for queries, at the cost of increased data redundancy. Some common de-normalization techniques include collapsing tables, pre-joining tables, and adding redundant columns. Any de-normalization should be carefully analyzed to balance the performance gains against increased storage needs.
Dwh lecture 07-denormalization
- 2. 2 Striking a balance between “good” & “evil” Flat Table Data Lists Data Cubes 1st Normal Form 2nd Normal Form 3rd Normal Form 4+ Normal Forms NormalizationDe-normalization One big flat file Too many tables
- 3. 3 What is De-normalization? the aim is to enhance performance without loss of information. Normalization is a rule of thumb in DBMS, but in DSS ease of use is achieved by way of denormalization. De-normalization comes in many flavors, such as combining tables, splitting tables, adding data etc., but all done very carefully.
- 4. Bringing “close” dispersed but related data items. Very early studies showed performance difference in orders of magnitude for different number de-normalized tables and rows per table. The level of de-normalization should be carefully considered. 4 Why De-normalization In DSS?
- 5. 5 How De-normalization improves performance? De-normalization specifically improves performance by either: Reducing the number of tables and hence the reliance on joins, which consequently speeds up performance. Reducing the number of joins required during query execution, or
- 6. 6 4 Guidelines for De-normalization 1. Carefully do a cost-benefit analysis (frequency of use, additional storage, join time). 2. Do a data requirement and storage analysis. 3. When in doubt, don’t denormalize.
- 7. 7 Areas for Applying De-Normalization Techniques Dealing with the abundance of star schemas. Fast access of time series data for analysis. Fast aggregate (sum, average etc.) results and complicated calculations. Multidimensional analysis (e.g. geography) in a complex hierarchy. Dealing with few updates but many join queries. De-normalization will ultimately affect the database size and query performance.
- 8. 8 Five principal De-normalization techniques 1. Collapsing Tables. - Two entities with a One-to-One relationship. - Two entities with a Many-to-Many relationship. 2. Splitting Tables (Horizontal/Vertical Splitting). 3. Pre-Joining. 4. Adding Redundant Columns (Reference Data). 5. Derived Attributes (Summary, Total, Balance etc).
- 9. 9 Collapsing Tables ColA ColB ColA ColC normalized ColA ColB ColC denormalized Reduced storage space. Reduced update time. Does not changes business view. Reduced foreign keys.
- 10. 9 Collapsing Tables ColA ColB ColA ColC normalized ColA ColB ColC denormalized Reduced storage space. Reduced update time. Does not changes business view. Reduced foreign keys.
Editor's Notes
- #3: In the reality of the “real world”, the enhancement in performance delivered by some selective de-normalization technique can be a very valuable tool.
- #4: De-normalization does not mean chaos or disorder or indiscipline. De-normalization is the process of selectively transforming normalized relations into un-normalized physical record specifications, with the aim ofreducing query processing time.
- #8: De-normalization will ultimately affect the database size and query performance. De-normalization is especially useful while dealing with the abundance of star schemas that are found in many data warehouse installations. For such cases, de-normalization provides better performance and a more natural data structure for supporting decision making. The goal of most analytical processes in a typical data warehouse environment is to access aggregates such as averages, sums, complicated formula calculations, top 10 customers etc. Typical OLTP systems contain only the raw transaction data, whiledecision makers expect to find aggregated and time-series data in their data warehouse to get the big picture through immediate query and display.
- #10: One of the most common and safe de-normalization techniques is combining of One-to One relationships. This situation occurs when for each row of entity A, there is only one related row in entity B. For example, if users frequently need to see COLA, COLB, and COLC together and the data from the two tables are in a One-to-One relationship, the solution is to collapse the two tables into one. For example, SID and gender in one table, and SID and degree in the other table. In general, collapsing tables in One-to-One relationship has fewer drawbacks than others. There are several advantages of this technique, some of the obvious ones being reduced storage space, reduced amount of time for data update, some of the other not so apparent advantages are reduced number of foreign keys on tables, reduced number of indexes since most indexes are created based on primary/foreign keys). Furthermore, combiningthe columns does not change the business view, but does decrease access time by having fewer physical objects and reducing overhead.