1. (C) Copyright Fabian Pascal

2. DENORMALIZATION FOR PERFORMANCE
A COSTLY ILLUSION
SQLSaturday, Milan, October 2013
Fabian Pascal

3. (C) Copyright Fabian Pascal
INDUSTRY SOP
“A traditional normalized structure will not outperform a denormalized schema.”
--Practitioner with 20 years experience
“No major application will run in third normal form.”
--G. Koch, ORACLE COMPLETE REFERENCE
“Denormalization can be described a a process for reducing the degree of normalization with the aim of improving query processing performance.”
--Sanders & Shin, University of NY Buffalo
(C) Copyright Fabian Pascal

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THE ARGUMENT
The higher the normal form, the more tables in the database;
More tables  more joins;
Join queries perform worse than single table queries;
Denormalize for performance.
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5. NORMALIZATION DEGREES
<1NF: Unnormalized
1NF: Normalized *
2NF-4NF: Further normalized
5NF: Fully normalized **
* R-tables;
** “Improved” R-tables; (6NF)
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RULE OF THUMB
3NF R-tables are usually also in 5NF. It is not very often that 3NF R-tables are not fully normalized. *
* Composite keys
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7. (C) Copyright Fabian Pascal
BUSINESS MODEL
Every employee is assigned to one or more projects;
Every project has one or more employees assigned to it;
Every employee is assigned to one or more activities;
Every activity has one or more employees assigned to it;
Project and activity assignments are independent;
Business rules
Employees-to-projects, employees-to-tasks (N:M)
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8. (C) Copyright Fabian Pascal
LOGICAL MODEL (5NF)
EMP_PROJ EMP_ACT
Project assignment of employee identified by employee number (EMP#) is to project identified by project name (PROJNAME)
Activity assignment of employee identified by employee number (EMP#) is to activity identified by activity name (ACTNAME)
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9. (C) Copyright Fabian Pascal
“BUNDLING” (<4NF)
Redundancy *
Update anomalies
INSERT: proj. of empl. assigned to no act
DELETE: sole proj. of empl. Assined to multiple act.
Database bias
Harder to understand database
Error proneness
* Due to bundling
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10. (C) Copyright Fabian Pascal
DESIGN OPTIONS
Two 5NF R-tables:
Domain
Column
Key *
* FD, RI, Arb.
One R-table:
Domain
Column
Key
Redundancy control!
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RC CONSTRAINT
a = b1 JOIN b2
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THE THEORY & …
CREATE ASSERTION a_rd
CHECK (SELECT *
FROM a) =
(SELECT *
FROM b1)
JOIN
FROM b2);
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13. (C) Copyright Fabian Pascal
… PRACTICE
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DATA FUNDAMENTALS
“IT professionals and users require databases and DBMS’s that produce correct results and are efficient, but most of them do not know whether the practices and tools they employ are sound and optimal what the real sources of problems are if they are not.”
-- PRACTICAL ISSUES IN DATABASE MANAGEMENT
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15. MODEL & IMPLEMENTATION
“Consider a mathematical principle, say:
(a+b) x (a-b) = a² - b²
If you are using a calculator that uses a method involving this principle and it is slow relative to other method, which would you blame, the method or the calculator?”
-- A. Sen
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BACKWARDS
“Deferred to Design are the compromises between more tables to eliminate redundancy and and acceptable performance … But this normalization is not a concern as the [logical] model is built. Indeed, there is no realistic way of knowing whether the designer will chose to [further] normalize data and to what level.”
-- Coad & Yourdon, OBJECT-ORIENTED ANALYSIS
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17. PRACTICAL IMPLICATIONS
Full normalization
Purely logical
Neutral database
Formal guide
No update anomalies
Max integrity
Min constraints
Denormalization
Log-phys conf.
Database bias
Ad-hoc
Update anomalies
Max integrity risk
RC constraints defeat purpose
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18. (C) Copyright Fabian Pascal
RECOMMENDATIONS
Learn data fundamentals *
Don’t trust what you hear/read
Don’t confuse
Levels of representation
Model with implementation
Design logically sound databases
Demand TRDBMS’s
(C) Copyright Fabian Pascal

19. (C) Copyright Fabian Pascal

20. (C) Copyright Fabian Pascal
EDUCATION SERVICES
Education--distinct from tool-specific training--useful for any and all DBMS products used;
Correct myths and misconceptions about
Explain the practical implications of
data fundamentals
concepts, principles and methods
that receive little, no, or incorrect coverage in the industry
in simple, accessible language;
Data professionals and users who interacts with databases and prefer
to think for themselves
understanding to "cookbooks"
soundness to marketing fads and fashion;
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21. (C) Copyright Fabian Pascal
SEMINARS & PAPERS
Business Modeling for Database Design
The Costly Illusion: Normalization, Integrity and Performance
The Final NULL in the Coffin: A Relational Solution to Missing Data
Truly Relational: What It Really Means
(C) Copyright Fabian Pascal

22. (C) Copyright Fabian Pascal
DBDEBUNK BLOG
Debunkings of industry claims;
Articles on data fundamentals;
Weekly Quotes & To Laugh or Cry?
Industry material for which it is difficult to know which of the two reactions is warranted;
Illustrates the poor state of foundation knowledge;
Offer opportunity to test oneself on knowledge and comprehension of data fundamentals;
(C) Copyright Fabian Pascal