1 Informal Design Guidelines for Relational Databases
What is relational database design?
The grouping of attributes to form "good" relation schemas
Two levels of relation schemas:
- The logical "user view" level
- The storage "base relation" level
Design is concerned mainly with base relations
What are the criteria for "good" base relations?
We first discuss informal guidelines for good relational design
Then we discuss formal concepts of functional dependencies and normal forms
1NF (First Normal Form)
2NF (Second Normal Form)
3NF (Third Normal Form)
BCNF (Boyce-Codd Normal Form)
Semantics of the Relation Attributes
GUIDELINE 1: Informally, each tuple in a relation should represent one
entity or relationship instance. (Applies to individual relations and
their attributes).
- Attributes of different entities (EMPLOYEEs, DEPARTMENTs, PROJECTs)
should not be mixed in the same relation
- Only foreign keys should be used to refer to other entities
- Entity and relationship attributes should be kept apart as much as possible.
Bottom Line: Design a schema that can be explained easily relation by
relation.
Redundant Information in Tuples and Update Anomalies
- Mixing attributes of multiple entities may cause problems
- Information is stored redundantly wasting storage
- Problems with update anomalies:
- Insertion anomalies
- Deletion anomalies
Modification anomalies
EXAMPLE OF AN UPDATE ANOMALY:
Consider the relation:
EMP_PROJ ( Emp#, Proj#, Ename, Pname, No_hours)
Update Anomaly: Changing the name of project number P1 from “Billing”
to “Customer-Accounting” may cause this update to be made for all 100
employees working on project P1.
Insert Anomaly: Cannot insert a project unless an employee is assigned to .
Inversely- Cannot insert an employee unless an he/she
is assigned to a project.
Delete Anomaly: When a project is deleted, it will result in
deleting all the employees who work on that project. Alternately, if
an employee is the sole employee on a project, deleting that employee
would result in deleting the corresponding project.
GUIDELINE 2: Design a schema that does not suffer from the insertion,
deletion and update anomalies. If there are any present, then note
them so that applications can be made to take them into account
Null Values in Tuples
GUIDELINE 3: Relations should be designed such that their tuples will
have as few NULL values as possible
- Attributes that are NULL frequently could be placed in separate
relations (with the primary key)
- Reasons for nulls:
a. attribute not applicable or invalid
b. attribute value unkown (may exist)
c. value known to exist, but unavailable
Spurious Tuples
- Bad designs for a relational database may result in erroneous
results for certain JOIN operations
- The "lossless join" property is used to guarantee meaningful results
for join operations
GUIDELINE 4: The relations should be designed to satisfy the lossless
join condition. No spurious tuples should be generated by doing a
natural-join of any relations.
- There are two important properties of decompositions: (a)
non-additive or losslessness of the corresponding join, (b)
preservation of the functional dependencies. Note that property (a) is
extremely important and cannot be sacrificed. property (b) is less
stringent and may be sacrificed.
Functional Dependencies
- Functional dependencies (FDs) are used to specify formal measures
of the "goodness" of relational designs
- FDs and keys are used to define normal forms for relations
- FDs are constraints that are derived from the meaning and
interrelationships of the data attributes
- A set of attributes X functionally determines a set of attributes Y
if the value of X determines a unique value for Y
- X -> Y holds if whenever two tuples have the same value for X, they
must have the same value for Y
- For any two tuples t1 and t2 in any relation instance r(R):
If t1[X]=t2[X], then t1[Y]=t2[Y]
- X -> Y in R specifies a constraint on all relation instances r(R)
- Written as X -> Y; can be displayed graphically on a relation schema
as in Figures. ( denoted by the arrow: ).
- FDs are derived from the real-world constraints on the attributes
Examples of FD constraints:
- social security number determines employee name
SSN -> ENAME
- project number determines project name and location
PNUMBER -> {PNAME, PLOCATION}
- employee ssn and project number determines the hours per week that
the employee works on the project
{SSN, PNUMBER} -> HOURS
- An FD is a property of the attributes in the schema R
- The constraint must hold on every relation instance r(R)
- If K is a key of R, then K functionally determines all attributes in
R (since we never have two distinct tuples with t1[K]=t2[K])
Introduction to Normalization-
Normalization: Process of decomposing unsatisfactory "bad" relationsby breaking up their attributes into smaller relations-
Normal form: Condition using keys and FDs of a relation to certifywhether a relation schema is in a particular normal form- 2NF, 3NF, BCNF based on keys and FDs of a relation schema
- 4NF based on keys, multi-valued dependencies : MVDs; 5NF based onkeys, join dependencies : JDs
First Normal Form- Disallows composite attributes, multivalued attributes, and nestedrelations; attributes whose values for an individual tuple arenon-atomic
Second Normal Form- Uses the concepts of FDs, primary keyDefinitions:- Prime attribute - attribute that is member of the primary key K- Full functional dependency - a FD Y -> Z where removal of anyattribute from Y means the FD does not hold any more
Examples: - {SSN, PNUMBER} -> HOURS is a full FD since neither SSN -> HOURS nor PNUMBER -> HOURS hold - {SSN, PNUMBER} -> ENAME is not a full FD (it is called apartialdependency ) since SSN -> ENAME also holds-
A relation schema R is in second normal form (2NF) if everynon-prime attribute A in R is fully functionally dependent on theprimary key-
R can be decomposed into 2NF relations via the process of 2NF normalization
Third Normal FormDefinition:- Transitive functional dependency - a FD X -> Z that can be derivedfrom two FDs X -> Y and Y -> Z Examples:
- SSN -> DMGRSSN is a transitive FD since SSN -> DNUMBER and DNUMBER -> DMGRSSN hold - SSN -> ENAME is non-transitive since there is no set of attributes X where SSN -> X and X -> ENAME-
A relation schema R is in third normal form (3NF) if it is in 2NFand no non-prime attribute A in R is transitively dependent on theprimary keyR can be decomposed into 3NF relations via the process of 3NF normalization
NOTE:In X -> Y and Y -> Z, with X as the primary key, we consider this aproblem only if Y is not a candidate key. When Y is a candidate key,there is no problem with the transitive dependency .E.g., Consider EMP (SSN, Emp#, Salary ).Here, SSN -> Emp# -> Salary and Emp# is a candidate key.
BCNF (Boyce-Codd Normal Form)- A relation schema R is in Boyce-Codd Normal Form (BCNF) if whenever
an FD X -> A holds in R, then X is a superkey of R-
Each normal form is strictly stronger than the previous one: Every 2NF relation is in 1NF Every 3NF relation is in 2NF Every BCNF relation is in 3NF- There exist relations that are in 3NF but not in BCNF- The goal is to have each relation in BCNF (or 3NF)
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