Hubbry Logo
Third normal formThird normal formMain
Open search
Third normal form
Community hub
Third normal form
logo
7 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Third normal form
Third normal form
Third normal form
View on Wikipedia
from Wikipedia

Third normal form (3NF) is a level of database normalization defined by English computer scientist Edgar F. Codd. A relation (or table, in SQL) is in third normal form if it is in second normal form and also lacks non-key dependencies, meaning that no non-prime attribute is functionally dependent on (that is, contains a fact about) any other non-prime attribute. In other words, each non-prime attribute must depend solely and non-transitively on each candidate key.[1] William Kent summarised 3NF with the dictum that "a non-key field must provide a fact about the key, the whole key, and nothing but the key".[2][citation needed]

An example of a violation of 3NF would be a Patient relation with the attributes PatientID, DoctorID and DoctorName, in which DoctorName would depend first and foremost on DoctorID and only transitively on the key, PatientID (via DoctorID's dependency on PatientID). Such a design would cause a doctor's name to be redundantly duplicated across each of their patients. A database compliant with 3NF would store doctors' names in a separate Doctor relation which Patient could reference via a foreign key.

3NF was defined, along with 2NF (which forbids dependencies on proper subsets of composite keys), in Codd's paper "Further Normalization of the Data Base Relational Model" in 1971,[3] which came after 1NF's definition in "A Relational Model of Data for Large Shared Data Banks" in 1970.[citation needed] 3NF was itself followed by the definition of Boyce–Codd normal form in 1974, which seeks to prevent anomalies possible in relations with several overlapping composite keys.

Definition of third normal form

[edit]

Codd's definition states that a relation R is in 3NF if and only if it is in second normal form (2NF) and every non-prime attribute of R is non-transitively dependent on each candidate key. A non-prime attribute of R is an attribute that does not belong to any candidate key of R.[4]

Codd defines a transitive dependency of an attribute set Z on an attribute set X as a functional dependency chain X → Y → Z that must be satisfied for some attribute set Y, where it is not the case that Y → X, and all three sets must be disjoint.[5]

A 3NF definition that is equivalent to Codd's, but expressed differently, was given by Carlo Zaniolo in 1982. This definition states that a table is in 3NF if and only if for each of its functional dependencies X → Y, at least one of the following conditions holds:[6][7][need quotation to verify]

  • X contains Y (that is, Y is a subset of X, meaning X → Y is a trivial functional dependency),
  • X is a superkey,
  • every element of Y \ X, the set difference between Y and X, is a prime attribute (i.e., each attribute in Y \ X is contained in some candidate key).

To rephrase Zaniolo's definition more simply, the relation is in 3NF if and only if for every non-trivial functional dependency X → Y, X is a superkey or Y \ X consists of prime attributes. Zaniolo's definition gives a clear sense of the difference between 3NF and the more stringent Boyce–Codd normal form (BCNF). BCNF simply eliminates the third alternative ("Every element of Y \ X, the set difference between Y and X, is a prime attribute.").

The definition offered by Zaniolo can be shown to be equivalent to the Codd definition in the following way: let X → A be a nontrivial functional dependency (i.e., one where X does not contain A) and let A be a non-prime attribute. Also let Y be a candidate key of R. Then Y → X. Further since A is a non-prime attribute, therefore A cannot determine X (A → X not possible) because in that case AY would form the super key. Therefore, A is not transitively dependent on Y (X is not prime attribute as per 2NF but both Y and X can be non-primes without following the Codd definition for 3NF) if and only if there is a functional dependency X → Y (simply reversing one of the dependency to avoid transitivity), i.e., if and only if X is a superkey of R. It is to be noted that either or each of A, X and Y can be single attributes or a combination thereof but are necessarily disjoint. One can write X → Y equivalently as X → XY and one may thus observe the Zaniolo equivalence for Codd by performing the set difference between the dependent and the determinant.

Example

[edit]

Design which violates 3NF

[edit]

The following relation, with the composite key {Name, Year}, fails to meet the requirements of 3NF. The non-prime attributes WinnerName and WinnerBirthdate are only transitively dependent on the composite key via their dependence on the non-prime attribute WinnerID. This creates redundancy and the potential for inconsistency in the case that a winner of multiple tournaments is accidentally given different dates of birth in different tuples.

Tournament
Name Year WinnerID WinnerName WinnerBirthdate
Indiana Invitational 1998 1 Al Fredrickson 1975-07-21
Cleveland Open 1999 2 Bob Albertson 1968-09-28
Des Moines Masters 1999 1 Al Fredrickson 1975-07-21
Indiana Invitational 1999 3 Chip Masterson 1977-03-14

Design which complies with 3NF

[edit]

To bring the relation into compliance with 3NF, WinnerID, WinnerName and WinnerBirthdate can be transferred to a separate table.

Tournament
Name Year WinnerID
Indiana Invitational 1998 1
Cleveland Open 1999 2
Des Moines Masters 1999 1
Indiana Invitational 1999 3
Winner
WinnerID Name Birthdate
1 Al Fredrickson 1975-07-21
2 Bob Albertson 1968-09-28
3 Chip Masterson 1977-03-14

Tournament's WinnerID attribute now acts as a foreign key referencing the primary key of Winner. Unlike before, it is not possible for a winner to be associated with multiple dates of birth.

"Nothing but the key"

[edit]

A paraphrase of Codd's definition of 3NF parodying the traditional oath to tell the truth in a court of law was given by William Kent: "a non-key field must provide a fact about the key, the whole key, and nothing but the key".[2] Requiring that non-key attributes be dependent on "the whole key" ensures compliance with 2NF, and further requiring their dependency on "nothing but the key" ensures compliance with 3NF. A common variation supplements the paraphrase with the addendum "so help me Codd".[8]

While the phrase is a useful mnemonic, the mention of only a single key makes fulfilling it necessary but not sufficient to satisfy 2NF and 3NF, both of which are concerned with all candidate keys of a relation and not just any one.[citation needed]

Christopher J. Date notes that, adapted to refer to all fields rather than just non-key fields, the summary can also encompass the slightly stronger Boyce–Codd normal form, in which prime attributes must not be functionally dependent at all.[9] Prime attributes are considered to provide a fact about the key in the sense of providing part or all of the key itself. (This rule applies only to functionally dependent attributes, as applying it to all attributes would implicitly prohibit composite keys, since each part of any such key would violate the "whole key" clause.)

Computation

[edit]

A relation can always be decomposed in third normal form, that is, the relation R is rewritten to projections R1, ..., Rn whose join is equal to the original relation. Further, this decomposition does not lose any functional dependency, in the sense that every functional dependency on R can be derived from the functional dependencies that hold on the projections R1, ..., Rn. What is more, such a decomposition can be computed in polynomial time.[10]

To decompose a relation into 3NF from 2NF, break the table into the canonical cover functional dependencies, then create a relation for every candidate key of the original relation which was not already a subset of a relation in the decomposition.[11]

Considerations for use in reporting environments

[edit]

While 3NF was ideal for machine processing, the segmented nature of the data model can be difficult to intuitively consume by a human user. Analytics via query, reporting, and dashboards were often facilitated by a different type of data model that provided pre-calculated analysis such as trend lines, period-to-date calculations (month-to-date, quarter-to-date, year-to-date), cumulative calculations, basic statistics (average, standard deviation, moving averages) and previous period comparisons (year ago, month ago, week ago) e.g. dimensional modeling and beyond dimensional modeling, flattening of stars via Hadoop and data science.[12][13] Hadley Wickham's "tidy data" framework is 3NF, with "the constraints framed in statistical language".[14]

See also

[edit]

References

[edit]

Further reading

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Third normal form

View on Grokipedia
from Grokipedia
Third normal form (3NF) is a level of in design that eliminates transitive dependencies, ensuring that non-key attributes depend only on s and not on other non-key attributes. Introduced by in his paper, it builds upon (2NF) by requiring that a relation schema is in 3NF if every non-trivial X → A holds only when X is a or A is a prime attribute (part of some candidate key). The primary goal of 3NF is to reduce and prevent insertion, update, and deletion anomalies that arise from transitive dependencies, where a non-prime attribute indirectly depends on a key through another non-prime attribute. For example, in a relation with attributes for employee ID, department, and department location, if department location depends on department (not directly on employee ID), decomposing the relation into separate tables for employees-departments and departments-locations achieves 3NF. This form promotes and efficient querying in relational databases, though it may not always eliminate all redundancies addressed in higher normal forms like Boyce-Codd normal form (BCNF). In practice, achieving 3NF involves verifying that the relation is in 2NF (no partial dependencies) and then removing any transitive dependencies by decomposing into multiple relations, each with its own . While 3NF is a foundational in , its application balances normalization benefits against potential performance costs from excessive decomposition, often guiding modern design in systems like SQL-based relational databases.

Normalization Prerequisites

Overview of Database Normalization

Database normalization is a systematic process for organizing data in a to minimize redundancy and dependency, thereby enhancing and consistency. This technique structures tables and their relationships to ensure that data is stored efficiently without unnecessary duplication, which can lead to inconsistencies during operations. The concept originated with Edgar F. Codd's seminal 1970 paper, "A Relational Model of Data for Large Shared Data Banks," which introduced the and laid the foundation for normalization as a means to maintain and logical structure in shared systems. Normalization addresses key goals in , including the elimination of insertion anomalies (difficulty adding new data without extraneous information), update anomalies (inconsistent changes across duplicated ), and deletion anomalies (loss of unrelated when removing records), ultimately promoting consistency and reducing maintenance efforts. In relational databases, core terminology includes relations (equivalent to tables), attributes (columns defining data properties), and tuples (rows representing individual records). Normalization progresses through a of normal forms, starting from (1NF) and advancing to higher levels such as (2NF) and beyond, with each subsequent form building on the previous to provide incremental refinements in data organization and anomaly prevention. This progression relies on concepts like functional dependencies, which describe how attribute values determine others, guiding the decomposition of relations into more refined structures.

First and Second Normal Forms

First normal form (1NF) requires that a relation consists of atomic values in each attribute, ensuring no repeating groups or multivalued attributes within a single cell. This means every entry in the relation must be indivisible and represent a single value from its domain, eliminating nested s or lists that could complicate and . The purpose of 1NF is to establish a foundational where each can be uniquely identified by a , facilitating consistent querying and updates without ambiguity from composite or non-atomic data. Consider an unnormalized table tracking student enrollments, where the "Courses" attribute contains multiple values separated by commas:
StudentIDStudentNameCourses
101AliceMath, Physics
102BobChemistry, Math
This violates due to the repeating groups in the Courses column. To achieve , decompose it into separate tuples for each course, resulting in:
StudentIDStudentNameCourse
101AliceMath
101AlicePhysics
102BobChemistry
102BobMath
Here, each attribute holds a single atomic value, allowing a (e.g., StudentID and Course) to uniquely identify rows. Second normal form (2NF) builds on 1NF by requiring that every non-prime attribute be fully functionally dependent on the entire , with no partial dependencies on only part of a . A relation is in 2NF if it is in 1NF and all non-key attributes depend on the whole rather than a of it. Prime attributes are those that belong to at least one , while non-prime attributes are all others in the relation. For example, suppose a 1NF relation tracks orders with a (OrderID, ProductID), but includes a SupplierName that depends only on ProductID (a partial dependency):
OrderIDProductIDSupplierNameQuantity
001P1Acme Corp5
001P2Beta Inc3
002P1Acme Corp2
Here, SupplierName is not fully dependent on the entire key {OrderID, ProductID}, as it repeats for the same ProductID across orders, leading to update anomalies. To reach 2NF, decompose into two relations: one for order details (fully dependent on the composite key) and one for product-supplier information (dependent on ProductID alone): OrderDetails:
OrderIDProductIDQuantity
001P15
001P23
002P12
Products:
ProductIDSupplierName
P1Acme Corp
P2Beta Inc
This elimination of partial dependencies reduces redundancy and ensures data integrity. The transition from 1NF to 2NF typically involves such decomposition to isolate attributes with partial dependencies into separate relations, preserving all information while adhering to full dependency rules.

Core Definition

Formal Statement of 3NF

Third normal form (3NF) was introduced by E. F. Codd in 1972 to further refine structures by addressing redundancies beyond those handled in , as part of establishing rules for relational integrity. A relation schema RR with attributes divided into prime attributes (those belonging to some ) and non-prime attributes is in 3NF if it is already in (2NF) and every non-prime attribute is non-transitively dependent on every of RR. This condition ensures that no non-prime attribute depends indirectly on a key through another non-key attribute. A in this context arises from a of functional dependencies X→YX \to Y and Y→ZY \to Z, where XX is a , YY is a non-prime attribute (not a ), and ZZ is another non-prime attribute, implying an indirect dependency X→ZX \to Z that violates direct dependence on the key. An equivalent formulation states that a relation RR is in 3NF if, for every non-trivial functional dependency X→AX \to A holding in RR, either XX is a superkey of RR, or AA is a prime attribute. This transitive dependency condition is equivalent to the functional dependency-based definition commonly used in modern database theory. By extending 2NF—which eliminates partial dependencies—3NF specifically targets transitive dependencies among non-key attributes, thereby reducing update anomalies and improving data consistency in relational models.

Role of Functional Dependencies

Functional dependencies (FDs) serve as the cornerstone for analyzing and achieving third normal form in relational databases by capturing the semantic constraints that dictate how attribute values are interrelated within a relation. A functional dependency X → Y holds in a relation R if, for every pair of tuples in R that agree on all attributes in X, they also agree on all attributes in Y; this means X uniquely determines Y. FDs are classified into several types based on their structure and implications: a trivial FD occurs when Y is a subset of X, as it always holds without additional constraints; a non-trivial FD has Y not entirely contained in X; full FDs indicate that no proper subset of X determines Y, contrasting with partial FDs where a subset does; and transitive FDs arise when X → Z → Y implies X → Y indirectly. The logical implications of FDs are governed by Armstrong's axioms, a complete set of inference rules for deriving all valid FDs from a given set. These include reflexivity (if Y ⊆ X, then X → Y), augmentation (if X → Y, then XZ → YZ for any Z), and transitivity (if X → Y and Y → Z, then X → Z). Derived rules extend these, such as union (if X → Y and X → Z, then X → YZ), (if X → YZ, then X → Y and X → Z), and pseudotransitivity (if X → Y and WY → Z, then WX → Z). These axioms enable systematic reasoning about dependencies, ensuring that any FD inferred logically holds in the relation. Identifying FDs typically involves semantic , where domain experts derive them from business rules and relationships to reflect real-world constraints. Alternatively, empirical methods mine FDs directly from samples using algorithms that scan relations to detect dependencies, such as heuristic-driven searches for minimal covers in large datasets. To facilitate , FD sets are often simplified into a canonical cover, a minimal equivalent set where each FD is non-redundant, left-reduced (no extraneous attributes on the left side), and right-reduced (no extraneous on the right). The process involves repeatedly removing redundant FDs and extraneous attributes using closure computations until no further simplifications are possible. The closure of an attribute set X, denoted X+X^+, comprises all attributes in the relation that are functionally determined by X, computed iteratively by applying the given FDs and Armstrong's axioms starting from X until no new attributes are added. This closure is essential for verifying keys, testing implications, and simplifying FD sets.

Illustrative Examples

Design Violating 3NF

A design violates third normal form (3NF) when it is in second normal form (2NF) but contains transitive dependencies, where a non-prime attribute depends on another non-prime attribute rather than directly on a candidate key. Consider a relation schema called Employee with attributes EmpID (primary key), Dept, DeptLocation, and Skill. The functional dependencies (FDs) are: EmpID → Dept, EmpID → Skill, and Dept → DeptLocation. Here, Skill and Dept depend directly on the primary key EmpID, but DeptLocation depends on Dept (a non-key attribute), creating a transitive dependency EmpID → Dept → DeptLocation. This relation is in 2NF because the primary key is a single attribute, so there are no partial dependencies on only part of a composite key. However, it fails 3NF due to the transitive dependency on the non-prime attribute DeptLocation. To illustrate, suppose the Employee relation contains the following data, where multiple employees share the same department and thus the same department location, leading to redundancy:
EmpIDSkillDeptDeptLocation
101JavaITNew York
102PythonITNew York
103SQLHRLondon
The redundancy of "New York" and "London" across rows highlights the transitive dependency. This design leads to data anomalies. An update anomaly occurs if the IT department relocates to Boston: updating DeptLocation for all IT employees (rows 101 and 102) risks inconsistency if one row is missed, resulting in some records showing "New York" while others show "Boston". An insertion anomaly arises when adding a new department, such as Finance in Tokyo, without any employees yet: no row can be inserted for the department's location without assigning an employee, preventing storage of the department information. A deletion anomaly happens if the HR employee (row 103) is deleted: the HR department's location "London" is lost, even though the department still exists. After such a deletion, the relation might look like this, missing the HR location entirely:
EmpIDSkillDeptDeptLocation
101JavaITNew York
102PythonITNew York
These anomalies demonstrate how the transitive dependency causes inefficiencies and potential issues in the design.

Design Complying with 3NF

To achieve compliance with third normal form (3NF), a database schema exhibiting transitive dependencies must be refactored by decomposing tables such that no non-prime attribute is dependent on another non-prime attribute. Consider a typical violating design where an Employee table includes attributes for employee ID (EmpID, ), department (Dept), (), and department location (DeptLocation), with functional dependencies (FDs) EmpID → Dept, EmpID → , and Dept → DeptLocation. The transitive dependency Dept → DeptLocation violates 3NF because DeptLocation depends on Dept, which is not a key. To comply, decompose into two tables: Employee (EmpID [PK], Dept [FK], ) and Department (Dept [PK], DeptLocation). This separation ensures all non-prime attributes depend directly on candidate keys, eliminating the . Verification of 3NF compliance involves confirming the schema meets (2NF) prerequisites and has no transitive dependencies. In the Employee table, FDs are EmpID → Dept and EmpID → Skill, with both non-prime attributes (Dept, Skill) depending solely on the primary key EmpID; no partial or transitive issues exist. In the Department table, the FD Dept → DeptLocation ensures DeptLocation depends only on the primary key Dept. Joins via the foreign key Dept in Employee referencing Department allow reconstruction of the original data without redundancy. The following markdown tables illustrate a side-by-side comparison of the original violating and the refactored 3NF-compliant , assuming sample for three employees in two departments: Original Violating Table: Employee
EmpIDDeptSkillDeptLocation
101MarketingNew York
102SalesNew York
103ITCoding
Refactored 3NF-Compliant Tables Employee
EmpIDDeptSkill
101Marketing
102Sales
103ITCoding
Department
DeptDeptLocation
New York
IT
This structure supports key database operations without anomalies. For insertion, a new department can be added to the Department table (e.g., | HR | |) without requiring an associated employee, avoiding forced nulls or dummy records. Updates to department locations occur in one place in Department (e.g., changing to ), preventing inconsistent data across multiple rows. Deletions allow removing an employee from Employee without losing department information, as it persists independently. While 3NF compliance reduces storage redundancy (e.g., DeptLocation repeated only once per department) and mitigates update/insert/delete anomalies, it introduces trade-offs such as increased query complexity requiring joins (e.g., SELECT * FROM Employee JOIN Department ON Employee.Dept = Department.Dept) and potential performance overhead in large-scale systems due to additional table accesses.

Theoretical Foundations

The "Nothing but the Key" Principle

The "Nothing but the Key" principle serves as an informal mnemonic to intuitively grasp the requirements of third normal form (3NF) in design. Coined by , it states that every non-key attribute in a relation must provide a fact about the key, the whole key, and nothing but the key. This slogan parallels the oath taken in court to emphasize completeness and exclusivity in attribute dependencies. The breakdown of the phrase highlights key aspects of normalization. The "whole key" portion ensures that non-key attributes depend on the entire , thereby eliminating partial dependencies addressed in (2NF). Meanwhile, "nothing but the key" prevents non-key attributes from depending on other non-key attributes, avoiding transitive dependencies that could lead to update anomalies. In relation to functional dependencies, the principle ensures that every non-key attribute is functionally determined solely by the key, without any non-key attribute being determined by another non-key attribute. This mnemonic originated as an elaboration on E.F. Codd's formal introduction of 3NF in , aimed at making normalization concepts more accessible to database practitioners. However, the principle oversimplifies and fails to account for edge cases like multivalued dependencies, which require higher normal forms such as (4NF) for resolution.

Algorithms for 3NF Decomposition

The synthesis algorithm for third normal form (3NF) decomposition, originally proposed by , provides a systematic procedure to transform a relational into a set of 3NF relations while preserving key properties of the original design. Given a relation RR with attribute set Attr⁡(R)\operatorname{Attr}(R) and a set FF of functional dependencies (FDs) over Attr⁡(R)\operatorname{Attr}(R), the algorithm first computes a canonical cover FcF_c of FF, which is a minimal equivalent set of FDs with single attributes on the right-hand side and no redundant FDs or attributes. This step involves iteratively removing redundant FDs by checking if each FD is implied by the others using attribute closure computations and decomposing multi-attribute right-hand sides into individual FDs. The core decomposition proceeds as follows: for each FD X→AX \to A in FcF_c, create a relation schema consisting of the attributes X∪{A}X \cup \{A\}. If none of the resulting schemas contains a of RR, add a new schema comprising one such key. Finally, if any schema has only a single attribute and is subsumed by another schema, merge it into the superseding schema to avoid trivial relations; similarly, combine schemas sharing the same set of attributes. This process yields a into relations, each of which satisfies the 3NF condition as defined by Codd, where for every non-trivial FD X→AX \to A in the projected FDs, either XX is a or AA is a prime attribute. The algorithm guarantees a lossless-join decomposition, meaning the natural join of the decomposed relations reconstructs the original relation without spurious tuples, as each relation includes determinants from the canonical cover, ensuring the join dependencies align with the FDs. It also preserves dependencies, such that the union of the FDs projected onto each decomposed relation logically implies the original set FF, allowing enforcement of all constraints locally without recomputing closures across relations. To verify whether a given is already in 3NF, a checking examines the FDs for violations of the form. Compute the cover FcF_c of the given FDs. For each FD X→AX \to A in FcF_c where AA is a non-prime attribute, determine if XX is a by computing the attribute closure X+X^+ (under FF) and checking if Attr⁡(R)⊆X+\operatorname{Attr}(R) \subseteq X^+; if not, and no such violation holds for prime AA, the is in 3NF. This verifies the absence of transitive dependencies by ensuring no non- implies a non-prime attribute transitively. The following pseudocode outlines the synthesis algorithm abstractly:

Input: Relation R, FD set F Output: Set of 3NF relations D 1. Compute canonical cover Fc of F // Using closure checks to remove redundancies 2. D = [empty set](/page/Empty_set) 3. For each FD X → A in Fc: Add relation (X ∪ {A}) to D 4. If no relation in D contains a [candidate key](/page/Candidate_key) K of R: Add relation K to D 5. For each pair of relations Ri, Rj in D where Ri ⊆ Rj: Remove Ri from D // Merge subsumed relations Return D

Input: Relation R, FD set F Output: Set of 3NF relations D 1. Compute canonical cover Fc of F // Using closure checks to remove redundancies 2. D = [empty set](/page/Empty_set) 3. For each FD X → A in Fc: Add relation (X ∪ {A}) to D 4. If no relation in D contains a [candidate key](/page/Candidate_key) K of R: Add relation K to D 5. For each pair of relations Ri, Rj in D where Ri ⊆ Rj: Remove Ri from D // Merge subsumed relations Return D

Each step relies on attribute closure computations, which can be performed in polynomial time relative to the number of attributes, making the overall synthesis polynomial-time executable.

Practical Applications

Benefits and Anomalies Addressed

Third normal form (3NF) primarily addresses insertion, update, and deletion anomalies that arise from transitive dependencies in lower normal forms. An insertion anomaly occurs when new data cannot be added without including extraneous , such as entering details for a new supplier only when an order is placed; 3NF prevents this by ensuring all non-key attributes depend solely on the , allowing independent insertion of entities. Update anomalies are avoided because facts are stored in a single location, eliminating the risk of inconsistent modifications, for instance, changing a supplier's requires only one update rather than multiple across related . Deletion anomalies are eliminated as well, since removing one entity—such as an order—does not inadvertently erase unrelated data like supplier details, preserving during removals. Beyond anomaly prevention, 3NF reduces by decomposing relations to remove transitive dependencies, which lowers storage requirements and simplifies . This minimization of duplication also facilitates easier maintenance, as changes propagate consistently without affecting multiple copies of the same data. Furthermore, 3NF supports through the use of primary and foreign keys in separated tables, ensuring relationships remain valid and reducing the potential for orphaned or inconsistent references. In practice, while 3NF serves as a foundational baseline for robust , denormalization may be applied selectively in read-heavy systems to improve query performance by reintroducing some redundancy and reducing join operations.

Considerations in Reporting and OLAP Systems

In reporting and OLAP systems, adhering strictly to third normal form (3NF) presents significant challenges due to the need for frequent joins across normalized tables, which can degrade query performance during complex aggregations and analytical processing over large datasets. To mitigate this, techniques such as star and snowflake schemas are prevalent, intentionally incorporating to consolidate related attributes into fewer tables, thereby minimizing join operations and accelerating aggregation speeds for queries. Despite these performance trade-offs, 3NF remains applicable in reporting contexts involving , where maintaining and eliminating transitive dependencies is essential to ensure consistent across reports. It is particularly beneficial when update frequencies are high, as normalization reduces redundancy and prevents update anomalies that could propagate errors into analytical outputs. Hybrid approaches offer a practical compromise, normalizing core operational tables to 3NF for data integrity while denormalizing derived views or materialized tables specifically for reporting to optimize read-heavy workloads. This strategy leverages (ETL) processes to populate denormalized structures from normalized sources, balancing storage efficiency with query responsiveness. SQL extensions, such as window functions, enhance the efficiency of handling 3NF data in OLAP environments by enabling row-level computations like rankings, running totals, and moving averages over partitioned datasets without requiring full or excessive joins. These functions support analytical operations directly on normalized schemas, improving performance in systems like or SQL Server for tasks such as time-series analysis in reporting. Emerging trends in cloud databases, such as , facilitate balancing OLTP and OLAP workloads through scalable architectures that support normalized designs for transactional integrity while allowing efficient querying via integrated analytical extensions, potentially incorporating automated scaling to handle mixed normalization strategies without manual intervention.

References

  1. https://www.semanticscholar.org/paper/Further-Normalization-of-the-Data-Base-Relational-Codd/7be40103ca2c4114e07bb327bf5f902d5b081808
  2. https://www.sciencedirect.com/topics/computer-science/third-normal-form
  3. https://www.ibm.com/think/topics/database-normalization
  4. https://www.datacamp.com/tutorial/third-normal-form
  5. https://orc.library.atu.edu/cgi/viewcontent.cgi?article=1002&context=atu_oer
  6. https://dl.acm.org/doi/10.1145/362384.362685
  7. https://cvw.cac.cornell.edu/RelationalDBs/design-create/database_normalization
  8. https://www.cs.dartmouth.edu/~tjp/cs61/slides/Day12.pdf
  9. https://home.adelphi.edu/~siegfried/cs443/443l5.pdf
  10. https://forum.thethirdmanifesto.com/wp-content/uploads/asgarosforum/987737/00-efc-further-normalization.pdf
  11. https://www.cs.purdue.edu/homes/bb/CS348-F13/other_handouts/design_theory.pdf
  12. https://www.cs.utexas.edu/~mitra/csSpring2012/cs327/lectures/normal.html
  13. https://dl.acm.org/doi/10.1145/358024.358054
  14. https://learn.microsoft.com/en-us/troubleshoot/microsoft-365-apps/access/database-normalization-description
  15. https://www.freecodecamp.org/news/database-normalization-1nf-2nf-3nf-table-examples/
  16. https://dl.acm.org/doi/10.1145/320493.320489
  17. https://jcsites.juniata.edu/faculty/rhodes/dbms/normalization.htm
  18. https://condor.depaul.edu/gandrus/240IT/accesspages/normalization3.htm
  19. https://www.seas.upenn.edu/~zives/03f/cis550/codd.pdf
  20. https://www.bu.edu/tech/files/2018/06/intro2DB20180611.pdf
  21. http://users.csc.calpoly.edu/~dekhtyar/366-Spring2019/lectures/lec08.366.pdf
  22. https://ocw.mit.edu/courses/11-521-spatial-database-management-and-advanced-geographic-information-systems-spring-2003/a69668cb3f76aeb74b005dbbaa446d09_lect7.pdf
  23. https://cvw.cac.cornell.edu/RelationalDBs/design-create/denormalization
  24. https://www.matillion.com/blog/3nf-vs-star-schema
  25. https://www.singlestore.com/blog/third-normal-form/
  26. https://www.sciencedirect.com/topics/computer-science/star-schema
  27. https://www.cloudquery.io/blog/explainer-3nf_vs_star-schema
  28. https://www.integrate.io/blog/data-normalization/
  29. https://celerdata.com/glossary/normalization-vs-denormalization-the-trade-offs-you-need-to-know
  30. https://guides.visual-paradigm.com/balancing-data-integrity-and-performance-normalization-vs-denormalization-in-database-design/
  31. https://hevodata.com/learn/data-denormalization/
  32. https://www.vldb.org/pvldb/vol8/p1058-leis.pdf
  33. https://mode.com/sql-tutorial/sql-window-functions/
  34. https://aws.amazon.com/compare/the-difference-between-olap-and-oltp/
  35. https://www.cloudoptimo.com/blog/how-oltp-olap-and-etl-drive-modern-cloud-data-management/
Add your contribution
Related Hubs
User Avatar
No comments yet.