modeling trees

8/4/2019 Modeling Trees

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Modeling Trees in SQL

Richard Broersma Jr.Mangan Inc. System Integrator


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From a data modeling perspective,

what is a Tree ?

A specialized form of Graph (data structure).


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From a data modeling perspective,

what is a Graph ?

For a set of Entities( Vertexes ), a set of

relationships( edges ) can be defined to

represent all associations between entities.


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Simple Graph Illustration

Notice: Provides a flexible way to relate entities Multiple relationship paths exist for entities Graph Analysis is inefficient using SQL


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Graph Analysis In Practice

Which path between two terminals: has the smallest transit time has the least number of stops passes through a certain terminal


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Okay but what about TREES!

Notice:

Have a root that all entities relate to Limits singular relationships between entities Relationships from root to leaf are hierarchical

constraints make SQL analysis performant


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Trees In Practice: Org Chart

Models the Has zero or more: Who is the manager of any given employee Which emps directly report to a manager Which emps ultimately managed by a manager


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Trees In Practice: Assembly (BOM)

Models the Contains zero or more: What sub assemblies compose an assembly What is an assembly ultimately composed of


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Logic Entity Relationship Diagram

Barker'sNotation/Style

Chen like Style


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Good Practice Tree Design

Liberal add Check Constraints to eliminate

possible sources of tree corruptionDecouple entity and relationship in separate

tables. Eliminates Entity/Relationship Denormalization Allows multiple tree relationship tables to exist

to represent different types of relationships


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Physical Designs

Adjacency ListModel

A.K.A. BOM Model

Nested Set ModelA.K.A. Celko Tree

Path EnumerationModel

(Impetus ForPostgreSQL LTREEContrib Module)


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Adjacency List Model

CREATE TABLE OrgChart(emp VARCHAR(10) NOT NULL PRIMARY KEYREFERENCES Personnel( emp ),

boss VARCHAR(10) null means rootREFERENCES Personnel( emp ),

... );

Albert NULL

Bert Albert

Chuck Albert

Donna Chuck

Eddie Chuck

Fred Chuck

emp boss


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Adjacency List Model

Performs well with Inserts & Deletes.

Using SQL92, can only return at most one levelof the tree per statement (until PG 8.4 CTE) and

can perform badly.

With SQL03, With Recursive makes ALM a goodchoice.


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Nested Set Model

CREATE TABLE OrgChart(lft INTEGER NOT NULL,rgt INTEGER NOT NULL,

CONSTRAINT lftlessthanrgtCHECK( lft < rgt ),

emp VARCHAR(10) NOT NULL PRIMARY KEYREFERENCES Personnel( emp )

... );

1 12 Albert

2 3 Bert

4 11 Chuck

5 6 Donna

7 8 Eddie

9 10 Fred

lft rgt emp


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Nested Set Model

Performs badly with numerous Inserts & Deletes. Selects Perform very well Using SQL92, easily returns all levels of the tree

per statement. (hard to limit to immediate levels) Nature of Nested Set allow for easy analysis of

tree structure ex. WHERE lft >= 4 AND rgt


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Path Enumeration Model

CREATE TABLE OrgChart(path VARCHAR NOT NULL,emp VARCHAR(10) NOT NULL PRIMARY KEY

REFERENCES Personnel( emp ),CONSTRAINT validPathCHECK( path LIKE '%' || emp ),

... );

path

Albert Albert

Bert

Chuck

Donna

Eddie

Fred

emp

AlbertBert

AlbertChuck

AlbertChuckDonna

AlbertChuckEddie

AlbertChuckFred


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Path Enumeration Model

Performs well numerous Inserts & Deletes. Selects performs very but has typical LIKE '%.%'

gotchas. (LTREE improves this with gist support) Using SQL92, easily returns all levels of the tree

per statement and can limit to immediate levels Analyzing tree structure is complicated ex. WHERE emp LIKE 'AlbertChuck%'


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References

Batini, Carol, Stefano Ceri, and Shamkant B. Navathe. Conceptual Database Design : An Entity Relationship Approach. Boston:

Benjamin Cummings Company, 1991.

Celko, Joe. Joe Celko's SQL for Smarties : Advanced SQL Programming. 3rd ed. Greensboro: Morgan Kaufmann, 2005.

Celko, Joe. Joe Celko's Thinking in Sets : Auxiliary, Temporal, and Virtual Tables in SQL. New York: Elsevier Science &

Technology Books, 2008.

Celko, Joe. Joe Celko's Trees and Hierarchies in SQL for Smarties. Greensboro: Morgan Kaufmann, 2004.

Douglas, Korry. PostgreSQL. 2nd ed. Indianapolis: Sams, 2005.

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