How to Implement a Hierarchy in SQL Server

1. How to Implement a Hierarchy in SQL Server Louis Davidson (drsql.org) drsql@hotmail.com

2. Who am I? • Been in IT for over 19 years • Microsoft MVP For 10 Years • Corporate Data Architect • Written five books on database design • Ok, so they were all versions of the same book. They at least had slightly different titles each time

3. Hierarchies

4. Hierarchies • Trees - Single Parent Hierarchies • Graphs – Multi Parent Hierarchies • Note: Graphs can be complex to deal with as a whole, but often you can deal with them as a set of trees Wood with Tape Screw and Tape Screw Piece of Wood Tape

5. Cycles in Hierarchies Grandparent Parent Child • “I’m my own grandpa” syndrome • Must be understood or can cause infinite loop in processing • Generally disallowed in trees • May be supported in graphs, particularly for establishing relationships

6. Hierarchy Uses • Trees • Species • Jurisdictions • “Simple” Organizational Charts (Or at least the base manager-employee part of the organization) • Directory folders • Graph • Bill of materials • Complex Organization Chart (all those dotted lines!) • Genealogies • Biological (Typically with limit cardinality of parents to 2 ) • Family Tree – (Sky is the limit) • Social Networking Relationships • Example: (Bob is connected to Sue, Sue is connected to Fred, Fred is connected to Bob)

7. Implementation of a Hierarchy • “There is more than one way to shave a dog” • None of which are pleasant for the dog or the shaver • And the doctor who orders it only asks for a bald dog • Hierarchies are not at all natural to manipulate/query using relational code • And the natural, recursive processing of a node at a time is horribly difficult and slow in relational code • So, multiple methods of processing them have arisen through the years • The topic (much like the topic of how cruel it is to shave a dog), inspires religious-like arguments • I find all of the implementation possibilities fascinating, so I set out to do an overview of them all…

8. Working with Trees - Background • Node recursion • Relational Recursion

9. Tree Processing Algorithms • There are several methods for processing trees in SQL • We will look at • Fixed Levels • Adjacency List • HierarchyId • Path Technique • Nested Sets • Kimball Helper Table • Without giving away too much, pretty much all of the methods have some use…

10. Coding for trees • Manipulation: • Creating a new node • Moving/Reparenting a node • Deleting a node (without children) • Note: No tree algorithms allow for “simple” SQL solutions to all of these problems • Usage • Getting the children of a node • Getting the parent of a node • Aggregating along the tree • We will have demos of all of these operations…available at least

11. Reparenting Example Dragging along all of it’s child nodes along with it • Starting with: • Perhaps ending with:

12. Implementing a tree – Fixed Levels CREATE TABLE CompanyHierarchy( Company varchar(100) NOT NULL, Headquarters varchar(100) NOT NULL, Branch varchar(100) NOT NULL, PRIMARY KEY (Company, Headquarters, Branch)) Very limited, but very fast and easy to work with I will not demo this structure today because it’s use is both extremely obvious and limited

13. Implementing a tree – Adjacency List • Every row includes the key value of the parent in the row • Parent-less rows have NULL parent value • Code is the most complex to write (though not as inefficient as it might seem) • CREATE TABLE CompanyHierarchy( Organization varchar(100) NOT NULL PRIMARY KEY,ParentOrganizationvarchar(100) NULL REFERENCES CompanyHierarchy (Organization), Name varchar(100) NOT NULL)

14. Adjacency List – Adding a Node New Node

16. Simply set the parent and done!

17. Implementing a tree – Path Method 900 Bytes allows for indexed manipulations Every row includes a representation of the path to their parent Processing makes use of like and string processing (I have seen a case that used fixed length binary values) Limitation on path size for string manipulation/indexing CREATE TABLE CompanyHierarchy(OrganizationIdint NOT NULL PRIMARY KEY, Name varchar(100) NOT NULL, Path varchar(900))

18. Path Method Adding a Node New Node

19. New Id = 9

20. Plus the New Id Path from the parent

21. Implementing a tree – Path Method Every row includes a representation of the path to their parent Processing makes use of like and string processing (I have seen a case that used fixed length binary values) Limitation on path size for string manipulation/indexing CREATE TABLE CompanyHierarchy(OrganizationIdint NOT NULL PRIMARY KEY, Name varchar(100) NOT NULL, Path varchar(900))

22. Implementing a tree – HierarchyId Somewhat unnatural method to the typical SQL Programmer Similar to the Path Method, and has some of the same limitations when moving around nodes Node path does not use data natural to the table, but rather positional locationing CREATE TABLE CompanyHierarchy(OrganizationIdint NOT NULL PRIMARY KEY, Name varchar(100) NOT NULL,OrgNodehierarchyId not null)

23. Implementing a tree – Nested Sets • Query processing is done using range queries • Structure is quite slow to maintain due to fragile structure • Can produce excellent performance for queries • CREATE TABLE CompanyHierarchy( Organization varchar(100) NOT NULL PRIMARY KEY, Name varchar(100) NOT NULL, Left int NOT NULL, Right int NOT NULL)

24. Nested Sets – Adding a Node New Node

25. Updating Right Values

26. And the One Left value right of the new node

27. Renumber, leaving gap for child

28. The New Node

29. Set the New Node’s Left/Right

30. Implementing a tree – Nested Sets • Query processing is done using range queries • Structure is quite slow to maintain due to fragile structure • Can produce excellent performance for queries • CREATE TABLE CompanyHierarchy( Organization varchar(100) NOT NULL PRIMARY KEY, Name varchar(100) NOT NULL, Left int NOT NULL, Right int NOT NULL)

31. Implementing a tree – Kimball Helper • Developed initially for data warehousing since data is modified all at once with a fixed cost • Basically explodes the hierarchy into a table that turns all hierarchy manipulations into a relational query • Maintenance can be slightly costly, but using the data is extremely fast

32. Implementing a tree – Kimball Helper • For the rows in yellow, expands to the table shown:

33. Performance Examples and Limitations • The following tests were run multiple times, and the results were taken from one such run. • Clearly the results are not scientific, and done with random data. • However, they very much match my expectations from my research. • Load times were captured loading one row at a time. • Test machine (this laptop I am using tonight) was a: • Lenovo Yoga Pro 2, Haswell ULT i7 (4th Gen Intel Mobile Processor), 2.4Ghz Dual Core (Hyperthreaded), 8GB RAM, 256 GB SSD • Note: All load times include time to load 5 transactions per node

34. Performance Example Explanation • For each performance test (which I will show the code later), I ran three query sets on each data set: • Load the tree (until my computer couldn’t do it in a reasonable number of hours) • Fetch all children from the root node • Aggregate data for all children at all levels

35. Performance Comparisons

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46. Method Comparison

47. Demo Code • Example code for all examples available for download. Will demo hierarchies and graphs.

48. Method Applicability

49. Future Improvements • Use SQL Server 2014 In-Memory Database to help with locking and brute force operations • Adjust Nested Sets to use fractional numbers to reduce load time costs • Load an order of magnitude more data • Try these examples on a “real” computer!

50. Graphs • Generally implemented in same manner as adjacency list • Can be processed in the same manner as an adjacency list • Primary difference is child can have > 1 parent node • Cycles are generally acceptable • Graph structure will always be external to data structure • Graphs are even more natural data structures than trees