Tutorials
Practical T-SQL
Practical T-SQL
  • Practical T-SQL Pocket Guide For Beginners
  • Preface
    • Section I. A Note From The Author
    • Section II. Tutorial Overview
    • Section III. Running The Examples
    • Section IV. How To Report An Issue
    • Section V. Join The MSU Community On Discord
    • Section VI. Supplementary Material
  • Language Basics
    • Lesson 1. Obligatory Hello World
    • Lesson 2. Code Comments With T-SQL
    • Lesson 3. Basic Syntax
    • Lesson 4. Your First Query
    • Lesson 5. Filtering Data
    • Lesson 6. Sorting Data
    • Lesson 7. Complex Data Filtering
    • Lesson 8. Aliases
    • Lesson 9. String Functions
    • Lesson 10. Creating New Columns From Existing Data (Calculated Fields)
    • Lesson 11. Displaying Data Based On Conditions (Case Statement)
    • Lesson 12. Aggregate Functions
    • Lesson 13. Grouping And Summarizing Data
    • Lesson 14. Querying More Than One Table
    • Lesson 15. Combining Queries
    • Lesson 16. Subqueries
    • Lesson 17. Creating Data
    • Lesson 18. Updating Data
    • Lesson 19. Deleting Data
    • Lesson 20. Common Table Expressions (CTEs)
    • Lesson 21. Derived Tables
    • Lesson 22. Putting It All Together
  • Advanced Topics
    • Lesson 23. Selecting Unique Values
    • Lesson 24. Updating Data With A Join
    • Lesson 25. Data Types
    • Lesson 26. Casting Data Types
    • Lesson 27. Creating Tables
    • Lesson 28. Altering Tables
    • Lesson 29. Dropping Tables
    • Lesson 30. Variables
    • Lesson 31. Controlling Flow
    • Lesson 32. Looping
    • Lesson 33. Error Processing
    • Lesson 34. Temporary Tables
    • Lesson 35. Views
    • Lesson 36. Indexed Views
    • Lesson 37. User Defined Functions
    • Lesson 38. Stored Procedures
    • Lesson 39. BULK INSERT
    • Lesson 40. Loading Tables With MERGE
    • Lesson 41. Partitioning A Dataset
    • Lesson 42. Pivoting Data
    • Lesson 43. Dynamic SQL
    • Lesson 44. Cursors
  • Solutions To Real World Problems
    • Lesson 45. Listing All Tables In A SQL Server Database
    • Lesson 46. Listing All Columns In A SQL Server Database
    • Lesson 47. Pull Records From A Table At Random
    • Lesson 48. A Better Alternative To WITH (NOLOCK)
    • Lesson 49. Boost Performance When Calling A Stored Proc From SSIS
    • Lesson 50. Setting Up Queries For Ablation Testing
    • Lesson 51. Reduce Code And Save Time With Default Column Values
    • Lesson 52. Finding Duplicate Records In A Table
    • Lesson 53. Why You Cannot Have More Than One Clustered Index On A Table
    • Lesson 54. Converting Dates To YYYYMMDD
    • Lesson 55. Sending Notification Emails With T-SQL Without Using Hardcoded Email Addresses
    • Lesson 56. Troubleshooting Long Running Queries
    • Lesson 57. Loading Large CSVs Into Data Warehouse Staging Tables
    • Lesson 58. The Only Bloody Good Reason To Use Cursors
    • Lesson 59. Loading A Type II Slowly Changing Dimension With SQL Merge
    • Lesson 60. A Clearer Explanation Of The Parameters Of The Numeric Data Type
    • Lesson 61. Why You Cannot Join On Null Values
    • Lesson 62. A Deep Dive On How The Where Clause Functions
    • Lesson 63. Using HASHBYTES() To Compare Character Strings
    • Lesson 64. Using Pipe To Hash Multiple Columns For Matching
    • Lesson 65. Why People That Indent Code Drive Me Nuts
    • Lesson 66. How To Rapidly Stand Up A Data Warehouse From Scratch
    • Lesson 67. How To Pivot Data With T-SQL When Columns Are Not Predefined
    • Lesson 68. Prepopulating A Junk Dimension
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  1. Solutions To Real World Problems

Lesson 68. Prepopulating A Junk Dimension

The mathematical properties of junk dimensions are fascinating to me. Let me explain.

Junk dimensions are usually a collection of flags and other values with low cardinality. The records in a junk dimension represent unique combinations of all the columns. Since the number of possible values in each column is finite, using basic combinatorics, you can calculate how many records are going to be in that table if you know all the values a priori.

If we let x represent the number of possible values in a column, then total number of records in a table is defined by:

Total number of records = x1 * x2 * x3*…xn

Suppose we had a table with the following profile.

Column Name

Number Of Possible Values

Color

5

Size

3

isActiveProduct

2

This means that the total number of records in this table will be:

5 * 3 * 2 = 30

This is not just an academic exercise. You can use this information to determine if there will be too many records in this table and maybe consider splitting up some of the columns into smaller logical groupings.

But the real fun of all this is loading the data completely before the first load even hits! Yeah, yeah. We could just let the table populate naturally but what fun is that?!

In our example above, we already know what all the values are. Additionally, there are not that many. We can take advantage of CROSS JOIN to put the data together and load the table!

Examples

Watch While I Science The **** Out Of This

In [ ]:

USE demo

--create necessary tables
DECLARE @ProductInformation AS TABLE (Color NVARCHAR(20), Size NVARCHAR(1), isActiveProduct BIT)
DECLARE @Color AS TABLE(Color NVARCHAR(20))
DECLARE @Size AS TABLE(Size NVARCHAR(1))
DECLARE @isActiveProduct AS TABLE(isActiveProduct BIT)

--populate component tables
INSERT INTO @Color(Color)
SELECT 'Red'
UNION
SELECT 'Blue'
UNION
SELECT 'Brown'
UNION
SELECT 'Green'
UNION
SELECT 'Yellow'

INSERT INTO @Size(Size)
SELECT 'S'
UNION
SELECT 'M'
UNION
SELECT 'L'

INSERT INTO @isActiveProduct(isActiveProduct)
SELECT '1'
UNION
SELECT '0'

--Prepopulate table
INSERT INTO @ProductInformation(Color, Size, isActiveProduct)
SELECT Color, Size, isActiveProduct 
FROM @Color
CROSS JOIN @Size
CROSS JOIN @isActiveProduct

--gloat over my math superiority
SELECT COUNT(*) AS TotalRecords
FROM @ProductInformation

--see the results
SELECT *
FROM @ProductInformation
PreviousLesson 67. How To Pivot Data With T-SQL When Columns Are Not Predefined

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