1. Silent Killers Lurking in Your Schema Mickey Stuewe
Sr. Database Developer
Microsoft Geek
Red Gate Nerd

2. Your Background DBA Database Developer Programmer Manager
Just Checking Things Out

3. Objectives Data Types Smart Keys Naming Named Value Pairs Nullability
Constraints
Indexes
Hard Coded Lists
Minimal Auditing

4. Data types can be data hogs Data Types
Affects range of possible values
Affects storage
Affects behavior of functions
Affects choices of indexes
Affects quality of your data
Data types can
be data hogs

5. Data Types TINYINT SMALLINT INT BIGINT Size 1 Byte 2 Bytes 4 Bytes
Range
0 to 255
-32,768 to 32,768
-2,147,483,648 to
2,147,483,648
-2^63 to 2^63

6. Data Types TINYINT SMALLINT INT BIGINT Size 1 Byte 2 Bytes 4 Bytes
Range
0 to 255
-32,768 to 32,768
-2,147,483,648 to
2,147,483,648
-2^63 to 2^63
Bad Use
Year
PK of trans data
Age
Number of dependents
PK of “click data”
Birth month
PK for a lookup table

7. Data Types TINYINT SMALLINT INT BIGINT Size 1 Byte 2 Bytes 4 Bytes
Range
0 to 255
-32,768 to 32,768
-2,147,483,648 to
2,147,483,648
-2^63 to 2^63
Bad Use
Year
PK of trans data
Age
Number of dependents
PK of “click data”
Birth month
PK for a lookup table
Good Use

8. Data Types CHAR VARCHAR NVARCHAR NVARCHAR (MAX) Size
1 byte per char. (fixed length)
2 bytes + 1 byte per char.
2 bytes + 2 bytes per char.
Up to 2 GB
Range
1 to 8000 char.
1 to 4000 char.
1 char to 2GB

9. Data Types CHAR VARCHAR NVARCHAR NVARCHAR (MAX) Size
1 byte per char. (fixed length)
2 b + 1 byte per char.
2 bytes + 2 byte per char.
Up to 2 GB
Range
1 to 8000 char.
1 to 4000 char.
1 char to 2GB
Bad Use
Order Notes
Middle Name
Gender (F/M)
Middle Initial
State Capitals
Lookup Values
Almost Everything

10. Data Types CHAR VARCHAR NVARCHAR NVARCHAR (MAX) Size
1 byte per char. (fixed length)
2 b + 1 byte per char.
2 bytes + 2 byte per char.
Up to 2 GB
Range
1 to 8000 char.
1 to 4000 char.
1 char to 2GB
Bad Use
Order Notes
Middle Name
Gender (F/M)
Middle Initial
State Capitals
Lookup Values
Almost Everything
Good Use
Capitals of the world
suser_name()
Notes that can go over 4000 characters

11. Data Types No Indexes on NVARCHAR (MAX)…EVER CHAR VARCHAR NVARCHAR
Size
1 byte per char. (fixed length)
2 b + 1 byte per char.
2 bytes + 2 byte per char.
Up to 2 GB
Range
1 to 8000 char.
1 to 4000 char.
1 char to 2GB
Bad Use
Order Notes
Middle Name
Gender (F/M)
Middle Initial
State Capitals
Lookup Values
Almost Everything
Good Use
Capitals of the world
suser_name()
Notes that can go over 4000 characters
Max str. fields in index
<= 900 Char
<= (900) – (2 x no.fields)
<= (450) – (2 x no.fields)
Can’t touch this.

12. Data Types Hint: DateTime2 is the best thing since sliced bread
Homework:
Compare date data types

13. Smart Keys MED15 – 000456 - F Smart Keys Are Not So Smart Pro
Easy to decipher
Multiple pieces of information in one column
Con
Fragmented indexes
Can get out of hand
Breaks Codd’s rules
MED15 – F

14. Bad Naming AEC AER IOU FICO Return Review Split Id Field1 Sheet!1
It Shouldn’t Be Aa Box Of Chocolates
Generic columns named Column1…n
Abbreviations
Reserved Words
Default Names
Bit columns: Active vs isActive
Spelling
AEC
AER
IOU
FICO
Return
Review
Split Id
Field1
Sheet!1
Sheet1$
YourNameHere
From
User
Create
UserColumn01
UserColumn02 …
UserColumn08
AEC Average Efficiency Cost
AER Annual Equivalent Return
AER Annual Equivalent Rate
Return
Review

15. Named Value Pairs Long Distance Dating Pro Easy to insert
Easier for applications
Con
VARCHAR/NVARCHAR only
Hard for reporting
Lack of Foreign Keys
Breaks Codd’s rules
Maintenance
Lack of Constraints
Possible Dirty Data
Rule 1: The information rule:
All information in a relational database (including table and column names) is represented in only one way, namely as a value in a table.

16. Nullability To NULL Or Not To NULL, That Is The Question
Has a purpose – represents “no data”
Column of type BIT should not be nullable
Be careful with ISNULL function
Rule 3: Systematic treatment of null values:
The DBMS must allow each field to remain null (or empty). Specifically, it must support a representation of "missing information and inapplicable information" that is systematic, distinct from all regular values (for example, "distinct from zero or any other number", in the case of numeric values), and independent of data type. It is also implied that such representations must be manipulated by the DBMS in a systematic way.
Rule 3: Systematic treatment of null values:
The DBMS must allow each field to remain null (or empty). Specifically, it must support a representation of "missing information and inapplicable information" that issystematic, distinct from all regular values (for example, "distinct from zero or any other number", in the case of numeric values), and independent of data type. It is also implied that such representations must be manipulated by the DBMS in a systematic way.

17. Constraints Constrain the data, she’s goanna blow!
Protect against bad data
Check Constraints
Foreign Key Constraints
Unique Constraints
Check end date is after begin date
Check a month is a month
Check birthdate is not in the future

18. Indexes Resource: JasonStrate.com
She can’t take any more. She’s maxed at 900 bytes!
Not all Primary Keys should be clustered Indexes
Add Indexes to heavily used columns
Have a good balance of indexes
Resource: JasonStrate.com
Surrogate primary keys vs natural keys
How is the table used?
Does it have more reads than writes?
Is it meant for reporting?
Is the data retrieved in large ranges or one row at a time?

19. Hard Coded Lists Danger Will Robinson!
Unable to change them efficiently
Unable to validate against them outside of application or query
Hard to maintain over time
No referential integrity
Dirty data will happen

20. Minimal Auditing CreatedBy CreatedOn UpdatedBy UpdatedOn
Who’s on first. When’s on second.
Can provide quick insight as to when data was changed
Can provide quick insight as to who changed the data
Can provide insight to bugs
Can be used by ETL processes
Can be used for partitioning
CreatedBy
CreatedOn
UpdatedBy
UpdatedOn

21. Show Me The Demo

22. Objectives Data Types Smart Keys Naming Named Value Pairs Nullability
Constraints
Indexes
Hard Coded Lists
Minimal Auditing

23. Questions?

24. Presentation Contact Online www.MickeyStuewe.com/Resources
Online
MickeyStuewe.com
Bigpass.sqlpass.org
YouTube.com: Mickey Stuewe