This session will explore the new JSON functionality introduced in SQL Server 2016. We will use T-SQL examples to learn how these functions can be used to parse, create, and modify JSON data. More importantly, we will discuss how to optimize performance when using these functions. By the end of this session DBAs and developers will know how to efficiently work with JSON in SQL Server 2016. Demo code available at https://bertwagner.com/presentations

1. DBAs vs Developers:
JSON in SQL Server 2016
| Bert Wagner | July 22, 2017
1

3. Background
• BI developer @ Progressive Insurance for 6+ years
• I ❤ JSON – I use it in APIs, hardware projects, websites
• I also ❤ SQL – relational database structures
3

4. Overview
• What is JSON?
• Why use JSON?
• When is it appropriate to store JSON in SQL?
• Usage examples:
• ETL and reporting
• Database object maintenance
• Performance parsing
• Performance comparisons
4

5. 5
What does JSON look like?
{
“Make” : “Volkswagen”,
“Year” : 2003,
“Model” : {
“Base” : “Golf”,
“Trim” : “GL”
},
“Colors” : [“White”, “Pearl”, “Rust”],
“PurchaseDate” : “2006-10-05T00:00:00.000Z”
}

6. 6
Why use JSON?
Easy Processing
var car = { "Make" : "Volkswagen" };
console.log(car.Make);
// Output: Volkswagen
car.Year = 2003;
console.log(car);
// Output: { "Make" : "Volkswagen", "Year" : 2003" }
Javascript:

7. 7
Why use JSON?
APIs

8. 8
Why use JSON?
Storage Size
<Car>
<Make>Volkswagen</Make>
<Year>2003</Year>
<Model>
<Base>Golf</Base>
<Trim>GL</Trim>
</Model>
<Colors>
<Color>White</Color>
<Color>Pearl</Color>
<Color>Rust</Color>
</Colors>
<PurchaseDate>
2006-10-05 00:00:00.000
</PurcaseDate>
</Car>
{
“Make” : “Volkswagen”,
“Year” : 2003,
“Model” : {
“Base” : “Golf”,
“Trim” : “GL”
},
“Colors” :
[“White”, “Pearl”, Rust”],
“PurchaseDate” :
“2006-10-05T00:00:00.000Z”
}
XML: 225 Characters JSON: 145 Characters

9. 9
Appropriate Usage
Staging Data
• Load data raw
• Validate
• Transform

10. 10
Appropriate Usage
Error Logging
ErrorDate Component Data
2016-03-17 21:23:39 GetInventory { "Make : "Volkswagen", "Year" : 2003}
2016-03-19 12:59:31 Login { "User" : "Bert", "Referrer" : "http://google.com",
"AdditionalDetails" : "Invalid number of login attempts" }

11. 11
Appropriate Usage
Non-Analytical Data
• Sessions
• User preferences
• Non-frequently changing variables
• Admin emails
• Static dropdown menus

12. 12
Inappropriate Usage
High-Performance Requirements

13. 13
Inappropriate Usage
Validation/Integrity Requirements

14. 14
Inappropriate Usage
Being Lazy

15. Demos
1. ETL and reporting
2. Database object maintenance
3. Performance parsing w/ computed column indexes
4. SQL JSON vs XML vs .NET performance comparisons
15

16. Performance Results - XML
16
• JSON faster in almost all categories
• If considering entire app performance, maybe faster in
all categories

17. Performance Results - .NET
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• Competitive with C# libraries
• Indexes on computed columns are BLAZING!

18. Recap
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• Many good (and bad) uses for JSON in SQL exist
• JSON can be fully manipulated in SQL Server 2016
• JSON is preferable to XML for new projects
• JSON performance is comparable to .NET, faster with
computed column indexes

19. Thank you!
Twitter: @bertwagner
Blog: https://blog.bertwagner.com <- new post every Tuesday
YouTube: http://bit.ly/bertsyoutube <- new video every Tuesday
Email: bertwagner@bertwagner.com
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20. 20
Appendix
Software for keeping screen region on top
• On Top Replica
Blog posts and YouTube videos:
• SQL Server JSON Usage - Parsing
• SQL Server JSON Usage - Creating
• SQL Server JSON Usage - Updating, Adding, Deleting
• Performance Comparisons - .NET
• Performance Comparisons - XML
• Performance Comparisons - .NET and XML Redux
• JSON Computed Column Indexes
Microsoft Connect
• Add an option to JSON_MODIFY() to fully delete values from arrays