Working With Temporal Data in SQL Server


Working With Temporal Data in SQL Server
Working With Temporal Data in SQL Server
WEBRip | English | MP4 | 1024 x 768 | AVC ~430 kbps | 15 fps
AAC | 123 Kbps | 44.1 KHz | 2 channels | 3h 06mn | 591.2 MB


Genre: Video Tutorial / Development, Programming
Working with temporal data in SQL Server is not supported out of the box. Learn how to implement temporal support in a SQL Server database with all of the constraints needed, and how to optimize your temporal queries.

Although temporal data is part of many business applications, most RDBMS', including SQL Server, do not support it out of the box. However, before solving the problem, you need to understand it. After an introduction to temporal problems and constraints, you will learn how to implement the solutions. Many solutions are much simpler with the help of a special Interval CLR data type. Additional relational operators PACK and UNPACK are handy as well. Of course, these two operators do not exist in the Transact-SQL language. You will learn how to implement them with help of other language elements. Having a SQL Server solution for a problem does not mean the job is done. Of course, the next immediate issue is performance. Temporal queries that involve intervals are typically very IO and CPU intensive. For example, a test for overlapping intervals was solved with inefficient queries for years. However, a handful of solutions with fast queries was developed recently. This course introduces five different methods to get efficient queries that search for overlapping intervals, one of the most complex temporal problems. Of course, these solutions can be implemented for other temporal problems as well. All the solutions presented should work on any edition of SQL Server from 2008 to 2014.

Content:
Introduction
Introduction 1m 56s
Modules 1m 27s

1. Temporal Databases, Problems, and Constraints
Introduction 0m 44s
Theoretical and Practical Background 3m 38s
Temporal Databases 2m 9s
Demo: Mapping Dates to Integers 1m 46s
Semi-Temporal Problems 2m 28s
Demo: Semi-Temporal Problems 7m 43s
Full-Temporal Problems 3m 38s
Additional Discussions 3m 19s
Summary 1m 1s

2. The Interval Data Type
Introduction 0m 43s
IntervalCID Data Type 1m 0s
Allen's Interval Boolean Operators 3m 4s
Allen's Interval Algebra Operators 2m 51s
Demo: IntervalCID 16m 36s
Full-Temporal Constraints Revisited 2m 11s
Demo: Full-Temporal Tables 9m 16s
Summary 0m 41s

3. Packing and Unpacking Intervals
Introduction 0m 35s
Theoretical and Practical Background 2m 42s
Temporal Databases 5m 13s
Demo: Mapping Dates to Integers 1m 29s
Semi-Temporal Problems 1m 15s
Demo: Semi-Temporal Problems 2m 10s
Full-Temporal Problems 6m 42s
Additional Discussions 2m 28s
Summary 0m 40s

4. Optimizing Temporal Queries Part 1
Introduction 1m 52s
Classical T-SQL Solution 2m 59s
Demo: Classical T-SQL Solution 5m 56s
Relational Interval Tree (RIT) 1m 51s
RIT Calculation 3m 59s
Demo: Fork Node Calculation 2m 44s
Querying RIT 3m 5s
Demo: Querying RIT 6m 0s
Summary 1m 32s

5. Optimizing Temporal Queries Part 2
Introduction 1m 18s
Spatial Data Type 2m 28s
Demo: Geometry Data Type 4m 46s
XML Data Type 2m 26s
Demo: XML Data Type 4m 54s
Enhanced T-SQL Solution 4m 2s
Demo: Enhanced T-SQL Solution 5m 1s
Summary 1m 38s

6. Optimizing Temporal Queries Part 3
Introduction 1m 11s
IntervalCID Data Type 3m 19s
Demo: IntervalCID Data Type 6m 8s
Unpacked Form 3m 27s
Demo: Unpacked Form 6m 23s
Compact Unpacked Form 7m 25s
Demo: Compact Unpacked Form 10m 15s
Summary 2m 9s

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