Joe Celko's Thinking in Sets: Auxiliary, Temporal, and Virtual Tables in SQL

5.4 Encryption via Tables
5.5 Random Numbers
5.6 Interpolation

6 Views
6.1 Mullins VIEW Usage Rules
6.1.1 Effi cient Access and Computations
6.1.2 Column Renaming
6.1.3 Proliferation Avoidance
6.1.4 The VIEW Synchronization Rule
6.2 Updatable and Read-Only VIEWs
6.3 Types of VIEWs
6.3.1 Single-Table Projection and Restriction
6.3.2 Calculated Columns
6.3.3 Translated Columns
6.3.4 Grouped VIEWs
6.3.5 UNIONed VIEWs
6.3.6 JOINs in VIEWs
6.3.7 Nested VIEWs
6.4 Modeling Classes with Tables
6.4.1 Class Hierarchies in SQL
6.4.2 Subclasses via ASSERTIONs and TRIGGERs
6.5 How VIEWs Are Handled in the Database System
6.5.1 VIEW Column List
6.5.2 VIEW Materialization
6.6 In-Line Text Expansion
6.7 WITH CHECK OPTION Clause
6.7.1 WITH CHECK OPTION as CHECK( ) Clause
6.8 Dropping VIEWs
6.9 Outdated Uses for VIEWs
6.9.1 Domain Support
6.9.2 Table Expression VIEWs
6.9.3 VIEWs for Table Level CHECK( ) Constraints
6.9.4 One VIEW per Base Table

7 Virtual Tables
7.1 Derived Tables
7.1.1 Column Naming Rules
7.1.2 Scoping Rules
7.1.3 Exposed Table Names
7.1.4 LATERAL() Clause
7.2 Common Table Expressions
7.2.1 Nonrecursive CTEs
7.2.2 Recursive CTEs
7.3 Temporary Tables
7.3.1 ANSI/ISO Standards
7.3.2 Vendors Models
7.4 The Information Schema
7.4.1 The INFORMATION_SCHEMA Declarations
7.4.2 A Quick List of VIEWS and Their Purposes
7.4.3 DOMAIN Declarations
7.4.4 Defi nition Schema
7.4.5 INFORMATION_SCHEMA Assertions

8 Complicated Functions via Tables
8.1 Functions without a Simple Formula
8.1.1 Encryption via Tables
8.2 Check Digits via Tables
8.2.1 Check Digits Defi ned
8.2.2 Error Detection versus Error Correction
8.3 Classes of Algorithms
8.3.1 Weighted-Sum Algorithms
8.3.2 Power-Sum Check Digits
8.3.3 Luhn Algorithm
8.3.4 Dihedral Five Check Digit
8.4 Declarations, Not Functions, Not Procedures
8.5 Data Mining for Auxiliary Tables

9 Temporal Tables
9.1 The Nature of Time
9.1.1 Durations, Not Chronons
9.1.2 Granularity
9.2 The ISO Half-Open Interval Model
9.2.1 Use of NULL for "Eternity”
9.2.2 Single Timestamp Tables
9.2.3 Overlapping Intervals
9.3 State Transition Tables
9.4 Consolidating Intervals
9.4.1 Cursors and Triggers
9.4.2 OLAP Function Solution
9.4.3 CTE Solution
9.5 Calendar Tables
9.5.1 Day of Week via Tables
9.5.2 Holiday Lists
9.5.3 Report Periods
9.5.4 Self-Updating Views
9.6 History Tables
9.6.1 Audit Trails

10 Scrubbing Data with Non-1NF Tables
10.1 Repeated Groups
10.1.1 Sorting within a Repeated Group
10.2 Designing Scrubbing Tables
10.3 Scrubbing Constraints
10.4 Calendar Scrubs
10.4.1 Special Dates
10.5 String Scrubbing
10.6 Sharing SQL Data
10.6.1 A Look at Data Evolution
10.6.2 Databases
10.7 Extract, Transform, and Load Products
10.7.1 Loading Data Warehouses
10.7.2 Doing It All in SQL
10.7.3 Extract, Load, and then Transform

11 Thinking in SQL
11.1 Warm-up Exercises
11.1.1 The Whole and Not the Parts
11.1.2 Characteristic Functions
11.1.3 Locking into a Solution Early
11.2 Heuristics
11.2.1 Put the Specification into a Clear Statement
11.2.2 Add the Words "Set of All.” in Front of the Nouns
11.2.3 Remove Active Verbs from the Problem Statement
11.2.4 You Can Still Use Stubs
11.2.5 Do Not Worry about Displaying the Data
11.2.6 Your First Attempts Need Special Handling
11.2.7 Do Not Be Afraid to Throw Away Your First Attempts at DDL
11.2.8 Save Your First Attempts at DML
11.2.9 Do Not Think with Boxes and Arrows
11.2.10 Draw Circles and Set Diagrams
11.2.11 Learn Your Dialect
11.2.12 Imagine that Your WHERE Clause Is "Super Amoeba”
11.2.13 Use the Newsgroups, Blogs, and Internet
11.3 Do Not Use BIT or BOOLEAN Flags in SQL
11.3.1 Flags Are at the Wrong Level
11.3.2 Flags Confuse Proper Attributes

12 Group Characteristics
12.1 Grouping Is Not Equality
12.2 Using Groups without Looking Inside
12.2.1 Semiset-Oriented Approach
12.2.2 Grouped Solutions
12.2.3 Aggregated Solutions
12.3 Grouping over Time
12.3.1 Piece-by-Piece Solution
12.3.2 Data as a Whole Solution
12.4 Other Tricks with HAVING Clauses
12.5 Groupings, Rollups, and Cubes
12.5.1 GROUPING SET Clause
12.5.2 The ROLLUP Clause
12.5.3 The CUBE Clause
12.5.4 A Footnote about Super Grouping
12.6 The WINDOW Clause
12.6.1 The PARTITION BY Clause
12.6.2 The ORDER BY Clause
12.6.3 The RANGE Clause
12.6.4 Programming Tricks

13 Turning Specifications into Code
13.1 Signs of Bad SQL
13.1.1 Is the Code Formatted Like Another Language?
13.1.2 Assuming Sequential Access
13.1.3 Cursors
13.1.4 Poor Cohesion
13.1.5 Table-Valued Functions
13.1.6 Multiple Names for the Same Data Element
13.1.7 Formatting in the Database
13.1.8 Keeping Dates in Strings
13.1.9 BIT Flags, BOOLEAN, and Other Computed Columns
13.1.10 Attribute Splitting Across Columns
13.1.11 Attribute Splitting Across Rows
13.1.12 Attribute Splitting Across Tables
13.2 Methods of Attack
13.2.1 Cursor-Based Solution
13.2.2 Semiset-Oriented Approach
13.2.3 Pure Set-Oriented Approach
13.2.4 Advantages of Set-Oriented Code
13.3 Translating Vague Specifications
13.3.1 Go Back to the DDL
13.3.2 Changing Specifications

14 Using Procedure and Function Calls
14.1 Clearing out Spaces in a String
14.1.1 Procedural Solution #1
14.1.2 Functional Solution #1
14.1.3 Functional Solution #2
14.2 The PRD( ) Aggregate Function
14.3 Long Parameter Lists in Procedures and Functions
14.3.1 The IN( ) Predicate Parameter Lists

15 Numbering Rows
15.1 Procedural Solutions
15.1.1 Reordering on a Numbering Column
15.2 OLAP Functions
15.2.1 Simple Row Numbering
15.2.2 RANK( ) and DENSE_RANK( )
15.3 Sections

16 Keeping Computed Data
16.1 Procedural Solution
16.2 Relational Solution
16.3 Other Kinds of Computed Data

17 Triggers for Constraints
17.1 Triggers for Computations
17.2 Complex Constraints via CHECK( ) and CASE Constraints
17.3 Complex Constraints via VIEWs
17.3.1 Set-Oriented Solutions
17.4 Operations on VIEWs as Constraints
17.4.1 The Basic Three Operations
17.4.2 WITH CHECK OPTION Clause
17.4.3 WITH CHECK OPTION as CHECK( ) clause
17.4.4 How VIEWs Behave
17.4.5 UNIONed VIEWs
17.4.6 Simple INSTEAD OF Triggers
17.4.7 Warnings about INSTEAD OF Triggers

18 Procedural and Data Driven Solutions
18.1 Removing Letters in a String
18.1.1 The Procedural Solution
18.1.2 Pure SQL Solution
18.1.3 Impure SQL Solution
18.2 Two Approaches to Sudoku
18.2.1 Procedural Approach
18.2.2 Data-Driven Approach
18.2.3 Handling the Given Digits
18.3 Data Constraint Approach
18.4 Bin Packing Problems
18.4.1 The Procedural Approach
18.4.2 The SQL Approach
18.5 Inventory Costs over Time
18.5.1 Inventory UPDATE Statements
18.5.2 Bin Packing Returns

Index