Buch, Englisch, 384 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 780 g
Buch, Englisch, 384 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 780 g
ISBN: 978-0-12-374137-0
Verlag: William Andrew Publishing
Perfectly intelligent programmers often struggle when forced to work with SQL. Why? Joe Celko believes the problem lies with their procedural programming mindset, which keeps them from taking full advantage of the power of declarative languages. The result is overly complex and inefficient code, not to mention lost productivity.This book will change the way you think about the problems you solve with SQL programs. Focusing on three key table-based techniques, Celko reveals their power through detailed examples and clear explanations. As you master these techniques, you’ll find you are able to conceptualize problems as rooted in sets and solvable through declarative programming. Before long, you’ll be coding more quickly, writing more efficient code, and applying the full power of SQL
Zielgruppe
Data analysts and database developers from all backgrounds, regardless of which database technology they use; this is the market for all of Joe's other books, all our data modeling books in general as well. This includes database developers working on transactional (OLTP) systems as well as data warehouse design (OLAP systems).
Unlike most of Joe's other books which are for very experienced SQL programmers who want to become gurus, this book has the widest possible audience of programmers new to SQL as well as those who are very experienced.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1 SQL Is Declarative, Not Procedural1.1 Different Programming Models1.2 Different Data Models1.2.1 Columns Are Not Fields1.2.2 Rows Are Not Records1.2.3 Tables Are Not Files 1.2.4 Relational Keys Are Not Record Locators 1.2.5 Kinds of Keys 1.2.6 Desirable Properties of Relational Keys 1.2.7 Unique But Not Invariant 1.3 Tables as Entities 1.4 Tables as Relationships1.5 Statements Are Not Procedures 1.6 Molecular, Atomic, and Subatomic Data Elements 1.6.1 Table Splitting 1.6.2 Column Splitting 1.6.3 Temporal Splitting 1.6.4 Faking Non-1NF Data 1.6.5 Molecular Data Elements 1.6.6 Isomer Data Elements 1.6.7 Validating a Molecule 2 Hardware, Data Volume, and Maintaining Databases 2.1 Parallelism 2.2 Cheap Main Storage 2.3 Solid-State Disk 2.4 Cheaper Secondary and Tertiary Storage 2.5 The Data Changed 2.6 The Mindset Has Not Changed 3 Data Access and Records 3.1 Sequential Access 3.1.1 Tape-Searching Algorithms 3.2 Indexes 3.2.1 Single-Table Indexes 3.2.2 Multiple-Table Indexes 3.2.3 Type of Indexes 3.3 Hashing 3.3.1 Digit Selection 3.3.2 Division Hashing 3.3.3 Multiplication Hashing 3.3.4 Folding 3.3.5 Table Lookups 3.3.6 Collisions 3.4 Bit Vector Indexes 3.5 Parallel Access 3.6 Row and Column Storage 3.6.1 Row-Based Storage 3.6.2 Column-Based Storage 3.7 JOIN Algorithms 3.7.1 Nested-Loop Join Algorithm 3.7.2 Sort-Merge Join Method 3.7.3 Hash Join Method 3.7.4 Shin’s Algorithm 4 Lookup Tables 4.1 Data Element Names 4.2 Multiparameter Lookup Tables 4.3 Constants Table 4.4 OTLT or MUCK Table Problems 4.5 Defi nition of a Proper Table5 Auxiliary Tables 5.1 Sequence Table 5.1.1 Creating a Sequence Table 5.1.2 Sequence Constructor 5.1.3 Replacing an Iterative Loop 5.2 Permutations 5.2.1 Permutations via Recursion 5.2.2 Permutations via CROSS JOIN 5.3 Functions 5.3.1 Functions without a Simple Formula 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 R
