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Refactoring has proven its value in a wide range of development projects–helping software professionals improve system designs, maintainability, extensibility, and performance. Now, for the first time, leading agile methodologist Scott Ambler and renowned consultant Pramodkumar Sadalage introduce powerful refactoring techniques specifically designed for database systems.
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Ambler and Sadalage demonstrate how small changes to table structures, data, stored procedures, and triggers can significantly enhance virtually any database design–without changing semantics. You’ll learn how to evolve database schemas in step with source code–and become far more effective in projects relying on iterative, agile methodologies.
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This comprehensive guide and reference helps you overcome the practical obstacles to refactoring real-world databases by covering every fundamental concept underlying database refactoring. Using start-to-finish examples, the authors walk you through refactoring simple standalone database applications as well as sophisticated multi-application scenarios. You’ll master every task involved in refactoring database schemas, and discover best practices for deploying refactorings in even the most complex production environments.
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The second half of this book systematically covers five major categories of database refactorings. You’ll learn how to use refactoring to enhance database structure, data quality, and referential integrity; and how to refactor both architectures and methods. This book provides an extensive set of examples built with Oracle and Java and easily adaptable for other languages, such as C#, C++, or VB.NET, and other databases, such as DB2, SQL Server, MySQL, and Sybase.
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Using this book’s techniques and examples, you can reduce waste, rework, risk, and cost–and build database systems capable of evolving smoothly, far into the future.
- Sales Rank: #310016 in eBooks
- Published on: 2006-03-03
- Released on: 2006-03-03
- Format: Kindle eBook
From the Back Cover
Refactoring has proven its value in a wide range of development projects–helping software professionals improve system designs, maintainability, extensibility, and performance. Now, for the first time, leading agile methodologist Scott Ambler and renowned consultant Pramodkumar Sadalage introduce powerful refactoring techniques specifically designed for database systems.
Ambler and Sadalage demonstrate how small changes to table structures, data, stored procedures, and triggers can significantly enhance virtually any database design–without changing semantics. You’ll learn how to evolve database schemas in step with source code–and become far more effective in projects relying on iterative, agile methodologies.
This comprehensive guide and reference helps you overcome the practical obstacles to refactoring real-world databases by covering every fundamental concept underlying database refactoring. Using start-to-finish examples, the authors walk you through refactoring simple standalone database applications as well as sophisticated multi-application scenarios. You’ll master every task involved in refactoring database schemas, and discover best practices for deploying refactorings in even the most complex production environments.
The second half of this book systematically covers five major categories of database refactorings. You’ll learn how to use refactoring to enhance database structure, data quality, and referential integrity; and how to refactor both architectures and methods. This book provides an extensive set of examples built with Oracle and Java and easily adaptable for other languages, such as C#, C++, or VB.NET, and other databases, such as DB2, SQL Server, MySQL, and Sybase.
Using this book’s techniques and examples, you can reduce waste, rework, risk, and cost–and build database systems capable of evolving smoothly, far into the future.
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About the Author
Scott W. Ambler is a software process improvement (SPI) consultant living just north of Toronto. He is founder and practice leader of the Agile Modeling (AM) (www.agilemodeling.com), Agile Data (AD) (www.agiledata.org), Enterprise Unified Process (EUP) (www.enterpriseunifiedprocess.com), and Agile Unified Process (AUP) (www.ambysoft.com/unifiedprocess) methodologies. Scott is the (co-)author of several books, including Agile Modeling (John Wiley & Sons, 2002), Agile Database Techniques (John Wiley & Sons, 2003), The Object Primer, Third Edition (Cambridge University Press, 2004), The Enterprise Unified Process (Prentice Hall, 2005), and The Elements of UML 2.0 Style (Cambridge University Press, 2005). Scott is a contributing editor with Software Development magazine (www.sdmagazine.com) and has spoken and keynoted at a wide variety of international conferences, including Software Development, UML World, Object Expo, Java Expo, and Application Development. Scott graduated from the University of Toronto with a Master of Information Science. In his spare time Scott studies the Goju Ryu and Kobudo styles of karate.
Pramod J. Sadalage is a consultant for ThoughtWorks, an enterprise application development and integration company. He first pioneered the practices and processes of evolutionary database design and database refactoring in 1999 while working on a large J2EE application using the Extreme Programming (XP) methodology. Since then, Pramod has applied the practices and processes to many projects. Pramod writes and speaks about database administration on evolutionary projects, the adoption of evolutionary processes with regard to databases, and evolutionary practices’ impact upon database administration, in order to make it easy for everyone to use evolutionary design in regards to databases. When he is not working, you can find him spending time with his wife and daughter and trying to improve his running.
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Excerpt. � Reprinted by permission. All rights reserved.
Refactoring DatabasesRefactoring Databases Evolutionary Database DesignPreface
Evolutionary, and often agile, software development methodologies, such as Extreme Programming (XP), Scrum, the Rational Unified Process (RUP), the Agile Unified Process (AUP), and Feature-Driven Development (FDD), have taken the information technology (IT) industry by storm over the past few years. For the sake of definition, an evolutionary method is one that is both iterative and incremental in nature, and an agile method is evolutionary and highly collaborative in nature. Furthermore, agile techniques such as refactoring, pair programming, Test-Driven Development (TDD), and Agile Model-Driven Development (AMDD) are also making headway into IT organizations. These methods and techniques have been developed and have evolved in a grassroots manner over the years, being honed in the software trenches, as it were, instead of formulated in ivory towers. In short, this evolutionary and agile stuff seems to work incredibly well in practice.
In the seminal book Refactoring, Martin Fowler describes a refactoring as a small change to your source code that improves its design without changing its semantics. In other words, you improve the quality of your work without breaking or adding anything. In the book, Martin discusses the idea that just as it is possible to refactor your application source code, it is also possible to refactor your database schema. However, he states that database refactoring is quite hard because of the significant levels of coupling associated with databases, and therefore he chose to leave it out of his book.
Since 1999 when Refactoring was published, the two of us have found ways to refactor database schemas. Initially, we worked separately, running into each other at conferences such as Software Development (http://www.sdexpo.com) and on mailing lists (http://www.agiledata.org/feedback.html). We discussed ideas, attended each other's conference tutorials and presentations, and quickly discovered that our ideas and techniques overlapped and were highly compatible with one another. So we joined forces to write this book, to share our experiences and techniques at evolving database schemas via refactoring.
The examples throughout the book are written in Java, Hibernate, and Oracle code. Virtually every database refactoring description includes code to modify the database schema itself, and for some of the more interesting refactorings, we show the effects they would have on Java application code. Because all databases are not created alike, we include discussions of alternative implementation strategies when important nuances exist between database products. In some instances we discuss alternative implementations of an aspect of a refactoring using Oracle-specific features such as the SE,T UNUSED or RENAME TO commands, and many of our code examples take advantage of Oracle's COMMENT ON feature. Other database products include other features that make database refactoring easier, and a good DBA will know how to take advantage of these things. Better yet, in the future database refactoring tools will do this for us. Furthermore, we have kept the Java code simple enough so that you should be able to convert it to C#, C++, or even Visual Basic with little problem at all.
Why Evolutionary Database Development?
Evolutionary database development is a concept whose time has come. Instead of trying to design your database schema up front early in the project, you instead build it up throughout the life of a project to reflect the changing requirements defined by your stakeholders. Like it or not, requirements change as your project progresses. Traditional approaches have denied this fundamental reality and have tried to "manage change," a euphemism for preventing change, through various means. Practitioners of modern development techniques instead choose to embrace change and follow techniques that enable them to evolve their work in step with evolving requirements. Programmers have adopted techniques such as TDD, refactoring, and AMDD and have built new development tools to make this easy. As we have done this, we have realized that we also need techniques and tools to support evolutionary database development.
Advantages to an evolutionary approach to database development include the following:
You minimize waste. An evolutionary, just-in-time (JIT) approach enables you to avoid the inevitable wastage inherent in serial techniques when requirements change. Any early investment in detailed requirements, architecture, and design artifacts is lost when a requirement is later found to be no longer needed. If you have the skills to do the work up front, clearly you must have the skills to do the same work JIT.
You avoid significant rework. As you will see in Chapter 1, "Evolutionary Database Development," you should still do some initial modeling up front to think major issues through, issues that could potentially lead to significant rework if identified late in the project; you just do not need to investigate the details early.
You always know that your system works. With an evolutionary approach, you regularly produce working software, even if it is just deployed into a demo environment, which works. When you have a new, working version of the system every week or two, you dramatically reduce your project's risk.
You always know that your database design is the highest quality possible. This is exactly what database refactoring is all about: improving your schema design a little bit at a time.
You work in a compatible manner with developers. Developers work in an evolutionary manner, and if data professionals want to be effective members of modern development teams, they also need to choose to work in an evolutionary manner.
You reduce the overall effort. By working in an evolutionary manner, you only do the work that you actually need today and no more.
There are also several disadvantages to evolutionary database development:
Cultural impediments exist. Many data professionals prefer to follow a serial approach to software development, often insisting that some form of detailed logical and physical data models be created and baselined before programming begins. Modern methodologies have abandoned this approach as being too inefficient and risky, thereby leaving many data professionals in the cold. Worse yet, many of the "thought leaders" in the data community are people who cut their teeth in the 1970s and 1980s but who missed the object revolution of the 1990s, and thereby missed gaining experience in evolutionary development. The world changed, but they did not seem to change with it. As you will learn in this book, it is not only possible for data professionals to work in an evolutionary, if not agile, manner, it is in fact a preferable way to work.
Learning curve. It takes time to learn these new techniques, and even longer if you also need to change a serial mindset into an evolutionary one.
Tool support is still evolving. When Refactoring was published in 1999, no tools supported the technique. Just a few years later, every single integrated development environment (IDE) has code-refactoring features built right in to it. At the time of this writing, there are no database refactoring tools in existence, although we do include all the code that you need to implement the refactorings by hand. Luckily, the Eclipse Data Tools Project (DTP) has indicated in their project prospectus the need to develop database-refactoring functionality in Eclipse, so it is only a matter of time before the tool vendors catch up.
Agility in a Nutshell
Although this is not specifically a book about agile software development, the fact is that database refactoring is a primary technique for agile developers. A pro...
Most helpful customer reviews
42 of 47 people found the following review helpful.
Excellent refactoring reference and eye-opening book
By Michael Cohn
This is an excellent book that, in my opinion, serves two purposes. First, it is a compendium of well thought-out ways to evolve a database design. Each refactoring includes descriptions of why you might make this change, tradeoffs to consider before making it, how to update the schema, how to migrate the data, and how applications that access the data will need to change. Some of the refactorings are simple ones that even the most change-resistant DBAs will have used in the past ("Add index"). Most others (such as "Merge tables" or "Replace LOB with Table") are ones many conventional thinking DBAs avoid, even to the detriment of the applications their databases support.
This brings me to the second purpose of this book. Many DBAs view their jobs as protectors of the data. While that is admirable, they sometimes forget that they are part of a software development team whose job is to provide value to the organization through the development of new (and enhancement of existing) applications. One of the best DBAs I ever worked with viewed himself as a "Data Valet." He said his job was to make sure the data was presented to applications when and where they wanted and to protect the doors from getting dinged while under his care. Through its first five chapters and then the refactorings that follow, this book will help DBAs expand their view of their role in the organization from one of simply protecting data to one of enhancing the value of data to the organization.
This book is one that you'll keep on your reference shelf for many years to come. Highly recommended.
16 of 17 people found the following review helpful.
Some important ideas but much work remains to be done
By Piers C.
In software development a 'refactoring' is a change that improves code quality without changing functionality. Refactoring helps keep an application maintainable over its life-cycle as requirements evolve, and is particularly of interest to those adopting modern 'agile' methodologies. This book comprises five general chapters on database refactoring - about 70 pages - followed by a 200 page catalog of various refactorings. The refactorings are classified as 'structural', 'data quality', 'referential integrity', 'architectural' and 'methods'. An additional chapter catalogs 'transformations', more on which in a moment. Each catalog entry uses a template including 'Motivation', 'Tradeoffs', 'Schema Update Mechanics', 'Data-Migration Mechanics' and 'Access Program Update Mechanics'. The 'Mechanics' sections include example code fragments for Oracle, JDBC and Hibernate.
Several of the structural refactorings are just simple database schema changes: rename/drop column/table/view. Adding is not really a refactoring so add column/table/view were cataloged as 'transformations' - changes that do affect the application, a distinction that appears to me a little clumsy. Some structural refactorings are more interesting: merge/split columns/tables, move column, introduce/remove surrogate key, introduce calculated column, introduce associative table.
The data quality refactorings include introduce/drop default values, null or check constraints, standardize codes, formats and data types, use consistent keys and lookup tables. Most of these are common best practices, seeing them cataloged as refactorings didn't yield me any new insights. Only replacing type codes with flags was of special interest.
Referential integrity refactorings include the obvious add/drop foreign keys with optional cascading delete, but also using triggers to create a change history and hard vs. soft deletes. Using before and after delete triggers to implement soft deletes is probably the best example in the book.
Architectural refactorings include using CRUD methods (ie. stored procedures or functions to select/insert/update/delete records), query functions that return cursor refs, interchanging methods and views, implementing methods in stored procedures, using materialized views and using local mirror tables vs. remote 'official' data sources. All these are common design techniques and the discussion of motivation and tradeoffs is particularly relevant.
The final section on method refactorings is more abbreviated and covers typical code refactorings. These qualify for inclusion only because databases include stored procedures, but they have nothing to do with schema evolution.
An important aspect of this book is that the catalog of refactorings is presented in the context of evolutionary database development described in the first five chapters: this approach emphasises an iterative approach, automated regression testing, configuration control of schema objects and easy availability of personalized application database environments for developers. Refactorings and transformations are intended to be applied one by one, and an automated regression test suite used to maintain confidence that a change does not introduce an application defect. Change control and a change tracking mechanism are essential to manage the application of schema changes to integration, QA and production environments.
What do I like about this book? The catalog of refactorings is thorough (some might say pedantic) which makes it a good learning tool for new database developers and DBAs, and as a shared reference for communicating on larger projects and in larger organizations. Experienced DBAs working on smaller projects are less likely to find it useful.
What don't I like? Relatively little is provided about the tools required to make regular refactoring practical, the authors simply state that these are being worked on. utPLSQL is not mentioned at all. The discussion on tracking changes is thin (but check out the LiquiBase project on Sourceforge). No guidance is provided on how you might use Ant to build and maintain developer database environments. Little is covered on the tough topic of building and maintaining test data sets. A final pet peeve: no discussion of refactoring across multiple schemas shared by an application suite.
In summary this book sketches out some important ideas but much work remains to be done. The catalog takes a number of established techniques and best practices and places them in a new framework which at least provides value to some for now.
18 of 21 people found the following review helpful.
on the right path
By arzewski
This book is a much needed exploration on the subject. It tries to categorize those operations that developers and DBAs do on a database, who, for various reasons, must address a specific problem, need. I only gave it three stars because it is somewhat insufficient. It also doesn't make much of a distinction between a database in development and one in production. In the latter case, it is really difficult to make changes when there is already a data structure in place with data, being updated constantly by users, and plans to migrate to a different data model while a system is in production is really not for the faint of heart. What I am really loooking for is a thick book of bad designs that, for various reasons (unclear or evolving requirements, political), a database model presents itself with a bunch of problems, real problems and not just theoretical, and the ways a DBA and developers came about after a big pow-wow on how to solve it.
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