In the past decade, we've seen the rise of agile methodologies. Compared to their predecessors they change the demands on database design. One of the most central of these demands is the idea of evolutionary architecture. On an agile project you assume that you cannot fix the requirements of the system up-front. As a result having a detailed design phase at the beginning of a project becomes impractical. The architecture of the system has to evolve through the various iterations of the software. Agile methods, in particular extreme programming (XP), have a number of practices that make this evolutionary architecture practical.

When we and our ThoughtWorks colleagues started doing agile projects, we realized that we needed to solve the problem of how to evolve a database to support this evolution of architecture. We began around 2000 with a project whose database ended up with around 600 tables. As we worked on this project we developed techniques that allowed to change the schema and migrate existing data comfortably. This allowed our database to be completely flexible and evolvable. We described these techniques in an earlier version of this article, a description that's inspired other teams and toolsets. Since then we've used and further developed these techniques on hundreds of projects world-wide, from small teams to large multi-national programs of work. We've been long intending to bring this article up to date, and now we've had the chance to give it a proper refresh.

A small user story like this has only a single database migration, larger stories are often broken down into several separate migrations for each change to the database. Our usual rule is to make each database change as small as possible. The smaller it is, the easier it is to get right, and any errors are quick to spot and debug. Migrations like this compose easily, so it's best to do many small ones.

Once the changes are in mainline they will be picked up by the Continuous Integration server. It will run the migration scripts on the mainline copy of the database, and then run all the application tests. If all is green, this process will be repeated across the whole Deployment Pipeline , including QA and Staging environments. The same code finally is run against production, now updating the live database's schema and data.

If Jen isn't too familiar with making this change, she's fortunate that it's a common one to make to a database. So she can look up it the database refactoring book , there's also a summary online .

Jen runs this migration script on a local copy of database on her machine. She then proceeds to update the application code to use these new columns. As she does this she runs the existing test suite against this code to detect any changes in the application's behavior. Some tests, those that relied on the combined column need to be updated. Some tests may need to be added. Once Jen has done all this, and the application has all its tests green on her machine, Jen pushes all her changes to the shared project version control repository - which we call the mainline . These changes include the migration scripts and the application code changes.

To add new columns and migrate the data, Jen writes a SQL migration script that she can run against the current schema. This will both change the schema and also migrate all the existing data in the inventory.

To get a feel for how all of this works, lets outline what happens when a developer (Jen) writes some code to implement a new user story. The story states that the user should be able to see, search, and update the location, batch, and serial numbers of a product in inventory. Looking at the database schema, Jen sees that currently there's no such fields in the inventory table, just a single inventory_code field which is the concatenation of these three fields. She has to take this single code and split it into three separate fields: location_code , batch_number and serial_number .

We don't consider these problems to be inherently unsolvable. After all when we wrote the original version of this article we hadn't solved the problems of 24*7 uptime or integration databases. We found ways to deal with those, and expect we'll push the limits of evolutionary database design further too. But until we do, we won't claim we can solve such problems.

Increasingly we are seeing people use multiple schemas as part of a single database environment. We've worked with projects using a handful of schemas like this, but haven't yet cranked this up to tens or hundreds of schemas. This is a situation that we anticipate having to deal with in the next few years.

We have had projects with hundreds of retail stores having their own database, all of which need to upgraded together. However we haven't yet explored the situation where there is a lot of customization on such a large group of sites. An example might be a small business application that allows customization of the schema, deployed into thousands of different small companies.

Before we dive into the techniques, it's important to state that we haven't solved all the problems of evolutionary database design.

Since the early days we have tried to spread these techniques over more of our projects, gaining more experience from more cases and now all our projects use this approach. We've also found inspiration, ideas, and experience from other agile practitioners.

Over the course of the last decade and half we've been involved in many large projects that have used evolutionary database design and made it work. Some projects involved more than 100 people in multiple sites world-wide. Others involved over half-million lines of code, over 500 tables. Some had multiple versions of the application in production and applications that needed 24*7 uptime. During these projects we have seen iterations of a month and 1 week duration, shorter iterations worked better. The techniques we describe below are the ones that we used to make this work.

While these techniques have grown in use and interest, one of the biggest questions is how to make evolutionary design work for databases. For a long time, people in the database community considered database design as something that absolutely needs up-front planning. Changing the database schema late in the development tended to cause wide-spread breakages in application software. Furthermore changing a schema after deployment resulted in painful data migration problems.

An important part of this approach is iterative development, where you run the entire software life-cycle many times during the life of a project. Agile processes run complete life cycles in each iteration, completing the iteration with working, tested, integrated code for a small subset of the requirements of the final product. These iterations are short, anywhere from a few hours to a couple of weeks, more skillful teams use shorter iterations.

One of the vital contributions of agile methods is that they have come up with practices that allow evolutionary design to work in a controlled manner. So instead of the common chaos that often happens when design isn't planned up-front, these methods provide techniques to control evolutionary design and make them practical.

In order to make this work, you need a different attitude to design. Instead of thinking of design as a phase, which is mostly completed before you begin construction, you look at design as an on-going process that is interleaved with construction, testing, and even delivery. This is the contrast between planned and evolutionary design.

Agile processes approach change differently. They seek to embrace change, allowing changes to occur even late in a development project. Changes are controlled, but the attitude of the process is to enable change as much as possible. Partly this is in response to the inherent instability of requirements in many projects, partly it is to better support dynamic business environments by helping them change with the competitive pressures.

Such approaches look to minimize changes by doing extensive up-front work. Once the early work is done, changes cause significant problems. As a result such approaches run into trouble if requirements are changing, and requirements churn is a big problem for such processes.

As agile methods have spread in popularity in the early 2000s, one of their most obvious characteristics is their towards change. Before they appeared on the scene most of the thinking about software process was about understanding requirements early, signing off on these requirements, using the requirements as a basis for design, signing off on that, and then proceeding with construction. This is a plan-driven cycle, often referred to (usually with derision) as the waterfall approach

The Practices

Our approach to evolutionary database design depends on several important practices.

DBAs collaborate closely with developers One of the tenets of agile methods is that people with different skills and backgrounds need to collaborate very closely together. They can't communicate mainly through formal meetings and documents. Instead they need to be out talking with each other and working with each other all the time.Everybody is affected by this: analysts, PMs, domain experts, developers... and DBAs. Every task that a developer works on potentially needs a DBA's help. Both the developers and the DBA need to consider whether a development task is going to make a significant change to the database schema. If so, the developer needs to consult with the DBA to decide how to make the change. The developer knows what new functionality is needed, and the DBA has a global view of the data in the application and other surrounding applications. Many times developers have visibility into the application they are working on and not necessarily into all the other upstream or downstream dependencies on the schema. Even if it's a single database application, there could be dependencies in the database that a developer isn't aware of. At any time a developer can call on the DBA and ask to pair to sort out a database change. By pairing, the developer learns about how the database works, and the DBA learns the context of the demands on the database. For most changes it's up to the developers to call on the DBA if they are concerned about the database impact of changes. But DBAs also take initiative. When they see stories that they think are likely to have a significant data impact, they can seek out the developers involved to talk through the database impact. DBAs can also review the migrations as they get committed into version control. While it's annoying to reverse a migration, we again gain from each migration being small, which makes it easier to reverse. To make this happen the DBA has to make herself approachable and available. She needs to make it easy for a developer to just pop over for a few minutes and ask some questions, maybe on the slack channel or hipchat room or whatever communication medium the developers are using. When setting up the project space, make sure the DBAs and developers sit close to each other so they can easily get together. Ensure that DBAs are told about any application design sessions so they can pop in easily. In many environments we see people erecting barriers between the DBA and application development functions. These barriers must come down for an evolutionary database design process to work.

All database artifacts are version controlled with application code Developers benefit a lot from using version control for all their artifacts: application code, unit and functional tests, and other code such as build scripts, and puppet or chef scripts used to create an environment. Figure 1: All database artifacts are in version control along with other project artifacts Similarly all the database artifacts should be in version control, in the same repository that's being used by everyone else. The benefits for this are: There's only one place to look, making it easier for anyone on the project to find things.

Every change to the database is stored, allowing easy audit if any problems occur. We can trace every deployment of the database to the exact state of the schema and supporting data.

We prevent deployments where the database is out of sync with the application, which leads to errors retrieving and updating data.

We can easily create new environments: for development, testing, and indeed production. Everything needed to create a running version of the software should be in a single repository, so it can be quickly checked out and built.

All database changes are migrations In many organizations we see a process where developers make changes to a development database using schema editing tools and ad-hoc SQL for standing data. Once they've finished their development task, then DBAs compare the development database to the production database and make the corresponding changes to the production database when promoting the software to live. But doing this at production time is tricky as the context for the changes in development is lost. The purpose of the changes needs be understood again, by a different group of people. To avoid this we prefer to capture the change during development, and keep the change as a first class artifact that can be tested and deployed to production with the same process and control as changes to application code. We do this by representing every change to the database as a database migration script which is version controlled together with application code changes. These migration scripts include: schema changes, database code changes, reference data updates, transaction data updates, and fixes to production data problems caused by bugs. Here is a change adding min_insurance_value and max_insurance_value to the equipment_type table, with some default values. ALTER TABLE equipment_type ADD( min_insurance_value NUMBER(10,2), max_insurance_value NUMBER(10,2) ); UPDATE equipment_type SET min_insurance_value = 3000, max_insurance_value = 10000000; This change adds some standing data to the location and equipment_type tables. -- Create new warehouse locations #Request 497 INSERT INTO location (location_code, name , location_address_id, created_by, created_dt) VALUES ('PA-PIT-01', 'Pittsburgh Warehouse', 4567, 'APP_ADMIN' , SYSDATE); INSERT INTO location (location_code, name , location_address_id, created_by, created_dt) VALUES ('LA-MSY-01', 'New Orleans Warehouse', 7134, 'APP_ADMIN' , SYSDATE); -- Create new equipment_type #Request 562 INSERT INTO equipment_type (equipment_type_id, name, min_insurance_value, max_insurance_value, created_by, created_dt) VALUES (seq_equipment_type.nextval, 'Lift Truck', 40000, 4000000, 'APP_ADMIN', SYSDATE); With this way of working, we never use schema editing tools such as Navicat, DBArtisan or SQL Developer to alter schemas, nor do we ever run ad hoc DDL or DML in order to add standing data or fix problems. Other than updates to the database that occur due to the application software, all changes are made by migrations. Defining migrations as sets of SQL commands is part of the story, but in order to apply them properly we need some extra things to manage them. Each migration needs a unique identification.

We need to track which migrations have been applied to the database

We need to manage the sequencing constraints between the migrations. In the example above, we must apply the ALTER TABLE migration first, otherwise the second migration will fail to insert the equipment type. We handle these demands by giving each migration a sequence number. This acts as a unique identifier and ensures we can maintain the order that they're applied to the database. When a developer creates a migration she puts the SQL into a text file inside a migrations folder within the project's version control repository. She looks up the highest currently used number in the migrations folder, and uses that number together with a description to name the file. Thus the earlier pair of migrations might be called 0007_add_insurance_value_to_equipment_type.sql and 0008_data_location_equipment_type . To track the application of the migrations to the database we use a changelog table. Database migration frameworks typically create this table and automatically update it whenever a migration is applied. This way the database can always report which migration it is synchronized with. If we don't use such a framework, after all they didn't exist when we starting doing this, we automate this with a script. Figure 2: changelog table maintained by database migration frameworks With this numbering scheme in place, we can then track changes as they apply to the many databases that we manage. Figure 3: Life of a migration script from its creation to its deployment in production Some of these data migrations may have to be released more frequently than the migrations related to new features, in that scenario, we have found it to be useful to have separate migration repository or folder for data related bug fixes. Figure 4: Separate folders to manage new feature database changes and production data fixes Each of these folders can be tracked separately by the database migration tools such as Flyway, dbdeploy, MyBatis or similar tools, with a separate table to store the migration numbers. The property flyway.table in Flyway is used to change the name of the table where the migration metadata is stored

Everybody gets their own database instance Most development organizations share a single development database, which is used by all members of the organization. Perhaps a separate database is used for QA or staging, but the notion is to limit how many databases are running live. Sharing databases like this is a consequence of database instances being difficult to set up and manage, leading organizations to minimize how many there are. Controls on who can alter the schema in such situations varies, some places require all changes to be made through the DBA team, others allow any developers to change the schema of the development database, and the DBAs get involved when changes are promoted downstream. When we started work with agile database projects, we noted that application developers usually follow a pattern where they work in a private working copy of the code. People learn by trying things out, so in programming terms developers experiment with how to implement a certain feature and may make a few attempts before picking one. It's important to be able to experiment in private workspace and pushing to a shared area when things are more stable. If everyone is working in a shared area, then they are constantly interrupting each other with half-done changes. Although we favor Continuous Integration, where integrations occur after no more than a few hours, the private working copy is still important. Version control systems support this work, allowing developers to work independently while supporting integrating their work in a mainline copy. This separate working works with files, but it can also work with databases. Each developer gets their own database instance which they can freely modify without touching other people's work. When they are ready they can push and share their changes, as we'll see in the next section. These separate databases can either be separate schemas on a shared server or, more commonly these days, a separate database running on a developer's laptop or workstation. A decade ago, database licensing costs could make individual database instances prohibitively expensive - but these days this is rarely the case, particularly as open-source databases have grown in popularity. We've found it handy to run a database in a virtual machine running on a developer's machine. We define the build of the database VM using Vagrant and Infrastructure As Code, so the developer doesn't need to know the details of setting up the database VM, or have to do it manually. Figure 5: Problem using a single database schema for all members on the team in development Figure 6: Every member of the team gets their own database schema for development and testing Many DBAs still see multiple databases as anathema, too difficult to work in practice, but we've found that you can easily manage a hundred or so application database instances. The vital thing is to have tools to allow you to manipulate databases much as you would manipulate files. <target name="create_schema" description="create a schema as defined in the user properties"> <echo message="Admin UserName: ${admin.username}"/> <echo message="Creating Schema: ${db.username}"/> <sql password="${admin.password}" userid="${admin.username}" url="${db.url}" driver="${db.driver}" classpath="${jdbc.classpath}" > CREATE USER ${db.username} IDENTIFIED BY ${db.password} DEFAULT TABLESPACE ${db.tablespace}; GRANT CONNECT,RESOURCE, UNLIMITED TABLESPACE TO ${db.username}; GRANT CREATE VIEW TO ${db.username}; ALTER USER ${db.username} DEFAULT ROLE ALL; </sql> </target> Creation of developer schemas can be automated, using the build script to reduce workload on the DBA. This automation can also be restricted just to environments in development. <target name="drop_schema"> <echo message="Admin UserName: ${admin.username}"/> <echo message="Working UserName: ${db.username}"/> <sql password="${admin.password}" userid="${admin.username}" url="${db.url}" driver="${db.driver}" classpath="${jdbc.classpath}" > DROP USER ${db.username} CASCADE; </sql> </target> For example, a developer joins a project, checks out the code base and starts to setup her local development environment. She uses the template build.properties file and makes changes, such as setting db.username to Jen and so forth for the rest of the settings. Once these settings are done she can just run ant create_schema and get a schema of her own on the team development database server or database server on his laptop. With the schema created, she can then run the database migration script to build all the database content to populate her database instance: tables, indexes, views, sequences, stored procedures, triggers, synonyms and other database specific objects. Similarly, there are scripts to delete schemas - either because they are no longer needed, or merely because the developer wishes to clean up and start again with a fresh schema. Database environments should be phoenixes - regularly burnt down and rebuilt at will. That way there's less danger of environments accumulating characteristics that aren't reproducible, or audited. This need for a private workspace is true for developers, but also true for everyone else on the team. QA staff should create their own databases, so they also can work without danger of getting confused by changes outside their knowledge. DBAs should be able to experiment with their own database copy as they explore modeling options, or performance tuning.

Developers continuously integrate database changes Although developers can experiment frequently in their own sandbox, it's vital to integrate their different changes back together frequently using Continuous Integration (CI). CI involves setting up an integration server that automatically builds and tests the mainline software. Our rule of thumb is that each developer should integrate into mainline at least once a day. Many tools exist to help with CI including: GoCD, Snap CI, Jenkins, Bamboo and Travis CI Figure 7: Database changes, migrations being developed and integrated just like application code Figure 7 shows the flow of how database migrations are developed, tested locally, checked into source control, picked up by the CI server and applied to the integration database, tested again, and packaged for downstream use. Let's take an example 1 Jen starts a development that include a database schema change. If the change is easy, such as adding a column, Jen decides how to make the change directly. If it's complicated she grabs the DBA and talks it over with her. Once she has sorted out the change, she writes the migration. ALTER TABLE project ADD projecttypeid NUMBER(10) NULL; ALTER TABLE project ADD (CONSTRAINT fk_project_projecttype FOREIGN KEY (projecttypeid) REFERENCES projecttype DEFERRABLE INITIALLY DEFERRED); UPDATE project SET projecttypeid = (SELECT projecttypeid FROM projecttype WHERE name='Integration'); Adding a nullable column is a backwards compatible change, so she can integrate the change without needing to change any application code. However, if it isn't a backwards compatible change, such as splitting a table, then Jen would need to modify the application code too. 2 Once Jen has finished her changes, she is ready to integrate. The first step in integration is updating her local copy from mainline. These are changes other members of the team have done while she's been working on her task. She then checks her changes work with these updates by rebuilding the database and running all the tests. If she runs into problems, due to the other developers’ changes interfering with hers, she needs to fix those problems on her copy. Usually such clashes are easy to sort out, but occasionally they are more involved. Often these more complex conflicts trigger a conversation between Jen and her teammates so they can sort out how to resolve overlapping changes. Once she has her local copy working again, she checks to see if any more changes have been pushed to master while she's working, if so she needs to repeat the integration with the new changes. Usually, however, it doesn't take more than one or two of these cycles before her code is fully integrated with mainline. 3 Jen pushes the change to mainline. Since the change is backwards compatible with the existing application code, she can integrate the database change before updating the application code to use it - a common example of Parallel Change. 4 The CI server detects the change in mainline and starts a new build which contains the database migration. 5 The CI server uses its own database copy for the build, so applies the database migration script to this database to apply the changes in the migration. In addition it runs the rest of the build steps: compile, unit tests, functional tests etc. 6 Once the build finishes successfully, the CI server packages the build artifacts and publishes them. These build artifacts contain the database migration scripts, so that they can be applied to the databases in downstream environments, such as a Deployment Pipeline. The build artifacts also contain the application code packaged into a jar, war, dll etc. This is exactly the practice of Continuous Integration, which is commonly used with application source code management. The steps above are just about treating the database code as another piece of source code. As such the database code - DDL, DML, Data, views, triggers, stored procedures - is kept under configuration management in the same way as the source code. Whenever we have a successful build, by packaging the database artifacts along with the application artifacts, we have a complete and synchronized version history of both application and database. With application source code, much of the pain of integrating with changes can be handled by source code control systems and using various tests in local environments. For databases there's a bit more effort involved as there is data (state) in the database that needs to preserve its business meaning. (We'll talk more about automated database refactorings like this shortly.) In addition the DBA needs to look at any database changes and ensure that they fit within the overall scheme of the database schema and data architecture. For all this to work smoothly, big changes shouldn't come as surprises at integration time - hence the need for the DBA to collaborate closely with the developers. We emphasize integrating frequently because we've found that it's much easier to do frequent small integrations rather than infrequent large integrations - a case of Frequency Reduces Difficulty. The pain of integration increases exponentially with the size of the integration, so doing many small changes is much easier in practice, even though it appears counter-intuitive to many.

A database consists of schema and data When we talk about a database here, we mean not just the schema of the database and database code, but also a fair amount of data. This data consists of common standing data for the application, such as the inevitable list of all the states, countries, currencies, address types and various application specific data. We may also include some sample test data such as a few sample customers, orders etc. This sample data would not make it to production, unless specifically needed for sanity testing or semantic monitoring. This data is there for a number of reasons. The main reason is to enable testing. We are great believers in using a large body of automated tests to help stabilize the development of an application. Such a body of tests is a common approach in agile methods. For these tests to work efficiently, it makes sense to work on a database that is seeded with some sample test data, which all tests can assume is in place before they run. This sample data needs to be version controlled, so we know where to look for it when we need to populate a new database, and so we have a record of changes that's synchronized with the tests and application code. As well as helping test the code, this sample test data also allows to test our migrations as we alter the schema of the database. By having sample data, we are forced to ensure that any schema changes also handle sample data. In most projects we've seen this sample data be fictional. However in a few projects we've seen people use real data for the samples. In these cases this data's been extracted from prior legacy systems with automated data conversion scripts. Obviously you can't convert all the data right away, as in early iterations only a small part of the new database is actually built. But we can use Incremental Migration to develop the conversion scripts to provide necessary data just in time. Not just does this help flush out data conversion problems early, it makes it much easier for domain experts to work with the growing system as they are familiar with the data they are looking at and can often help to identify cases that may cause problems for the database and application design. As a result we are now of the view that you should try to introduce real data from the very first iteration of your project. We've found Jailer to be a useful tool to help with this process.

All database changes are database refactorings The changes that we make to the database alter the way the database stores information, introduces new ways to store information, or removes storage that's no longer needed. But none of the database changes, on their own, change the overall behavior of the software. Consequently we can see them as fitting the definition of a refactoring. a change made to the internal structure of software to make it easier to understand and cheaper to modify without changing its observable behavior -- Refactoring (chapter 2) Recognizing this, we collected and documented many of these refactorings. By writing such a catalog, we make it easier to make these changes correctly since we can follow the steps we've successfully used before. Scott and Pramod's book detail the steps needed for most of the database refactorings you'll need. One of the big differences about database refactorings is that they involve three different changes that have to be done together Changing the database schema

Migrating the data in the database

Changing the database access code Thus whenever we describe a database refactoring, we have to describe all three aspects of the change and ensure that all three are applied before we apply any other refactorings. Like code refactoring, database refactorings are very small. The concept of chaining together a sequence of very small changes is much the same for databases as it is for code. The three dimensional nature of the change makes it all the more important to keep to small changes. Many database refactorings, such as Introduce New Column, can be done without having to update all the code that accesses the system. If code uses the new schema without being aware of it, the column will just go unused. Many changes, however don't have this property, we call these destructive changes. Destructive changes need a bit more care, the degree of which depends on the degree of destruction involved. An example of a minor destructive change is Make Column Non Nullable, which changes a nullable column to not nullable. This is destructive because if any existing code doesn't set it to a value, then we'll get an error. We'll also get problems if there are any nulls in the existing data. We can avoid the problem with existing nulls (at the cost of slightly different ones) by assigning default data to any rows that have nulls here. For the problem of application code not assigning (or assigning null) we have two options. One is to set a default value to the column. ALTER TABLE customer MODIFY last_usage_date DEFAULT sysdate; UPDATE customer SET last_usage_date = (SELECT MAX(order_date) FROM order WHERE order.customer_id = customer.customer_id) WHERE last_usage_date IS NULL; UPDATE customer SET last_usage_date = last_updated_date WHERE last_usage_date IS NULL; ALTER TABLE customer MODIFY last_usage_date NOT NULL; The other way of dealing with a lack of assignment is to change the application code as part of the refactoring. This is the option we prefer if we can confidently get to all the code that updates the database, which is usually easy if the database is only used by a single application, but is hard if it's a shared database. A more complex case is Split Table, particularly if the access to the table is spread widely across the application code. If this is the case it's important to let everyone know that the change is coming up so they can prepare themselves for it. It may also be wise to wait for a relatively quiet moment, such as the start of an iteration. Any destructive change is much easier if database access is all channeled through a few modules of the system. That makes it easier to find and update the database access code. The important thing overall is to choose a procedure that's appropriate for the kind of change that you're making. If in doubt try to err on the side of making changes easier. Our experience is that we got burned much less frequently than many people would think, and with a strong configuration control of the entire system it's not difficult to revert should the worst happen. Taking care of database changes including DDL, DML and data migration during development, provides the most context for the data team, avoiding batch migration of all changes by data team during deployment without context. Transition phase We've already alluded to the difficulties we run into when we get a destructive database refactoring and we can't easily change the access code. These problems grow horns and big sharp teeth when you have a shared database, which may have many applications and reports using it. In this situation, you have to take much more care over something like a Rename Table. To keep us safe from such horns and teeth, we turn to the transition phase. A transition phase is a period of time when the database supports both the old access pattern and the new ones simultaneously. This allows older systems time to migrate over to the new structures at their own pace. Figure 8: Database refactorings, being applied to legacy database and the phases it needs to take before being implemented ALTER TABLE customer RENAME to client; CREATE VIEW customer AS SELECT id, first_name, last_name FROM client; For the Rename Table example, the developer would create a script that renames the table customer to client and also creates a view named customer that existing applications can use. This Parallel Change supports new and old access. It does add complexity, so it's important that it gets removed once downsteam systems have had time to migrate. In some organizations this can be done in a couple of months and in some other organizations it may take years. Views are one technique to enable transition phases. We also make use of database triggers, which are handy for things like Rename Column

Automate the refactorings Since refactoring became well known for application code, many languages have seen good support for automated refactorings. These simplify and speed up refactoring by swiftly carrying out the various steps with no human involved to make mistakes. Such automation is also available for databases. Frameworks like Liquibase and Active Record Migrations provide a DSL to apply database refactorings, allowing a standard way to apply database migrations. However these kinds of standardized refactorings don't work so well for databases since the rules for dealing with data migration and legacy data are very dependent on a team's specific context. So we prefer to handle database refactoring by writing scripts for migration and focus on tools to automate how to apply them. We write each script, as we've shown so far, by combining SQL DDL (for the schema change) and DML (for the data migration) and putting the result in a folder in our version-controlled repository. Our automation ensures we never apply these changes manually, they are only applied by the automation tooling. That way we maintain the ordering of the refactorings and update the database metadata. We can apply the refactorings to any database instance, to bring them up to date with the latest master, or to any previous version. The tooling uses the metadata information in the database to find out its current version, then applies each refactoring between it and the desired version. We can use this approach to update development instances, test instances, and production databases. Updating production databases isn't any different to test databases, we run the same set of scripts against different data. We do prefer releasing frequently as that keeps the updates small, as that means that the updates occur more quickly and it is easier to deal with any problems that come up. The easiest way to do these updates is to take the production database down while we apply the updates, this works well for most situations. If we have to do them while keeping the application live, it is possible, but the techniques we use will need another article to explain. So far, we've found that this technique has worked remarkably well. By breaking down all the database changes into a sequence of small, simple changes; we've been able to make quite large changes to production data without getting ourselves in trouble. As well as automating the forward changes, you can consider automating reverse changes for each refactoring. If you do this you'll be able to back out changes to a database in the same automated way. We haven't found this to be cost effective and beneficial enough to try all the time, also we've not had much demand for it, but it's the same basic principle. On the whole we prefer to write our migrations so that the database access section can work with both the old and new version of the database. This allows us to update the database to support a future need and make it live, have it running in production for a while, and then only push the update that uses the new data structures once we've found they have settled down without problems. These days there are many tools that automate applying database migrations, including: Flyway, Liquibase, MyBatis migrations, DBDeploy. Here's applying a migration with Flyway. psadalag:flyway-4 $ ./flyway migrate Flyway 4.0.3 by Boxfuse Database: jdbc:oracle:thin:@localhost:1521:xe (Oracle 11.2) Successfully validated 9 migrations (execution time 00:00.021s) Creating Metadata table: "JEN_DEV"."schema_version" Current version of schema "JEN_DEV": << Empty Schema >> Migrating schema "JEN_DEV" to version 0 - base version Migrating schema "JEN_DEV" to version 1 - asset Migrating schema "JEN_DEV" to version 2 - asset type Migrating schema "JEN_DEV" to version 3 - asset parameters Migrating schema "JEN_DEV" to version 4 - inventory Migrating schema "JEN_DEV" to version 5 - split inventory Migrating schema "JEN_DEV" to version 6 - equipment type Migrating schema "JEN_DEV" to version 7 - add insurance value to equipment type Migrating schema "JEN_DEV" to version 8 - data location equipment type Successfully applied 9 migrations to schema "JEN_DEV" (execution time 00:00.394s). psadalag:flyway-4 $

Clearly separate all database access code To understand the consequences of database refactorings, it's important to be able to see how the database is used by the application. If SQL is scattered willy-nilly around the code base, this is very hard to do. As a result it's important to have a clear database access layer to show where the database is being used and how. To do this we suggest following one of the data source architectural patterns from P ofEAA. Having a clear database layer has a number of valuable side benefits. It minimizes the areas of the system where developers need SQL knowledge to manipulate the database, which makes life easier to developers who often are not particularly skilled with SQL. For the DBA it provides a clear section of the code that he can look at to see how the database is being used. This helps in preparing indexes, database optimization, and also looking at the SQL to see how it could be reformulated to perform better. This allows the DBA to get a better understanding of how the database is used.