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Many projects need addition of identical columns to all the tables created by the project. Audit columns are an example of such a requirement. The requirement is to add columns such as created_by, created_date, modified_by and modified_date to all the tables, these columns store, who created the row, when the row was created, who modified the row last and when was it modified. created_by and created_date are required to be present when the row is inserted and thus are required to be not nullable. Adding these columns to each and every table is a lot of work for developers.

In many development shops, developers are not allowed to access the database schema directly, and are not allowed to create tables, indexes, views etc, instead are given access via a different schema that allows SELECT, UPDATE and DELETE access on data. The general reason is to avoid developers creating database objects without

When Behavior Driven Development BDD was introduced, some of the key principles were Requirements are behavior, Provides “ubiquitous language” for analysis, Acceptance criteria should be executable. Design constraints should be made into executable tests.

IN many projects, there are tables which need default audit columns such as Created_By, Created_Date, Modified_By, Modified_date and other columns that need to be updated every time some actions are done against the tables and/or columns. This type of functionality can be implemented using triggers.

In every enterprise and every project we end up having multiple environments, especially the database side of the enterprise tends to stick around for a longer period of time and has much more dependencies or application integration as opposed to application urls etc. Given this, how to name the servers, databases and schemas becomes a very important decision, do these names provide for an easy way to use the application and not make it harder or the developers to access the database.

Many of the projects we end up working on are replacing existing systems with existing data either wholly or in part. In all of the above projects we end up writing data migration or data conversion code to move the data from legacy systems to the new systems. Many stake holders of the project such as business users, project managers, business analysts really care about the data conversion scripts and the quality of the conversion. Since this conversion is business entity related and matters a lot as future business/functionality depends on the data being logically equivalent to the legacy system.

In relational database usage the pattern of migrations is well understood and has gained widespread acceptance. Frameworks such as DBDeploy, DBMaintain, MyBatis migrations, Flyway, Liquibase, Active Record Migrations and many others. These tools allow to migrate the database and maintain the version history of the database in the database.

While doing evalauation of NoSQL databases, we had a 10 node riak cluster and wanted check how a similar setup would work with mongodb. So started to setup a 10 node mongodb cluster. Since this was for initial spikes, we decided to set this up on a single machine as with the other test setup using Riak.

Its been about a month since my blog moved to octopress, wanted to write about my experience. I had been running my blog for a some time now using Movable Type upgrading as and when new versions where released. Over time I realized that upgrading was fraught with errors as lot of steps had to be done manually. Customizing the layout was risky as there was no way to preview your changes and commit only when I was comfortable. With the release of Movable Type 6 there is no longer a free version to download.