It’s been more than a decade that DevOps as a practice has been bridging the gap between the development and operations teams. It’s breaking down the silos between the two and automating the delivery cycle. Just like we implement DevOps to the delivery pipeline (to deal with the varying development environment), it is time that we start doing the same for databases.
Changes in the database are quite tedious. Any changes to the DB are reviewed by a database administrator (DBA). The DBA identifies how these database changes are going to affect the data integration and performance of the application. Manual workflows to manage the database result in bottlenecks, slowed down release cycles, crashes, unwanted downtime, etc. Thus, automation is necessary to implement futuristic changes in a repeatable and predictable manner.
Automation is important when we have a large-sized database. When there is too much data in the database, altering a table can block upcoming changes such as inserts, updates, or deletes and takes a lot of time. To avoid these blockers, it is better to manage the DB changes side by side (as the product grows) rather than waiting for changes to get implemented manually.
DevOps for Database: Best Practices
One of the major problems with databases is the tightly coupled architectures. Such architectures have a central database which is usually large in size and thus any changes in the DB would have a major impact on the system.
Fortunately, we have modular architectures these days such as Microservices, SOA where we have the opportunity to create an individual, small-sized database for every. Such architectures keep the databases less burdened as changes in the database for a specific service are made.
According to a Redgate Database DevOps report, 45% of the databases lack version control, compared with only 17% for application code.
Traditional methods for tracking and monitoring are time & resource-consuming. Besides that, they prove to be ineffective when it comes to managing database version releases and changes. That is why it is necessary that the release automation process (CI/CD) involves a database. It will bring agility to the development cycle.
Maintaining a detailed history of changes made to the database by all stakeholders means a DBA has to waste several hours of manual work. Automating it would ensure that a complete record is maintained for database compliance and can be accessed in a few clicks, whenever required.
What could ease the automation of database versioning is a database change policy. Even when a database is a part of the DevOps pipeline, errors can happen. To avoid them, the organization should have a database change policy in place. Thus, despite a dynamic coding environment, the development stakeholders will ensure that database changes are standardized and automation results in error-free output.
For database management, rules and permissions are a must. Define who can do what with the database so that alerts can be passed to control unauthorized activities with the database and ensure compliance. For example, you can define who can deploy database changes or who has access to the database.
Also, when it comes to maintaining governance, database audit history can be a major roadblock. If multiple developers are working on a project and there are thousands of changes being made, then manually curating a report is not a feasible solution. Automation helps in creating an audit report that helps to maintain the security and governance of the database.
CI/CD pipeline is no longer optional. It is one of the must-haves for continuous, on-time releases. Once the developers and the database teamwork in collaboration, it will result in more stable and consistent builds. The CI process validates the DB schema changes automatically, which ensures that the DB is structurally intact.
Tools are the lifelines of the CI/CD pipeline. So, when the database is included in the CI/CD pipeline, make sure that the database is automated using tools that are compatible with the tools already in use. Database automation tools can validate DB scripts, run tests, and sync the DB with the source code.
It is essential to avoid last-minute errors. To ensure that the release is successful in every scenario post-production, analyze the configuration drifts ad errors beforehand.
Another important test is the integration test. The code might work flawlessly after the change but it should be not be creating any conflict with the integration done before. That is why integration testing is necessary.
Conclusion:
Including databases in CI/CD pipeline is a new norm. At Daffodil, we practice database DevOps to ensure that any flaw in the database should not affect the releases in long term.
Although database DevOps should be a part of the development cycle from the start if you haven’t done that already, then there is nothing to fear. The database can be included in the DevOps cycle to ensure that the DB doesn’t get affected later on.
If you have more queries regarding DevOps for database or to understand best practices that work best for your business, then reach out to our DevOps experts.