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The Evolution of Database Schema Management​

In today's world, where software is developed and deployed at a rapid pace, selecting the right tools for your team is crucial. The right tools can help you move faster, be more productive, and reduce the risk of errors and bugs. Most backend applications are backed by a database, and maintaining the database schema is a critical part of the development process.

Our industry has seen many trends in the way database schemas evolve over the years. In the early days, DBAs would manually run SQL DDL commands directly against the database through a terminal. Then came GUI based, point-and-click database management tools that included schema editing capabilities.

In the early 2000s, we saw the rise of "database migration tools" that allowed developers to write versioned database upgrade scripts (usually in SQL), called "migrations", that would be executed by the tool itself. This trend was further intensified with the rise of DevOps and CI/CD, as it became the de-facto standard way for automating database schema management.

The Rise of Database Schema-as-Code​

In recent years, we have witnessed a seismic shift in the way cloud infrastructure is managed. The rise of tools such as Terraform and Kubernetes has led to the concept of "Infrastructure-as-Code". As the complexity of infrastructure grew, it became clear that the only way to manage it effectively was to use a declarative approach and treat it "as code".

Infrastructure-as-code, in this context, means that the desired state of the infrastructure is defined in a formal language ("as code") and stored in a version control system (such as Git). The infrastructure is then managed by a tool that compares the desired state to the actual state and makes the necessary changes to bring the actual state to the desired state.

This approach has many advantages over the traditional imperative approach of manually running commands against the infrastructure. It allows for better collaboration between team members, it is more robust and less susceptible to errors, it is self documenting, and allows for better visibility, governance and control into the state of the infrastructure.

Following this trend, some projects such as Atlas and Skeema have started to apply the same principles to database schema management. Instead of using an imperative approach, where developers do the planning and tools are only used to execute the plan and keep track of what has been done, they bring to the table a simpler approach. This approach, which is gaining the name "Database Schema-as-Code", applies a declarative approach to database schema management: developers provide the desired state of the database and the tool automatically plans the required migrations.

A practical example​

Let's use Atlas to show a quick example of how database schema-as-code tools work.

Atlas allows users to represent their database schema in plain SQL or in HCL. The schema is saved in a schema file, which holds the desired state of our database.

We will start with a simple schema that represents a users table, in which each user has an ID and a name:

table "users" {
  schema = schema.example
  column "id" {
    null = false
    type = int
  }
  column "name" {
    null = true
    type = varchar(100)
  }
  primary_key {
    columns = [column.id]
  }
}

atlas schema apply \
  -u "mysql://root:pass@localhost:3306/example" \
  --to file://schema.hcl \
  --dev-url "docker://mysql/8/example"

-- create "users" table
CREATE TABLE `users` (
  `id` int NOT NULL,
  `name` varchar(100) NULL,
  PRIMARY KEY (`id`)
) CHARSET utf8mb4 COLLATE utf8mb4_0900_ai_ci;

atlas schema apply \
  -u "mysql://root:pass@localhost:3306/example" \
  --to file://schema.sql \
  --dev-url "docker://mysql/8/example"

Atlas will compare the desired state from our schema file to the database URL provided, and plan the migration:

-- Planned Changes:
-- Create "users" table
CREATE TABLE `users` (
  `id` int NOT NULL,
  `name` varchar(100) NULL,
  PRIMARY KEY (`id`)
) CHARSET utf8mb4 COLLATE utf8mb4_0900_ai_ci;
? Are you sure?:
  ▸ Apply
    Lint and edit
    Abort

After applying this to our database, we will run the command once more and see that Atlas recognizes there is no drift between the two states:

Schema is synced, no changes to be made

Let's make a simple change to our schema by adding an email column:

table "users" {
  schema = schema.example
  column "id" {
    null = false
    type = int
  }
  column "name" {
    null = true
    type = varchar(100)
  }
  column "email" {
    null = true
    type = varchar(255)
  }
  primary_key {
    columns = [column.id]
  }
}

-- create "users" table
CREATE TABLE `users` (
  `id` int NOT NULL,
  `name` varchar(100) NULL,
  `email` varchar(255) NULL,
  PRIMARY KEY (`id`)
) CHARSET utf8mb4 COLLATE utf8mb4_0900_ai_ci;

Now let's run the schema apply command again, and view the migration plan that Atlas will create for us:

-- Planned Changes:
-- Modify "users" table
ALTER TABLE `users` ADD COLUMN `email` varchar(255) NULL;
? Are you sure?:
  ▸ Apply
    Lint and edit
    Abort

To conclude, in this example, we got a glimpse of how a tool like Atlas works with a database schema using the declarative approach.

Do you need a Database Schema-as-Code tool?​

If you are using an older more traditional database migration tool or not using one at all, you might be wondering if you should consider switching to a Database Schema-as-Code tool. Here are five signs that you might need to consider it:

1. Your team is making changes to the database schema directly in production​

The worst kind of tool for managing database schemas is no tool at all. Running migrations manually against production databases should be avoided at all costs. First, humans are notoriously prone to errors, and the risk of making a mistake that will cause downtime or data loss is very high. Second, any changes made directly in production are not tracked anywhere, so it is very hard to know what has been done and by whom. Third, it means that your deployments cannot be fully automated and you are missing out on the benefits of CI/CD.

Using a Database Schema-as-Code tool as the exclusive way of making changes to the database schema is a great way to avoid these problems. It allows you to automate your deployments, track all changes to the database schema in source control, and collaborate more effectively with your team.

2. You don't have a single source of truth for your database schema​

Having a single source of truth for how your application is deployed is a key principle of DevOps. It allows you to have a clear picture of the state of your application at any given time, and it allows you to automate your deployments and avoid human errors.

By requiring all migrations to be checked in to source control, traditional migration tools can go a long way to ensure that the database schema is in a consistent state, but they suffer from two issues in this regard:

1. They only keep track of the changes to the database ("migrations"). To figure out the actual schema of the database at any given version, you need to apply all the migrations up to that version to an empty database and inspect the result with an additional tool. Database Schema-as-Code tools, on the other hand, naturally document the schema of the database in any given version.

2. They have no way to ensure that the actual database's schema is consistent with the migrations. If someone makes a change directly in the database, there is no way to know about it. When you apply your schema to a target database using a Database Schema-as-Code tool, it will compare the desired state to the actual state and make the necessary changes to bring the actual state to the desired state.

   This ensures that over time your actual databases will not drift from the desired state.

3. You don't have any database experts on your team​

In the past, managing database schemas was under the responsibility of DBAs. They were the ones who were responsible for making sure that the database schema is consistent and that all changes are done safely.

In recent years, as cloud-native, microservices-based architectures have become more popular, the amount of databases that each team manages has grown significantly. This has led to a situation where many teams don't have a DBA , and the responsibility for managing the database schema falls on the shoulders of the developers.

If you maintain a small schema with a few tables, you might not feel the need for a tool to help you manage it. But as schemas grow in size and complexity, to contain thousands of database objects (tables, views, functions, stored procedures, etc.), it becomes very hard to manage them manually. A Database Schema-as-Code tool can help you manage your schema more effectively, even if you don't have any DBAs on your team.

4. You manually verify the safety of your database migrations​

If your application is small and the stakes for making a mistake are low, you might be able to get away with messing up a schema change now and then. But if your workload is mission-critical, if application downtime or data loss can have a significant impact on your business, you need to make sure that your migrations are safe.

Many teams have been through the unpleasant experience of a failed migration that caused a significant outage. This is usually followed by a change in review processes where migrations are reviewed by multiple team members, and a manual verification process is added to make sure that the migration is safe to run.

This process is time-consuming and error-prone. Even worse, it turns team members with more database experience into bottlenecks, slowing down the development process and team velocity.

Database Schema-as-Code tools can help you automate this process and make it more robust. Atlas, for example, comes with built-in support for Migration Linting, Diff Policies, and other safety mechanisms that help you catch errors and issues before they lead to a full-blown production outage.

5. Your app and infra delivery are way better than your database deployments​

When interviewing platform engineers about how their deployment infrastructure works, we keep hearing the same story: "Our deployments are 100% pure Infrastructure-as-Code GitOps goodness, we can do them 50 times a day!" And when we ask about databases… "Oh yeah, there’s that part... It’s not as great."

A lot has changed in the way applications are deployed since the early days of database migrations. The versioned migrations paradigm was a great improvement over the previous manual approach, but it is still very much an imperative approach. Modern application delivery principles such as GitOps and Infrastructure-as-Code work in a declarative way, and so they need a database schema management tool that works in a similar way.

If your application and infra delivery feel like a well-oiled machine, but your database deployments are still semi-manual, or feel clunky and error-prone, you should consider switching to a Database Schema-as-Code tool where you can manage your database schema in a Kubernetes Operator or a Terraform Provider.

Enter: Atlas​

Atlas is a Database Schema-as-Code tool that allows you to manage your database schema in a declarative way. With Atlas, developers provide the desired state of the database schema and Atlas automatically plans the required migrations. To get started with Atlas, you can follow our quickstart guide..

Wrapping up​

In this article, we have discussed the evolution of database schema management tools and the rise of Database Schema-as-Code tools. We have also discussed five signs that you might need to consider switching to a Database Schema-as-Code tool. I hope you found this article useful.

As always, we would love to hear your feedback and suggestions on the Atlas Discord server.