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Table of Contents
Version control is the basis, but many people don't do it right
Automated testing is indispensable, especially regression testing
Be careful with order and dependency when integrating SQL changes in CI/CD
Data migration should be cautious, especially in production environments
Home Database SQL SQL Best Practices for DevOps Integration

SQL Best Practices for DevOps Integration

Jul 30, 2025 am 04:08 AM

The key to combining SQL with DevOps is to treat database changes as code management to ensure version control, automated testing and deployment; 1. Database structure changes should be changed using version control, and orderly SQL scripts that can be executed repeatedly, and automatically executed with Flyway or Liquibase; 2. Automated testing is essential, query tests and unit tests should be written to automatically run in CI to prevent regression problems; 3. When integrating SQL changes in CI/CD, scripts should be idempotent, management changes history should be managed, and dry runs should be performed before deployment; 4. Data migration should be operated with caution, performed in stages, processed at low peaks, backups and drills in advance to ensure the safety of the production environment.

SQL Best Practices for DevOps Integration

The combination of SQL and DevOps is actually not that complicated, but many teams will still get trapped in practice. The key point is to enable database changes to be versioned, tested and deployed automatically like code. The following aspects are relatively important but easy to ignore in actual operation.

SQL Best Practices for DevOps Integration

Version control is the basis, but many people don't do it right

If the database structure changes (such as adding fields and changing indexes) are not implemented, it is easy to cause inconsistency in the environment. Many teams are also manually executing SQL scripts, which is prone to errors.

The recommended approach is:

SQL Best Practices for DevOps Integration
  • Each structure change is written as a repeatable SQL script
  • Save it in the repository in sequence, such as V1_01__add_user_table.sql
  • Automatic execution of these scripts using tools such as Flyway or Liquibase
  • Put database changes and application code in the same repository to maintain consistency

This not only facilitates rollback, but also facilitates automatic execution in the CI/CD pipeline.


Automated testing is indispensable, especially regression testing

Once a database change occurs, the impact is often great. For example, if a field is changed in type, it may make the entire service unable to be raised. Therefore, automated testing is particularly important.

SQL Best Practices for DevOps Integration

What you can do is:

  • Write some basic query tests to verify whether the table structure is correct
  • Logically write unit tests on key data (such as using tSQLt or pgTAP )
  • Automatically run these tests in the CI before each merge change
  • The test data must also be versioned to ensure that each test environment is consistent

Many people will ignore this part, but once it is established, it can greatly reduce online accidents.


Be careful with order and dependency when integrating SQL changes in CI/CD

Performing database changes in pipelines is more sensitive than deploying code. For example, if multiple services rely on a table at the same time, if the order is wrong, problems may arise.

A few points to note:

  • Each change script must be idempotent or can detect whether it has been executed
  • Use tools to manage change history and avoid repeated execution
  • Do a "dry run" before deployment, such as generating a change plan
  • If it is a multi-environment deployment (dev/staging/prod), make sure that the script can run in every environment

For example, in GitLab CI or GitHub Actions, SQL changes can be used as part of the deployment process and scripts to connect to the database to perform changes.


Data migration should be cautious, especially in production environments

Sometimes structure changes will also involve data migration, such as demolishing fields, merging tables, converting types, etc. This type of operation is particularly dangerous in production environments.

Consider:

  • In stages: add new fields first, then migrate data, and finally delete old fields
  • Execute at low peaks, or in the deployment window
  • Back up data in advance, even if you export a SQL file
  • Write a rollback script in case

This type of operation is recommended to practice it in the test environment first to confirm that the table will not be locked for too long and will not cause the service to be unavailable.


Basically that's it. The key to combining SQL and DevOps is to manage the database as "code", and with testing and automation, safe and controllable changes can be achieved.

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