How to Implement CI/CD with MySQL in PostgreSQL

Galaxy Glossary

How do I build a CI/CD pipeline for MySQL databases?

CI/CD with MySQL automates schema migrations, seed data and tests through version-controlled scripts executed in every pipeline run.

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Description

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Why integrate MySQL into a CI/CD pipeline?

Automating MySQL migrations catches breaking schema changes early, guarantees repeatable environments, and speeds up releases. CI validates structure; CD deploys the new schema alongside application code.

Which tools fit best for MySQL CI/CD?

GitHub Actions, GitLab CI, Jenkins, and CircleCI all support MySQL through Docker images like mysql:8. Migration frameworks such as Flyway or Liquibase handle versioned SQL files.

How should migration files be organized?

Store numbered scripts (e.g., V001__create_customers.sql) in a db/migrations folder. Each script is idempotent and contains DDL for one change set.

What does a minimal GitHub Actions workflow look like?

The job spins up a MySQL service, runs migrations, loads seed data, executes unit tests, and publishes an artifact containing the new dump.

Sample `mysql-ci.yml` steps

1. Checkout code
2. Start mysql:8 container
3. Apply migrations with Flyway
4. Run integration tests
5. Export updated dump for CD

How is continuous delivery handled?

CD pipelines apply the same migrations against staging and production. Use --baselineOnMigrate to skip already-applied scripts, ensuring safety.

Best practices for MySQL CI/CD?

• Maintain backward-compatible migrations.
• Keep test data small.
• Tag Docker images with commit SHA.
• Guard destructive DDL with feature flags.

Why How to Implement CI/CD with MySQL in PostgreSQL is important

How to Implement CI/CD with MySQL in PostgreSQL Example Usage


-- Seed data script executed during CI
INSERT INTO Customers (id, name, email, created_at)
VALUES (1, 'Alice', 'alice@example.com', NOW());

INSERT INTO Orders (id, customer_id, order_date, total_amount)
VALUES (1, 1, CURDATE(), 120.00);

INSERT INTO Products (id, name, price, stock)
VALUES (1, 'Keyboard', 40.00, 50);

INSERT INTO OrderItems (id, order_id, product_id, quantity)
VALUES (1, 1, 1, 3);

How to Implement CI/CD with MySQL in PostgreSQL Syntax


# Flyway CLI syntax for MySQL migrations in CI
flyway migrate \
  -url="jdbc:mysql://localhost:3306/ecommerce" \
  -user="root" \
  -password="root" \
  -locations="filesystem:db/migrations" \
  -baselineOnMigrate=true 

# Example migration V002__add_stock_to_products.sql
ALTER TABLE Products ADD COLUMN stock INT NOT NULL DEFAULT 0;

# GitHub Actions service declaration
services:
  mysql:
    image: mysql:8
    env:
      MYSQL_ROOT_PASSWORD: root
      MYSQL_DATABASE: ecommerce
    ports: ["3306:3306"]
    options: >-
      --health-cmd="mysqladmin ping -h 127.0.0.1 -uroot -proot" 
      --health-interval=10s --health-timeout=5s --health-retries=3

Common Mistakes

Running schema-changing <code>DROP</code> or <code>ALTER</code> statements directly in production. Fix by wrapping them in version-controlled migration scripts and testing them in CI first.
Using <code>mysql dump</code> files as the only source of truth. Fix by storing raw DDL/DML in migration files so every environment can be rebuilt from scratch.

Frequently Asked Questions (FAQs)

Can I run MySQL CI tests without Docker?

Yes, use in-memory MySQL variants like MariaDB4j or local services, but Docker ensures environment parity.

How are rollback scenarios handled?

Flyway and Liquibase support undo scripts. Alternatively, keep backward-compatible migrations and deploy a forward fix.

Want to learn about other SQL terms?

dbt tests

dbt tests are declarative assertions in dbt that automatically validate data quality during model builds.

dbt utils

dbt utils is a community-maintained dbt package that adds time-saving utility macros for analytics engineers, such as generating surrogate keys, safely unioning models, and automating date spines.

dbt Tutorial Glossary: Build, Test & Deploy SQL Models

dbt is an open-source framework that lets data teams transform raw warehouse tables into tested, documented, production-ready datasets using version-controlled SQL.

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