Convert SQL to YML

Why Convert SQL to YML?

Converting SQL files to YML format produces configuration-ready output files using the .yml extension that is preferred by many popular DevOps and development tools. Docker Compose, GitHub Actions, GitLab CI/CD, Ansible, and Spring Boot all conventionally use the .yml extension for their configuration files. By converting SQL data to YML, you create files that seamlessly integrate into these ecosystems.

The .yml extension is functionally identical to .yaml but has become the de facto standard in many toolchains. Docker Compose defaults to docker-compose.yml, GitHub Actions expects files in .github/workflows/*.yml, and many Spring Boot applications use application.yml. Converting SQL data to YML ensures your database configuration and seed data files match the naming conventions expected by these tools.

SQL database schemas and data convert cleanly to YML's indentation-based structure. Table definitions become nested mappings with column metadata, INSERT data becomes sequences of records with typed values, and database configuration parameters become simple key-value pairs. The resulting YML files are easy to read, edit, and merge in version control, with clean diffs that show exactly what changed between commits.

This conversion is especially useful for development teams that manage database initialization alongside application configuration. Instead of maintaining separate SQL scripts for database setup, teams can convert SQL to YML and manage all configuration in a consistent format. This simplifies CI/CD pipelines where database setup, application configuration, and deployment are all defined in YML files.

Key Benefits of Converting SQL to YML:

• Docker Compatible: Output matches docker-compose.yml naming conventions
• CI/CD Ready: Works with GitHub Actions, GitLab CI, and other pipeline tools
• Native Types: Integers, booleans, dates, and nulls preserved without quotes
• Clean Syntax: Indentation-based format is easy to read and edit
• Version Control: YML produces meaningful, reviewable diffs
• Framework Integration: Compatible with Spring Boot, Django, Rails configurations
• Consistent Naming: .yml extension matches DevOps tooling conventions

Practical Examples

Example 1: Docker-Style Database Configuration

Input SQL file (config.sql):

INSERT INTO service_config (service, host, port, protocol, replicas) VALUES
('web-app', 'frontend.local', 3000, 'http', 3),
('api-gateway', 'gateway.local', 8080, 'http', 2),
('database', 'postgres.local', 5432, 'tcp', 1),
('redis-cache', 'redis.local', 6379, 'tcp', 1);

Output YML file (config.yml):

# Service configuration
services:
  - service: web-app
    host: frontend.local
    port: 3000
    protocol: http
    replicas: 3
  - service: api-gateway
    host: gateway.local
    port: 8080
    protocol: http
    replicas: 2
  - service: database
    host: postgres.local
    port: 5432
    protocol: tcp
    replicas: 1
  - service: redis-cache
    host: redis.local
    port: 6379
    protocol: tcp
    replicas: 1

Example 2: CI/CD Pipeline Variables

Input SQL file (pipeline.sql):

INSERT INTO pipeline_vars (stage, variable_name, value, secret) VALUES
('build', 'NODE_VERSION', '20', FALSE),
('build', 'BUILD_TARGET', 'production', FALSE),
('test', 'TEST_DB_HOST', 'localhost', FALSE),
('test', 'TEST_DB_PORT', '5432', FALSE),
('deploy', 'DEPLOY_REGION', 'us-east-1', FALSE),
('deploy', 'AUTO_SCALE', 'true', FALSE);

Output YML file (pipeline.yml):

# CI/CD Pipeline Variables
stages:
  build:
    variables:
      NODE_VERSION: "20"
      BUILD_TARGET: production
  test:
    variables:
      TEST_DB_HOST: localhost
      TEST_DB_PORT: 5432
  deploy:
    variables:
      DEPLOY_REGION: us-east-1
      AUTO_SCALE: true

Example 3: Application Feature Flags

Input SQL file (features.sql):

INSERT INTO feature_flags
(feature_name, enabled, rollout_pct, description) VALUES
('dark_mode', TRUE, 100, 'Dark theme for UI'),
('new_checkout', TRUE, 50, 'Redesigned checkout flow'),
('ai_recommendations', FALSE, 0, 'AI-powered product suggestions'),
('beta_dashboard', TRUE, 25, 'New analytics dashboard'),
('two_factor_auth', TRUE, 100, 'Two-factor authentication');

Output YML file (features.yml):

# Feature Flags Configuration
feature_flags:
  - name: dark_mode
    enabled: true
    rollout_pct: 100
    description: Dark theme for UI
  - name: new_checkout
    enabled: true
    rollout_pct: 50
    description: Redesigned checkout flow
  - name: ai_recommendations
    enabled: false
    rollout_pct: 0
    description: AI-powered product suggestions
  - name: beta_dashboard
    enabled: true
    rollout_pct: 25
    description: New analytics dashboard
  - name: two_factor_auth
    enabled: true
    rollout_pct: 100
    description: Two-factor authentication

Frequently Asked Questions (FAQ)

Q: What is the difference between YML and YAML?

A: There is no functional difference between .yml and .yaml files. Both use the same YAML specification and are parsed identically by all YAML libraries. The .yml extension is a shorter alternative that has become the convention for many tools like Docker Compose (docker-compose.yml), GitHub Actions, GitLab CI, and Spring Boot. The .yaml extension is the official recommendation from the YAML specification, but .yml is equally valid and more commonly used in DevOps contexts.

Q: How are SQL tables converted to YML structure?

A: SQL table data from INSERT statements is converted to YML sequences (lists) of mappings. Each row becomes a list item with column names as keys and values as YML-typed values. Schema definitions from CREATE TABLE become nested mappings documenting columns, types, and constraints. The table name becomes the top-level key under which all data is organized.

Q: Are SQL data types preserved in YML?

A: Yes! YML's native type system handles SQL types naturally. SQL INTEGER becomes YML integer (unquoted number), BOOLEAN becomes true/false, NULL becomes null, DECIMAL becomes float, and VARCHAR/TEXT becomes string. Date and timestamp values use YML's native date format. Strings that look like numbers or booleans are properly quoted to prevent type confusion.

Q: Can I use the YML output with Docker Compose?

A: The generated YML file uses valid YAML syntax and the .yml extension that Docker Compose expects. While the data structure represents database content rather than Docker service definitions, you can incorporate the output into docker-compose.yml as environment variables, config sections, or use it alongside Docker Compose for database initialization. SQL configuration data converts well to Docker environment variables.

Q: How are SQL comments preserved in YML?

A: SQL comments (-- single-line and /* multi-line */) are converted to YML comments (# prefix). This preserves the documentation value of the original SQL file. YML comments appear on their own lines or at the end of data lines, providing context about the data's purpose and origin. Comments are preserved in the output to maintain documentation continuity.

Q: How does YML handle multi-line SQL values?

A: SQL TEXT or VARCHAR values containing multiple lines are represented using YML's literal block scalar (|) or folded block scalar (>) syntax. The literal style preserves line breaks exactly as they are, while the folded style joins lines with spaces. This allows long SQL descriptions, stored procedure text, and multi-line comments to be represented cleanly in the YML output.

Q: Can I use this for Spring Boot application.yml?

A: Yes! The YML output follows the standard YAML syntax used by Spring Boot's application.yml configuration format. Database connection parameters, application properties, and configuration settings extracted from SQL can be reorganized into Spring Boot's hierarchical property structure. The native type support means port numbers remain integers and boolean flags remain true/false.

Q: Is there a size limit for SQL to YML conversion?

A: There is no inherent format limitation. YML files can be any size and the conversion handles SQL files with thousands of INSERT statements efficiently. However, very large YML files (hundreds of MB) may be slow to parse with some YAML libraries. For very large datasets, consider splitting into multiple YML files or using a more efficient format like JSON Lines or Parquet for bulk data storage.