📄️ Schema Generator
SchemaGenerator can do harm to your database. It will drop or alter tables, indexes, sequences and such. Please use this tool with caution in development and not on a production server. It is meant for helping you develop your Database Schema, but NOT with migrating schema from A to B in production. A safe approach would be generating the SQL on development server and saving it into SQL Migration files that are executed manually on the production server.
📄️ Migrations
MikroORM has integrated support for migrations. It allows you to generate migrations based on the current schema difference.
📄️ Seeding
When initializing your application or testing it can be exhausting to create sample data for your database. The solution is to use seeding. Create factories for your entities and use them in the seed script or combine multiple seed scripts.
📄️ Entity Generator
To generate entities from existing database schema, you can use the EntityGenerator helper. It lives in its own package called @mikro-orm/entity-generator:
📄️ Naming Strategy
When mapping your entities to database tables and columns, their names will be defined by naming strategy. There are 3 basic naming strategies you can choose from:
📄️ Using Multiple Schemas
In MySQL, PostgreSQL, and SQLite (via ATTACH DATABASE) it is possible to define your entities in multiple schemas. In MySQL terminology, it is called database, but from an implementation point of view, it is a schema.
📄️ Virtual Entities
Virtual entities don't represent any database table. Instead, they dynamically resolve to an SQL query (or an aggregation in MongoDB), allowing to map any kind of results onto an entity. Such entities are meant for read purposes, they don't have a primary key and therefore cannot be tracked for changes.
📄️ View Entities
View entities represent actual database views that are created and managed by MikroORM's schema generator. Unlike virtual entities which evaluate expressions at query time, view entities create persistent CREATE VIEW statements in your database.
📄️ Materialized Views
Materialized views store the results of a query physically, providing faster read performance at the cost of data freshness. Unlike regular views, materialized views must be explicitly refreshed to reflect changes in the underlying data.