What exactly does db.metadatas['auth'].tables['user'] in Flask-SQLAlchemy do?

Flask-SQLAlchemy multi-database binding and metadata detailed explanation

This article discusses the meaning of db.metadatas["auth"].tables["user"] code snippet, and explains the multi-database binding mechanism and metadata usage methods of Flask-SQLAlchemy.

The core of this code lies in db.metadatas["auth"] . It does not directly operate on the user table in the database, but first obtains the metadata object bound to the database named "auth" through metadatas["auth"] , reflecting the multi-database binding capability of Flask-SQLAlchemy.

Flask-SQLAlchemy allows multiple database connections to be configured via SQLALCHEMY_BINDS , for example:

 SQLALCHEMY_BINDS = {
    "meta": "sqlite:////path/to/meta.db",
    "auth": {
        "url": "mysql://localhost/users",
        "pool_recycle": 3600,
    },
}

This configuration defines two database bindings: "meta" and "auth", which connect to different databases respectively. "meta" connects to SQLite database, and "auth" connects to MySQL database.

db.metadatas is a dictionary, the key is the database binding name (such as "auth", "meta"), and the value is the corresponding metadata object. A metadata object is a collection that contains multiple table objects and their database association information. Therefore, db.metadatas["auth"] returns the metadata object associated with the "auth" binding.

.tables["user"] accesses the tables property of the metadata object. tables is a dictionary with the key as the table name (such as "user") and the value is the corresponding table object. Finally, db.metadatas["auth"].tables["user"] gets the table object named "user" in the "auth" database.

The code assigns this table object to the __table__ attribute of user class to realize the direct mapping between user class and the "user" table in the "auth" database. In this way, the "user" table in the "auth" database can be easily operated without explicitly defining the table structure, thereby simplifying the code and improving maintainability.

Understanding db.metadatas and database binding mechanisms is crucial to efficiently use Flask-SQLAlchemy to handle complex multi-database applications.

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