So let's create an Author model with two fields: id and name, which means we'll have a table with two columns. Next, that attribute is going to be set equal to the mapped_column function call, where we could set additional properties on our column like primary key, max length, nullable, etc. So for an ID column, we would have id: Mapped. So the format of each column is the name of the column, followed by a class called Mapped with the Python type that closest matches the type you want in the database. Starting in SQLAlchemy 2.0, we can use the Python typing system to define the columns for us. The idea here is to first define a tablename, which is the attribute _tablename_. Let's first create an Author model which will map to an author table. Python class Base ( DeclarativeBase ): pass Create models # This makes it easy to add additional settings to our Base class in the future since all of our models will inherit from this one. Even though our classes could inherit directly from DeclarativeBase, we will instead create our own Base class that inherits it and then call pass. Classes that represent tables in an ORM are called models.īefore we can do the mapping, we need something called the DeclarativeBase from SQLAlchemy. So the first step to using ORM is to define classes that map to your tables. The classes represent the tables in your database, and the objects of those classes represent rows. The idea behind ORM (object-relational mapping) is to create a code representation of your database using classes and objects instead of writing raw SQL statements. If you want to use it as an ORM, you can do that as well. mappings (): print ( "Author:", row)Īs you can see, you only need to know a few things to write raw queries using SQLAlchemy. execute ( text ( "SELECT * FROM example WHERE name = :name" ), dict (name = "Ashley" )) for row in result. This makes it easy for us to retrieve the data and display it in a loop. We can assign the result of connection.execute() to a variable called result, and if we loop over result.mappings(), we'll see that we get dictionaries for each row, where the key in each dictionary represents the column name. Query data #įinally, now that we have some data in the database, let's go ahead and query that data so we can see it again. commit() after we execute our insert statements. Unlike CREATE statements, INSERT statements happen in transactions, so we have to save them to the database by calling. Just make sure the keys in the dictionary match the placeholders you have with a colon in front of the name.Įven though we aren't dealing with user input here, it's still a good idea to make a habit of passing in parameters instead of the data directly inside of an insert statement or select statement. But the second statement will create two rows since a list of two dictionaries was passed. The first execute statement will create just one row because a single dictionary was passed. ![]() execute ( text ( "INSERT INTO example (name) VALUES (:name)" ), ]) connection. We need to call nnect() to get a connection, and because the connect method is defined with a context manager, we can use a with statement to work with the connection. ![]() Since the engine manages connections, you need to ask the engine for a connection before you can send statements over to the database. To start sending queries over, you'll need to create a connection object. Now that we have our engine object working let's use it to send raw SQL statements to the database and receive the results in return. This article covers how to use it to send raw queries to your database and how to use it as an ORM. With the engine object working, you can then continue with SQLAlchemy in various ways. If you get an error like "access denied" or "server not found," then you'll need to fix your connection string before proceeding. ![]() If you run the code and get no errors, then SQLAlchemy has no trouble connecting to your database. See the Python connection arguments MySQL docs for more info and to see all of the possible arguments. This means you do not need to pass it into create_engine() as a connection arguement. Typically, you don't need echo set to True, but it's here so you can see the SQL statements that SQLAlchemy sends to your database.īy default, SSL/TLS usage in mysql-connector-python is enabled, which is required to connect to PlanetScale. Python from sqlalchemy import create_engine connection_string = engine = create_engine (connection_string, echo = True )
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