Relationships API — SQLAlchemy Documentation
from sqlalchemy import Table, Column, Integer, ForeignKey from promovare-site.info import relationship from promovare-site.infoative import declarative_base. promovare-site.info relationship (argument, secondary=None, primaryjoin=None, This corresponds to a parent-child or associative table relationship. from sqlalchemy import Integer, ForeignKey, String, Column from promovare-site.info declarative import declarative_base from promovare-site.info import relationship.
Returns a MappedCollection factory with a keying function generated from keyfunc, a callable that takes an entity and returns a key value.
In other cases, special decorators are needed to tell SQLAlchemy more detail about how the collection operates. Do I need a custom collection implementation? In most cases not at all! For the first use case, the orm. See Simple Validators for an example of this. This approach leaves the underlying mapped collection unaffected and avoids the need to carefully tailor collection behavior on a method-by-method basis. They can of course be combined with the above two approaches. Collections in SQLAlchemy are transparently instrumented.
Instrumentation means that normal operations on the collection are tracked and result in changes being written to the database at flush time. Additionally, collection operations can fire events which indicate some secondary operation must take place. The collections package understands the basic interface of lists, sets and dicts and will automatically apply instrumentation to those built-in types and their subclasses. Object-derived types that implement a basic collection interface are detected and instrumented via duck-typing: The ORM needs to know which methods to use to append, remove and iterate over members of the collection.
When using a type like list or set, the appropriate methods are well-known and used automatically when present. This set-like class does not provide the expected add method, so we must supply an explicit mapping for the ORM via a decorator. SQLAlchemy will add instances via the append method. Default methods can be changed as well: There is no requirement to be list- or set-like at all.
Append and remove methods will be called with a mapped entity as the single argument, and iterator methods are called with no arguments and must return an iterator. The decorators fall into two groups: They are not written with parens: The recipe decorators all require parens, even those that take no arguments: The decorator argument indicates which method argument holds the SQLAlchemy-relevant value.
Arguments can be specified positionally i. The appender method is called with one positional argument: Something to remember is that the appender will be called for each object mapped by a database query. If the database contains rows that violate your collection semantics, you will need to get creative to fix the problem, as access via the collection will not work.
Deprecated since version 1. The default converter implementation will use duck-typing to do the conversion. For example, on a blog engine, an instance of the Article class could be associated with many instances of the Comment class. In this case, we would map the mentioned classes and its relation as follows: To give a different example, let's say that we want to map the relationship between instances of Tire to an instance of a Car.
As many tires belong to one car and this car contains many tires, we would map this relation as follows: As an example, consider the relationship between a Person and a MobilePhone. Usually, one person possesses one mobile phone and this mobile phone belongs to this person only. To map this relationship on SQLAlchemy, we would create the following code: The official Relationships API documentation provides a complete explanation of these parameters and also covers other parameters not mentioned here.
The last type supported by SQLAlchemy, Many To Many, is used when instances of a particular class can have zero or more associations to instances of another class. For example, let's say that we are mapping the relationship of instances of Student and instances of Class in a system that manages a school.
As many students can participate in many classes, we would map the relationship as follows: Note that, to make SQLAlchemy aware of the helper table, we passed it in the secondary parameter of the relationship function.
The above code snippets show just a subset of the mapping options supported by SQLAlchemy. In the following sections, we are going to take a more in-depth look into each one of the available relationship patterns. Besides that, the official documentation is a great reference to learn more about relationship patterns on SQLAlchemy. These changes can be simple updates, which are called cascade updates, or full deletes, known as cascade deletes.
Otherwise we will end up with a lot of garbage and unfulfilled references in our database. To make this kind of operation easy to maintain, SQLAlchemy ORM enables developers to map cascade behavior when using relationship constructs. Indicates that when a parent object is deleted, children of this object will be deleted as well. Indicates that when a child object loses reference to a parent, it will get deleted. Indicates that merge operations propagate from parent to children.
If more information about this feature is needed, the SQLAlchemy documentation provides an excellent chapter about Cascades. As explained by Martin Fowler, a Unit of Work is used to maintain a list of objects affected by a business transaction and to coordinate the writing out of these changes.
This means that all modifications tracked by Sessions Units of Works will be applied to the underlying database together, or none of them will. In other words, Sessions are used to guarantee the database consistency.
The official SQLAlchemy ORM documentation about Sessions gives a great explanation how changes are tracked, how to get sessions, and how to create ad-hoc sessions.
However, in this article, we will use the most basic form of session creation: We need to create a session factory that is bound to the SQLAlchemy engine. After that, we can just issue calls to this session factory to get our sessions. In the following sections, we will create a small project based on pipenv —a Python dependency manager—and add some classes to it.
Then we will map these classes to tables persisted to a PostgreSQL database and learn how to query data. Starting the Tutorial Project To create our tutorial project, we have to have Python installed on our machine and pipenv installed as a global Python package.
The following commands will install pipenv and set up the project. These commands are dependent on Python, so be sure to have it installed before proceeding: There are many ways to get an instance of PostgreSQL. Another possibility is to install PostgreSQL locally on our current environment. The third option is probably the best choice because it has the performance of an instance running locally, it's free forever, and because it's easy to create and destroy Docker instances.
- SQLAlchemy 1.3 Documentation
The only small disadvantage is that we need to install Docker locally. After having Docker installed, we can create and destroy dockerized PostgreSQL instances with the following commands: Defines the name of the Docker instance.
Defines the password to connect to PostgreSQL. Defines the user to connect to PostgreSQL. Defines the main and only database available in the PostgreSQL instance. Defines that the local port will tunnel connections to the same port in the Docker instance.
SQLAlchemy 1.3 Documentation
To install these dependencies, we will use pipenv as shown: Note that to run the scripts that we are going to create, we first need to spawn the virtual environment shell. That is, before executing python somescript. Otherwise, Python won't be able to find the installed dependencies, as they are just available in our new virtual environment. In this tutorial, we will map four simple classes that represent movies, actors, stuntmen, and contact details.
The following diagram illustrates these entities' characteristics and their relations. To start, we will create a file called base. Now let's create and map the Movie class. To do this, let's create a new file called movie.
We start by making this class extend the Base class defined in the base.
An id to represent the primary key in the table. A title of type String.
Basic Relationship Patterns — SQLAlchemy Documentation
The next class that we will create and map is the Actor class. Let's create a file called actor. As many movies can have many actors and vice-versa, we will need to create a Many To Many relationship between these two classes. Let's create this relationship by updating the movie. The next class that we will create is Stuntman.
In our tutorial, a particular Actor will have only one Stuntman and this Stuntman will work only with this Actor.