Configuring how Relationship Joins — SQLAlchemy Documentation
To establish the relationship as many-to-one, an extra directive is added known as remote_side, which is a Column or collection of Column objects that indicate . Flask – SQLAlchemy Self Referential Relationship. February 3 For the relationship, we define a collection named as catogories. The first. SQLAlchemy ORM: Setting Up Self-Referential Many-to-Many Relationships for you as I assume some familiarity with Python, SQL, and SQLAlchemy. Next, we set parents equal to SQLAlchemy's relationship function.
I can say that a student has many teachers, and a teacher has many students. It's like two overlapped one-to-many relationships from both ends. For a relationship of this type I should be able to query the database and obtain the list of teachers that teach a given student, and the list of students in a teacher's class.
This is actually non-trivial to represent in a relational database, as it cannot be done by adding foreign keys to the existing tables.
The representation of a many-to-many relationship requires the use of an auxiliary table called an association table. Here is how the database would look for the students and teachers example: While it may not seem obvious at first, the association table with its two foreign keys is able to efficiently answer all the queries about the relationship.
Many-to-One and One-to-One A many-to-one is similar to a one-to-many relationship. The difference is that this relationship is looked at from the "many" side. A one-to-one relationship is a special case of a one-to-many. The representation is similar, but a constraint is added to the database to prevent the "many" side to have more than one link. While there are cases in which this type of relationship is useful, it isn't as common as the other types.
Representing Followers Looking at the summary of all the relationship types, it is easy to determine that the proper data model to track followers is the many-to-many relationship, because a user follows many users, and a user has many followers. But there is a twist. In the students and teachers example I had two entities that were related through the many-to-many relationship. But in the case of followers, I have users following other users, so there is just users.
So what is the second entity of the many-to-many relationship? The second entity of the relationship is also the users. A relationship in which instances of a class are linked to other instances of the same class is called a self-referential relationship, and that is exactly what I have here.
Here is a diagram of the self-referential many-to-many relationship that keeps track of followers: The followers table is the association table of the relationship. The foreign keys in this table are both pointing at entries in the user table, since it is linking users to users.
Each record in this table represents one link between a follower user and a followed user.
- SQLAlchemy ORM: Setting Up Self-Referential Many-to-Many Relationships
- Flask – SQLAlchemy Self Referential Relationship
- SQLAlchemy 1.3 Documentation
Like the students and teachers example, a setup like this one allows the database to answer all the questions about followed and follower users that I will ever need. Database Model Representation Let's add followers to the database first. Here is the followers association table: Note that I am not declaring this table as a model, like I did for the users and posts tables. Since this is an auxiliary table that has no data other than the foreign keys, I created it without an associated model class.
Now I can declare the many-to-many relationship in the users table: Many-to-many followers relationship class User UserMixin, db. Like I did for the posts one-to-many relationship, I'm using the db. This relationship links User instances to other User instances, so as a convention let's say that for a pair of users linked by this relationship, the left side user is following the right side user.
I'm defining the relationship as seen from the left side user with the name followed, because when I query this relationship from the left side I will get the list of followed users i. Let's examine all the arguments to the db. Since this is a self-referential relationship, I have to use the same class on both sides. From the left side, the relationship is named followed, so from the right side I am going to use the name followers to represent all the left side users that are linked to the target user in the right side.
The additional lazy argument indicates the execution mode for this query. A mode of dynamic sets up the query to not run until specifically requested, which is also how I set up the posts one-to-many relationship. Don't worry if this is hard to understand. I will show you how to work with these queries in a moment, and then everything will become clearer.
The Flask Mega-Tutorial Part VIII: Followers - dubaiairporthotel.info
The changes to the database need to be recorded in a new database migration: For example, if I had two users stored in user1 and user2 variables, I can make the first follow the second with this simple statement: Instead, I'm going to implement the "follow" and "unfollow" functionality as methods in the User model.
It is always best to move the application logic away from view functions and into models or other auxiliary classes or modules, because as you will see later in this chapter, that makes unit testing much easier. Below are the changes in the user model to add and remove relationships: Add and remove followers class User UserMixin, db. One such example is the materialized path pattern, where we compare strings for overlapping path tokens in order to produce a tree structure.
Through careful use of foreign and remotewe can build a relationship that effectively produces a rudimentary materialized path system.
The New Flask Mega-Tutorial
Support has been added to allow a single-column comparison to itself within a primaryjoin condition, as well as for primaryjoin conditions that use ColumnOperators. A common situation which involves the usage of primaryjoin and secondaryjoin is when establishing a many-to-many relationship from a class to itself, as shown below: In the Declarative form above, as we are declaring these conditions within the Python block that corresponds to the Node class, the id variable is available directly as the Column object we wish to join with.
Alternatively, we can define the primaryjoin and secondaryjoin arguments using strings, which is suitable in the case that our configuration does not have either the Node. When referring to a plain Table object in a declarative string, we use the string name of the table as it is present in the MetaData: Sometimes, when one seeks to build a relationship between two tables there is a need for more than just two or three tables to be involved in order to join them.
In more recent versions of SQLAlchemy, the secondary parameter can be used in some of these cases in order to provide a composite target consisting of multiple tables.
Below is an example of such a join condition requires version 0. A query from A to D looks like: New in version 0. Support is improved for allowing a join construct to be used directly as the target of the secondary argument, including support for joins, eager joins and lazy loading, as well as support within declarative to specify complex conditions such as joins involving class names as targets.
There is one complex join case where even this technique is not sufficient; when we seek to join from A to B, making use of any number of C, D, etc. When this extremely advanced case arises, we can resort to creating a second mapping as a target for the relationship.
Flask - SQLAlchemy Self Referential Relationship - CodeOmitted
This is where we use mapper in order to make a mapping to a class that includes all the additional tables we need for this join. Below illustrates a relationship with a simple join from A to B, however the primaryjoin condition is augmented with two additional entities C and D, which also must have rows that line up with the rows in both A and B simultaneously: Columns in the join with the same name must be disambiguated within the mapping, using named properties.
To remove the persistence part of the equation, use the flag viewonly on the relationshipwhich establishes it as a read-only attribute data written to the collection will be ignored on flush.