Flask+SQLAlchemy Serializer: A Comprehensive Guide to Serialization

Serialization, the process of converting data into a format suitable for transmission and storage, is a crucial aspect of working with databases. In the realm of Flask web applications powered by SQLAlchemy, a powerful Object Relational Mapping (ORM) library, effective serialization plays a pivotal role. Enter the Flask+SQLAlchemy SerializerMixin, a tool that enhances the capabilities of your SQLAlchemy models by making them serializable. In this comprehensive guide, we will delve into the concept of serialization, introduce the SerializerMixin, explore its various applications, and provide a step-by-step tutorial for implementation.

Serialization involves transforming complex data structures into a format that can be easily transmitted over networks or stored in databases. In the context of web applications, serialization is commonly used to convert data into JSON (JavaScript Object Notation) or XML (eXtensible Markup Language) format. Serialized data is compact, platform-independent, and easily interpreted by both humans and machines.

When dealing with databases, such as in a Flask application using SQLAlchemy, serialization becomes a critical component. SQLAlchemy allows developers to define database models using Python classes, making it seamless to interact with the database using Python code. However, when you need to send this data over the network or store it in a database, you often require a serialized representation.

The Flask+SQLAlchemy SerializerMixin is a valuable tool for effortlessly serializing your SQLAlchemy models. By integrating the SerializerMixin into your model classes, you gain the ability to convert instances of these classes into easily transportable dictionaries. This transformation simplifies data interchange between your Flask application and external systems, such as frontend clients, APIs, or other services.

from sqlalchemy_serializer import SerializerMixin
...
class RandomModel(db.Model, SerializerMixin):
    # Your model attributes and relationships
...
...
item = RandomModel.query.filter(...).first()
result = item.to_dict()

Here, the serializer is imported and implemented. This grants access to .to_dict(). When you call the .to_dict() method on an instance of a model, it moves through the model's attributes and relationships, collecting their values and structuring them into a dictionary. This dictionary can then be easily converted into a serialized format, making it suitable for sending over a network, storing in a database, or other similar use cases.

From within .to_dict(),we can further specify what we do or don’t want to see, send, and receive.

result = item.to_dict(rules=(
    '-somefield', 
    '-some_relation.nested_one.another_nested_one')
    )

Passing a rules argument inside .to_dict() can exclude information using negative rules, denoted by the “-” that precludes the text inside the strings. However, this can go both ways:

result = item.to_dict(rules=(
    'random_table', 
    '-another_random_table')
    )

By leaving off the “-”, one can set a positive rule to include certain information as opposed to solely excluding information. And this can even go one step further:

result = item.to_dict(only=('single_instance_variable', ))

Using only will exclude everything except what is given permission. Negative rules can still be used here, as well.

This image is from a database that is returning an entire user instance. Instead of tracing every single relationship and association to exclude and exclude and exclude, only can be used to pare this information down to just a single key-value pair, via a single argument, if needed. Nested in the user data, however, is a list of 20 movies. If we wanted to keep the user_movies information, but didn’t need the actual movie information, we could use only with a mix of positive and negative rules…

result = item.to_dict(only=(
'id', 'first_name', 'last_name', 'username',
'user_movies', '-user_movies.movies'
))

With those 6 arguments, only the information we need for whatever task is at hand is available. Additionally, by starting with positives rather than negatives, we could incrementally test what we were returning without forcing any recursion errors (or at least far fewer). We grabbed an entire nested data structure with only two arguments: 'user_movies' and '-user_movies.movies'.

Rules and only functionally accomplish the same goal, but one forces you to work backwards, while the other allows you to work forwards.