Top Flask-SQLAlchemy Interview Questions and Answers for Web Developers

Flask-SQLAlchemy is one of the most popular extensions for the Flask web framework that makes interacting with databases incredibly easy for Python web developers. Combining the power of SQLAlchemy and the simplicity of Flask, Flask-SQLAlchemy provides an incredibly convenient Object Relational Mapper (ORM) to abstract away complex SQL statements into simple Python code.

As Flask-SQLAlchemy continues to gain popularity, knowledge of this tool is becoming essential for Python web developers using Flask. This article provides a comprehensive set of Flask-SQLAlchemy interview questions that hiring managers commonly ask to evaluate a candidate’s skills. From basic concepts to advanced usage these questions cover key aspects of Flask-SQLAlchemy that every web developer should know.

Whether you’re preparing for an upcoming Flask job interview or simply want to strengthen your Flask-SQLAlchemy skills, reviewing these questions is a great way to build expertise. Read on for the top Flask-SQLAlchemy interview questions with detailed explanations and example code snippets!

Basic Questions

Q1. What is Flask-SQLAlchemy and how does it simplify working with databases in Flask?

Flask-SQLAlchemy is the ORM extension for Flask that simplifies database operations by providing a high-level, Pythonic interface to interact with databases. It serves as an abstraction layer between the app code and raw SQL, allowing developers to execute CRUD operations using Python expressions instead of SQL statements. This eliminates the need to write lengthy SQL queries to create, read, update or delete data. Additionally, Flask-SQLAlchemy handles connections, sessions and transactions automatically to reduce boilerplate code.

Q2. How does Flask-SQLAlchemy map Python classes to database tables?

Flask-SQLAlchemy leverages SQLAlchemy’s declarative base to map Python classes and model definitions to database tables and schemas. Each model class subclassing db.Model represents a database table, while columns correspond to table fields. For example:

from flask_sqlalchemy import SQLAlchemydb = SQLAlchemy()class User(db.Model):  id = db.Column(db.Integer, primary_key=True)  username = db.Column(db.String(80), unique=True)  email = db.Column(db.String(120))

Here, the User class will create a user table with id, username and email columns. The column types like db.Integer and db.String determine the column types in the database table.

Q3. How can you define relationships between models in Flask-SQLAlchemy?

Flask-SQLAlchemy allows defining one-to-many, many-to-many and one-to-one relationships between models using the db.relationship() function. For example:

class User(db.Model):  id = db.Column(db.Integer, primary_key=True)  name = db.Column(db.String(50))  addresses = db.relationship('Address', backref='user')class Address(db.Model):  id = db.Column(db.Integer, primary_key=True)  email = db.Column(db.String(120))  user_id = db.Column(db.Integer, db.ForeignKey('user.id'))  

Here a one-to-many relationship is defined between User and Address models where one user can have multiple addresses. The backref creates a user attribute on Address to access the parent User object.

Intermediate Questions

Q4. How can you lazy load or eager load relationships in Flask-SQLAlchemy?

By default, Flask-SQLAlchemy uses lazy loading for relationships, meaning the related objects are only loaded when first accessed. To enable eager loading so that relationships are loaded immediately, we can specify lazy='joined':

class User(db.Model):  addresses = db.relationship('Address', lazy='joined') 

This will use a JOIN query to eagerly load Address when querying User. Other options are lazy='subquery' using a subquery, and lazy='dynamic' which returns a query object.

Q5. How can you sort query results in Flask-SQLAlchemy?

The order_by() method is used to sort query results. For example:

User.query.order_by(User.name).all()

Will order users alphabetically by name. Chaining desc() reverses the order:

from sqlalchemy import descUser.query.order_by(desc(User.name))  

We can also order by multiple columns:

User.query.order_by(User.name, desc(User.id))

Q6. How can you use Flask-SQLAlchemy for CRUD operations?

The key ORM operations can be performed as:

• Create – db.session.add(object) followed by db.session.commit().
• Read – Model.query.filter_by(...).first() to fetch objects.
• Update – Modify object attributes and commit session.
• Delete – db.session.delete(object) then commit().

For example:

# Insert new useruser = User(name='John')db.session.add(user)db.session.commit()# Fetch useruser = User.query.filter_by(name='John').first() # Update useruser.name = 'Jane'db.session.commit()  # Delete userdb.session.delete(user)db.session.commit()

Q7. How can you define custom model methods in Flask-SQLAlchemy?

Flask-SQLAlchemy allows defining custom model methods to encapsulate business logic or reuse queries. For example:

class User(db.Model):  # ...  def full_name(self):    return f"{self.first_name} {self.last_name}"  @staticmethod  def authenticate(username, password):    user = User.query.filter_by(      username=username,       password=password    ).first()    return user 

These methods can be invoked on model instances like:

user = User() print(user.full_name())user = User.authenticate('john', 'secret')

Advanced Questions

Q8. How can you optimize and improve performance of Flask-SQLAlchemy queries?

Some ways to optimize Flask-SQLAlchemy performance include:

• Use lazy='joined' and lazy='subquery' eager loading to reduce queries.
• Set reasonable backref to avoid unnecessary extra queries.
• Use Query.with_entities() and Query.options() to select only needed columns.
• Add indexes on columns used for filters and ordering.
• Batch insert/update using session.bulk_save_objects().
• Use an in-memory cache like Redis to avoid redundant queries.

Q9. How can you use raw SQL with Flask-SQLAlchemy?

While Flask-SQLAlchemy promotes the use of the ORM, we can use raw SQL using text() and connection execution:

query = "SELECT * FROM users WHERE id=:user_id"from sqlalchemy import text  result = db.engine.execute(text(query), user_id=1)

The text string acts as a template while the parameters pass literal values to avoid SQL injection.

Q10. How can you handle concurrency and row locking in Flask-SQLAlchemy?

To handle concurrency issues like race conditions and row locking, Flask-SQLAlchemy provides isolation levels and pessimistic locking.

For example, to acquire a row-level lock when selecting:

from sqlalchemy.orm import with_lockmodeuser = User.query.with_lockmode('update').filter(User.id==1).first()

This will lock the row preventing concurrent updates. Other isolation levels like REPEATABLE READ can be set globally on the session.

Q11. How can you define polymorphic relationships in Flask-SQLAlchemy?

Flask-SQLAlchemy can map polymorphic relationships where a model connects to different related models. For example, an Employee model that can be related to either Manager or Engineer:

employees_table = db.Table('employees',  db.Column('employee_id', db.Integer, db.ForeignKey('employee.id')),  db.Column('manager_id', db.Integer, db.ForeignKey('manager.id')),  db.Column('engineer_id', db.Integer, db.ForeignKey('engineer.id')))class Employee(db.Model):  id = db.Column(db.Integer, primary_key=True)  type = db.Column(db.String(20))  __mapper_args__ = {    'polymorphic_on':type,    'polymorphic_identity':'employee'  }class Manager(db