If you’ve worked with SQLAlchemy long enough, especially on growing projects, you’ve probably used backref. It’s quick, it’s convenient, and it “just works”.

Until it doesn’t.

When your app grows, your models become more complex, and refactors become more frequent, that "quick and dirty" shortcut can start causing real pain. Suddenly, relationships become harder to trace, your IDE stops helping you, and bugs slip through reviews.

This post is a deep dive into how to do bidirectional relationships right focusing on backref vs back_populates, and why explicit is almost always better than implicit in large-scale projects.

 

First Things First → What’s a Bidirectional Relationship?

A bidirectional relationship means two models can reference each other.

Let’s take a basic example: a User and a Post. Each Post belongs to a User, and each User has many Posts. Here’s how you can define that relationship.

Option 1: Using backref (Quick and implicit)

# models.py
from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.orm import relationship, declarative_base

Base = declarative_base()

class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    name = Column(String)

    posts = relationship("Post", backref="author")

class Post(Base):
    __tablename__ = "posts"

    id = Column(Integer, primary_key=True)
    title = Column(String)
    user_id = Column(Integer, ForeignKey("users.id"))

With just one line backref="author", SQLAlchemy creates the reverse relationship on Post.author.

Option 2: Using back_populates (Explicit and symmetrical)

class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    name = Column(String)

    posts = relationship("Post", back_populates="author")

class Post(Base):
    __tablename__ = "posts"

    id = Column(Integer, primary_key=True)
    title = Column(String)
    user_id = Column(Integer, ForeignKey("users.id"))

    author = relationship("User", back_populates="posts")

This looks more verbose but it’s also more readable, debuggable, and maintainable.

Let’s explore why that matters, especially as your codebase scales.

 

When backref Becomes Dangerous

backref is fine in simple apps. But as things grow, it has real downsides:

1. Hidden Coupling

With backref, the relationship is only defined in one place. You can't easily “see” the reverse relationship unless you know where to look.

Problem:

user.posts   # defined in User
post.author  # implicitly created where is this defined?

In large teams or big codebases, this becomes a source of confusion. A dev teammate might ask: “Where is author coming from?” IDEs often won’t help.

2. Silent Clashes

You can accidentally define conflicting backrefs on both sides. SQLAlchemy won’t always stop you, especially if you're dynamically importing models or building them conditionally.

3. Weak IDE and Type Support

Most static type checkers and autocompletion tools struggle with backref because it's dynamically generated. With back_populates, the relationship is declared on both sides explicitly making it easier for tools like PyCharm, VS Code, and mypy to catch mistakes.

 

Why back_populates Wins in Real Life

Let’s simulate a real backend use case: an app with User, Post, and Comment. In this case the relationships would look like that:

• user has many posts
• post has many comments
• comment belongs to both post and user

Here’s how to do it right using back_populates.

Define your models explicitly

class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    username = Column(String)

    posts = relationship("Post", back_populates="author")
    comments = relationship("Comment", back_populates="commenter")

class Post(Base):
    __tablename__ = "posts"
    id = Column(Integer, primary_key=True)
    title = Column(String)
    user_id = Column(Integer, ForeignKey("users.id"))

    author = relationship("User", back_populates="posts")
    comments = relationship("Comment", back_populates="post")

class Comment(Base):
    __tablename__ = "comments"
    id = Column(Integer, primary_key=True)
    content = Column(String)
    user_id = Column(Integer, ForeignKey("users.id"))
    post_id = Column(Integer, ForeignKey("posts.id"))

    commenter = relationship("User", back_populates="comments")
    post = relationship("Post", back_populates="comments")

Let’s look at the benefits:

• Every relationship is clearly declared on both sides.
• Type hints and IDE autocompletion work flawlessly.
• Easy to reason about and navigate.
• Safer when refactoring field names or model logic.

 

Refactoring Legacy backref to back_populates

If you’re working in a legacy codebase, replacing backref may feel risky. But it’s doable with a step-by-step approach.

Step-by-step Refactor Plan

Identify the backref pairings

• look for relationship(..., backref="xyz")
• note the models and attribute names involved

Replace with matching back_populates

Before:

class A(Base):
    b = relationship("B", backref="a")

After:

class A(Base):
    b = relationship("B", back_populates="a")

class B(Base):
    a = relationship("A", back_populates="b")

Update all usages in the codebase.

• search for attribute access on both sides
• rename as needed (you may want more descriptive names)

Add type hints and run your linters.

• your IDE should now correctly infer types

 

A Few Advices For Robust Backend Projects

Avoid backref in APIs and service layers

If you're building APIs like with FastAPI or Flask, avoid relying on backref for anything exposed in serialization like .dict() or .json() output. It creates uncertainty about what's available.

Use back_populates with Pydantic & type checkers

Libraries like Pydantic, dataclasses, or even attrs work better when relationships are predictable. Explicit models make it easier to build response schemas, serializers, and test fixtures.

 

Final Thoughts

In small projects, backref feels like a timesaver. But in growing systems, clarity always wins. back_populates might look more verbose, but it's:

• easier to debug
• safer for refactors
• friendly to IDEs and type checkers
• more maintainable across teams