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plain.models

Model your data and store it in a database.

Overview

# app/users/models.py
from datetime import datetime

from plain import models
from plain.models import types
from plain.passwords.models import PasswordField


@models.register_model
class User(models.Model):
    email: str = types.EmailField()
    password = PasswordField()
    is_admin: bool = types.BooleanField(default=False)
    created_at: datetime = types.DateTimeField(auto_now_add=True)

    def __str__(self) -> str:
        return self.email

Every model automatically includes an id field which serves as the primary key. The name id is reserved and can't be used for other fields.

You can create, update, and delete instances of your models:

from .models import User


# Create a new user
user = User.query.create(
    email="[email protected]",
    password="password",
)

# Update a user
user.email = "[email protected]"
user.save()

# Delete a user
user.delete()

# Query for users
admin_users = User.query.filter(is_admin=True)

Database connection

To connect to a database, you can provide a DATABASE_URL environment variable:

DATABASE_URL=postgresql://user:password@localhost:5432/dbname

Or you can manually define the DATABASE setting:

# app/settings.py
DATABASE = {
    "NAME": "dbname",
    "USER": "user",
    "PASSWORD": "password",
    "HOST": "localhost",
    "PORT": "5432",
}

PostgreSQL is the only supported database. You need to install a PostgreSQL driver separately — psycopg is recommended:

uv add psycopg[binary]  # Pre-built wheels, easiest for local development
# or
uv add psycopg[c]       # Compiled against your system's libpq, recommended for production

Querying

Models come with a powerful query API through their QuerySet interface:

# Get all users
all_users = User.query.all()

# Filter users
admin_users = User.query.filter(is_admin=True)
recent_users = User.query.filter(created_at__gte=datetime.now() - timedelta(days=7))

# Get a single user
user = User.query.get(email="[email protected]")

# Complex queries with Q objects
from plain.models import Q
users = User.query.filter(
    Q(is_admin=True) | Q(email__endswith="@example.com")
)

# Ordering
users = User.query.order_by("-created_at")

# Limiting results
first_10_users = User.query.all()[:10]

For more advanced querying options, see the QuerySet class.

Custom QuerySets

You can customize QuerySet classes to provide specialized query methods. Define a custom QuerySet and assign it to your model's query attribute:

from typing import Self

class PublishedQuerySet(models.QuerySet["Article"]):
    def published_only(self) -> Self:
        return self.filter(status="published")

    def draft_only(self) -> Self:
        return self.filter(status="draft")

@models.register_model
class Article(models.Model):
    title = models.CharField(max_length=200)
    status = models.CharField(max_length=20)

    query = PublishedQuerySet()

# Usage - all methods available on Article.query
all_articles = Article.query.all()
published_articles = Article.query.published_only()
draft_articles = Article.query.draft_only()

# Chaining works naturally
recent_published = Article.query.published_only().order_by("-created_at")[:10]

For internal code that needs to create QuerySet instances programmatically, use from_model():

special_qs = SpecialQuerySet.from_model(Article)

Typing QuerySets

For better type checking of query results, you can explicitly type the query attribute:

from __future__ import annotations

from plain import models
from plain.models import types

@models.register_model
class User(models.Model):
    email: str = types.EmailField()
    is_admin: bool = types.BooleanField(default=False)

    query: models.QuerySet[User] = models.QuerySet()

With this annotation, type checkers will know that User.query.get() returns a User instance and User.query.filter() returns QuerySet[User]. This is optional but improves IDE autocomplete and type checking.

Raw SQL

For complex queries that can't be expressed with the ORM, you can use raw SQL.

Use Model.query.raw() to execute raw SQL and get model instances back:

users = User.query.raw("""
    SELECT * FROM users
    WHERE created_at > %s
    ORDER BY created_at DESC
""", [some_date])

for user in users:
    print(user.email)  # Full model instance with all fields

Raw querysets support prefetch_related() for loading related objects:

users = User.query.raw("SELECT * FROM users WHERE is_admin = %s", [True])
users = users.prefetch_related("posts")

For queries that don't map to a model, use the database cursor directly:

from plain.models import db_connection

with db_connection.cursor() as cursor:
    cursor.execute("SELECT COUNT(*) FROM users WHERE is_admin = %s", [True])
    count = cursor.fetchone()[0]

For SQL set operations (UNION, INTERSECT, EXCEPT), use raw SQL. For simple cases, use Q objects instead:

from plain.models import Q

# Equivalent to UNION (on same model)
users = User.query.filter(Q(is_admin=True) | Q(is_staff=True))

Avoiding N+1 queries

Accessing a FK in a loop without select_related() fires one query per row.

# Bad — N+1 queries
for post in Post.query.all():
    print(post.author.name)

# Good — single JOIN
for post in Post.query.select_related("author").all():
    print(post.author.name)

Reverse ForeignKey and ManyToMany relations need a separate prefetch query.

# Bad — N+1 queries
for author in Author.query.all():
    print(author.posts.count())

# Good — one extra query
for author in Author.query.prefetch_related("posts").all():
    print(author.posts.count())

Annotate instead of per-row aggregations

Use database-level aggregation instead of calling .count() or similar per row.

# Bad — N+1 queries
for category in Category.query.all():
    print(category.products.count())

# Good — single query with annotation
from plain.models.aggregates import Count
for category in Category.query.annotate(num_products=Count("products")).all():
    print(category.num_products)

Fetch all data in the view

Templates should only render data, never trigger queries. Prepare everything in the view.

# Bad — template triggers lazy queries
def get_template_context(self):
    return {"posts": Post.query.all()}  # related lookups happen in template

# Good — eagerly load everything
def get_template_context(self):
    return {"posts": Post.query.select_related("author").prefetch_related("tags").all()}

Query efficiency

Use .values_list() when you only need specific columns

# Bad — loads entire model objects
emails = [u.email for u in User.query.all()]

# Good — single column, flat list
emails = list(User.query.values_list("email", flat=True))

Use .exists() instead of .count() > 0

.exists() stops at the first match; .count() scans all matching rows.

# Bad
if User.query.filter(is_active=True).count() > 0: ...

# Good
if User.query.filter(is_active=True).exists(): ...

Use .count() instead of len(queryset)

len() loads all objects into memory just to count them.

# Bad
total = len(User.query.all())

# Good
total = User.query.count()

Use bulk_create / bulk_update for batch operations

Avoid calling .save() in a loop — each call is a separate query.

# Bad — N INSERT statements
for name in names:
    Tag(name=name).save()

# Good — single INSERT
Tag.query.bulk_create([Tag(name=name) for name in names])

Use queryset .update() / .delete() for mass operations

# Bad — N UPDATE statements
for user in User.query.filter(is_active=False):
    user.is_archived = True
    user.save()

# Good — single UPDATE statement
User.query.filter(is_active=False).update(is_archived=True)

Use .only() / .defer() for heavy columns

Skip large text or JSON fields when you don't need them.

# Bad — loads large body text for a listing page
posts = Post.query.all()

# Good — defers heavy column
posts = Post.query.defer("body").all()

Use .iterator() for large result sets

Process rows in chunks instead of loading everything into memory.

# Bad — entire table in memory
for row in HugeTable.query.all():
    process(row)

# Good — chunked iteration
for row in HugeTable.query.iterator(chunk_size=2000):
    process(row)

Migrations

Migrations track changes to your models and update the database schema accordingly. They are Python files stored in your app's migrations/ directory.

Creating migrations

plain makemigrations

Key flags:

  • --dry-run — Show what migrations would be created (with operations and SQL) without writing files
  • --check — Exit non-zero if migrations are needed (for CI)
  • --empty <package> — Create an empty migration for custom data migrations
  • --name <name> — Set the migration filename
  • -v 3 — Show full migration file contents

Only write migrations by hand if they are custom data migrations.

Running migrations

plain migrate --backup

Key flags:

  • --backup / --no-backup — Create a database backup before applying (default: on in DEBUG)
  • --plan — Show what migrations would run without applying them
  • --check — Exit non-zero if unapplied migrations exist (for CI)
  • --fake — Mark migrations as applied without running them

Viewing migration status

plain migrations list

migrate has no --list or --status flag. Use plain migrations list.

  • --format plan — Show in dependency order instead of grouped by package

Development workflow

During development, iterating on models often produces multiple small migrations (0002, 0003, 0004...). Clean these up before committing.

Consolidating uncommitted migrations (delete-and-recreate):

Use this when migrations exist only in your local dev environment and haven't been committed or deployed.

  1. Delete the intermediate migration files (keep the initial 0001 and any previously committed migrations)
  2. plain migrations prune --yes — removes stale DB records for the deleted files
  3. plain makemigrations — creates a single fresh migration with all the changes
  4. plain migrate --fake — marks the new migration as applied (the schema is already correct from the old migrations)

Consolidating committed migrations (squash):

Use this when migrations have already been committed or deployed to other environments.

plain migrations squash <package> <migration> creates a replacement migration with a replaces list. Keep the original files until all environments have migrated past the squash point, then delete them and run migrations prune.

Which method to use:

Scenario Method
Migrations are local only (not committed) Delete-and-recreate
Migrations are committed but not deployed Delete-and-recreate (if all developers reset) or squash
Migrations are deployed to production Squash

Other migration commands

  • plain migrations squash <package> <migration> — Squash migrations into one
  • plain migrations prune — Remove stale migration records

Fields

You can use many field types for different data:

from plain import models

class Product(models.Model):
    # Text fields
    name = models.CharField(max_length=200)
    description = models.TextField()

    # Numeric fields
    price = models.DecimalField(max_digits=10, decimal_places=2)
    quantity = models.IntegerField(default=0)

    # Boolean fields
    is_active = models.BooleanField(default=True)

    # Date and time fields
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)

Text fields:

Numeric fields:

Date and time fields:

Other fields:

For relationship fields, see Relationships.

Typed fields

For better IDE support and type checking, use plain.models.types with type annotations:

from plain import models
from plain.models import types

@models.register_model
class User(models.Model):
    email: str = types.EmailField()
    username: str = types.CharField(max_length=150)
    is_admin: bool = types.BooleanField(default=False)

For nullable fields, add | None to the annotation:

published_at: datetime | None = types.DateTimeField(allow_null=True, required=False)

All field types listed above are available through types. Typed and untyped fields can be mixed in the same model. The database behavior is identical — typed fields only add type checking.

Sharing fields across models

To share common fields across multiple models, use Python classes as mixins. The final, registered model must inherit directly from models.Model and the mixins should not.

from plain import models


# Regular Python class for shared fields
class TimestampedMixin:
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)


# Models inherit from the mixin AND models.Model
@models.register_model
class User(TimestampedMixin, models.Model):
    email = models.EmailField()
    password = PasswordField()
    is_admin = models.BooleanField(default=False)


@models.register_model
class Note(TimestampedMixin, models.Model):
    content = models.TextField(max_length=1024)
    liked = models.BooleanField(default=False)

Relationships

Use ForeignKeyField for many-to-one and ManyToManyField for many-to-many:

from plain import models

@models.register_model
class Book(models.Model):
    title = models.CharField(max_length=200)
    author = models.ForeignKeyField("Author", on_delete=models.CASCADE)
    tags = models.ManyToManyField("Tag")

Foreign keys are typed with the related model:

from plain.models import types

author: Author = types.ForeignKeyField(Author, on_delete=models.CASCADE)

Reverse relationships

When you define a ForeignKey or ManyToManyField, Plain automatically creates a reverse accessor on the related model (like author.book_set). You can explicitly declare these reverse relationships using ReverseForeignKey and ReverseManyToMany:

from plain import models

@models.register_model
class Author(models.Model):
    name = models.CharField(max_length=200)
    # Explicit reverse accessor for all books by this author
    books = models.ReverseForeignKey(to="Book", field="author")

@models.register_model
class Book(models.Model):
    title = models.CharField(max_length=200)
    author = models.ForeignKeyField(Author, on_delete=models.CASCADE)

# Usage
author = Author.query.get(name="Jane Doe")
for book in author.books.all():
    print(book.title)

# Add a new book
author.books.create(title="New Book")

For many-to-many relationships:

@models.register_model
class Feature(models.Model):
    name = models.CharField(max_length=100)
    # Explicit reverse accessor for all cars with this feature
    cars = models.ReverseManyToMany(to="Car", field="features")

@models.register_model
class Car(models.Model):
    model = models.CharField(max_length=100)
    features = models.ManyToManyField(Feature)

# Usage
feature = Feature.query.get(name="Sunroof")
for car in feature.cars.all():
    print(car.model)

Why use explicit reverse relations?

  • Self-documenting: The reverse accessor is visible in the model definition
  • Better IDE support: Autocomplete works for reverse accessors
  • Type safety: When combined with type annotations, type checkers understand the relationship
  • Control: You choose the accessor name instead of relying on automatic _set naming

Reverse relations are optional — if you don't declare them, the automatic {model}_set accessor still works.

Typing reverse relationships

To get type checking for custom QuerySet methods on reverse relations, specify the QuerySet type as a second parameter:

# Basic usage
books: types.ReverseForeignKey[Book] = types.ReverseForeignKey(to="Book", field="author")

# With custom QuerySet for proper method recognition
books: types.ReverseForeignKey[Book, BookQuerySet] = types.ReverseForeignKey(to="Book", field="author")

# Now type checkers recognize custom methods like .published()
author.books.query.published()

Constraints

Validation

You can validate models before saving:

@models.register_model
class User(models.Model):
    email = models.EmailField()
    age = models.IntegerField()

    model_options = models.Options(
        constraints=[
            models.UniqueConstraint(fields=["email"], name="unique_email"),
        ],
    )

    def clean(self):
        if self.age < 18:
            raise ValidationError("User must be 18 or older")

    def save(self, *args, **kwargs):
        self.full_clean()  # Runs validation
        super().save(*args, **kwargs)

Field-level validation happens automatically based on field types and constraints.

Indexes and constraints

You can optimize queries and ensure data integrity with indexes and constraints:

class User(models.Model):
    email = models.EmailField()
    username = models.CharField(max_length=150)
    age = models.IntegerField()

    model_options = models.Options(
        indexes=[
            models.Index(fields=["email"]),
            models.Index(fields=["-created_at"], name="user_created_idx"),
        ],
        constraints=[
            models.UniqueConstraint(fields=["email", "username"], name="unique_user"),
            models.CheckConstraint(check=models.Q(age__gte=0), name="age_positive"),
        ],
    )

Schema design

Index fields used in filters and ordering

Add indexes for columns that appear in .filter(), .order_by(), or .exclude().

# Bad — full table scan on every filtered query
class Order(models.Model):
    status = models.CharField(max_length=20)
    created_at = models.DateTimeField()

# Good — indexed for common queries
class Order(models.Model):
    status = models.CharField(max_length=20)
    created_at = models.DateTimeField()

    model_options = models.Options(
        indexes=[models.Index(fields=["status", "-created_at"])],
    )

Use database constraints, not app-only validation

Enforce uniqueness and data integrity at the database level.

# Bad — only validated in Python
def save(self):
    if MyModel.query.filter(email=self.email).exists():
        raise ValueError("duplicate")

# Good — database-enforced
model_options = models.Options(
    constraints=[models.UniqueConstraint(fields=["email"])],
)

Choose on_delete deliberately

CASCADE for owned children, PROTECT for referenced data, SET_NULL for optional references.

# Bad — blindly using CASCADE everywhere
company = models.ForeignKeyField("Company", on_delete=models.CASCADE)  # deleting company deletes invoices!

# Good — protect referenced data
company = models.ForeignKeyField("Company", on_delete=models.PROTECT)

No allow_null on string fields

Use default="" instead of allow_null=True to avoid two representations of "empty."

# Bad — NULL and "" both mean "empty"
nickname = models.CharField(max_length=50, allow_null=True)

# Good — single empty representation
nickname = models.CharField(max_length=50, default="")

Forms

Models integrate with plain.forms:

from plain import forms
from .models import User

class UserForm(forms.ModelForm):
    class Meta:
        model = User
        fields = ["email", "is_admin"]

# Usage
form = UserForm(request=request)
if form.is_valid():
    user = form.save()

Architecture

graph TB
    subgraph "User API"
        Model["Model"]
        QS["QuerySet"]
        Expr["Expressions<br/><small>F() Q() Value()</small>"]
    end

    subgraph "Query Layer"
        Query["Query"]
        Where["WhereNode"]
        Join["Join"]
    end

    subgraph "Compilation"
        Compiler["SQLCompiler"]
    end

    subgraph "Database"
        Connection["DatabaseWrapper"]
        DB[(Database)]
    end

    Model -- ".query" --> QS
    QS -- "owns" --> Query
    Expr -- "used by" --> Query
    Query -- "contains" --> Where
    Query -- "contains" --> Join
    Query -- "get_compiler()" --> Compiler
    Compiler -- "execute_sql()" --> Connection
    Connection -- "executes" --> DB

Query execution flow:

  1. Model.query returns a QuerySet bound to the model
  2. QuerySet methods like .filter() modify the internal Query object
  3. When results are needed, Query.get_compiler() creates the appropriate SQLCompiler
  4. SQLCompiler.as_sql() renders the Query to SQL
  5. SQLCompiler.execute_sql() runs the SQL via DatabaseWrapper and returns results

Key components:

  • Model - Defines fields, relationships, and provides the query attribute
  • QuerySet - Chainable API (.filter(), .exclude(), .order_by()) that builds a Query
  • Query - Internal representation of a query's logical structure (tables, joins, filters)
  • SQLCompiler - Transforms a Query into executable SQL
  • DatabaseWrapper - PostgreSQL connection and query execution

Settings

Setting Default Env var
DATABASE Required Via DATABASE_URL

When DATABASE_URL is set, the DATABASE setting is automatically configured:

Environment Variable Description Default
DATABASE_URL Database connection URL Required
DATABASE_CONN_MAX_AGE Connection persistence (seconds) 600
DATABASE_CONN_HEALTH_CHECKS Enable health checks true

See default_settings.py for more details.

Settings

Setting Default Env var
DATABASE Required Via DATABASE_URL

When DATABASE_URL is set, the DATABASE setting is automatically configured:

Environment Variable Description Default
DATABASE_URL Database connection URL Required
DATABASE_CONN_MAX_AGE Connection persistence (seconds) 600
DATABASE_CONN_HEALTH_CHECKS Enable health checks true

See default_settings.py for more details.

FAQs

How do I add a field to an existing model?

Add the field to your model class, then run plain makemigrations to create a migration. If the field is required (no default value and not nullable), you'll be prompted to provide a default value for existing rows.

What's the difference between CharField and TextField?

CharField requires a max_length and is typically used for short strings like names or emails. TextField has no length limit and is used for longer content like descriptions or body text.

How do I create a unique constraint on multiple fields?

Use UniqueConstraint in your model's model_options:

model_options = models.Options(
    constraints=[
        models.UniqueConstraint(fields=["email", "organization"], name="unique_email_per_org"),
    ],
)

Can I use multiple databases?

Currently, Plain supports a single database connection per application. For applications requiring multiple databases, you can use raw SQL with separate connection management.

Installation

Install the plain.models package from PyPI:

uv add plain.models psycopg[binary]

Then add to your INSTALLED_PACKAGES:

# app/settings.py
INSTALLED_PACKAGES = [
    ...
    "plain.models",
]