plain.postgres 0.88.2

Model your data and store it in a database.

plain.postgres

Model your data and store it in a database.

• Overview
• Database connection
• Querying
• Migrations
• Fields
• Relationships
• Constraints
• Forms
• Architecture
• Diagnostics
• Settings
• FAQs
• Installation

Overview

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

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


@postgres.register_model
class User(postgres.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="[e-mail]",
    password="password",
)

# Update a user
user.email = "[e-mail]"
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 set the individual POSTGRES_* settings (via PLAIN_POSTGRES_* environment variables or in app/settings.py):

# app/settings.py
POSTGRES_HOST = "localhost"
POSTGRES_PORT = 5432
POSTGRES_DATABASE = "dbname"
POSTGRES_USER = "user"
POSTGRES_PASSWORD = "password"

If DATABASE_URL is set, it takes priority and the individual connection settings are parsed from it.

To explicitly disable the database (e.g. during Docker builds where no database is available), set DATABASE_URL=none.

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="[e-mail]")

# Complex queries with Q objects
from plain.postgres 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
from plain.postgres import types

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

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

@postgres.register_model
class Article(postgres.Model):
    title: str = types.CharField(max_length=200)
    status: str = types.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 postgres
from plain.postgres import types

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

    query: postgres.QuerySet[User] = postgres.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.postgres import get_connection

with get_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.postgres import Q

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

Avoiding N+1 queries

Use select_related for ForeignKey access in loops

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)

Use prefetch_related for reverse/M2N access in loops

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.postgres.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)

Transactions

By default, each query runs in its own implicit transaction and is committed immediately (autocommit mode). When you need multiple queries to succeed or fail together — like creating a user and their profile — wrap them in an explicit transaction.

Atomic blocks

Wrap multiple queries in a transaction with transaction.atomic():

from plain.postgres import transaction

with transaction.atomic():
    user = User(email="[e-mail]")
    user.save()
    Profile(user=user).save()
    # Both saves commit together, or both roll back on error

Nesting atomic() creates savepoints:

with transaction.atomic():
    user.save()
    try:
        with transaction.atomic():
            risky_operation()  # If this fails...
    except SomeError:
        pass  # ...only the inner block rolls back
    safe_operation()  # This still runs in the outer transaction

Read-only connections

Enforce read-only mode on the current database connection using read_only(). Any write (INSERT, UPDATE, DELETE, DDL) raises psycopg.errors.ReadOnlySqlTransaction:

from plain.postgres.connections import read_only

with read_only():
    users = User.query.all()       # reads work
    User.query.create(name="x")   # raises psycopg.errors.ReadOnlySqlTransaction

This works with both autocommit queries and explicit atomic() blocks.

For sticky read-only mode (e.g., a shell session), use set_read_only() on the connection directly:

from plain.postgres.db import get_connection

conn = get_connection()
conn.set_read_only(True)   # all subsequent queries are read-only
conn.set_read_only(False)  # back to normal

Read-only mode must be set outside a transaction — calling it inside atomic() raises TransactionManagementError.

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 decimal import Decimal
from datetime import datetime

from plain import postgres
from plain.postgres import types

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

    # Numeric fields
    price: Decimal = types.DecimalField(max_digits=10, decimal_places=2)
    quantity: int = types.IntegerField(default=0)

    # Boolean fields
    is_active: bool = types.BooleanField(default=True)

    # Date and time fields
    created_at: datetime = types.DateTimeField(auto_now_add=True)
    updated_at: datetime = types.DateTimeField(auto_now=True)

Text fields:

• CharField - String with max length
• TextField - Unlimited text
• EmailField - Email address (validated)
• URLField - URL (validated)

Numeric fields:

• IntegerField - Integer
• BigIntegerField - Big (8 byte) integer
• SmallIntegerField - Small integer
• PositiveIntegerField - Positive integer
• PositiveBigIntegerField - Positive big integer
• PositiveSmallIntegerField - Positive small integer
• FloatField - Floating point number
• DecimalField - Fixed precision decimal

Date and time fields:

• DateField - Date (without time)
• DateTimeField - Date with time
• TimeField - Time (without date)
• DurationField - Time duration (timedelta)
• TimeZoneField - Timezone (stored as string, accessed as ZoneInfo)

Other fields:

• BooleanField - True/False
• UUIDField - UUID
• BinaryField - Raw binary data
• JSONField - JSON data
• GenericIPAddressField - IPv4 or IPv6 address

Encrypted fields:

• EncryptedTextField - Text encrypted at rest
• EncryptedJSONField - JSON encrypted at rest

See Encrypted fields for details.

For relationship fields, see Relationships.

For nullable fields, use | None in the annotation:

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

Sharing fields across models

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

from datetime import datetime

from plain import postgres
from plain.postgres import types


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


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


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

Encrypted fields

Encrypted fields transparently encrypt values before writing to the database and decrypt on read. Use them for third-party credentials, API keys, OAuth tokens, and other secrets your application needs back in plaintext.

This is not for passwords or tokens you issue — those should be hashed (one-way). This is for secrets you receive from others and need to use later.

from plain import postgres
from plain.postgres import types

@postgres.register_model
class Integration(postgres.Model):
    name: str = types.CharField(max_length=100)
    api_key: str = types.EncryptedTextField(max_length=200)
    credentials: dict = types.EncryptedJSONField(required=False, allow_null=True)

Values are encrypted using Fernet (AES-128-CBC + HMAC-SHA256) with a key derived from SECRET_KEY. The cryptography package is required — install it with pip install cryptography.

Available fields:

• EncryptedTextField — encrypts text, stored as text in the database regardless of max_length (ciphertext is longer than plaintext). max_length is enforced on the plaintext value during validation.
• EncryptedJSONField — serializes to JSON, encrypts, and stores as text. Supports custom encoder and decoder parameters (same as JSONField).

Limitations:

• No lookups — encrypted values are non-deterministic (same plaintext produces different ciphertext each time), so filtering on encrypted fields doesn't work. Only isnull lookups are supported.
• No indexes or constraints — encrypted fields cannot be used in indexes or unique constraints. Preflight checks will catch this.

Key rotation:

Encryption uses SECRET_KEY. When rotating keys, add the old key to SECRET_KEY_FALLBACKS — the field will decrypt with any fallback key and re-encrypt with the current key on save.

Gradual migration:

If you add encryption to an existing plaintext column, old unencrypted values are returned as-is on read (the field detects whether a value is encrypted by its $fernet$ prefix). They'll be encrypted on the next save.

Relationships

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

from plain import postgres
from plain.postgres import types

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

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 postgres
from plain.postgres import types

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

@postgres.register_model
class Book(postgres.Model):
    title: str = types.CharField(max_length=200)
    author: Author = types.ForeignKeyField(Author, on_delete=postgres.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:

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

@postgres.register_model
class Car(postgres.Model):
    model: str = types.CharField(max_length=100)
    features = types.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.

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:

@postgres.register_model
class User(postgres.Model):
    email: str = types.EmailField()
    age: int = types.IntegerField()

    model_options = postgres.Options(
        constraints=[
            postgres.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(postgres.Model):
    email: str = types.EmailField()
    username: str = types.CharField(max_length=150)
    age: int = types.IntegerField()

    model_options = postgres.Options(
        indexes=[
            postgres.Index(fields=["email"]),
            postgres.Index(fields=["-created_at"], name="user_created_idx"),
        ],
        constraints=[
            postgres.UniqueConstraint(fields=["email", "username"], name="unique_user"),
            postgres.CheckConstraint(check=postgres.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(postgres.Model):
    status: str = types.CharField(max_length=20)
    created_at: datetime = types.DateTimeField()

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

    model_options = postgres.Options(
        indexes=[postgres.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 = postgres.Options(
    constraints=[postgres.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: Company = types.ForeignKeyField("Company", on_delete=postgres.CASCADE)  # deleting company deletes invoices!

# Good — protect referenced data
company: Company = types.ForeignKeyField("Company", on_delete=postgres.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: str = types.CharField(max_length=50, allow_null=True)

# Good — single empty representation
nickname: str = types.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["DatabaseConnection"]
        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 DatabaseConnection 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
• DatabaseConnection - PostgreSQL connection and query execution

Diagnostics

You can run health checks against your database to find issues like missing indexes, redundant indexes, and configuration problems.

uv run plain postgres diagnose

Use --json for structured output (useful for scripting and AI agents):

uv run plain postgres diagnose --json

Use --all to include issues in installed packages (by default, only your app's issues are shown):

uv run plain postgres diagnose --all

Checks

Check	What it finds	Severity
Invalid indexes	Broken indexes from failed CREATE INDEX CONCURRENTLY — maintained on writes, never used for reads	Warning
Duplicate indexes	Indexes where one is a column-prefix of another on the same table (e.g., an auto FK index that's redundant with a composite index)	Warning
Unused indexes	Indexes with zero scans since stats reset (>1 MB). Excludes unique indexes, constraint-backing indexes, and indexes that are the sole coverage for a FK column	Warning
Missing FK indexes	Foreign key columns without any index coverage — parent DELETE/UPDATE operations will sequentially scan the child table	Warning
Sequence exhaustion	Identity sequences approaching their type max (>50% warning, >90% critical)	Warning/Critical
XID wraparound	Transaction ID age approaching the 2 billion wraparound limit (>25% warning, >40% critical)	Warning/Critical
Cache hit ratio	Heap buffer hit ratio below 98.5% — indicates insufficient shared_buffers or RAM	Warning
Index hit ratio	Index buffer hit ratio below 98.5%	Warning
Vacuum health	Tables with significant dead tuple accumulation (>10% of live rows) where autovacuum may be falling behind	Warning

App vs package issues

Each finding is tagged with its source:

• App — your code, fully actionable
• Package — owned by an installed package (e.g., plain-jobs). These appear in the footer summary by default; use --all to see details
• Unmanaged — tables not managed by any Plain model. The suggestion includes exact SQL to run

Production usage

Run diagnose against your production database to get meaningful stats. On Heroku:

heroku run -a your-app "plain postgres diagnose --json"

The --json flag must be quoted so Heroku passes it through to the command.

Preflight checks

Two related checks run automatically during uv run plain preflight (and uv run plain check):

• postgres.missing_fk_indexes — warns about FK fields without index coverage in your model definitions
• postgres.duplicate_indexes — warns about prefix-redundant indexes in your model constraints

These are static, code-level checks that catch issues before you deploy. The diagnose command complements them with runtime stats from the actual database.

Settings

Connection settings are configured via DATABASE_URL or individual POSTGRES_* settings.

When DATABASE_URL is set, it is parsed into the individual connection settings automatically. When DATABASE_URL is not set, the connection settings are required individually.

Set DATABASE_URL=none to explicitly disable the database (e.g. during Docker image builds).

Setting	Type	Default	Env var
POSTGRES_HOST	str	—	PLAIN_POSTGRES_HOST
POSTGRES_PORT	int | None	None	PLAIN_POSTGRES_PORT
POSTGRES_DATABASE	str	—	PLAIN_POSTGRES_DATABASE
POSTGRES_USER	str	—	PLAIN_POSTGRES_USER
POSTGRES_PASSWORD	Secret[str]	—	PLAIN_POSTGRES_PASSWORD
POSTGRES_CONN_MAX_AGE	int	600	PLAIN_POSTGRES_CONN_MAX_AGE
POSTGRES_CONN_HEALTH_CHECKS	bool	True	PLAIN_POSTGRES_CONN_HEALTH_CHECKS
POSTGRES_OPTIONS	dict	{}	—
POSTGRES_TIME_ZONE	str | None	None	PLAIN_POSTGRES_TIME_ZONE

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 = postgres.Options(
    constraints=[
        postgres.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.postgres package from PyPI:

uv add plain.postgres psycopg[binary]

Then add to your INSTALLED_PACKAGES:

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