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The most maintainable way to connect a FastAPI application to PostgreSQL on Ubuntu is to install PostgreSQL with apt, create a least-privilege database role, isolate your Python dependencies in a virtual environment, and use SQLAlchemy with Alembic migrations. Choose a synchronous stack (psycopg2 and regular def endpoints) for the simplest implementation, or an asynchronous stack (asyncpg, SQLAlchemy’s asyncio extension, and async def endpoints) when your application benefits from non-blocking database I/O.
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This guide builds both options, explains where they differ, and shows how to keep credentials out of source code, generate schema migrations, inject sessions into routes, and diagnose the failures that are most common on Ubuntu.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhat the FastAPI–PostgreSQL stack contains
FastAPI is the HTTP framework, PostgreSQL is the relational database, SQLAlchemy is the database toolkit and object-relational mapper, and Alembic tracks changes to your schema. A PostgreSQL driver translates SQLAlchemy operations into the wire protocol PostgreSQL understands.
| Layer | Typical choice | Purpose |
|---|---|---|
| API framework | FastAPI | Routing, validation, dependency injection and OpenAPI documentation. |
| ORM/toolkit | SQLAlchemy 2.x | Models, queries, transactions and connection pooling. |
| Migration tool | Alembic | Versioned, repeatable schema changes. |
| Database driver | psycopg2 for synchronous code; asyncpg for asynchronous code |
Connects Python to PostgreSQL. |
| Database server | PostgreSQL on Ubuntu | Durable relational storage, constraints, indexes and transactions. |
FastAPI’s official SQL tutorial uses SQLModel, which combines SQLAlchemy and Pydantic. It demonstrates SQLite, but the same SQLAlchemy-compatible approach can use PostgreSQL. FastAPI release notes identify version 0.115.1, released on 12 October 2024, as the release that added that SQLModel-based database tutorial. SQLAlchemy’s current documented line is 2.x; pin the versions you deploy rather than copying unverified version numbers from an old tutorial.
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Choose synchronous or asynchronous database access
Both designs are valid. The important rule is consistency: use a synchronous driver with synchronous sessions and route functions, or an asynchronous driver with asynchronous sessions and route functions.
| Decision point | Synchronous stack | Asynchronous stack |
|---|---|---|
| Install | sqlalchemy, psycopg2-binary |
sqlalchemy[asyncio], asyncpg |
| URL scheme | postgresql:// (SQLAlchemy selects the psycopg2 driver when installed) |
postgresql+asyncpg:// |
| Route style | def |
async def with awaited database calls |
| Session class | Session and sessionmaker |
AsyncSession and async_sessionmaker |
| Migrations | Use the same synchronous URL | Alembic’s runner is synchronous; configure a synchronous migration URL, commonly by removing +asyncpg and using psycopg2. |
| Operational trade-off | Fewer moving parts and easier debugging | Non-blocking I/O, but every database operation and transaction must be awaited correctly. |
Start synchronously unless you have a clear reason to make the database layer asynchronous. An async endpoint that calls a synchronous database session can still block the event loop, while an async stack adds a second connection URL for migrations and more lifecycle details.
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Use Ubuntu’s packaged PostgreSQL
Ubuntu’s server documentation recommends the distribution package and notes that it is configured with usable defaults:
sudo apt update
sudo apt install postgresql
sudo systemctl status postgresqlThe exact PostgreSQL major version depends on your Ubuntu release. Ubuntu ships one fixed major version in each release. Check what was installed before relying on version-specific behavior:
psql --version
sudo -u postgres psql -c "SHOW server_version;"Install a newer major version with PGDG
If your application requires a newer major release than Ubuntu provides, use the PostgreSQL project’s APT repository (PGDG). Its instructions describe adding the repository and installing versioned packages such as postgresql-18. The available major versions and supported Ubuntu releases change, so follow the current PGDG instructions for your release instead of hard-coding an old repository line. The PostgreSQL Linux download documentation also provides Ubuntu-specific package guidance.
Do not mix arbitrary packages from several repositories. Decide which repository owns the server version, install the matching client and server packages, and verify the resulting service and psql version.
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Create a database and an application role
Ubuntu’s package normally creates a local operating-system user and PostgreSQL superuser named postgres. Use that account only for administration. Create a separate login for the FastAPI application and a database it owns.
sudo -u postgres psqlAt the psql prompt, run commands like these, replacing the example names and password with values appropriate for your project:
CREATE ROLE fastapi_app WITH LOGIN PASSWORD 'replace-with-a-long-random-password';
CREATE DATABASE fastapi_db OWNER fastapi_app;
c fastapi_db
REVOKE CREATE ON SCHEMA public FROM PUBLIC;
GRANT USAGE, CREATE ON SCHEMA public TO fastapi_app;
qFor local development, PostgreSQL commonly listens on localhost:5432. A production deployment may use a managed or separate database host, TLS, firewall rules and a different port. Never commit the password, put it in shell history, or grant the application superuser privileges.
Create the FastAPI project and install dependencies
Set up an isolated environment
mkdir fastapi-postgres
cd fastapi-postgres
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pipInstall the synchronous baseline:
pip install fastapi uvicorn sqlalchemy alembic psycopg2-binaryFor the asynchronous variant, install the asyncio extra and driver instead:
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pip freeze > requirements.txtFor a team or deployment pipeline, maintain explicit version pins in a reviewed requirements file and update them deliberately. FastAPI, SQLAlchemy, Alembic, PostgreSQL and Ubuntu all evolve independently.
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Keep the connection string outside source code
Set a development variable in your shell or a local environment file that is excluded from version control:
export DATABASE_URL='postgresql+psycopg2://fastapi_app:replace-with-a-long-random-password@localhost:5432/fastapi_db'The async URL is:
export DATABASE_URL='postgresql+asyncpg://fastapi_app:replace-with-a-long-random-password@localhost:5432/fastapi_db'In production, inject the variable through the service manager or secret store. If the password contains characters such as @, : or /, URL-encode it or construct the URL with SQLAlchemy’s URL helper rather than creating an invalid string.
Build a synchronous SQLAlchemy database layer
Create database.py with one engine, a session factory and a declarative base. The pool_pre_ping option lets SQLAlchemy test an idle connection before using it, which helps after a server restart.
import os
from sqlalchemy import create_engine
from sqlalchemy.orm import DeclarativeBase, sessionmaker
DATABASE_URL = os.environ["DATABASE_URL"]
engine = create_engine(
DATABASE_URL,
pool_pre_ping=True,
)
SessionLocal = sessionmaker(
bind=engine,
autoflush=False,
autocommit=False,
)
class Base(DeclarativeBase):
passDefine a model in models.py. SQLAlchemy 2.x’s typed declarative style makes columns explicit:
from sqlalchemy import String, Text
from sqlalchemy.orm import Mapped, mapped_column
from .database import Base
class Article(Base):
__tablename__ = "articles"
id: Mapped[int] = mapped_column(primary_key=True)
title: Mapped[str] = mapped_column(String(200), nullable=False)
body: Mapped[str] = mapped_column(Text, nullable=False)The model describes the desired table; it does not automatically update an existing production database. Use Alembic, described below, to apply the schema change.
Inject and close a session per request
A generator dependency opens a session, yields it to the route, and closes it even when the request raises an exception:
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from collections.abc import Generator
from sqlalchemy.orm import Session
from .database import SessionLocal
def get_db() -> Generator[Session, None, None]:
db = SessionLocal()
try:
yield db
finally:
db.close()Use Pydantic models for request and response validation in schemas.py:
from pydantic import BaseModel, ConfigDict
class ArticleCreate(BaseModel):
title: str
body: str
class ArticleRead(BaseModel):
model_config = ConfigDict(from_attributes=True)
id: int
title: str
body: strThen wire the dependency into main.py:
from fastapi import Depends, FastAPI
from sqlalchemy import select
from sqlalchemy.orm import Session
from .database import Base, engine, get_db
from .models import Article
from .schemas import ArticleCreate, ArticleRead
app = FastAPI()
@app.post("/articles", response_model=ArticleRead)
def create_article(payload: ArticleCreate, db: Session = Depends(get_db)):
article = Article(title=payload.title, body=payload.body)
db.add(article)
db.commit()
db.refresh(article)
return article
@app.get("/articles", response_model=list[ArticleRead])
def list_articles(db: Session = Depends(get_db)):
return db.scalars(select(Article).order_by(Article.id)).all()Commit only after all required writes for the request have succeeded. If a transaction fails, call rollback() before attempting another operation with that session. Keep transaction boundaries in application code rather than relying on implicit, long-lived sessions.
Build the asynchronous SQLAlchemy variant
The async version uses an async engine and session factory. Every database operation that performs I/O is awaited.
import os
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
from sqlalchemy.orm import DeclarativeBase
DATABASE_URL = os.environ["DATABASE_URL"]
engine = create_async_engine(
DATABASE_URL,
pool_pre_ping=True,
)
SessionLocal = async_sessionmaker(
bind=engine,
class_=AsyncSession,
expire_on_commit=False,
)
class Base(DeclarativeBase):
pass
async def get_db():
async with SessionLocal() as db:
yield dbUse the same model declarations, importing the async base from your database module. An async route might look like this:
from fastapi import Depends, FastAPI
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from .database import get_db
from .models import Article
from .schemas import ArticleCreate, ArticleRead
app = FastAPI()
@app.post("/articles", response_model=ArticleRead)
async def create_article(payload: ArticleCreate, db: AsyncSession = Depends(get_db)):
article = Article(title=payload.title, body=payload.body)
db.add(article)
await db.commit()
await db.refresh(article)
return article
@app.get("/articles", response_model=list[ArticleRead])
async def list_articles(db: AsyncSession = Depends(get_db)):
result = await db.execute(select(Article).order_by(Article.id))
return result.scalars().all()Do not pass an AsyncSession to synchronous code or call synchronous methods without the required await. Also avoid sharing one session between concurrent tasks; create a session for each request or unit of work.
Configure Alembic migrations
Initialize the migration directory
From the project root, create Alembic’s configuration and script directory:
alembic init alembicSet the database URL used by Alembic. You can put a development value in alembic.ini, but an environment variable is safer. In alembic/env.py, import your model metadata and assign it to target_metadata:
from your_package.database import Base
from your_package import models # ensures model classes are imported
target_metadata = Base.metadataReplace your_package with the actual Python package name. Importing the models matters: if a model has not been imported, it is absent from Base.metadata and autogenerate cannot see it.
Generate and apply a revision
After the model exists and Alembic can connect, generate a candidate migration:
alembic revision --autogenerate -m "create articles table"
alembic upgrade headOpen the generated revision and review it before applying it. Autogenerate detects many table, column and index changes, but it cannot infer every semantic change safely; data transformations, renames and destructive operations often need hand-written migration code. Check the current state with:
alembic current
alembic historyWhen changing a model later, generate another revision, inspect its upgrade() and downgrade() functions, and apply it in a controlled deployment. Do not use Base.metadata.create_all() as a substitute for migrations in a database that already contains data.
Use a synchronous URL for async projects
Alembic’s standard runner is synchronous. For an async application, keep DATABASE_URL as postgresql+asyncpg://... for the app, but provide Alembic with a synchronous URL, commonly postgresql+psycopg2://... pointing to the same database. Removing +asyncpg from a URL is not enough unless the corresponding synchronous driver is installed and SQLAlchemy resolves the resulting scheme as intended. Explicitly configure the migration URL and test it separately.
Start and exercise the API
With the virtual environment active and the database migrated, start Uvicorn from the directory containing your package:
uvicorn your_package.main:app --reloadThe --reload option is for development; it watches files and restarts the process. Do not use it as a production process manager. Open /docs in the running application, create an article, and then request the collection endpoint. A successful insert should return a generated integer ID and the row should be visible through a subsequent GET.
SQLModel as an alternative model layer
If you prefer one class to serve as both a database model and a validated API model, SQLModel is the approach used in FastAPI’s official SQL tutorial. It is built on SQLAlchemy and Pydantic and can target PostgreSQL through SQLAlchemy’s dialect support. SQLModel does not remove the need to choose a PostgreSQL driver, manage sessions, protect credentials or run migrations. For larger systems, separate SQLAlchemy and Pydantic schemas can make read/write differences, relationships and security boundaries more explicit.
Quick Recap
Common failures and precise fixes
| Symptom | Likely cause | What to check |
|---|---|---|
connection refused |
PostgreSQL is stopped, listening on another address or blocked by a firewall. | Run systemctl status postgresql, verify the host and port, and inspect PostgreSQL’s listening configuration. |
password authentication failed |
Wrong password, role or authentication rule. | Try psql with the same URL, confirm the role exists, and reset the password as the PostgreSQL administrator if necessary. |
database does not exist |
The database name in DATABASE_URL differs from the one created with CREATE DATABASE. |
List databases with sudo -u postgres psql -l and correct the URL. |
ModuleNotFoundError: psycopg2 |
The synchronous driver is not installed in the active virtual environment. | Activate .venv and install psycopg2-binary, or use the driver that matches your URL. |
postgresql+asyncpg cannot load |
asyncpg is missing or a synchronous engine is being used with an async URL. |
Install asyncpg and use create_async_engine with AsyncSession. |
| Alembic says no changes detected | Models were not imported, or target_metadata points to the wrong base. |
Import every model module in env.py and verify that it uses the same Base as the models. |
| Tables are missing after a successful app start | The application started but migrations were never applied. | Run alembic upgrade head against the same database URL the app uses. |
| Async requests hang or block | Synchronous database calls are running inside async routes. | Use async SQLAlchemy APIs throughout, or change the route and dependency to the synchronous stack. |
current transaction is aborted |
A statement failed and the session remains in a failed transaction. | Call rollback(), fix the original constraint or SQL error, and retry in a new transaction. |
Production checklist for an Ubuntu deployment
- Use a dedicated application role; reserve the
postgresadministrator for maintenance. - Store
DATABASE_URLin a secret manager or protected service configuration, not in Git. - Require TLS when the database is not on the same trusted host and restrict network access to the application’s addresses.
- Run Alembic migrations as an explicit deployment step and review generated SQL, especially for renames and data deletion.
- Set a deliberate connection-pool size that fits PostgreSQL’s connection limit and the number of application workers.
- Use health checks that distinguish an application process being alive from a database being reachable.
- Back up PostgreSQL, test restoring those backups, and monitor disk space, connections and slow queries.
- Run Uvicorn behind a production process manager and reverse proxy rather than using
--reload. - Pin Python dependencies and record the Ubuntu and PostgreSQL major versions so upgrades are reproducible.
Recommended implementation path
- Install and verify PostgreSQL on Ubuntu; use PGDG only when Ubuntu’s packaged major version is not suitable.
- Create a separate database and login role with a strong password.
- Create a virtual environment and install FastAPI, Uvicorn, SQLAlchemy, Alembic and exactly one PostgreSQL driver for your chosen style.
- Put the URL in an environment variable and build the engine and session dependency.
- Define SQLAlchemy models and Pydantic request/response schemas, or adopt SQLModel if its combined model style fits the project.
- Initialize Alembic, connect
target_metadatato your model base, review an autogenerated revision and runalembic upgrade head. - Start Uvicorn, exercise a write and read endpoint, and verify the row directly with
psql. - Before deployment, add secret management, backups, TLS, connection limits and a migration gate to your release process.
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