Comprehensive Guide to Building a Lightweight PostgreSQL Testing Environment with py-pglite

In modern Python development, database testing is an essential task, especially when you rely on PostgreSQL as your primary data store. Traditional approaches to database testing involve installing and configuring a full PostgreSQL server, maintaining initialization scripts, and orchestrating cleanup logic after each test. These steps can be time-consuming, error-prone, and prone to environment inconsistencies. Fortunately, there is a tool designed specifically to address these challenges: py-pglite. Py-pglite allows you to simulate a full PostgreSQL environment entirely in memory, without needing to install the actual PostgreSQL server. In this comprehensive guide, we will explain what py-pglite is, how to install and configure it, how to integrate it into your Python projects and testing workflows, and best practices to ensure robust and efficient database testing. By the end of this article, you will understand why py-pglite is an attractive solution for running isolated, reproducible, and high-performance database tests in Python.

Table of Contents

  1. Why Choose py-pglite for PostgreSQL Testing?

  2. Installation and Environment Requirements

  3. Getting Started: Writing Your First Test with py-pglite

  4. Advanced Usage: Manual Management of the In-Memory PostgreSQL Instance

  5. py-pglite Utility Functions for Database Cleanup and Inspection

  6. Integrating py-pglite with FastAPI for API Testing

  7. Concurrency and Multi-Session Testing with py-pglite

  8. Best Practices and Recommendations for Effective Testing

  9. Common Issues and Troubleshooting Strategies

  10. Conclusion: Accelerate Your Database Testing with py-pglite

1. Why Choose py-pglite for PostgreSQL Testing?

When it comes to testing database-driven applications in Python, one primary goal is to replicate real PostgreSQL behavior without incurring the overhead of setting up and maintaining a full database instance for each developer or each continuous integration (CI) job. Traditional workflows often involve:

  • Installing and configuring a local or dockerized PostgreSQL server.
  • Creating and applying SQL migration scripts to shape the schema.
  • Writing custom teardown logic to clean up data after each test case.
  • Ensuring every developer’s machine or every CI runner has the correct version of PostgreSQL installed.

These steps introduce complexity, can slow down developer onboarding, and may result in unpredictable test failures if environments diverge. Py-pglite addresses these pain points by providing:

  1. In-Memory PostgreSQL Instances
    Py-pglite leverages PGlite, a lightweight in-memory implementation of PostgreSQL, to spin up a fully functional PostgreSQL-compatible database entirely in RAM. Tests run orders of magnitude faster than when using a disk-based server.

  2. Seamless Integration with Python Ecosystem
    Py-pglite works natively with SQLAlchemy, SQLModel, and popular web frameworks like FastAPI. There is zero impedance mismatch: your ORM models, migrations, and SQL queries behave exactly as they would against a “real” PostgreSQL instance.

  3. Automatic Lifecycle Management
    With pytest fixtures such as pglite_session and pglite_engine, py-pglite automatically starts, initializes, and shuts down the in-memory database for each test function or module, ensuring perfect isolation and reproducibility.

  4. Comprehensive PostgreSQL Feature Support
    Because PGlite is based on the actual PostgreSQL engine compiled for in-memory operation, py-pglite provides near-perfect compatibility with PostgreSQL SQL syntax, data types, constraints, and functions. This avoids the common pitfalls of using SQLite or other “lightweight” databases that lack certain PostgreSQL-specific features.

By choosing py-pglite for your PostgreSQL testing, you dramatically reduce setup time, avoid environment drift, and achieve lightning-fast test performance—all while keeping behavior consistent with your production database.

2. Installation and Environment Requirements

Before diving into py-pglite usage examples, let’s examine how to install the library, what optional extensions are available, and which system requirements must be satisfied to ensure smooth operation.

2.1 Basic Installation

Installing py-pglite is as simple as running a single pip command. In your terminal or CI pipeline, execute:

pip install py-pglite

This command installs:

  • The py-pglite Python package, which provides pytest fixtures, classes, and utility functions for managing the in-memory PostgreSQL instance.
  • Underlying PGlite dependencies. Behind the scenes, py-pglite will fetch the necessary Node.js modules to initialize and run PGlite. There is no need for you to manually run npm install or maintain any Node.js configuration.

Once installation completes successfully, you can immediately import py-pglite components in your Python test scripts.

2.2 Optional Extensions and Features

Depending on your project’s tech stack, you might want additional functionality. Py-pglite offers optional “extras” that bundle extra dependencies for specific use cases:

  • SQLModel Support
    If your project uses SQLModel (a lightweight ORM layer built on top of SQLAlchemy), you can install py-pglite with SQLModel extras:

    pip install "py-pglite[sqlmodel]"

    This ensures SQLModel and its required components are available, allowing you to seamlessly create and manipulate SQLModel-defined tables in your tests.

  • FastAPI Integration
    For projects built on FastAPI, py-pglite provides convenient helper fixtures to override the standard database dependency. To enable FastAPI-specific features, run:

    pip install "py-pglite[fastapi]"

    This extra installs FastAPI and its testing dependencies, enabling you to combine TestClient, dependency overrides, and the in-memory database in your integration tests.

  • Development Tools and Debugging Aids
    If you plan to extend py-pglite itself, debug its internals, or contribute to the library, you may want the development extras:

    pip install "py-pglite[dev]"

    This will install additional packages such as linters, test runners, and code formatting tools that ease contributing to the py-pglite codebase.

2.3 System and Language Prerequisites

To ensure py-pglite runs correctly, your development or CI environment should satisfy the following requirements:

  • Python 3.10 or Above
    Py-pglite leverages modern Python features, including type hints and advanced language constructs. Though the library may work on some earlier versions, it is recommended to use Python 3.10+.

  • Node.js 18 or Above
    PGlite relies on a Node.js runtime to manage its PostgreSQL-in-memory engine. Py-pglite will automatically download PGlite’s Node.js modules, but you must have a compatible Node.js (version 18 or higher) installed on your system. If you do not have Node.js, you can download it from the official Node.js website, or use a version manager like nvm to install it easily.

  • SQLAlchemy 2.0+ and SQLModel (Optional)
    If you are using SQLAlchemy directly, py-pglite is fully compatible with SQLAlchemy 2.0 or higher. If your project relies on SQLModel, ensure you have installed SQLModel 0.0.8 or higher (the latest stable version), so that the ORM mappings and table creation work smoothly.

No additional database server needs to be installed, and no special permissions are required to use Unix sockets, as PGlite runs entirely in a temporary directory by default. With these requirements in place, you are ready to integrate py-pglite into your Python testing workflows.

3. Getting Started: Writing Your First Test with py-pglite

In this section, we will walk through a minimal example to demonstrate how quickly and easily you can write a pytest-based database test using py-pglite. We will define a simple data model using SQLModel, configure pytest fixtures, and run assertions against the in-memory PostgreSQL.

3.1 Defining a Simple SQLModel Data Model

For the sake of example, let’s create a basic user data model using SQLModel. SQLModel sits on top of SQLAlchemy, offering declarative table definitions with type hints. Create a file named models.py (or integrate into your existing codebase) with the following content:

from sqlmodel import SQLModel, Field

class User(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str
    email: str

Here is a breakdown of what each line does:

  • SQLModel inheritance tells SQLModel to treat this class as a table definition.
  • table=True signals that User should be mapped to a database table named "user" (by default, SQLModel uses the class name in lowercase).
  • id: int | None = Field(default=None, primary_key=True) defines id as an integer primary key that will auto-increment. Using | None indicates that the ID can be null before insertion.
  • name: str and email: str define name and email columns both as non-nullable text fields.

By defining a model in this way, SQLModel will automatically generate the corresponding SQL CREATE TABLE statement under the hood when you invoke SQLModel.metadata.create_all(engine).

3.2 Writing a pytest Test Case Using pglite_session

With the User model in place, we can write a pytest test that uses the pglite_session fixture provided by py-pglite. Create a test file named test_user.py:

import pytest
from sqlmodel import select
from models import User
from py_pglite import pglite_session

def test_user_creation(pglite_session):
    """
    Verify that we can create and query a User record against an in-memory PostgreSQL instance.
    """

    # Create and insert a new User record
    new_user = User(name="Alice", email="alice@example.com")
    pglite_session.add(new_user)
    pglite_session.commit()
    # Refresh so that new_user.id is populated
    pglite_session.refresh(new_user)

    # Query the User table
    statement = select(User)
    results = pglite_session.exec(statement).all()

    # Assert that exactly one user exists and the name matches
    assert len(results) == 1
    assert results[0].name == "Alice"
    assert results[0].email == "alice@example.com"

Let’s break down the key elements:

  • Importing the pglite_session Fixture
    By importing pglite_session, you get a ready-to-use SQLAlchemy session connected to an in-memory PostgreSQL instance. Pytest will automatically spin up PGlite before the test and tear it down after.

  • Session Lifecycle
    At test startup, py-pglite will:

    1. Download and install PGlite’s Node.js dependencies if necessary.
    2. Launch a PGlite process to host a PostgreSQL-compatible server entirely in memory.
    3. Expose a SQLAlchemy Session object (the pglite_session) ready to accept ORM commands.

    When the test function completes (whether it passes or fails), py-pglite shuts down the process and cleans up any temporary files, guaranteeing that subsequent tests run in a fresh environment.

  • Adding, Committing, and Refreshing
    We create a new User instance, add it to the session, and call commit(). Calling refresh(new_user) ensures SQLModel fetches the generated id from the database for our new record. You can skip refresh if you do not need to reference the generated primary key.

  • Querying with select(User)
    SQLModel’s select() function integrates with SQLAlchemy’s new-style query API. We call pglite_session.exec(...) to execute the query and retrieve results. The all() method returns a list of model instances.

  • Assertions
    Finally, we assert that exactly one user exists and that the user’s name and email match our expectations. Since the database is brand new for each test, there is no risk of leftover data from other tests.

When you run pytest, the output should show a quick test pass, with no manual database management required:

$ pytest test_user.py
============================= test session starts =============================
platform linux -- Python 3.10.x, pytest-7.x.x, SQLModel-0.0.x
collected 1 item                                                                 

test_user.py .                                                            [100%]

============================== 1 passed in 0.05s ==============================

Notice how fast the test completes. Since the entire database is in memory, the overhead is minimal compared to spinning up a full PostgreSQL server in Docker or on the local file system.

4. Advanced Usage: Manual Management of the In-Memory PostgreSQL Instance

In simple cases, relying on pytest fixtures (pglite_session or pglite_engine) is sufficient. However, there are times when you need more control over how the in-memory PostgreSQL instance is configured, when it is started or stopped, and what logging or socket settings are used. For these scenarios, py-pglite provides the PGliteManager and PGliteConfig classes to let you manage the database instance manually.

4.1 Configuring Custom Settings with PGliteConfig

The PGliteConfig class allows you to specify various options for starting and managing the in-memory PostgreSQL process. Below is an example configuration:

from py_pglite import PGliteConfig

config = PGliteConfig(
    timeout=30,                # Maximum number of seconds to wait for PGlite to start
    cleanup_on_exit=True,      # Delete temporary database files when the manager stops
    log_level="INFO",          # Set PGlite log verbosity (DEBUG, INFO, WARNING, ERROR)
    socket_path="/tmp/pglite.sock",  # Custom Unix socket path to avoid conflicts
    work_dir=None,             # Working directory for PGlite; None defaults to system temp directory
    node_modules_check=True,   # Verify that Node.js modules are present before starting
    auto_install_deps=True     # Automatically run `npm install` if Node.js modules are missing
)

  • timeout
    Specifies how long (in seconds) to wait for the PGlite server to become ready. On machines with slower I/O or in restricted CI environments, the default timeout may be insufficient. Bumping this value helps avoid intermittent “connection refused” errors.

  • cleanup_on_exit
    By setting this to True, any temporary files created for the in-memory database (e.g., socket files, interim logs) will be deleted when PGliteManager exits. If you want to inspect the database files after a test run for debugging purposes, set this to False.

  • log_level
    Controls the verbosity of PGlite’s logging output. In most cases, INFO is adequate. However, if you’re troubleshooting connection or configuration issues, using DEBUG can provide additional insight into the startup sequence and SQL interactions.

  • socket_path
    By default, PGlite creates a Unix domain socket in a system-defined temporary directory. If you already have a PostgreSQL server running on a standard socket (e.g., /tmp/.s.PGSQL.5432) or if you need to isolate the PGlite socket to a specific path, override socket_path. This option prevents potential conflicts when multiple database processes co-exist on the same machine.

  • work_dir
    Determines where PGlite stores its temporary data (the “database files,” albeit in memory). If left as None, py-pglite chooses a location in the system’s temporary directory. You can specify a path to a dedicated “pg_data” directory for debugging or persistent caching purposes.

  • node_modules_check and auto_install_deps
    When you install py-pglite for the first time, PGlite’s Node.js modules (hosted on npm) may not be present locally. By default, node_modules_check=True makes py-pglite verify the existence of those modules at startup. If they are not found, and if auto_install_deps=True, py-pglite will automatically run npm install in its own environment to fetch the required packages. If you prefer to manage Node.js dependencies manually in your own project (e.g., pin a specific version), you can disable auto_install_deps and run npm install pglite on your own.

4.2 Using PGliteManager in a Context Manager

Once you have a PGliteConfig object, you can create a PGliteManager to start and stop the in-memory database:

from py_pglite import PGliteManager, PGliteConfig
from sqlmodel import SQLModel

# Step 1: Create a custom configuration
config = PGliteConfig(
    timeout=30,
    cleanup_on_exit=True,
    log_level="INFO",
    socket_path="/tmp/my_pglite.sock",
    work_dir=None,
    auto_install_deps=True
)

# Step 2: Use PGliteManager in a with-statement to ensure cleanup
with PGliteManager(config) as manager:
    # Step 3: Obtain a SQLAlchemy engine from the manager
    engine = manager.get_engine()

    # Step 4: Create tables based on your SQLModel metadata
    SQLModel.metadata.create_all(engine)

    # Step 5: Perform any database operations you need, manually or in tests
    from sqlmodel import Session
    with Session(engine) as session:
        # Example: Insert a dummy user
        user = User(name="Bob", email="bob@example.com")
        session.add(user)
        session.commit()

    # When the with-block exits, PGliteManager shuts down PGlite and cleans up

Key points to note:

  1. Context Manager Guarantees Cleanup
    By using with PGliteManager(config) as manager:, you ensure that, even if an exception is raised inside the block, the in-memory database process is cleanly terminated when leaving the block.

  2. Retrieving the SQLAlchemy Engine
    The manager.get_engine() method returns a SQLAlchemy Engine object configured to connect to the in-memory PostgreSQL instance. You can pass this engine to any ORM, raw SQL code, or migration tool just as you would with a normal PostgreSQL connection string.

  3. Schema Initialization
    Since PGlite does not persist data across runs (unless you explicitly configure a persistent working directory), you must run SQLModel.metadata.create_all(engine) inside the context to create all tables before using them.

  4. Flexibility for Complex Testing Scenarios
    Manual management mode is especially useful when:

    • You need to share one database instance across multiple test modules or functions.
    • You want to run a suite of integration tests that rely on pre-populated seed data.
    • You need to debug internal database state by leaving temporary files behind (cleanup_on_exit=False).
    • You want to orchestrate a multi-step testing flow in a single script that directly manipulates tables, schemas, and constraints.

By leveraging PGliteConfig and PGliteManager, you gain complete control over the in-memory PostgreSQL instance, while still avoiding the overhead of a full disk-based database server.

5. py-pglite Utility Functions for Database Cleanup and Inspection

When testing against an in-memory PostgreSQL instance, you often need to programmatically reset data, drop or create schemas, or gather quick statistics. Py-pglite includes a suite of utility functions under py_pglite.utils to streamline these tasks. Let’s explore each of these helpers.

5.1 clean_database_data – Bulk Data Cleanup

One common requirement is to wipe all data from all tables in the database, especially between test runs. The function clean_database_data(engine) deletes all rows from every table in the current schema. The call looks like:

from py_pglite import utils

# After you have an Engine instance:
utils.clean_database_data(engine)

This function generates and executes SQL TRUNCATE TABLE commands (or their equivalent) for every table present. If you want to preserve data in certain tables—perhaps configuration or reference tables—use the exclude_tables parameter:

# Clean all tables except for the 'users' and 'lookup_codes' tables
utils.clean_database_data(engine, exclude_tables=["users", "lookup_codes"])

This is particularly useful when you need to maintain seed data across multiple test cases, while still clearing out transactional or temporary tables. Internally, clean_database_data does the following:

  1. Queries the catalog to obtain a list of all tables in the current schema.
  2. Filters out any tables listed in exclude_tables.
  3. Executes a TRUNCATE TABLE <table_name> CASCADE (or the equivalent) for each remaining table.

This ensures that foreign key constraints and dependent objects are also handled, avoiding referential integrity errors during cleanup.

5.2 reset_sequences – Resetting Auto-Incrementing Sequences

After repeatedly inserting and deleting data in tests, auto-incrementing primary key sequences can jump to high numbers, which may lead to confusion when asserting on specific IDs. reset_sequences(engine) resets all sequences in the database back to their initial values (typically 1). Usage:

# Reset all auto-increment sequences
utils.reset_sequences(engine)

Under the hood, reset_sequences does the following:

  1. Queries the PostgreSQL catalog for sequences owned by tables.
  2. For each sequence, sets its current value to 1 (or the minimum value) using ALTER SEQUENCE <sequence_name> RESTART WITH 1.
  3. Ensures that nextval() on each sequence yields 1 (or nextval yields 1 for the next inserted row).

This helps maintain consistent test conditions where, for example, you expect the first inserted User to always have id = 1.

5.3 verify_database_empty – Ensuring a Clean State

Before or after running certain tests, you may want to programmatically assert that the database contains no rows in any table. The function verify_database_empty(engine) returns a boolean indicating whether all tables are empty:

from py_pglite import utils

# Returns True if no rows exist in any user-owned table
assert utils.verify_database_empty(engine)

Internally, verify_database_empty does a count check on each table in the current schema. If every table’s COUNT(*) is zero, it returns True; otherwise, it returns False, which might indicate that a previous test failed to clean up properly. By calling verify_database_empty(engine) at the start and end of critical test suites, you can catch residual data issues early.

5.4 Schema Management: create_test_schema and drop_test_schema

When building a multi-tenant application or simulating logically separate spaces for different modules, you may want to create and drop schemas on the fly. Py-pglite’s schema management helpers do exactly that:

  • create_test_schema(engine, "test_schema_name")
    Creates a new PostgreSQL schema named test_schema_name. Equivalent to CREATE SCHEMA test_schema_name;.

  • drop_test_schema(engine, "test_schema_name")
    Drops the specified schema and all its objects (tables, views, etc.), equivalent to DROP SCHEMA test_schema_name CASCADE;.

Example usage:

from py_pglite import utils

# Create a new schema
utils.create_test_schema(engine, "integration_tests")

# Create tables within that schema in your SQLModel metadata
# For example: `User.__table__.schema = "integration_tests"`
# Or run raw SQL: CREATE TABLE integration_tests.example (...);

# After tests complete, drop the schema
utils.drop_test_schema(engine, "integration_tests")

Using separate schemas can be very helpful when you want to:

  • Run parallel test suites in the same database instance without table name collisions.
  • Test multi-tenant logic by placing each tenant’s objects in its own schema.
  • Isolate specific test artifacts, then clean them up en masse by dropping the schema.

5.5 get_table_row_counts – Gathering Row Count Statistics

For debugging or performance analysis, you might want to know how many rows exist in each table at any point in time. The helper function get_table_row_counts(engine) returns a Python dictionary mapping table names to their row counts. Sample usage:

from py_pglite import utils

row_counts = utils.get_table_row_counts(engine)
# Example output: {'user': 2, 'order': 5, 'product': 0}
print(row_counts)

Internally, this function iterates over all user-defined tables in the current schema, executes a SELECT COUNT(*) FROM <table_name>, and builds a dictionary. This provides a quick snapshot of database state, useful when:

  • You want to verify that a test setup step inserted the correct number of records.
  • You’re diagnosing why a test’s cleanup code did not fully clear all tables.
  • You need to programmatically log database sizes at different test stages for monitoring.

6. Integrating py-pglite with FastAPI for API Testing

FastAPI is a modern Python web framework known for its speed, developer-friendly syntax, and built-in dependency injection capabilities. In many real-world applications, your database access is encapsulated in a dependency function, typically called get_db, which returns a SQLModel or SQLAlchemy Session. In production, get_db would connect to your real PostgreSQL database. During tests, we want to override that dependency so that it points to our in-memory py-pglite instance. In this section, we will illustrate how to set up a FastAPI endpoint that relies on a database session, then override that session to use py-pglite in a pytest test.

6.1 The Standard FastAPI Dependency Pattern for Database Access

Consider the following basic FastAPI application definition in main.py:

from fastapi import Depends, FastAPI
from sqlmodel import Session
from typing import Generator

from models import User
from database import get_real_db  # In production, this returns a real PostgreSQL Session

app = FastAPI()

def get_db() -> Generator[Session, None, None]:
    """
    In production, this would:
    - Create an SQLAlchemy engine pointing to PostgreSQL
    - Create a SessionLocal class
    - Yield a session and close it in a finally block
    """
    db = next(get_real_db())
    try:
        yield db
    finally:
        db.close()

@app.post("/users/")
def create_user(user_data: dict, db: Session = Depends(get_db)):
    """
    Endpoint to create a new user. The data model `User` is a SQLModel class.
    """
    new_user = User(name=user_data["name"], email=user_data["email"])
    db.add(new_user)
    db.commit()
    db.refresh(new_user)
    return {"id": new_user.id, "name": new_user.name, "email": new_user.email}

Key points:

  • Dependency Injection: By declaring db: Session = Depends(get_db), FastAPI automatically calls get_db() to provide a database session whenever this endpoint is invoked.
  • Session Lifecycle: In production, get_real_db() likely manages connection pooling, engine creation, and proper closing of the session. For tests, we want get_db() to be overridden to return a session from the py-pglite engine instead of the real database.

6.2 Overriding get_db in Tests to Use the In-Memory Database

In a pytest-based test, FastAPI’s TestClient allows us to override dependencies by modifying app.dependency_overrides. To begin, ensure you have py-pglite[fastapi] installed. Next, create a test file, for example test_api.py:

from fastapi.testclient import TestClient
from sqlmodel import Session, select
import pytest

from main import app, get_db
from models import User
from py_pglite import pglite_engine

@pytest.fixture(scope="module")
def initialize_db(pglite_engine):
    """
    Create all database tables in the in-memory PostgreSQL instance at the start of the test module.
    """
    from sqlmodel import SQLModel

    SQLModel.metadata.create_all(pglite_engine)
    yield
    # No explicit cleanup needed; pglite_engine will shut down at the end of the module.

@pytest.fixture(scope="function", autouse=True)
def override_get_db(pglite_engine, initialize_db):
    """
    Override the FastAPI dependency to yield sessions from the in-memory database.
    This fixture uses function scope so that each test function gets its own session lifecycle.
    """
    def _get_db_override():
        with Session(pglite_engine) as session:
            yield session

    # Override the dependency in FastAPI
    app.dependency_overrides[get_db] = _get_db_override
    yield
    # Remove the override after each test to avoid cross-test contamination
    app.dependency_overrides.pop(get_db, None)

def test_create_user_endpoint():
    """
    Test the /users/ endpoint to ensure that creating a user via HTTP works as expected.
    """
    client = TestClient(app)
    response = client.post("/users/", json={"name": "Charlie", "email": "charlie@example.com"})
    assert response.status_code == 200
    data = response.json()
    assert data["name"] == "Charlie"
    assert data["email"] == "charlie@example.com"
    assert "id" in data

    # Further verify that the user is actually persisted in the in-memory database
    with Session(pglite_engine) as db_session:
        statement = select(User).where(User.email == "charlie@example.com")
        user_list = db_session.exec(statement).all()
        assert len(user_list) == 1
        assert user_list[0].name == "Charlie"

Let’s explain the key components step by step:

  1. initialize_db Fixture (module scope)

    • Receives a pglite_engine parameter provided by py-pglite. This engine connects to the in-memory PostgreSQL instance.
    • Calls SQLModel.metadata.create_all(pglite_engine) once per module to create all tables defined in your SQLModel classes.
    • Yields control back to pytest. No explicit table-dropping logic is needed, since the in-memory database is torn down when pglite_engine is garbage-collected at module teardown.

  2. override_get_db Fixture (function scope, autouse)

    • Defines _get_db_override() which yields a SQLModel Session connected to the py-pglite engine.
    • Registers the override in app.dependency_overrides, so any call to the get_db dependency within FastAPI routes now uses the in-memory session.
    • After the test function completes, it removes the override to prevent bleed into other test functions.

  3. Writing the API Test

    • Instantiate TestClient(app). This launches the FastAPI application in a test environment.
    • Perform a POST request to "/users/" with a sample JSON payload, { "name": "Charlie", "email": "charlie@example.com" }.
    • Assert that the HTTP response status is 200 and that the returned JSON contains the correct name, email, and an auto-generated id.
    • For additional verification, open a new session using Session(pglite_engine) and query the User table directly to confirm that the row was inserted in the in-memory database.

By following this pattern, you ensure that all database interactions inside your FastAPI application during tests point to the lightweight py-pglite instance, never touching your production or development database. Tests run quickly, in full isolation, and remain fully reproducible.

6.3 Example: Testing a User Creation Endpoint

To recap the full flow for a user creation API test:

  1. Define the User Data Model

    • Use SQLModel to define tables and columns.
    # models.py
from sqlmodel import SQLModel, Field

class User(SQLModel, table=True):
    id: int | None = Field(default=None, primary_key=True)
    name: str
    email: str

  2. Implement the FastAPI Endpoint

    • Create a main.py with an endpoint that depends on get_db() to insert a new user into the database.
    # main.py
from fastapi import Depends, FastAPI
from sqlmodel import Session
from typing import Generator

from models import User
from database import get_real_db  # Production DB dependency

app = FastAPI()

def get_db() -> Generator[Session, None, None]:
    db = next(get_real_db())
    try:
        yield db
    finally:
        db.close()

@app.post("/users/")
def create_user(user_data: dict, db: Session = Depends(get_db)):
    new_user = User(name=user_data["name"], email=user_data["email"])
    db.add(new_user)
    db.commit()
    db.refresh(new_user)
    return {"id": new_user.id, "name": new_user.name, "email": new_user.email}

  3. Write the pytest Test with py-pglite

    • Create test_api.py that overrides get_db to use pglite_engine and performs the HTTP request and direct database verification.
    # test_api.py
from fastapi.testclient import TestClient
from sqlmodel import Session, select
import pytest

from main import app, get_db
from models import User
from py_pglite import pglite_engine

@pytest.fixture(scope="module")
def initialize_db(pglite_engine):
    from sqlmodel import SQLModel
    SQLModel.metadata.create_all(pglite_engine)
    yield

@pytest.fixture(scope="function", autouse=True)
def override_get_db(pglite_engine, initialize_db):
    def _get_db_override():
        with Session(pglite_engine) as session:
            yield session

    app.dependency_overrides[get_db] = _get_db_override
    yield
    app.dependency_overrides.pop(get_db, None)

def test_create_user_endpoint():
    client = TestClient(app)
    response = client.post("/users/", json={"name": "Charlie", "email": "charlie@example.com"})
    assert response.status_code == 200
    data = response.json()
    assert data["name"] == "Charlie"
    assert data["email"] == "charlie@example.com"
    assert isinstance(data["id"], int)

    # Verify the user was inserted in the database
    with Session(pglite_engine) as db_session:
        statement = select(User).where(User.email == "charlie@example.com")
        fetched_users = db_session.exec(statement).all()
        assert len(fetched_users) == 1
        assert fetched_users[0].name == "Charlie"

Running pytest test_api.py should pass in under a second, thanks to the in-memory database:

$ pytest test_api.py
============================= test session starts =============================
platform linux -- Python 3.10.x, pytest-7.x.x, SQLModel-0.0.x
collected 1 item                                                                 

test_api.py .                                                            [100%]

============================== 1 passed in 0.07s ==============================

7. Concurrency and Multi-Session Testing with py-pglite

Many real-world applications involve concurrent database operations—multiple threads or processes may read from and write to the database simultaneously. When writing tests for such scenarios, it’s crucial to ensure that your in-memory PostgreSQL instance can handle concurrent connections or sessions without collisions or deadlocks. Py-pglite supports concurrency well, provided you follow a few best practices.

7.1 Why Concurrency Matters in Database Tests

In production, your application might serve multiple HTTP requests at once, each spawning its own database session. You might also have background jobs, scheduled tasks, or asynchronous event processors all interacting with the same database. If your tests only cover single-threaded or single-session use cases, you miss a large swath of real-world behaviors. Common concurrency issues include:

  • Race Conditions on Record Updates
    Two sessions read a row simultaneously, update it differently, and attempt to write back conflicting data.

  • Transaction Isolation Violations
    How the database handles “dirty reads,” “non-repeatable reads,” and “phantom reads” depends on the isolation level. You want to test that your application’s logic behaves correctly under the chosen isolation level.

  • Connection Pool Limits
    Even in an in-memory environment, there may be a limit on how many connections or sessions can be opened simultaneously. It’s important to verify that pooling settings in SQLAlchemy or your framework do not lead to unexpected “too many connections” errors.

By testing concurrency scenarios against a py-pglite-powered in-memory PostgreSQL, you can catch potential deadlocks, verify isolation level effects, and ensure your application’s data integrity holds up under load.

7.2 Sharing a Single Engine Across Multiple Sessions

The recommended strategy for concurrency tests with py-pglite is to share a single SQLAlchemy Engine object across multiple threads or processes. Creating multiple engines against the same socket path can lead to connection pool conflicts. Instead, do the following:

from threading import Thread
from sqlmodel import Session
from py_pglite import PGliteManager, PGliteConfig
import time

def worker_task(engine, worker_id):
    """
    A dummy worker function that reads and updates a counter table.
    """
    with Session(engine) as session:
        # Simulate reading a shared counter
        row = session.exec(select(Counter).where(Counter.id == 1)).one_or_none()
        if row is None:
            # Insert a new counter if not found
            counter = Counter(id=1, value=0)
            session.add(counter)
            session.commit()
            value = 0
        else:
            value = row.value

        # Simulate some processing time
        time.sleep(0.1)

        # Increment the counter
        new_value = value + 1
        session.execute(
            update(Counter)
            .where(Counter.id == 1)
            .values(value=new_value)
        )
        session.commit()
        print(f"Worker {worker_id} updated counter to {new_value}")

if __name__ == "__main__":
    # Step 1: Configure and start PGlite
    config = PGliteConfig(timeout=30, cleanup_on_exit=True, log_level="INFO")
    with PGliteManager(config) as manager:
        engine = manager.get_engine()

        # Step 2: Create the Counter table
        from sqlmodel import SQLModel, Field
        from typing import Optional
        from sqlalchemy import Integer, Column

        class Counter(SQLModel, table=True):
            id: Optional[int] = Field(default=None, primary_key=True)
            value: int

        SQLModel.metadata.create_all(engine)

        # Step 3: Launch multiple threads to simulate concurrency
        threads = []
        for i in range(5):
            thread = Thread(target=worker_task, args=(engine, i + 1))
            threads.append(thread)
            thread.start()

        # Wait for all workers to finish
        for thread in threads:
            thread.join()

        # Step 4: Verify final counter value
        with Session(engine) as session:
            final_counter = session.exec(select(Counter).where(Counter.id == 1)).one()
            print(f"Final counter value: {final_counter.value}")

Important considerations:

  • Single Engine Instance
    We create one engine = manager.get_engine() at the beginning. Each thread receives the same engine, triggering SQLAlchemy’s connection pooling under the hood. This avoids the “too many engines pointing to the same socket” problem.

  • Session per Thread
    Each worker thread calls with Session(engine) as session: to obtain a session. SQLAlchemy allocates a connection from its pool to each session. Because PGlite is an in-memory database running a single process, multiple concurrent connections are multiplexed through the same socket in an efficient manner.

  • Transaction Management
    In this toy example, we read the Counter row, sleep for a short time, then update the row. In a production setup, you would want to explicitly manage transactions (e.g., using BEGIN ISOLATION LEVEL SERIALIZABLE) to prevent lost updates. Py-pglite respects PostgreSQL’s transaction isolation semantics, so you can test for deadlocks, serialization failures, or retry logic easily.

7.3 Best Practices to Avoid Connection Pool Conflicts

When using multiple concurrent sessions, keep the following guidelines in mind:

  • Limit Engine Creation
    Do not call manager.get_engine() inside each thread or test function. Instead, create one engine per PGlite instance and share it.

  • Configure Suitable Pool Size
    SQLAlchemy’s default connection pool size is typically 5. If you spawn more than 5 concurrent sessions, you may need to adjust the pool size via create_engine(..., pool_size=N). Py-pglite’s default engine creation uses a sensible default, but if you have 10 threads, you must explicitly configure pool_size=10 or higher.

  • Close Sessions Promptly
    Always use context managers (with Session(engine)) or explicitly call session.close() when done. Lingering sessions hold on to connections and can exhaust the pool.

  • Monitor for Deadlocks
    In a highly concurrent scenario with conflicting writes, you might encounter PostgreSQL deadlocks or “could not serialize access” errors. Use log_level="DEBUG" in PGliteConfig to examine the exact queries and lock orders if you suspect such issues.

By following these practices, you ensure that your concurrency tests remain stable and representative of real-world production behavior.

8. Best Practices and Recommendations for Effective Testing

Py-pglite offers powerful capabilities for database testing, but unlocking its full potential requires following some best practices. In this section, we share recommendations based on real-world experience to help you build reliable, fast, and maintainable test suites.

8.1 Leverage pytest Fixtures for Simplicity and Isolation

One of py-pglite’s biggest advantages is its pytest integration. Using built-in fixtures like pglite_session and pglite_engine ensures:

  • Automatic Startup and Teardown
    Each test function or test module automatically receives a brand-new in-memory database. No need to write custom setup or teardown functions.

  • Perfect Isolation
    When you use pglite_session (scope=function) or pglite_engine (scope=function or scope=module), pytest guarantees that no two tests share the same database instance. This prevents “test A leaves data that breaks test B” types of issues.

  • Minimal Boilerplate
    Instead of writing explicit PGliteManager and PGliteConfig code in every test, simply include the fixture in the test function signature. For example:

    def test_product_crud(pglite_session):
    # pglite_session is ready to use
    product = Product(name="Widget", price=9.99)
    pglite_session.add(product)
    pglite_session.commit()

    # Verify product exists
    ...

Use fixtures to handle database initialization, schema creation, and injection of the session or engine into test functions. Rely on py-pglite’s default configuration for most cases; only override when necessary.

8.2 Adjust Timeout and Logging Levels Appropriately

In environments with limited I/O performance—such as shared CI runners or resource-constrained containers—PGlite might take slightly longer than usual to start up or install its Node.js dependencies. If you see sporadic test failures citing “could not connect to database” or “connection refused,” consider:

  • Increasing timeout
    Pass a higher value (e.g., 60 seconds) to PGliteConfig(timeout=60) if you frequently see timeouts, particularly on first-run when NPM modules need to download.

  • Temporary Logging to DEBUG
    To troubleshoot why PGlite isn’t starting, set log_level="DEBUG". This lets you inspect logs such as:

    • “Waiting for socket to become available”
    • “Error: Node modules missing, installing…”
    • “Connected to in-memory PostgreSQL at /tmp/pglite.sock”

Once you’ve diagnosed and resolved the issue, revert log_level back to INFO or WARNING to reduce noise in your test logs.

8.3 Utilize Utility Functions for Data Management

Py-pglite’s utils module provides many handy functions that simplify test setup and teardown:

  1. clean_database_data(engine)
    Use this at the end of a test or a fixture’s teardown to wipe all tables, ensuring no residual data bleeds into subsequent tests.

  2. reset_sequences(engine)
    Call this if you rely on specific primary key values in tests. For example, if test A expects the first User to have id = 1, but an earlier test inserted a dozen users, calling reset_sequences will guarantee the next inserted user has id = 1.

  3. verify_database_empty(engine)
    Use this as an assertion in integration tests that employ multiple test steps. For example:

    def test_data_cleanup(pglite_engine):
    # Setup: Insert some rows
    ...
    # Teardown: Clean data
    utils.clean_database_data(pglite_engine)
    assert utils.verify_database_empty(pglite_engine)

  4. Schema Management
    If your application supports multi-tenancy or schema-based data partitioning, call create_test_schema at the start and drop_test_schema at the end of tests. This guarantees a truly fresh schema context for each suite.

  5. get_table_row_counts(engine)
    When debugging a complex test suite, log row counts at various checkpoints:

    counts_before = utils.get_table_row_counts(engine)
print("Row counts before cleanup:", counts_before)
utils.clean_database_data(engine)
counts_after = utils.get_table_row_counts(engine)
print("Row counts after cleanup:", counts_after)

By using these utilities judiciously, you keep test code concise and avoid reinventing database cleanup or inspection logic.

8.4 Choosing Between Automatic and Manual Management Modes

Py-pglite offers two main modes of operation:

  1. Automatic Mode (pytest Fixtures)

    • Use pglite_session or pglite_engine directly in test function parameters.
    • Pytest takes care of configuration, instance creation, and teardown.
    • Preferred for straightforward unit tests and small integration tests.

  2. Manual Mode (PGliteManager and PGliteConfig)

    • Create a PGliteConfig object to customize logging, socket paths, timeouts, and other settings.

    • Use PGliteManager to explicitly control when the in-memory database starts and stops.

    • Better for advanced scenarios such as:

      • Sharing one instance across multiple test modules or files.
      • Running ad-hoc scripts that require an isolated database environment.
      • Debugging internal PostgreSQL behaviors by setting cleanup_on_exit=False and examining temporary files.

As a rule of thumb, default to the automatic pytest fixtures to keep tests concise. Switch to manual mode when you need fine-grained control over the database lifecycle or when running tests outside of pytest.

8.5 Handling Concurrency and Connection Pooling

When you test concurrent interactions—multiple HTTP requests handled in parallel, multiple background workers, or asynchronous tasks—follow these guidelines to avoid connection pool exhaustion or lock conflicts:

  • Single Engine Across Threads
    Instantiate only one SQLAlchemy Engine via manager.get_engine() and pass it explicitly to each thread or asynchronous task. Do not call get_engine() in every thread.

  • Configure Pool Size
    If you know you’ll have N concurrent sessions, set the pool size manually:

    from sqlalchemy import create_engine

engine = create_engine(
    manager.get_connection_url(),
    pool_size=10,       # Maximum number of simultaneous connections
    max_overflow=0      # Prevent scaling beyond the pool size
)

  • Close Sessions
    Always close or dispose sessions promptly, e.g., using with Session(engine) as session: .... Lingering sessions hold on to connections and can exhaust the pool.

  • Test Isolation
    For tests that specifically examine transaction isolation levels (e.g., “serializable” vs. “repeatable read”), wrap critical sections in BEGIN TRANSACTION ISOLATION LEVEL statements to ensure you observe the exact concurrency behavior.

  • Monitor Deadlocks
    If you encounter deadlocks in tests, set log_level="DEBUG" in PGliteConfig and inspect the log output. Py-pglite faithfully records lock acquisition and release events, helping you pinpoint problematic transaction sequences.

By applying these strategies, you can confidently perform complex concurrency testing against an in-memory PostgreSQL, catching issues early before they escalate in production.

9. Common Issues and Troubleshooting Strategies

Even with a well-designed testing tool like py-pglite, you may encounter occasional issues when configuring or running your tests. Below is a table of common pitfalls, their likely root causes, and recommended solutions. Use this as a quick reference when you run into unexpected behavior.

Issue Likely Cause Solution
Startup Timeout or “Could Not Connect to Database” PGlite process took too long to initialize Node.js dependencies or to start the internal PostgreSQL server. Increase timeout in PGliteConfig, e.g., PGliteConfig(timeout=60). Ensure Node.js 18+ is installed properly on your system.
Residual Data Between Tests The test did not call pglite_session.commit(), or manual session cleanup was skipped. Call commit() on any pending transactions. Use utils.clean_database_data(engine) in a teardown fixture.
FastAPI Tests Reporting Connection Errors get_db dependency not correctly overridden in tests; tests still trying to connect to a real database. Use app.dependency_overrides[get_db] = override_fn inside a fixture. Remove the override after each test.
Concurrency Lock or Connection Pool Exhaustion Multiple threads/processes each creating their own engine pointing to the same socket. Create one engine = manager.get_engine() and share it across threads. Increase pool_size if needed.
Unexpected Sequence Number Jumps Tests inserted and deleted rows without resetting sequences. Call utils.reset_sequences(engine) between test runs if you expect IDs to start from 1 each time.
Schema Already Exists When Creating a Test Schema The schema was not dropped in a previous test run. Use utils.drop_test_schema(engine, schema_name) before create_test_schema. Or run tests with a fresh in-memory instance.
Socket Path Conflict (Address Already in Use) Another PostgreSQL or PGlite instance is already listening on the default socket path. Set a unique socket_path in PGliteConfig, such as "/tmp/pg_test_1234.sock".
Node.js Modules Not Found PGlite’s Node.js dependencies were not installed (run out-of-band or deleted). Set auto_install_deps=True in PGliteConfig, or manually run npm install pglite in the project root.
Test Hanging on Commit or Connection Close A session was left open, preventing the engine from shutting down cleanly. Ensure every Session(engine) is used within a context manager or that session.close() is called explicitly.
Transaction Rollback Not Occurring as Expected Test code did not handle exceptions properly, so uncommitted transactions persisted. In tests where you expect rollbacks, wrap ORM operations in with session.begin(): ... or explicitly call session.rollback().

9.1 Startup Timeouts or Failed Initialization

Symptoms:

  • pytest hangs or fails immediately with a message indicating it “could not connect to the database” or “timeout waiting for PGlite to start.”
  • Logs show repeated “Waiting for socket file” messages or “ECONNREFUSED”.

Potential Causes and Solutions:

  1. Slow NPM Installation

    • On first-run, PGlite tries to download and install Node.js packages. This can take time if your Internet connection is slow or your CI environment has limited bandwidth.
    • Solution: Increase the timeout parameter in PGliteConfig, e.g., PGliteConfig(timeout=60). Alternatively, run npm install pglite manually in a “warm-up” stage before running tests.

  2. Node.js Version Mismatch

    • If your system has an older Node.js version (<18), PGlite may not run correctly.
    • Solution: Install a supported Node.js version (18 or later). Use a version manager (e.g., nvm) to maintain isolated Node.js versions per project.

  3. Socket Path Permissions

    • In rare cases, the default Unix socket path might not be writable by your test user.
    • Solution: Specify a socket_path inside /tmp or any directory where you have write access, e.g., socket_path="/tmp/my_pglite.sock".

9.2 Residual Data Leading to Test Contamination

Symptoms:

  • Running the same test suite multiple times yields unexpected rows already present in tables.
  • Tests that assume an empty database fail intermittently depending on test order.

Potential Causes and Solutions:

  1. Missing commit() Calls

    • You may have created ORM objects and not called session.commit(), leaving data in uncommitted state, or committed but not cleaned up between tests.
    • Solution: Review test code to ensure all inserts, updates, and deletions call commit(). Alternatively, use session.begin() context managers for transaction control.

  2. Failure to Clear Data

    • Tests that rely on manual calls to utils.clean_database_data(engine) but that function wasn’t invoked.
    • Solution: Use a teardown fixture or autouse fixture to call utils.clean_database_data(engine) after every test, or rely on pytest’s function-scoped pglite_session which resets data automatically.

  3. Shared Engine Across Psuedo-Isolated Tests

    • You might have reused pglite_engine at the module scope, but not cleaned up data between function-scoped tests.
    • Solution: Either use pglite_session (function scope) or explicitly call clean_database_data(engine) at the start of each test when using a module-scoped engine.

9.3 FastAPI Endpoint Tests Reporting Connection Errors

Symptoms:

  • Tests attempting to call FastAPI endpoints get HTTP 500 or “connection refused” errors.
  • Logs show that the get_db dependency is trying to connect to a real PostgreSQL database.

Potential Causes and Solutions:

  1. Dependency Override Not Applied

    • The pytest fixture that sets app.dependency_overrides[get_db] = override_fn did not execute early enough, or was not autouse.
    • Solution: Ensure the fixture has autouse=True or explicitly mark it as a dependency for test functions.

  2. Override Not Removed After Test

    • If you modify app.dependency_overrides but never remove the override at the end, subsequent tests might fail.
    • Solution: In the fixture teardown (after yield), remove the override with app.dependency_overrides.pop(get_db, None).

  3. Missing Schema Creation

    • Even if the dependency override points to the in-memory database, if you forget to call SQLModel.metadata.create_all(pglite_engine), the users table might not exist.
    • Solution: Use a module-scoped fixture to call metadata.create_all before any tests run. For example:
    @pytest.fixture(scope="module")
def initialize_db(pglite_engine):
    from sqlmodel import SQLModel
    SQLModel.metadata.create_all(pglite_engine)
    yield

9.4 Concurrency-Related Connection Pool Conflicts

Symptoms:

  • Tests hang or throw “FATAL: sorry, too many clients already” errors.
  • Connection refused or database closed unexpectedly when multiple threads attempt to open sessions.

Potential Causes and Solutions:

  1. Multiple Engine Instances

    • Each call to manager.get_engine() creates a separate Engine instance pointing to the same Unix socket. When those engines attempt to open connections concurrently, the socket can only handle so many simultaneous connections before refusing new ones.
    • Solution: Create exactly one engine = manager.get_engine() and pass that same engine into every thread or coroutine that needs a session.

  2. Insufficient Pool Size

    • SQLAlchemy’s default pool size (e.g., 5) may be too small when you have many concurrent sessions.

    • Solution: Adjust pool parameters:

      engine = create_engine(
    manager.get_connection_url(),
    pool_size=10,       # Allow up to 10 concurrent connections
    max_overflow=0      # Prevent creating more than 10 total connections
)

  3. Lingering Open Sessions

    • If you forget to call session.close(), sessions remain open and tie up connections.
    • Solution: Always use with Session(engine) as session: or explicitly call session.close() in a finally block.

9.5 Unexpected Sequence Number Jumps

Symptoms:

  • You expect the first inserted record to have id = 1, but instead it has a higher number (e.g., id = 5).
  • Tests fail because they assert on specific IDs.

Potential Causes and Solutions:

  1. Sequences Not Reset Between Tests

    • PostgreSQL’s sequences continue incrementing even if rows are deleted. If you inserted and deleted records in a previous test, the next id might be higher.

    • Solution: Call utils.reset_sequences(engine) before your test runs to set all sequences back to start value. You can incorporate this into a fixture:

      @pytest.fixture(autouse=True)
def reset_sequences_fixture(pglite_engine):
    from py_pglite import utils
    utils.reset_sequences(pglite_engine)
    yield

  2. Manual SQL Insert Bypassing ORM

    • If you inserted rows via raw SQL with INSERT INTO ... DEFAULT nextval(...), the sequence may have advanced further than you expected.
    • Solution: Either rely solely on ORM inserts for tests, or after raw SQL inserts, explicitly reset the sequence before running assertive tests.

10. Conclusion: Accelerate Your Database Testing with py-pglite

Py-pglite transforms the way Python developers approach PostgreSQL database testing by providing:

  1. Ultra-Fast In-Memory PostgreSQL Instances
    With PGlite under the hood, you have a real PostgreSQL-compatible database that lives entirely in memory. Tests run in a fraction of the time compared to spinning up a full server.

  2. Seamless Python Integration
    Out-of-the-box support for SQLAlchemy, SQLModel, and FastAPI means you can plug py-pglite into your existing code with minimal changes. All your ORM models, tables, and migrations work exactly the same.

  3. Automatic Isolation with pytest Fixtures
    Fixtures such as pglite_session and pglite_engine take care of starting, configuring, and tearing down the database automatically between tests. This guarantees a clean slate for every test, preventing cross-test contamination and flaky failures.

  4. Manual Management for Complex Scenarios
    For advanced requirements—such as sharing a database across multiple test modules, customizing socket paths, or debugging PostgreSQL internals—you can use PGliteConfig and PGliteManager to tailor everything about the in-memory instance.

  5. Rich Utility Functions for Cleanup and Inspection
    Functions like clean_database_data, reset_sequences, verify_database_empty, create_test_schema, and get_table_row_counts help you manage data lifecycle, ensure consistency, and gather metrics with minimal boilerplate code.

  6. Concurrency-Ready for Real-World Testing
    Whether you need to spawn multiple threads, run asynchronous tasks, or simulate parallel HTTP requests, py-pglite supports concurrent session usage. By sharing a single SQLAlchemy Engine and configuring connection pool settings, you can thoroughly test transaction isolation, deadlock handling, and high-load scenarios.

  7. Simplified CI/CD Integration
    Because py-pglite does not require a separate database server or Docker container, your CI pipelines become simpler, faster, and more reliable. Installs happen via pip install py-pglite, and you do not need to maintain separate PostgreSQL services in your CI configuration.

  8. High Fidelity to Production Behavior
    With PGlite effectively running the same PostgreSQL engine code (compiled to run in memory), you can trust that SQL syntax, data types, functions, and concurrency semantics match your production environment. There is no need to write special-case logic for tests as widely encountered with SQLite or other limited databases.

By adopting py-pglite, you significantly reduce the friction of writing, maintaining, and running database tests in Python. Your team can focus on implementing business logic and validation rather than wrestling with database setup and teardown. The consistent, isolated test environment leads to fewer surprises in production and faster development cycles.

If you are developing any application—be it a web service, a data pipeline, or a background worker process—that relies on PostgreSQL, integrating py-pglite into your test suite will revolutionize how you think about database testing. From simple CRUD unit tests to full-scale integration tests with FastAPI endpoints and concurrency scenarios, py-pglite provides a comprehensive, developer-friendly, and high-performance solution.

Get started today by installing py-pglite with pip, defining your models, and writing your first pglite_session-backed pytest. You will soon experience the sheer speed and convenience of an in-memory PostgreSQL, perfectly aligned with the behavior of your production database. Let py-pglite take care of the database orchestration so you can focus on delivering high-quality, data-driven applications.