Asyncpg - Asynchronous client PostgreSQL

Introduction

With the growing popularity of asynchronous web frameworks such as FastAPI and Starlette, the need for fast, non‑blocking ways to interact with a database is increasing. asyncpg is an asynchronous PostgreSQL client built from the ground up for maximum performance.

asyncpg provides a low‑level, fast, and efficient way to work with PostgreSQL in an async/await style, making it an excellent choice for modern Python applications. The library is written in Python and Cython, delivering high execution speed and minimal overhead.

What Is asyncpg?

asyncpg is a full‑featured asynchronous PostgreSQL driver for Python that offers direct database interaction without an ORM. The library supports all modern PostgreSQL capabilities, including JSON, UUID, arrays, custom data types, and advanced functions.

Key advantages of asyncpg:

• Complete asynchronous support with asyncio
• High performance thanks to Cython implementation
• Native handling of all PostgreSQL data types
• Built‑in connection pool support
• Protection against SQL injection
• Easy integration with asynchronous frameworks

Installation and Initial Setup

System Requirements

To use asyncpg you need:

• Python 3.7 or newer
• PostgreSQL 9.5 or newer
• Operating system: Linux, macOS, Windows

Installing the Library

pip install asyncpg

For development it is recommended to install additional packages:

pip install asyncpg pytest-asyncio

Basic Database Connection

import asyncpg
import asyncio

async def main():
    # Connect with explicit parameters
    conn = await asyncpg.connect(
        user='postgres',
        password='secret',
        database='test_db',
        host='127.0.0.1',
        port=5432
    )
    
    # Verify connection
    version = await conn.fetchval('SELECT version()')
    print(f"Connected to: {version}")
    
    await conn.close()

asyncio.run(main())

Connecting via DSN

# Using a DSN string
dsn = "postgresql://postgres:secret@127.0.0.1:5432/test_db"
conn = await asyncpg.connect(dsn)

Asynchrony in Python and the Role of asyncpg

asyncpg uses async/await and is fully compatible with asyncio, enabling non‑blocking queries to PostgreSQL. This is critical for high‑load applications and APIs where blocking the event loop can cause severe performance degradation.

Differences from Synchronous Drivers

Unlike the synchronous psycopg2, asyncpg does not block other tasks:

# Synchronous approach (psycopg2)
import psycopg2
conn = psycopg2.connect(...)  # Blocks the thread
cursor = conn.cursor()
cursor.execute("SELECT * FROM users")  # Blocks the thread

# Asynchronous approach (asyncpg)
import asyncpg
conn = await asyncpg.connect(...)  # Does not block the event loop
rows = await conn.fetch("SELECT * FROM users")  # Does not block the event loop

Concurrent Query Execution

async def concurrent_queries():
    conn = await asyncpg.connect(...)
    
    # Run several queries in parallel
    tasks = [
        conn.fetch("SELECT * FROM users"),
        conn.fetch("SELECT * FROM orders"),
        conn.fetch("SELECT * FROM products")
    ]
    
    results = await asyncio.gather(*tasks)
    return results

Core Methods and Functions of asyncpg

Method Overview Table

Detailed Method Descriptions

execute() – executing commands

# Create a table
await conn.execute("""
    CREATE TABLE IF NOT EXISTS users (
        id SERIAL PRIMARY KEY,
        name TEXT NOT NULL,
        email TEXT UNIQUE,
        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    )
""")

# Insert data
await conn.execute(
    "INSERT INTO users (name, email) VALUES ($1, $2)",
    "Ivan Petrov", "ivan@example.com"
)

# Update data
await conn.execute(
    "UPDATE users SET name = $1 WHERE id = $2",
    "Ivan Ivanov", 1
)

# Delete data
await conn.execute("DELETE FROM users WHERE id = $1", 1)

fetch() – retrieving multiple results

# Get all users
users = await conn.fetch("SELECT * FROM users")

# Filtered query
active_users = await conn.fetch(
    "SELECT * FROM users WHERE created_at > $1",
    datetime.now() - timedelta(days=30)
)

# Sorted with limit
top_users = await conn.fetch(
    "SELECT name, email FROM users ORDER BY created_at DESC LIMIT $1",
    10
)

fetchrow() – retrieving a single row

# Get user by ID
user = await conn.fetchrow("SELECT * FROM users WHERE id = $1", 1)
if user:
    print(f"Name: {user['name']}, Email: {user['email']}")

# Get the most recent user
last_user = await conn.fetchrow(
    "SELECT * FROM users ORDER BY created_at DESC LIMIT 1"
)

fetchval() – retrieving a single value

# Count users
count = await conn.fetchval("SELECT COUNT(*) FROM users")

# Get maximum ID
max_id = await conn.fetchval("SELECT MAX(id) FROM users")

# Check existence
exists = await conn.fetchval(
    "SELECT EXISTS(SELECT 1 FROM users WHERE email = $1)",
    "test@example.com"
)

executemany() – bulk operations

# Bulk insert
users_data = [
    ("Anna", "anna@example.com"),
    ("Peter", "peter@example.com"),
    ("Maria", "maria@example.com")
]

await conn.executemany(
    "INSERT INTO users (name, email) VALUES ($1, $2)",
    users_data
)

Working with a Connection Pool

Creating and Configuring a Pool

# Create a pool with custom settings
pool = await asyncpg.create_pool(
    user='postgres',
    password='secret',
    database='test_db',
    host='127.0.0.1',
    port=5432,
    min_size=1,        # Minimum connections
    max_size=10,       # Maximum connections
    max_queries=50000, # Max queries per connection
    max_inactive_connection_lifetime=300,  # Idle timeout
    timeout=30,        # Connection timeout
    command_timeout=60 # Command execution timeout
)

Using the Pool

# Acquire a connection from the pool
async with pool.acquire() as conn:
    users = await conn.fetch("SELECT * FROM users")
    
# Alternative pattern
conn = await pool.acquire()
try:
    users = await conn.fetch("SELECT * FROM users")
finally:
    await pool.release(conn)

Closing the Pool

# Properly close the pool
await pool.close()

Working with Transactions

Automatic Transaction Management

# Transaction with automatic handling
async with conn.transaction():
    await conn.execute(
        "INSERT INTO users (name, email) VALUES ($1, $2)",
        "Test", "test@example.com"
    )
    await conn.execute(
        "UPDATE users SET name = $1 WHERE id = $2",
        "Test Updated", 1
    )
    # Any exception will roll back the transaction

Manual Transaction Management

# Manual transaction control
tr = conn.transaction()
await tr.start()

try:
    await conn.execute(
        "INSERT INTO users (name, email) VALUES ($1, $2)",
        "Test", "test@example.com"
    )
    
    # Conditional check
    count = await conn.fetchval("SELECT COUNT(*) FROM users")
    if count > 1000:
        raise Exception("Too many users")
    
    await tr.commit()
except Exception:
    await tr.rollback()
    raise

Nested Transactions (Savepoints)

async with conn.transaction():
    await conn.execute("INSERT INTO users (name) VALUES ('User1')")
    
    # Create a savepoint
    async with conn.transaction():
        await conn.execute("INSERT INTO users (name) VALUES ('User2')")
        # If an error occurs here, only this block is rolled back
        
    await conn.execute("INSERT INTO users (name) VALUES ('User3')")

PostgreSQL Data Type Support

Basic Data Types

# Create a table with various types
await conn.execute("""
    CREATE TABLE data_types (
        id SERIAL PRIMARY KEY,
        text_field TEXT,
        int_field INTEGER,
        float_field REAL,
        bool_field BOOLEAN,
        date_field DATE,
        timestamp_field TIMESTAMP,
        json_field JSON,
        uuid_field UUID,
        array_field INTEGER[]
    )
""")

# Insert mixed‑type data
import uuid
from datetime import datetime, date

await conn.execute("""
    INSERT INTO data_types (
        text_field, int_field, float_field, bool_field,
        date_field, timestamp_field, json_field, 
        uuid_field, array_field
    ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)
""", 
    "Sample text", 42, 3.14, True,
    date.today(), datetime.now(),
    {"key": "value", "number": 123},
    uuid.uuid4(), [1, 2, 3, 4, 5]
)

Working with JSON

# Insert JSON data
json_data = {
    "name": "Ivan",
    "age": 30,
    "skills": ["Python", "JavaScript", "SQL"],
    "address": {
        "city": "Moscow",
        "country": "Russia"
    }
}

await conn.execute(
    "INSERT INTO users (profile) VALUES ($1)",
    json_data
)

# Query JSON data
profiles = await conn.fetch("SELECT profile FROM users")
for profile in profiles:
    print(profile['profile']['name'])

Working with Arrays

# Insert an array
await conn.execute(
    "INSERT INTO data_types (array_field) VALUES ($1)",
    [1, 2, 3, 4, 5]
)

# Search inside an array
results = await conn.fetch(
    "SELECT * FROM data_types WHERE $1 = ANY(array_field)",
    3
)

Custom Type Conversions

# Set a codec for custom JSON handling
await conn.set_type_codec(
    'json',
    encoder=json.dumps,
    decoder=json.loads,
    schema='pg_catalog'
)

# Register a custom enum type
await conn.execute("""
    CREATE TYPE user_role AS ENUM ('admin', 'user', 'guest')
""")

# Use the enum
await conn.execute("""
    ALTER TABLE users ADD COLUMN role user_role DEFAULT 'user'
""")

Parameterized Queries and Security

Placeholders in asyncpg

# asyncpg uses numeric placeholders $1, $2, $3...
await conn.execute(
    "INSERT INTO users (name, email, age) VALUES ($1, $2, $3)",
    "Ivan", "ivan@example.com", 30
)

# Multiple conditions
users = await conn.fetch("""
    SELECT * FROM users 
    WHERE age BETWEEN $1 AND $2 
    AND name ILIKE $3
""", 20, 50, "%ivan%")

Protection Against SQL Injection

# SAFE – use parameters
user_id = "1; DROP TABLE users; --"  # Injection attempt
user = await conn.fetchrow(
    "SELECT * FROM users WHERE id = $1",
    user_id
)

# UNSAFE – do NOT do this!
# query = f"SELECT * FROM users WHERE id = {user_id}"
# await conn.execute(query)

Dynamic Queries

# Securely build dynamic queries
def build_filter_query(filters):
    conditions = []
    params = []
    param_count = 0
    
    for field, value in filters.items():
        param_count += 1
        conditions.append(f"{field} = ${param_count}")
        params.append(value)
    
    where_clause = " AND ".join(conditions) if conditions else "TRUE"
    query = f"SELECT * FROM users WHERE {where_clause}"
    
    return query, params

# Usage
filters = {"name": "Ivan", "age": 30}
query, params = build_filter_query(filters)
results = await conn.fetch(query, *params)

Performance and Optimization

Performance Benchmarks

asyncpg delivers superior performance compared to other drivers:

• 2–3× faster than psycopg2
• 1.5–2× faster than aiopg
• Lower memory consumption
• Better scalability under high load

Query Optimization

# Use EXPLAIN to analyze queries
explain_result = await conn.fetch("""
    EXPLAIN (FORMAT JSON) 
    SELECT * FROM users WHERE age > 25
""")

print(explain_result[0]['QUERY PLAN'])

Prepared Statements

# Prepare a statement for repeated use
stmt = await conn.prepare("""
    SELECT * FROM users WHERE age > $1 AND city = $2
""")

# Execute multiple times
results1 = await stmt.fetch(25, "Moscow")
results2 = await stmt.fetch(30, "Saint Petersburg")
results3 = await stmt.fetch(35, "Yekaterinburg")

Bulk Operations

# Efficient bulk insert
async def bulk_insert_users(conn, users_data):
    await conn.executemany("""
        INSERT INTO users (name, email, age) VALUES ($1, $2, $3)
    """, users_data)

# Use COPY for massive loads
async def copy_insert_users(conn, users_data):
    await conn.copy_records_to_table(
        'users',
        records=users_data,
        columns=['name', 'email', 'age']
    )

Integration with Popular Frameworks

FastAPI Integration

from fastapi import FastAPI, Depends, HTTPException
from contextlib import asynccontextmanager
import asyncpg

# Global pool
db_pool = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global db_pool
    # Initialize pool on startup
    db_pool = await asyncpg.create_pool(
        user='postgres',
        password='secret',
        database='test_db',
        host='127.0.0.1',
        min_size=1,
        max_size=10
    )
    yield
    # Close pool on shutdown
    await db_pool.close()

app = FastAPI(lifespan=lifespan)

# Dependency to get a connection
async def get_db():
    async with db_pool.acquire() as conn:
        yield conn

# Endpoints
@app.get("/users")
async def get_users(db=Depends(get_db)):
    users = await db.fetch("SELECT * FROM users")
    return [dict(user) for user in users]

@app.post("/users")
async def create_user(user_data: dict, db=Depends(get_db)):
    user_id = await db.fetchval("""
        INSERT INTO users (name, email) 
        VALUES ($1, $2) 
        RETURNING id
    """, user_data['name'], user_data['email'])
    
    return {"id": user_id, **user_data}

@app.get("/users/{user_id}")
async def get_user(user_id: int, db=Depends(get_db)):
    user = await db.fetchrow(
        "SELECT * FROM users WHERE id = $1", user_id
    )
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return dict(user)

Starlette Integration

from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Route
import asyncpg

# Global pool
db_pool = None

async def startup():
    global db_pool
    db_pool = await asyncpg.create_pool(
        user='postgres',
        password='secret',
        database='test_db'
    )

async def shutdown():
    await db_pool.close()

async def get_users(request):
    async with db_pool.acquire() as conn:
        users = await conn.fetch("SELECT * FROM users")
        return JSONResponse([dict(user) for user in users])

app = Starlette(
    routes=[
        Route('/users', get_users, methods=['GET']),
    ],
    on_startup=[startup],
    on_shutdown=[shutdown]
)

Quart Integration

from quart import Quart, jsonify
import asyncpg

app = Quart(__name__)

@app.before_serving
async def startup():
    app.db_pool = await asyncpg.create_pool(
        user='postgres',
        password='secret',
        database='test_db'
    )

@app.after_serving
async def shutdown():
    await app.db_pool.close()

@app.route('/users')
async def get_users():
    async with app.db_pool.acquire() as conn:
        users = await conn.fetch("SELECT * FROM users")
        return jsonify([dict(user) for user in users])

ORM Integration

Using with SQLAlchemy 2.0

from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker

# Async engine with asyncpg
engine = create_async_engine(
    "postgresql+asyncpg://postgres:secret@localhost/test_db",
    echo=True
)

# Session factory
async_session = sessionmaker(
    engine, class_=AsyncSession, expire_on_commit=False
)

# Example usage
async with async_session() as session:
    result = await session.execute(
        text("SELECT * FROM users WHERE age > :age"),
        {"age": 25}
    )
    users = result.fetchall()

Using with Tortoise ORM

from tortoise import Tortoise, fields
from tortoise.models import Model

class User(Model):
    id = fields.IntField(pk=True)
    name = fields.CharField(max_length=100)
    email = fields.CharField(max_length=255, unique=True)
    
    class Meta:
        table = "users"

# Initialize with asyncpg
async def init_db():
    await Tortoise.init(
        db_url='asyncpg://postgres:secret@localhost/test_db',
        modules={'models': ['__main__']}
    )
    await Tortoise.generate_schemas()

# Example usage
async def main():
    await init_db()
    
    # Create a user
    user = await User.create(name="Ivan", email="ivan@example.com")
    
    # Retrieve users
    users = await User.all()

Error Handling and Exceptions

Common Exception Types

import asyncpg

try:
    conn = await asyncpg.connect(
        user='wrong_user',
        password='wrong_password',
        database='test_db'
    )
except asyncpg.InvalidAuthorizationSpecificationError:
    print("Invalid credentials")
except asyncpg.InvalidCatalogNameError:
    print("Database not found")
except asyncpg.ConnectionDoesNotExistError:
    print("Connection unavailable")
except asyncpg.PostgresError as e:
    print(f"PostgreSQL error: {e}")

Handling Errors Inside Transactions

async def safe_transaction(conn):
    async with conn.transaction():
        try:
            await conn.execute(
                "INSERT INTO users (name, email) VALUES ($1, $2)",
                "Test", "test@example.com"
            )
            
            # This statement may raise a duplicate‑key error
            await conn.execute(
                "INSERT INTO users (name, email) VALUES ($1, $2)",
                "Test2", "test@example.com"
            )
            
        except asyncpg.UniqueViolationError:
            print("A user with this email already exists")
            raise  # Transaction will be rolled back
        except asyncpg.PostgresError as e:
            print(f"Database error: {e}")
            raise

Retry Logic

import asyncio
from typing import Optional

async def execute_with_retry(
    conn, 
    query: str, 
    *args, 
    max_retries: int = 3,
    delay: float = 1.0
) -> Optional[str]:
    for attempt in range(max_retries):
        try:
            return await conn.execute(query, *args)
        except asyncpg.ConnectionDoesNotExistError:
            if attempt < max_retries - 1:
                await asyncio.sleep(delay * (2 ** attempt))
                continue
            raise
        except asyncpg.PostgresError:
            raise

Testing Applications that Use asyncpg

Setting Up a Test Database

import pytest
import asyncpg
from unittest.mock import AsyncMock

@pytest.fixture
async def db_connection():
    # Connect to a test database
    conn = await asyncpg.connect(
        user='postgres',
        password='secret',
        database='test_db',
        host='localhost'
    )
    
    # Create test tables
    await conn.execute("""
        CREATE TABLE IF NOT EXISTS users (
            id SERIAL PRIMARY KEY,
            name TEXT NOT NULL,
            email TEXT UNIQUE
        )
    """)
    
    yield conn
    
    # Clean up after the test
    await conn.execute("DROP TABLE IF EXISTS users")
    await conn.close()

@pytest.fixture
async def db_pool():
    pool = await asyncpg.create_pool(
        user='postgres',
        password='secret',
        database='test_db',
        min_size=1,
        max_size=5
    )
    
    yield pool
    
    await pool.close()

Example Tests

@pytest.mark.asyncio
async def test_create_user(db_connection):
    # Insert a user
    await db_connection.execute(
        "INSERT INTO users (name, email) VALUES ($1, $2)",
        "Test", "test@example.com"
    )
    
    # Verify insertion
    user = await db_connection.fetchrow(
        "SELECT * FROM users WHERE email = $1",
        "test@example.com"
    )
    
    assert user is not None
    assert user['name'] == "Test"
    assert user['email'] == "test@example.com"

@pytest.mark.asyncio
async def test_duplicate_email(db_connection):
    # First insertion
    await db_connection.execute(
        "INSERT INTO users (name, email) VALUES ($1, $2)",
        "Test1", "test@example.com"
    )
    
    # Attempt duplicate insertion
    with pytest.raises(asyncpg.UniqueViolationError):
        await db_connection.execute(
            "INSERT INTO users (name, email) VALUES ($1, $2)",
            "Test2", "test@example.com"
        )

Mocking for Tests

@pytest.fixture
def mock_db_connection():
    mock_conn = AsyncMock()
    mock_conn.fetch.return_value = [
        {'id': 1, 'name': 'Test', 'email': 'test@example.com'}
    ]
    mock_conn.fetchrow.return_value = {
        'id': 1, 'name': 'Test', 'email': 'test@example.com'
    }
    mock_conn.execute.return_value = "INSERT 0 1"
    return mock_conn

@pytest.mark.asyncio
async def test_with_mock(mock_db_connection):
    # Test using the mock
    users = await mock_db_connection.fetch("SELECT * FROM users")
    assert len(users) == 1
    assert users[0]['name'] == 'Test'

Database Migrations

Simple Migration System

import asyncpg
import os
from pathlib import Path

class MigrationRunner:
    def __init__(self, db_url: str, migrations_dir: str = "migrations"):
        self.db_url = db_url
        self.migrations_dir = Path(migrations_dir)
    
    async def init_migrations_table(self, conn):
        await conn.execute("""
            CREATE TABLE IF NOT EXISTS migrations (
                id SERIAL PRIMARY KEY,
                filename VARCHAR(255) NOT NULL UNIQUE,
                applied_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        """)
    
    async def get_applied_migrations(self, conn):
        applied = await conn.fetch(
            "SELECT filename FROM migrations ORDER BY id"
        )
        return {row['filename'] for row in applied}
    
    async def run_migrations(self):
        conn = await asyncpg.connect(self.db_url)
        try:
            await self.init_migrations_table(conn)
            applied = await self.get_applied_migrations(conn)
            
            migration_files = sorted(self.migrations_dir.glob("*.sql"))
            
            for migration_file in migration_files:
                if migration_file.name not in applied:
                    print(f"Applying migration: {migration_file.name}")
                    
                    with open(migration_file, 'r') as f:
                        sql = f.read()
                    
                    async with conn.transaction():
                        await conn.execute(sql)
                        await conn.execute(
                            "INSERT INTO migrations (filename) VALUES ($1)",
                            migration_file.name
                        )
                    
                    print(f"Migration {migration_file.name} applied")
        finally:
            await conn.close()

# Usage
runner = MigrationRunner("postgresql://postgres:secret@localhost/test_db")
await runner.run_migrations()

Best Practices and Recommendations

Connection Management

# Recommended pattern for applications
class Database:
    def __init__(self):
        self.pool = None
    
    async def connect(self):
        self.pool = await asyncpg.create_pool(
            user='postgres',
            password='secret',
            database='test_db',
            min_size=1,
            max_size=10,
            command_timeout=60
        )
    
    async def disconnect(self):
        if self.pool:
            await self.pool.close()
    
    async def execute(self, query, *args):
        async with self.pool.acquire() as conn:
            return await conn.execute(query, *args)
    
    async def fetch(self, query, *args):
        async with self.pool.acquire() as conn:
            return await conn.fetch(query, *args)
    
    async def fetchrow(self, query, *args):
        async with self.pool.acquire() as conn:
            return await conn.fetchrow(query, *args)

# Usage
db = Database()
await db.connect()

# In the app
users = await db.fetch("SELECT * FROM users")

Query Logging

import logging
from functools import wraps

logger = logging.getLogger(__name__)

def log_query(func):
    @wraps(func)
    async def wrapper(*args, **kwargs):
        query = args[1] if len(args) > 1 else kwargs.get('query', '')
        params = args[2:] if len(args) > 2 else []
        
        logger.info(f"Executing query: {query}")
        logger.debug(f"Parameters: {params}")
        
        try:
            result = await func(*args, **kwargs)
            logger.info("Query executed successfully")
            return result
        except Exception as e:
            logger.error(f"Query failed: {e}")
            raise
    
    return wrapper

# Apply to database methods
class DatabaseWithLogging:
    def __init__(self, pool):
        self.pool = pool
    
    @log_query
    async def fetch(self, query, *args):
        async with self.pool.acquire() as conn:
            return await conn.fetch(query, *args)

Production Configuration

import os
from urllib.parse import urlparse

def get_db_config():
    db_url = os.getenv('DATABASE_URL')
    if db_url:
        parsed = urlparse(db_url)
        return {
            'user': parsed.username,
            'password': parsed.password,
            'database': parsed.path[1:],  # Strip leading '/'
            'host': parsed.hostname,
            'port': parsed.port or 5432,
        }
    
    return {
        'user': os.getenv('DB_USER', 'postgres'),
        'password': os.getenv('DB_PASSWORD', ''),
        'database': os.getenv('DB_NAME', 'postgres'),
        'host': os.getenv('DB_HOST', 'localhost'),
        'port': int(os.getenv('DB_PORT', 5432)),
    }

# Create a production pool
async def create_production_pool():
    config = get_db_config()
    
    return await asyncpg.create_pool(
        **config,
        min_size=5,
        max_size=20,
        max_queries=50000,
        max_inactive_connection_lifetime=300,
        timeout=30,
        command_timeout=300,
        server_settings={
            'application_name': 'MyApp',
            'jit': 'off'  # Disable JIT for stability
        }
    )

Comparison with Other PostgreSQL Drivers

Detailed Comparison

Code Samples for Comparison

# asyncpg
conn = await asyncpg.connect(dsn)
users = await conn.fetch("SELECT * FROM users")

# psycopg2 (synchronous)
conn = psycopg2.connect(dsn)
cursor = conn.cursor()
cursor.execute("SELECT * FROM users")
users = cursor.fetchall()

# aiopg
conn = await aiopg.connect(dsn)
cursor = await conn.cursor()
await cursor.execute("SELECT * FROM users")
users = await cursor.fetchall()

Frequently Asked Questions

What is asyncpg and why do I need it?

asyncpg is a high‑performance asynchronous driver for PostgreSQL, built specifically for Python. It provides fast, efficient database interaction without blocking the event loop, which is essential for modern async applications.

Why is asyncpg faster than other drivers?

asyncpg is written with Cython and optimized for the PostgreSQL wire protocol. It avoids unnecessary abstractions and communicates directly with the binary protocol, delivering maximum throughput.

Can I use asyncpg in synchronous code?

No. asyncpg is designed exclusively for asynchronous code. For synchronous projects you should use psycopg2 or psycopg3.

How should I handle connection errors?

Wrap connection attempts in try/except blocks that catch specific asyncpg exceptions. Implement retry logic for transient network failures.

Is asyncpg safe for production?

Yes. Many companies run asyncpg in production. The library is stable, well‑tested, and receives regular updates.

How can I optimise performance for large data volumes?

Use a connection pool, prepared statements, bulk operations (executemany, copy_to_table), and tune pool parameters to match your workload.

Does asyncpg support all PostgreSQL features?

Yes. asyncpg supports the full range of PostgreSQL capabilities, including transactions, triggers, functions, JSON, UUID, arrays, and custom data types.

How do I integrate asyncpg into an existing application?

Start by creating a connection pool at application startup, replace synchronous queries with asynchronous equivalents, and adjust your handlers to use async/await.

Are there ready‑made migration tools for asyncpg?

asyncpg does not ship with a migration system, but you can use Alembic together with SQLAlchemy or implement a simple custom migration runner.

How should I test code that uses asyncpg?

Use pytest‑asyncio for asynchronous tests, spin up a dedicated test database, and employ fixtures to set up and tear down test data.

Conclusion

asyncpg is a powerful and efficient tool for working with PostgreSQL in asynchronous Python applications. Its outstanding performance, full PostgreSQL feature support, and straightforward usability make it an ideal choice for modern web apps, APIs, and microservices.

Key benefits of asyncpg:

• Exceptional performance thanks to an optimized architecture
• Complete asynchrony without blocking the event loop
• Seamless integration with popular frameworks
• Built‑in connection pool support
• Protection against SQL injection
• Active community and regular updates

asyncpg is the optimal solution for developers who need high performance, reliability, and ease of use when working with PostgreSQL in an asynchronous environment. The library continues to evolve and remains one of the top choices for building scalable, efficient Python applications.