Re-usable code for Python 3 projects.
Installation
uv add python3-commonsOptional Dependencies
Some features require extra dependencies. You can install them individually or all at once:
api-client: For python3_commons.api_client
audit: For python3_commons.audit
authn: For python3_commons.auth
authz: For python3_commons.permissions
cache: For python3_commons.cache
database: For python3_commons.db
object-storage: For python3_commons.object_storage
soap-client: For python3_commons.soap_client
all: Install all optional dependencies
uv add "python3-commons[all]"Features
Async LRU Cache
An LRU cache decorator for asynchronous functions that handles await and prevents the dogpile effect (mitigating multiple concurrent calls for the same key by waiting for the first one to finish).
from python3_commons.async_functools import async_lru_cache
@async_lru_cache(maxsize=128)
async def fetch_expensive_data(item_id: int):
# This function will only be called once for a given item_id
# even if multiple tasks await it simultaneously.
return await db.get(item_id)
# Usage
result = await fetch_expensive_data(42)API Client
A context manager for aiohttp requests with built-in audit logging to S3 and standardized error mapping to Python exceptions.
from python3_commons.api_client import request
from aiohttp import ClientSession
async with ClientSession() as session:
async with request(
session,
base_url="https://api.example.com",
uri="/data",
method="get",
audit_name="my_service_audit"
) as response:
data = await response.json()SOAP Client
Async SOAP client support for zeep with S3 auditing capabilities.
from python3_commons.soap_client import soap_client
async with soap_client("https://example.com/service?wsdl") as client:
result = await client.service.GetData(id=42)Database Management
SQLAlchemy async engine and session management with pool tuning, health checks, and dynamic query builders.
from python3_commons.db import AsyncSessionManager
from python3_commons.conf import DBSettings
# Configuration
configs = {"default": DBSettings(dsn="postgresql+asyncpg://user:pass@localhost/db")}
manager = AsyncSessionManager(configs)
# Usage
async with manager.get_session_context("default") as session:
result = await session.execute(...)
# Health check
from python3_commons.db import is_healthy
await is_healthy(manager.get_engine("default"))Object Storage
Utilities for async S3 operations using aiobotocore.
from python3_commons import object_storage
import io
# Upload an object
await object_storage.put_object(
bucket_name="my-bucket",
path="uploads/file.txt",
data=io.BytesIO(b"Hello World"),
length=11
)
# Download an object
content = await object_storage.get_object("my-bucket", "uploads/file.txt")
# List objects
async for obj in object_storage.list_objects("my-bucket", "uploads/"):
print(obj['Key'])OIDC Authentication
Client for OpenID Connect authentication, supporting configuration fetching, JWKS, and token acquisition.
from python3_commons.auth import OIDCClient
from pydantic import HttpUrl
client = OIDCClient(
authority_url=HttpUrl("https://auth.example.com/realms/myrealm"),
client_id="my-app-client",
client_secret="secret"
)
async with client:
token_response = await client.fetch_token(username="user", password="password")
print(token_response.access_token)Valkey Cache
Async caching using Valkey (Redis-compatible) with automatic Msgpack serialization for complex types.
from python3_commons import cache
# Store a dictionary
await cache.store("user:123", {"name": "Alice", "role": "admin"}, ttl=3600)
# Retrieve it
user_data = await cache.get("user:123")
# Set operations
await cache.add_set_item("active_users", "user:123")
is_active = await cache.has_set_item("active_users", "user:123")Structured Logging
A JSONFormatter for structured logging, compatible with standard Python logging.
import logging
from python3_commons.log.formatters import JSONFormatter
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logging.getLogger().addHandler(handler)
logger = logging.getLogger("app")
logger.info("User logged in", extra={"user_id": "abc-123", "ip": "1.2.3.4"})Serialization
Enhanced JSON and Msgpack serialization for types not handled by default (Decimal, datetime, date, dataclasses, Pydantic models).
from python3_commons.serializers.msgspec import serialize_msgpack, deserialize_msgpack
from decimal import Decimal
from datetime import datetime
data = {
"amount": Decimal("150.75"),
"timestamp": datetime.now(),
"tags": {"finance", "internal"}
}
# Serialize to Msgpack
binary = serialize_msgpack(data)
# Deserialize back
restored = deserialize_msgpack(binary)RBAC Permissions
Database-backed Role-Based Access Control (RBAC) permission checking.
from python3_commons.permissions import has_user_permission
from uuid import UUID
user_uuid = UUID("...")
allowed = await has_user_permission(session, user_uuid, "reports.view")General Helpers
A collection of useful utility functions:
to_snake_case(text): Converts strings to snake_case.
round_decimal(value, places): Rounds Decimal values.
tries(n): An async retry decorator.
log_execution_time: An async decorator to log how long a function takes.
date_from_string / datetime_from_string: Flexible date/time parsing.
request_to_curl: Converts request parameters to a curl command string.
from python3_commons.helpers import tries, log_execution_time
@tries(3)
@log_execution_time
async def flaky_network_call():
...Async CSV Stream
Efficiently generate CSV data as a byte stream from an async generator of tuples.
from python3_commons.generators import tuple_csv_stream
async def generate_rows():
for i in range(1000):
yield (i, f"Name {i}", 10.5 * i)
async for chunk in tuple_csv_stream(generate_rows(), header=("ID", "Name", "Value")):
# Send chunk to HTTP response or write to file
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