python3-commons

Re-usable code for Python 3 projects.

Installation

uv add python3-commons

Optional 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.

Standardized Exception Mapping

HTTP error status codes and network failures are mapped to built-in Python exceptions:

• 400 Bad Request -> ValueError
• 401 Unauthorized, 403 Forbidden -> PermissionError
• 404 Not Found -> LookupError
• 429 Too Many Requests -> InterruptedError
• Other 4xx/5xx responses -> aiohttp.ClientResponseError
• Connection errors -> ConnectionRefusedError / ConnectionResetError

Enabling Audit Logging

Audit logging captures the outgoing request (formatted as an executable curl command) and the incoming response body, writing them asynchronously to Object Storage.

The destination path is resolved as follows:

• If ApiClientSettings.audit_path is configured (via API_CLIENT_AUDIT_PATH), it is used directly as the audit root for api_client (without prepending "audit/"). Note that API_CLIENT_AUDIT_PATH is a full object storage path like s3://bucket/audit: <audit_path>/<YYYY/MM/DD>/<audit_name>/<uri>/<method>_<timestamp>_<request_id>_{request,response}.txt
• Otherwise, if ObjectStorageSettings.work_path is configured (via OBJECT_STORAGE_WORK_PATH), it is used as the root with "audit/" prepended: <work_path>/audit/<YYYY/MM/DD>/<audit_name>/<uri>/<method>_<timestamp>_<request_id>_{request,response}.txt
• If neither is configured, the logs default to: audit/<YYYY/MM/DD>/<audit_name>/<uri>/<method>_<timestamp>_<request_id>_{request,response}.txt

To enable audit logging:

1. Configure Object Storage & S3 credentials:

   # API client audit path (full object storage path) or Object storage root
   export API_CLIENT_AUDIT_PATH="s3://bucket/audit"
   # or export OBJECT_STORAGE_WORK_PATH="my-audit-bucket"
   
   # S3 credentials
   export S3_ACCESS_KEY_ID="your-access-key-id"
   export S3_SECRET_ACCESS_KEY="your-secret-access-key"
   export S3_REGION="us-east-1"
   # Optional configurations:
   # export S3_ALLOW_HTTP="false"
   # export S3_ADDRESSING_STYLE="virtual"  # or "path"
   # export S3_CERT_VERIFY="true"

   Alternatively, you can configure them directly in Python:

   from python3_commons.conf import api_client_settings, object_storage_settings, s3_settings
   from pydantic import SecretStr
   
   api_client_settings.audit_path = "s3://bucket/audit"
   # or object_storage_settings.work_path = "my-audit-bucket"
   
   s3_settings.access_key_id = SecretStr("your-access-key-id")
   s3_settings.secret_access_key = SecretStr("your-secret-access-key")
   s3_settings.region = "us-east-1"

2. Pass audit_name to request(): Provide a non-empty string identifier (such as the target service or operation name) in the audit_name argument. If audit_name is omitted (default None), audit logging is disabled for that call. If S3 credentials are not configured, audit logging is safely bypassed.

Usage Example

from aiohttp import ClientSession
from python3_commons.api_client import request

async with ClientSession() as session:
    # 1. GET request with audit logging enabled
    async with request(
        session,
        base_url="https://api.example.com",
        uri="/users/123",
        method="get",
        headers={"Accept": "application/json"},
        audit_name="user_service",
    ) as response:
        user = await response.json()

    # 2. POST request with JSON body and audit logging
    async with request(
        session,
        base_url="https://api.example.com",
        uri="/users",
        method="post",
        json={"name": "Alice", "role": "admin"},
        audit_name="user_service",
    ) as response:
        result = await response.json()

    # 3. Request without audit logging (audit_name omitted)
    async with request(
        session,
        base_url="https://api.example.com",
        uri="/health",
        method="get",
    ) as response:
        status = 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, dynamic query builders, and declarative base models.

from python3_commons.db import AsyncSessionManager, is_healthy
from python3_commons.db.helpers import get_query
from python3_commons.conf import DBSettings

# Configuration
configs = {
    "default": DBSettings(
        dsn="postgresql+asyncpg://user:pass@localhost/db",
        pool_size=20,
        pool_timeout=30,
        statement_timeout=30,
    )
}
manager = AsyncSessionManager(configs)

# Usage
async with manager.get_session_context("default") as session:
    result = await session.execute(...)

# Engine health check (executes SELECT 1 with timeout)
healthy = await is_healthy(manager.get_engine("default"))

# Dynamic query filtering and sorting
where_clause, order_clauses = get_query(
    search={"name": "Alice"},
    order_by="-created_at,name",
    columns={
        # name: (column, case_insensitive, cast_func, exact_match)
        "name": (User.name, True, str, False),
        "created_at": (User.created_at, False, str, True),
    },
)

Object Storage

Thin wrapper for object-storage-client supporting S3, GCS, Azure, and local file storage, configured via object_storage_settings (OBJECT_STORAGE_WORK_PATH) and s3_settings (S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY, S3_REGION). Storage functions accept a required work_path parameter to define the base storage location.

from python3_commons import object_storage
import io

# Upload an object
await object_storage.put_object(
    work_path="my-bucket", 
    path="uploads/file.txt", 
    data=io.BytesIO(b"Hello World"), 
    length=11
)

# Download an object as bytes
content = await object_storage.get_object("my-bucket", "uploads/file.txt")

# Stream an object
async with object_storage.get_object_stream("my-bucket", "uploads/file.txt") as stream:
    data = await stream.read()

# List objects with a prefix
async for obj in object_storage.list_objects("my-bucket", "uploads/"):
    print(obj['Key'])

# Delete single or multiple objects
await object_storage.remove_object("my-bucket", "uploads/file.txt")
await object_storage.remove_objects("my-bucket", object_names=["uploads/file1.txt", "uploads/file2.txt"])

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 JSON logging and a filter_maker utility for level-based log filtering, compatible with standard Python logging.

import logging
from python3_commons.log.formatters import JSONFormatter
from python3_commons.log.filters import filter_maker

handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
# Filter to process logs up to INFO level only
handler.addFilter(filter_maker("INFO"))
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 for users and API keys.

from python3_commons.permissions import has_user_permission, has_api_key_permission
from uuid import UUID

user_uuid = UUID("...")
allowed = await has_user_permission(session, user_uuid, "reports.view")

api_key_uuid = UUID("...")
key_allowed = await has_api_key_permission(session, api_key_uuid, "reports.view")

Configuration Management

Typed application settings powered by Pydantic Settings:

• CommonSettings / settings: General logging level and format.
• DBSettings / db_settings: PostgreSQL database settings (DB_ environment prefix, with DB_PASS alias for password).
• S3Settings / s3_settings: S3 credentials and connection configuration (S3_ environment prefix: ACCESS_KEY_ID, SECRET_ACCESS_KEY, REGION).
• ObjectStorageSettings / object_storage_settings: Object storage root configuration (OBJECT_STORAGE_ environment prefix: WORK_PATH).
• ApiClientSettings / api_client_settings: API client configuration (API_CLIENT_ environment prefix: AUDIT_PATH full object storage path like s3://bucket/audit).
• OIDCSettings: OpenID Connect configuration (OIDC_ environment prefix).
• ValkeySettings / valkey_settings: Valkey/Redis cache configuration (VALKEY_ environment prefix).

File System Utilities

File system traversal helpers:

from python3_commons.fs import iter_files
from pathlib import Path

# Recursively iterate files, ignoring hidden directories
for file_path in iter_files(Path("./data"), recursive=True):
    print(file_path)

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.
• date_range(start, end): Generator yielding dates in a range.
• request_to_curl: Converts request parameters to a curl command string.
• SingletonMeta: Thread-safe singleton metaclass.
• replace_origin(url, host_url): Replaces scheme, host, and port of a URL.
• parse_string_list: Parses comma-separated string lists into tuples.

from python3_commons.helpers import tries, log_execution_time, SingletonMeta

@tries(3)
@log_execution_time
async def flaky_network_call():
    ...

class AppService(metaclass=SingletonMeta):
    pass

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
    pass