s3func 0.9.1

Simple functions for working with S3

s3func

Simple functions for working with S3-compatible object storage

---

s3func is a lightweight Python library providing a simplified interface for interacting with S3-compatible object storage services (AWS S3, Backblaze B2, MEGA S4, and others). It removes the boto3 dependency in favor of a fast, urllib3-based client with custom SigV4 signing.

Key Features

• Zero Boto3 Dependency: Minimal overhead and faster imports.
• Provider-Agnostic: One S3Session for AWS S3 and any S3-compatible provider (Backblaze B2, MEGA S4, ...), with provider quirks handled portably (strict RFC3986 signing of paths and queries).
• Distributed Locking: Verified shared/exclusive locking on plain object storage - no CAS required (see docs/locking.md).
• Streaming Support: Efficiently stream large objects.
• Automatic Retries: Built-in adaptive retry logic for transient network issues.

Installation

pip install s3func

Usage Examples

S3 Operations

from s3func import S3Session

# Initialize session for AWS S3
session = S3Session(
    access_key_id='YOUR_ACCESS_KEY',
    access_key='YOUR_SECRET_KEY',
    bucket='my-bucket',
    region='us-east-1'
)

# Also works with other S3-compatible providers (Contabo, Wasabi, DigitalOcean, etc.)
# by providing an endpoint_url.
session = S3Session(
    access_key_id='YOUR_ACCESS_KEY',
    access_key='YOUR_SECRET_KEY',
    bucket='my-bucket',
    endpoint_url='https://eu2.contabostorage.com'
)

# Upload an object
session.put_object('hello.txt', b'Hello, S3!')

# Download an object
resp = session.get_object('hello.txt')
print(resp.data.decode())

# List objects
for obj in session.list_objects(prefix='logs/').iter_objects():
    print(obj['key'], obj['content_length'])

Custom Metadata

You can easily read and write custom metadata headers.

# Upload with metadata
session.put_object(
    'data.csv', 
    b'col1,col2\n1,2', 
    metadata={'processed': 'false', 'source': 'sensor-1'}
)

# Read metadata
resp = session.head_object('data.csv')
print(resp.metadata['processed']) # 'false'

Distributed Locking

s3func provides a powerful distributed lock that mimics Python's threading.Lock API.

# Using S3 Lock via context manager
with session.lock('process-1'):
    # This block is protected by a distributed lock
    print("Doing some exclusive work...")

# Explicit acquire/release with timeout
lock = session.lock('my-resource')
if lock.acquire(blocking=True, timeout=10):
    try:
        # Perform operation
        pass
    finally:
        lock.release()

Performance Tips

• Streaming: Set stream=True in the session (default) or individual requests to handle large files without loading them entirely into memory.
• Adaptive Retries: The library uses urllib3 retry logic configured for high-concurrency environments to handle rate limiting and network blips automatically.

How Distributed Locking Works

Full walk-through with diagrams: docs/locking.md.

The lock is a Lamport-bakery-style election over plain object storage (no compare-and-swap needed):

1. Acquisition: A worker writes two small ticket objects (seq-0 and seq-1).
2. Self-visibility gate (0.9.0): it polls the listing until its OWN ticket is visible - a listing that cannot show your own writes cannot be trusted to show competitors (raises after visibility_timeout, default 30s).
3. Election: it lists all tickets and yields to older ones (seq-1 timestamp; lexicographic lock_id breaks ties). Shared tickets yield only to older exclusive tickets.
4. Confirming re-list (0.9.0): winning requires a second clear listing taken settle_delay (default 1.0s) later - a violation now needs two independent stale listings.
5. Own-ticket invariant (0.9.0): every decisive listing must still contain the worker's own ticket; if another client deleted it (e.g. break_other_locks), acquisition raises instead of "winning" without a ticket. Recovering a ticket via lock_id= restores the ticket only - acquire() re-runs the election.
6. Auto-Cleanup: weakref.finalize deletes ticket objects even if the process exits unexpectedly (best effort).

Guarantee and residual window: on storage with strongly consistent listings the election is safe. On eventually-consistent listings the hardening reduces the failure mode to two consecutive independently-stale listings (measured on B2: 80/80 listings were first-poll consistent - see benchmarks/results_visibility_lag.md). No provider we tested currently offers atomic conditional writes (benchmarks/conditional_write_probe.py is the qualification gate for adding a true CAS lock per provider; MEGA S4 accepts the headers but is not atomic under concurrency). Tune via session.lock(key, settle_delay=..., visibility_timeout=...).

Development

Setup environment

We use uv to manage the development environment and production build.

uv sync --all-extras --dev

Running Tests

uv run pytest

License

This project is licensed under the terms of the Apache Software License 2.0.