Metadata-Version: 2.2
Name: acme_s3
Version: 0.0.5
Summary: Efficient access to S3
Author-email: guner.stan@gmail.com
Project-URL: homepage, https://example.com
Project-URL: repository, https://example.com
Classifier: Programming Language :: Python :: 3.12
Description-Content-Type: text/markdown
Requires-Dist: python-dotenv
Requires-Dist: boto3
Requires-Dist: tqdm
Requires-Dist: backoff
Requires-Dist: botocore
Requires-Dist: pandas
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: uv; extra == "dev"
Requires-Dist: mkdocs; extra == "dev"
Requires-Dist: mkdocs-material; extra == "dev"
Requires-Dist: mkdocstrings[python]; extra == "dev"
Requires-Dist: jupyterlab; extra == "dev"
Requires-Dist: isort; extra == "dev"

# acme-s3

Efficient operations on S3 with retries, progress tracking and parallel processing

# Problem

Interactive with boto3 client can at times lack some features. This library aims to provide a more user-friendly interface to S3 operations.

# Features

* Multithreaded opeations for opeations on multiple files
* Progress tracking for all operations
* Retries for recoverable errors
* Benchmarking tool to test the performance of the operations
* Graceful error handling with useful messages

# Dev environment

The project comes with a python development environment.
To generate it, after checking out the repo run:

    chmod +x create_env.sh

Then to generate the environment (or update it to latest version based on state of `uv.lock`), run:

    ./create_env.sh

This will generate a new python virtual env under `.venv` directory. You can activate it via:

    source .venv/bin/activate

If you are using VSCode, set to use this env via `Python: Select Interpreter` command.

## Example usage

    as3 bench --bucket $TEST_AWS_BUCKET_NAME --prefix s3-benchmark

# Project template

This project has been setup with `acme-project-create`, a python code template library.

# Required setup post use

* Enable GitHub Pages to be published via [GitHub Actions](https://docs.github.com/en/pages/getting-started-with-github-pages/configuring-a-publishing-source-for-your-github-pages-site#publishing-with-a-custom-github-actions-workflow) by going to `Settings-->Pages-->Source`
* Create `release` environment for [GitHub Actions](https://docs.github.com/en/actions/managing-workflow-runs-and-deployments/managing-deployments/managing-environments-for-deployment#creating-an-environment) to enable uploads of the library to PyPi
* Setup auth to PyPI for the GitHub Action implemented in `.github/workflows/release.yml` via [Trusted Publisher](https://docs.pypi.org/trusted-publishers/adding-a-publisher/) `uv publish` [doc](https://docs.astral.sh/uv/guides/publish/#publishing-your-package)
* Once you create the python environment for the first time add the `uv.lock` file that will be created in project directory to the source control and update it each time environment is rebuilt
* In order not to replicate documentation in `docs/docs/index.md` file and `README.md` in root of the project setup a symlink from `README.md` file to the `index.md` file.
To do this, from `docs/docs` dir run:

    ln -sf ../../README.md index.md
