data-loader-plugin 0.0.1

Python plugin/extra to load data files from an external source (such as AWS S3) to a local directory

Data Loader Plugin - Python

Table of Content (ToC)

Table of contents generated with markdown-toc

Overview

The data loader plugin, aims at supporting running programs (e.g., API service backends) when downloading data from cloud services such as AWS S3. It provides a base Python library, namely data-loader-plugin, offering a few methods to download data files from AWS S3.

References

Python module

• GitHub: https://github.com/cloud-helpers/python-plugin-data-loader/tree/master/data_loader_plugin
• PyPi: https://pypi.org/project/data-loader-plugin/
• Read the Docs (RTD): https://readthedocs.org/projects/data-loader-plugin/

Python virtual environments

• Pyenv and pipenv: http://github.com/machine-learning-helpers/induction-python/tree/master/installation/virtual-env

Installation

Clone this Git repository

$ mkdir -p ~/dev/infra && \
  git clone [e-mail]:cloud-helpers/python-plugin-data-loader.git ~/dev/infra/python-plugin-data-loader
$ cd ~/dev/infra/python-plugin-data-loader

Python environment

• If not already done so, install pyenv, Python 3.9 and, pip and pipenv
  • PyEnv:

$ git clone https://github.com/pyenv/pyenv.git ${HOME}/.pyenv
$ cat >> ~/.profile2 << _EOF

# Python
eval "\$(pyenv init --path)"

_EOF
$ cat >> ~/.bashrc << _EOF

# Python
export PYENV_ROOT="\${HOME}/.pyenv"
export PATH="\${PYENV_ROOT}/bin:\${PATH}"
. ~/.profile2
if command -v pyenv 1>/dev/null 2>&1
then
        eval "\$(pyenv init -)"
fi
if command -v pipenv 1>/dev/null 2>&1
then
        eval "\$(pipenv --completion)"
fi

_EOF
$ . ~/.bashrc

• Python 3.9:

$ pyenv install 3.9.8 && pyenv local 3.9.8

• pip:

$ python -mpip install -U pip

• pipenv:

$ python -mpip install -U pipenv

Usage

Install the data-loader-plugin module

• There are at least two ways to install the data-loader-plugin module, in the Python user space with pip and in a dedicated virtual environment with pipenv.

  • Both options may be installed in parallel
  • The Python user space (typically, /usr/local/opt/python@3.9 on MacOS or ~/.pyenv/versions/3.9.8 on Linux) may already have many other modules installed, parasiting a fine-grained control over the versions of every Python dependency. If all the versions are compatible, then that option is convenient as it is available from the whole user space, not just from this sub-directory

• In the remainder of that Usage section, it will be assumed that the data-loader-plugin module has been installed and readily available from the environment, whether that environment is virtual or not. In other words, to adapt the documentation for the case where pipenv is used, just add pipenv run in front of every Python-related command.

Install in the Python user space

• Install and use the data-loader-plugin module in the user space (with pip):

$ python -mpip uninstall data-loader-plugin
$ python -mpip install -U data-loader-plugin

Installation in a dedicated Python virtual environment

• Install and use the data-loader-plugin module in a virtual environment:

$ pipenv shell
(python-...-JwpAHotb) ✔ python -mpip install -U data-loader-plugin
(python-...-JwpAHotb) ✔ python -mpip install -U data-loader-plugin
(python-...-JwpAHotb) ✔ exit

Use data-loader-plugin as a module from another Python program

• Check the data file with the AWS command-line (CLI):

$ aws s3 ls --human s3://nyc-tlc/trip\ data/yellow_tripdata_2021-07.csv --no-sign-request
2021-10-29 20:44:34  249.3 MiB yellow_tripdata_2021-07.csv

• Module import statements:

>>> import importlib
>>> from types import ModuleType
>>> from data_loader_plugin.base import DataLoaderBase

• Create an instance of the DataLoaderBase Python class:

>>> plugin: ModuleType = importlib.import_module("data_loader_plugin.copyfile")
>>> data_loader: DataLoaderBase = plugin.DataLoader(
        local_path='/tmp/yellow_tripdata_2021-07.csv',
        external_url='s3://nyc-tlc/trip\ data/yellow_tripdata_2021-07.csv',
    )
>>> data_load_success, message = data_loader.load()

Development / Contribution

• Build the source distribution and Python artifacts (wheels):

$ rm -rf _skbuild/ build/ dist/ .tox/ __pycache__/ .pytest_cache/ MANIFEST *.egg-info/
$ pipenv run python setup.py sdist bdist_wheel

• Upload to Test PyPi (no Linux binary wheel can be uploaded on PyPi):

$ PYPIURL="https://test.pypi.org"
$ pipenv run twine upload -u __token__ --repository-url ${PYPIURL}/legacy/ dist/*
Uploading distributions to https://test.pypi.org/legacy/
Uploading datamonitor-0.0.4-py2.py3-none-any.whl
100%|███████████████████████████████████████| 9.85k/9.85k [00:02<00:00, 4.48kB/s]
Uploading datamonitor-0.0.4.tar.gz
100%|███████████████████████████████████████| 18.8k/18.8k [00:01<00:00, 17.1kB/s]

View at:
https://test.pypi.org/project/datamonitor/0.0.4/

• Upload/release the Python packages onto the PyPi repository:
  • Register the authentication token for access to PyPi:

$ PYPIURL="https://upload.pypi.org"
$ pipenv run keyring set ${PYPIURL}/ __token__
Password for '__token__' in '${PYPIURL}/':

• Register the authentication token for access to PyPi:

$ pipenv run twine upload -u __token__ --repository-url ${PYPIURL}/legacy/ dist/*
Uploading distributions to https://upload.pypi.org/legacy/
Uploading datamonitor-0.0.4-py2.py3-none-any.whl
100%|███████████████████████████████████████| 11.5k/11.5k [00:02<00:00, 5.84kB/s]
Uploading datamonitor-0.0.4.tar.gz
100%|███████████████████████████████████████| 20.7k/20.7k [00:01<00:00, 15.8kB/s]

View at:
https://pypi.org/project/datamonitor/0.0.4/

• Note that the documentation is built automatically by ReadTheDocs (RTD)

  • The documentation is available from https://datamonitoring.readthedocs.io/en/latest/
  • The RTD project is setup on https://readthedocs.org/projects/datamonitoring/
  • As of July 2020, the documentation is built from RST files, e.g., README.rst and documentation files in the docs sub-directory. The author is a lot more familiar with MarkDown (MD) format, and would welcome help in translating the documentation generation configuration to use MD- rather than RST-based files. Do not hesitate to create an issue or, even better, submit a pull request

• Build the documentation manually (with Sphinx):

$ pipenv run python setup.py build_sphinx
running build_sphinx
Running Sphinx v3.1.1
loading pickled environment... done
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 9 source files that are out of date
updating environment: [new config] 9 added, 0 changed, 0 removed
looking for now-outdated files... none found
no targets are out of date.
build succeeded.

The HTML pages are in build/sphinx/html.

• Re-generate the Python dependency files (requirements.txt) for the CI/CD pipeline (currently Travis CI):

$ pipenv --rm; rm -f Pipfile.lock; pipenv install; pipenv install --dev
$ git add Pipfile.lock
$ pipenv lock -r > datamonitor/ci/requirements.txt
$ pipenv lock --dev -r > datamonitor/ci/requirements-dev.txt
$ git add datamonitor/ci/requirements.txt datamonitor/ci/requirements-dev.txt
$ git commit -m "[CI] Upgraded the Python dependencies for the Travis CI pipeline"

Test the DataMonitor Python module

• The tests use ElasticMock, which emulates, in a very simple and limited way, an Elasticsearch (ES) cluster/service.

• If ES cluster/service is already running locally on the ES default port (9200), it must be temporarily shutdown, as the default parameters for ElasticMock would otherwise have both ES services step on each other toes. For instance:

  • On MacOs, brew stop elasticsearch-full (and brew start elasticsearch-full to restart it afterwards)
  • On SystemD-based Linux distributions, sudo systemctl stop elasticsearch (and sudo systemctl start elasticsearch to restart afterwards)

• Enter into the pipenv Shell:

$ pipenv shell
(python-iVzKEypY) ✔ python -V
Python 3.8.3

• Uninstall any previously installed datamonitor module/library:

(python-iVzKEypY) ✔ python -m pip uninstall datamonitor

• Launch a simple test with pytest

(python-iVzKEypY) ✔ python -m pytest test_datamonitor.py
=================== test session starts ===========================
platform darwin -- Python 3.8.3, pytest-5.4.3, py-1.9.0, pluggy-0.13.1
rootdir: ~/dev/infra/induction-monitoring/python/datamonitor, inifile: setup.cfg
collected 1 item
datamonitor/tests/test_datamonitor.py

========================== 1 passed in 0.06s ======================

• Check that a document has been created on ES. When the ES service is made from a single node (e.g., on a laptop or a local installation), the status of the index will be yellow. That is because the documents cannot be replicated:

(python-iVzKEypY) ✔ curl -XGET http://localhost:9200/_cat/indices/dm-test-v0
yellow open dm-test-v0 GXEUJjtkRjev3_wSn-5HOg 1 1 1 0 3.7kb 3.7kb

• Drop the replication requirement:

(python-iVzKEypY) ✔ curl -XPUT http://localhost:9200/dm-test-v0/_settings -H "Content-Type: application/json" --data "@../elasticseearch/settings/kibana-read-only.json"

• Check again the status of the dm-test-v0 index, which should have become green:

(python-iVzKEypY) ✔ curl -XGET http://localhost:9200/_cat/indices/dm-test-v0
green open dm-test-v0 GXEUJjtkRjev3_wSn-5HOg 1 0 1 0 3.8kb 3.8kb

• Exit the pipenv Shell:

(python-iVzKEypY) ✔ exit

• To run all the tests:

$ pipenv run tox