Contributing to the Databricks Labs Data Generator

We happily welcome contributions to dbldatagen.

We use GitHub Issues to track community reported issues and GitHub Pull Requests for accepting changes.

License

When you contribute code, you affirm that the contribution is your original work and that you license the work to the project under the project’s Databricks license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s Databricks license and warrant that you have the legal authority to do so.

Building the code

Package Dependencies

See the contents of the file python/require.txt to see the Python package dependencies. Dependent packages are not installed automatically by the dbldatagen package.

Python compatibility

The code has been tested with Python 3.8.12 and later.

Older releases were tested with Python 3.7.5 but as of this release, it requires the Databricks runtime 9.1 LTS or later.

Checking your code for common issues

Run ./lint.sh from the project root directory to run various code style checks. These are based on the use of prospector, pylint and related tools.

Setting up your build environment

Run make buildenv from the root of the project directory to setup a pipenv based build environment.

Run make create-dev-env from the root of the project directory to set up a conda based virtualized Python build environment in the project directory.

You can use alternative build virtualization environments or simply install the requirements directly in your environment.

Build steps

Our recommended mechanism for building the code is to use a conda or pipenv based development process.

But it can be built with any Python virtualization environment.

Building with Conda

To build with conda, perform the following commands:

make create-dev-env from the main project directory to create your conda environment, if using
activate the conda environment - e.g conda activate dbl_testdatagenerator
install the necessary dependencies in your conda environment via make install-dev-dependencies
use the following to build and run the tests with a coverage report
- Run make dev-test-with-html-report from the main project directory.
Use the following command to make the distributable:
- Run make dev-dist from the main project directory
The resulting wheel file will be placed in the dist subdirectory

Building with Pipenv

To build with pipenv, perform the following commands:

make buildenv from the main project directory to create your conda environment, if using
install the necessary dependencies in your conda environment via make install-dev-dependencies
use the following to build and run the tests with a coverage report
- Run make test-with-html-report from the main project directory.
Use the following command to make the distributable:
- Run make dist from the main project directory
The resulting wheel file will be placed in the dist subdirectory

The resulting build has been tested against Spark 3.0.1

Creating the HTML documentation

Run make docs from the main project directory.

The main html document will be in the file (relative to the root of the build directory) ./docs/docs/build/html/index.html

Building the Python wheel

Run make clean dist from the main project directory.

Testing

Developing new tests

New tests should be created using PyTest with classes combining multiple Pytest tests.

Existing test code contains tests based on Python’s unittest framework but these are run on pytest rather than unitest.

To get a spark instance for test purposes, use the following code:

import dbldatagen as dg

spark = dg.SparkSingleton.getLocalInstance("<name to flag spark instance>")

The name used to flag the spark instance should be the test module or test class name.

Running unit / integration tests

If using an environment with multiple Python versions, make sure to use virtual env or similar to pick up correct python versions. The make target create

If necessary, set PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON to point to correct versions of Python.

To run the tests using a conda environment:

Run make dev-test from the main project directory to run the unit tests.
Run make dev-test-with-html-report to generate test coverage report in htmlcov/inxdex.html

To run the tests using a pipenv environment:

Run make test from the main project directory to run the unit tests.
Run make test-with-html-report to generate test coverage report in htmlcov/inxdex.html

Using the Databricks Labs data generator

The recommended method for installation is to install from the PyPi package

You can install the library as a notebook scoped library when working within the Databricks notebook environment through the use of a %pip cell in your notebook.

To install as a notebook-scoped library, create and execute a notebook cell with the following text:

%pip install dbldatagen

This installs from the PyPi package

You can also install from release binaries or directly from the Github sources.

The release binaries can be accessed at:

Databricks Labs Github Data Generator releases - https://github.com/databrickslabs/dbldatagen/releases

The %pip install method also works on the Databricks Community Edition.

Alternatively, you use download a wheel file and install using the Databricks install mechanism to install a wheel based library into your workspace.

The %pip install method can also down load a specific binary release. For example, the following code downloads the release V0.2.1

‘%pip install https://github.com/databrickslabs/dbldatagen/releases/download/v021/dbldatagen-0.2.1-py3-none-any.whl’

Coding Style

The code follows the Pyspark coding conventions.

Basically it follows the Python PEP8 coding conventions - but method and argument names used mixed case starting with a lower case letter rather than underscores following Pyspark coding conventions.

See https://legacy.python.org/dev/peps/pep-0008/