Getting Started with Koheesio#
Requirements#
Installation#
hatch / hatchling
If you're using hatch (or hatchling), simply add `koheesio` to the `dependencies` or section in your `pyproject.toml` file:pip
If you're using pip, run the following command to install Koheesio: Requires [pip](https://pip.pypa.io/en/stable/).Basic Usage#
Once you've installed Koheesio, you can start using it in your Python scripts. Here's a basic example:
my_first_step.py
from koheesio import Step
# Define a step
class MyStep(Step):
def execute(self):
# Your step logic here
# Create an instance of the step
step = MyStep()
# Run the step
step.execute()
Advanced Usage#
my_first_etl.py
from pyspark.sql.functions import lit
from pyspark.sql import DataFrame, SparkSession
# Step 1: import Koheesio dependencies
from koheesio.context import Context
from koheesio.spark.readers.dummy import DummyReader
from koheesio.spark.transformations.camel_to_snake import CamelToSnakeTransformation
from koheesio.spark.writers.dummy import DummyWriter
from koheesio.spark.etl_task import EtlTask
# Step 2: Set up a SparkSession
spark = SparkSession.builder.getOrCreate()
# Step 3: Configure your Context
context = Context({
"source": DummyReader(),
"transformations": [CamelToSnakeTransformation()],
"target": DummyWriter(),
"my_favorite_movie": "inception",
})
# Step 4: Create a Task
class MyFavoriteMovieTask(EtlTask):
my_favorite_movie: str
def transform(self, df: DataFrame = None) -> DataFrame:
df = df.withColumn("MyFavoriteMovie", lit(self.my_favorite_movie))
return super().transform(df)
# Step 5: Run your Task
task = MyFavoriteMovieTask(**context)
task.run()
Contributing#
If you want to contribute to Koheesio, check out the CONTRIBUTING.md
file in this repository. It contains guidelines
for contributing, including how to submit issues and pull requests.
Testing#
To run the tests for Koheesio, use the following command:
This will run all the tests in the tests
directory.