Skip to main content
Version: 0.13.1

Developing a Transformer

In this guide, we will outline each step to developing a custom Transformer for the DataHub Actions Framework.


Developing a DataHub Actions Transformer is a matter of extending the Transformer base class in Python, installing your Transformer to make it visible to the framework, and then configuring the framework to use the new Transformer.

Step 1: Defining a Transformer

To implement an Transformer, we'll need to extend the Transformer base class and override the following functions:

  • create() - This function is invoked to instantiate the action, with a free-form configuration dictionary extracted from the Actions configuration file as input.
  • transform() - This function is invoked when an Event is received. It should contain the core logic of the Transformer. and will return the transformed Event, or None if the Event should be filtered.

Let's start by defining a new implementation of Transformer called CustomTransformer. We'll keep it simple-- this Transformer will print the configuration that is provided when it is created, and print any Events that it receives.

from datahub_actions.transform.transformer import Transformer
from datahub_actions.event.event import EventEnvelope
from datahub_actions.pipeline.pipeline_context import PipelineContext
from typing import Optional

class CustomTransformer(Transformer):
def create(cls, config_dict: dict, ctx: PipelineContext) -> "Transformer":
# Simply print the config_dict.
return cls(config_dict, ctx)

def __init__(self, ctx: PipelineContext):
self.ctx = ctx

def transform(self, event: EventEnvelope) -> Optional[EventEnvelope]:
# Simply print the received event.
# And return the original event (no-op)
return event

Step 2: Installing the Transformer

Now that we've defined the Transformer, we need to make it visible to the framework by making it available in the Python runtime environment.

The easiest way to do this is to just place it in the same directory as your configuration file, in which case the module name is the same as the file name - in this case it will be custom_transformer.

Advanced: Installing as a Package

Alternatively, create a file in the same directory as the new Transformer to convert it into a package that pip can understand.

from setuptools import find_packages, setup

# if you don't already have DataHub Actions installed, add it under install_requires
# install_requires=["acryl-datahub-actions"]

Next, install the package

pip install -e .

inside the module. (alt.python

Once we have done this, our class will be referencable via custom_transformer_example.custom_transformer:CustomTransformer.

Step 3: Running the Action

Now that we've defined our Transformer, we can create an Action configuration file that refers to the new Transformer. We will need to provide the fully-qualified Python module & class name when doing so.

Example Configuration

# custom_transformer_action.yaml
name: "custom_transformer_test"
type: "kafka"
bootstrap: ${KAFKA_BOOTSTRAP_SERVER:-localhost:9092}
schema_registry_url: ${SCHEMA_REGISTRY_URL:-http://localhost:8081}
- type: "custom_transformer_example.custom_transformer:CustomTransformer"
# Some sample configuration which should be printed on create.
config1: value1
# Simply reuse the default hello_world action
type: "hello_world"

Next, run the datahub actions command as usual:

datahub actions -c custom_transformer_action.yaml

If all is well, your Transformer should now be receiving & printing Events.

(Optional) Step 4: Contributing the Transformer

If your Transformer is generally applicable, you can raise a PR to include it in the core Transformer library provided by DataHub. All Transformers will live under the datahub_actions/plugin/transform directory inside the datahub-actions repository.

Once you've added your new Transformer there, make sure that you make it discoverable by updating the entry_points section of the file. This allows you to assign a globally unique name for you Transformer, so that people can use it without defining the full module path.


Prerequisites to consideration for inclusion in the core Transformer library include

  • Testing Define unit tests for your Transformer
  • Deduplication Confirm that no existing Transformer serves the same purpose, or can be easily extended to serve the same purpose