Developing on Metadata Ingestion

If you just want to use metadata ingestion, check the user-centric guide.

Architecture

The architecture of this metadata ingestion framework is heavily inspired by Apache Gobblin (also originally a LinkedIn project!). We have a standardized format - the MetadataChangeEvent - and sources and sinks which respectively produce and consume these objects. The sources pull metadata from a variety of data systems, while the sinks are primarily for moving this metadata into DataHub.

Getting Started

Requirements

Python 3.6+ must be installed in your host environment.
You also need to build the mxe-schemas module as below.
```
(cd .. && ./gradlew :metadata-events:mxe-schemas:build)
```
This is needed to generate MetadataChangeEvent.avsc which is the schema for the MetadataChangeEvent_v4 Kafka topic.
On MacOS: brew install librdkafka
On Debian/Ubuntu: sudo apt install librdkafka-dev python3-dev python3-venv
On Fedora (if using LDAP source integration): sudo yum install openldap-devel

Set up your Python environment

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip wheel setuptools
pip uninstall datahub || true ; rm -r src/*.egg-info || true
pip install -e .
./scripts/codegen.sh

Common setup issues

Common issues (click to expand):

datahub command not found with PyPI install

If you've already run the pip install, but running datahub in your command line doesn't work, then there is likely an issue with your PATH setup and Python.

The easiest way to circumvent this is to install and run via Python, and use python3 -m datahub in place of datahub.

python3 -m pip install --upgrade acryl-datahub
python3 -m datahub --help

Wheel issues e.g. "Failed building wheel for avro-python3" or "error: invalid command 'bdist_wheel'"

This means Python's wheel is not installed. Try running the following commands and then retry.

pip install --upgrade pip wheel setuptools
pip cache purge

Failure to install confluent_kafka: "error: command 'x86_64-linux-gnu-gcc' failed with exit status 1"

This sometimes happens if there's a version mismatch between the Kafka's C library and the Python wrapper library. Try running pip install confluent_kafka==1.5.0 and then retrying.

Using Plugins in Development

The syntax for installing plugins is slightly different in development. For example:

- pip install 'acryl-datahub[bigquery,datahub-rest]'
+ pip install -e '.[bigquery,datahub-rest]'

Code layout

The CLI interface is defined in entrypoints.py.
The high level interfaces are defined in the API directory.
The actual sources and sinks have their own directories. The registry files in those directories import the implementations.
The metadata models are created using code generation, and eventually live in the ./src/datahub/metadata directory. However, these files are not checked in and instead are generated at build time. See the codegen script for details.
Tests live in the tests directory. They're split between smaller unit tests and larger integration tests.

Contributing

Contributions welcome!

Testing

# Follow standard install from source procedure - see above.

# Install, including all dev requirements.
pip install -e '.[dev]'

# Run unit tests.
pytest tests/unit

# Run integration tests. Note that the integration tests require docker.
pytest tests/integration

Sanity check code before committing

# Assumes: pip install -e '.[dev]'
black .
isort .
flake8 .
mypy .
pytest

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