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Ingestion Workflows as a Python Virtualenv Operator

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PROs

  • Installation does not clash with existing libraries
  • Simpler than ECS

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CONs

  • We need to install an additional plugin in MWAA
  • DAGs take longer to run due to needing to set up the virtualenv from scratch for each run.
We need to update the requirements.txt file from the MWAA environment to add the following line: 
virtualenv
Then, we need to set up a custom plugin in MWAA. Create a file named virtual_python_plugin.py. Note that you may need to update the python version (eg, python3.7 -> python3.10) depending on what your MWAA environment is running. 
"""
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Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
from airflow.plugins_manager import AirflowPlugin
import airflow.utils.python_virtualenv
from typing import List
import os


def _generate_virtualenv_cmd(tmp_dir: str, python_bin: str, system_site_packages: bool) -> List[str]:
    cmd = ['python3', '/usr/local/airflow/.local/lib/python3.7/site-packages/virtualenv', tmp_dir]
    if system_site_packages:
        cmd.append('--system-site-packages')
    if python_bin is not None:
        cmd.append(f'--python={python_bin}')
    return cmd


airflow.utils.python_virtualenv._generate_virtualenv_cmd = _generate_virtualenv_cmd

os.environ["PATH"] = f"/usr/local/airflow/.local/bin:{os.environ['PATH']}"


class VirtualPythonPlugin(AirflowPlugin):
    name = 'virtual_python_plugin'
This is modified from the AWS sample. Next, create the plugins.zip file and upload it according to AWS docs. You will also need to disable lazy plugin loading in MWAA. A DAG deployed using the PythonVirtualenvOperator would then look like: 
from datetime import timedelta

from airflow import DAG

from airflow.operators.python import PythonVirtualenvOperator

from airflow.utils.dates import days_ago


default_args = {
    "retries": 3,
    "retry_delay": timedelta(seconds=10),
    "execution_timeout": timedelta(minutes=60),
}

def metadata_ingestion_workflow():
    from metadata.workflow.metadata import MetadataWorkflow


    import yaml

    config = """
YAML config
    """
    workflow_config = yaml.loads(config)
    workflow = MetadataWorkflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()

with DAG(
    "redshift_ingestion",
    default_args=default_args,
    description="An example DAG which runs a OpenMetadata ingestion workflow",
    start_date=days_ago(1),
    is_paused_upon_creation=False,
    catchup=False,
) as dag:
    ingest_task = PythonVirtualenvOperator(
        task_id="ingest_redshift",
        python_callable=metadata_ingestion_workflow,
        requirements=['openmetadata-ingestion==1.0.5.0',
            'apache-airflow==2.4.3',  # note, v2.4.3 is the first version that does not conflict with OpenMetadata's 'tabulate' requirements
            'apache-airflow-providers-amazon==6.0.0',  # Amazon Airflow provider is necessary for MWAA
            'watchtower',],
        system_site_packages=False,
        dag=dag,
    )
Where you can update the YAML configuration and workflow classes accordingly. Further examples on how to run the ingestion can be found on the documentation (e.g., Snowflake). You will also need to determine the OpenMetadata ingestion extras and Airflow providers you need. Note that the Openmetadata version needs to match the server version. If we are using the server at 0.12.2, then the ingestion package needs to also be 0.12.2. An example of the extras would look like this openmetadata-ingestion[mysql,snowflake,s3]==0.12.2.2. For Airflow providers, you will want to pull the provider versions from the matching constraints file. Since this example installs Airflow Providers v2.4.3 on Python 3.7, we use that constraints file. Also note that the ingestion workflow function must be entirely self-contained as it will run by itself in the virtualenv. Any imports it needs, including the configuration, must exist within the function itself.

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Ingestion Workflow classes

We have different classes for different types of workflows. The logic is always the same, but you will need to change your import path. The rest of the method calls will remain the same. For example, for the Metadata workflow we’ll use: 
import yaml

from metadata.workflow.metadata import MetadataWorkflow

def run():
    workflow_config = yaml.safe_load(CONFIG)
    workflow = MetadataWorkflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()
The classes for each workflow type are:
  • Metadata: from metadata.workflow.metadata import MetadataWorkflow
  • Lineage: from metadata.workflow.metadata import MetadataWorkflow (same as metadata)
  • Usage: from metadata.workflow.usage import UsageWorkflow
  • dbt: from metadata.workflow.metadata import MetadataWorkflow
  • Profiler: from metadata.workflow.profiler import ProfilerWorkflow
  • Data Quality: from metadata.workflow.data_quality import TestSuiteWorkflow
  • Data Insights: from metadata.workflow.data_insight import DataInsightWorkflow
  • Elasticsearch Reindex: from metadata.workflow.metadata import MetadataWorkflow (same as metadata)