Run the ingestion from AWS MWAA

When running ingestion workflows from MWAA we have three approaches:

1. Install the openmetadata-ingestion package as a requirement in the Airflow environment. We will then run the process using a PythonOperator
2. Configure an ECS cluster and run the ingestion as an ECSOperator.
3. Install a plugin and run the ingestion with the PythonVirtualenvOperator.

We will now discuss pros and cons of each aspect and how to configure them.

Ingestion Workflows as a Python Operator

PROs

• It is the simplest approach
• We don’t need to spin up any further infrastructure

CONs

• We need to install the openmetadata-ingestion package in the MWAA environment
• The installation can clash with existing libraries
• Upgrading the OM version will require to repeat the installation process

To install the package, we need to update the requirements.txt file from the MWAA environment to add the following line:

openmetadata-ingestion[<plugin>]==x.y.z

Where x.y.z is the version of the OpenMetadata ingestion package. Note that the version needs to match the server version. If we are using the server at 1.3.1, then the ingestion package needs to also be 1.3.1.

The plugin parameter is a list of the sources that we want to ingest. An example would look like this openmetadata-ingestion[mysql,snowflake,s3]==1.3.1.

A DAG deployed using a Python Operator would then look like follows

import json
from datetime import timedelta

from airflow import DAG

try:
    from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
    from airflow.operators.python_operator import PythonOperator

from airflow.utils.dates import days_ago

from metadata.workflow.metadata import MetadataWorkflow


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

config = """
YAML config
"""

def metadata_ingestion_workflow():
    workflow_config = json.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 = PythonOperator(
        task_id="ingest_redshift",
        python_callable=metadata_ingestion_workflow,
    )

Where you can update the YAML configuration and workflow classes accordingly. accordingly. Further examples on how to run the ingestion can be found on the documentation (e.g., Snowflake).

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)

Ingestion Workflows as an ECS Operator

PROs

• Completely isolated environment
• Easy to update each version

CONs

• We need to set up an ECS cluster and the required policies in MWAA to connect to ECS and handle Log Groups.

We will now describe the steps, following the official AWS documentation.

1. Create an ECS Cluster & Task Definition

• The cluster needs a task to run in FARGATE mode.
• The required image is docker.getcollate.io/openmetadata/ingestion-base:x.y.z
  • The same logic as above applies. The x.y.z version needs to match the server version. For example, docker.getcollate.io/openmetadata/ingestion-base:1.3.1

We have tested this process with a Task Memory of 512MB and Task CPU (unit) of 256. This can be tuned depending on the amount of metadata that needs to be ingested.

When creating the Task Definition, take notes on the log groups assigned, as we will need them to prepare the MWAA Executor Role policies.

For example, if in the JSON from the Task Definition we see:

"logConfiguration": {
    "logDriver": "awslogs",
    "options": {
        "awslogs-create-group": "true",
        "awslogs-group": "/ecs/openmetadata",
        "awslogs-region": "us-east-2",
        "awslogs-stream-prefix": "ecs"
    },
    "secretOptions": []
}

We'll need to use the /ecs/openmetadata below when configuring the policies.

2. Task Definition ARN & Networking

1. From the AWS Console, copy your task definition ARN. It will look something like this arn:aws:ecs:<region>:<account>:task-definition/<name>:<revision>.
2. Get the network details on where the task should execute. We will be using a JSON like:

{
  "awsvpcConfiguration": {
    "subnets": [
      "subnet-xxxyyyzzz",
      "subnet-xxxyyyzzz"
    ],
    "securityGroups": [
      "sg-xxxyyyzzz"
    ],
    "assignPublicIp": "ENABLED"
  }
}

3. Update MWAA Executor Role policies

• Identify your MWAA executor role. This can be obtained from the details view of your MWAA environment.
• Add the following two policies to the role, the first with ECS permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Allow",
            "Action": [
                "ecs:RunTask",
                "ecs:DescribeTasks"
            ],
            "Resource": "*"
        },
        {
            "Action": "iam:PassRole",
            "Effect": "Allow",
            "Resource": [
                "*"
            ],
            "Condition": {
                "StringLike": {
                    "iam:PassedToService": "ecs-tasks.amazonaws.com"
                }
            }
        }
    ]
}

And for the Log Group permissions

{
    "Effect": "Allow",
    "Action": [
        "logs:CreateLogStream",
        "logs:CreateLogGroup",
        "logs:PutLogEvents",
        "logs:GetLogEvents",
        "logs:GetLogRecord",
        "logs:GetLogGroupFields",
        "logs:GetQueryResults"
    ],
    "Resource": [
        "arn:aws:logs:<region>:<account-id>:log-group:<airflow-environment-name>*",
        "arn:aws:logs:*:*:log-group:<ecs-mwaa-group>:*"
    ]
}

Note how you need to replace the region, account-id and the log group names for your Airflow Environment and ECS.

4. Prepare the DAG

A DAG created using the ECS Operator will then look like this:

from airflow import DAG
# If using Airflow < 2.5
# from airflow.providers.amazon.aws.operators.ecs import ECSOperator
# If using Airflow > 2.5
from airflow.providers.amazon.aws.operators.ecs import EcsRunTaskOperator
from airflow.utils.dates import days_ago


CLUSTER_NAME="openmetadata-ingestion"  # Replace value for CLUSTER_NAME with your information.
CONTAINER_NAME="openmetadata-ingestion"  # Replace value for CONTAINER_NAME with your information.
LAUNCH_TYPE="FARGATE"

TASK_DEFINITION = "arn:aws:ecs:<region>:<account>:task-definition/<name>:<revision>"
NETWORK_CONFIG = {
  "awsvpcConfiguration": {
    "subnets": [
      "subnet-xxxyyyzzz",
      "subnet-xxxyyyzzz"
    ],
    "securityGroups": [
      "sg-xxxyyyzzz"
    ],
    "assignPublicIp": "ENABLED"
  }
}

config = """
YAML config
"""


with DAG(
    dag_id="ecs_fargate_dag",
    schedule_interval=None,
    catchup=False,
    start_date=days_ago(1),
    is_paused_upon_creation=True,
) as dag:
    ecs_operator_task = EcsRunTaskOperator(
        task_id = "ecs_ingestion_task",
        dag=dag,
        cluster=CLUSTER_NAME,
        task_definition=TASK_DEFINITION,
        launch_type=LAUNCH_TYPE,
        overrides={
            "containerOverrides":[
                {
                    "name":CONTAINER_NAME,
                    "command":["python", "main.py"],
                    "environment": [
                      {
                        "name": "config",
                        "value": config
                      },
                      {
                        "name": "pipelineType",
                        "value": "metadata"
                      },
                    ],
                },
            ],
        },

        network_configuration=NETWORK_CONFIG,
        awslogs_group="/ecs/ingest",
        awslogs_stream_prefix=f"ecs/{CONTAINER_NAME}",
    )

Note that depending on the kind of workflow you will be deploying, the YAML configuration will need to updated following the official OpenMetadata docs, and the value of the pipelineType configuration will need to hold one of the following values:

• metadata
• usage
• lineage
• profiler
• TestSuite

Which are based on the PipelineType JSON Schema definitions

Moreover, one of the imports will depend on the MWAA Airflow version you are using:

• If using Airflow < 2.5: from airflow.providers.amazon.aws.operators.ecs import ECSOperator
• If using Airflow > 2.5: from airflow.providers.amazon.aws.operators.ecs import EcsRunTaskOperator

Make sure to update the ecs_operator_task task call accordingly.

Ingestion Workflows as a Python Virtualenv Operator

PROs

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

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.

"""
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

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. 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.

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)