Annotating Task Lineage with Inlets and Outlets
This skill guides you through adding manual lineage annotations to Airflow tasks using inlets and outlets.
When to Use This Approach
| Scenario | Use Inlets/Outlets? |
|---|---|
| Operator has OpenLineage methods | No, modify the OL method directly |
| Operator has no built-in OpenLineage extractor | Yes |
| Simple table-level lineage is sufficient | Yes |
| Quick lineage setup without custom code | Yes |
| Need column-level lineage | No, use OpenLineage methods or custom extractor |
| Complex extraction logic needed | No, use OpenLineage methods or custom extractor |
Supported Types for Inlets/Outlets
OpenLineage Datasets (recommended)
python
from openlineage.client.event_v2 import Dataset
source_table = Dataset(
namespace="postgres://mydb:5432",
name="public.orders",
)
Airflow Assets (Airflow 3+)
python
from airflow.sdk import Asset orders_asset = Asset(uri="s3://my-bucket/data/orders")
Airflow Datasets (Airflow 2.4+)
python
from airflow.datasets import Dataset orders_dataset = Dataset(uri="s3://my-bucket/data/orders")
Basic Usage
Setting Inlets and Outlets on Operators
python
from airflow import DAG
from airflow.operators.bash import BashOperator
from openlineage.client.event_v2 import Dataset
import pendulum
source_table = Dataset(namespace="snowflake://account", name="raw.orders")
target_table = Dataset(namespace="snowflake://account", name="staging.orders_clean")
output_file = Dataset(namespace="s3://my-bucket", name="exports/orders.parquet")
with DAG(
dag_id="etl_with_lineage",
start_date=pendulum.datetime(2024, 1, 1, tz="UTC"),
schedule="@daily",
) as dag:
transform = BashOperator(
task_id="transform_orders",
bash_command="echo 'transforming...'",
inlets=[source_table],
outlets=[target_table],
)
export = BashOperator(
task_id="export_to_s3",
bash_command="echo 'exporting...'",
inlets=[target_table],
outlets=[output_file],
)
transform >> export
Multiple Inputs and Outputs
python
from openlineage.client.event_v2 import Dataset
customers = Dataset(namespace="postgres://crm:5432", name="public.customers")
orders = Dataset(namespace="postgres://sales:5432", name="public.orders")
products = Dataset(namespace="postgres://inventory:5432", name="public.products")
daily_summary = Dataset(namespace="snowflake://account", name="analytics.daily_summary")
customer_metrics = Dataset(namespace="snowflake://account", name="analytics.customer_metrics")
aggregate_task = PythonOperator(
task_id="build_daily_aggregates",
python_callable=build_aggregates,
inlets=[customers, orders, products],
outlets=[daily_summary, customer_metrics],
)
Custom Operators
Option 1: Implement OpenLineage Methods (recommended)
python
from airflow.models import BaseOperator
class MyCustomOperator(BaseOperator):
def __init__(self, source_table: str, target_table: str, **kwargs):
super().__init__(**kwargs)
self.source_table = source_table
self.target_table = target_table
def execute(self, context):
self.log.info(f"Processing {self.source_table} -> {self.target_table}")
def get_openlineage_facets_on_complete(self, task_instance):
from openlineage.client.event_v2 import Dataset
from airflow.providers.openlineage.extractors import OperatorLineage
return OperatorLineage(
inputs=[Dataset(namespace="warehouse://db", name=self.source_table)],
outputs=[Dataset(namespace="warehouse://db", name=self.target_table)],
)
Option 2: Set Inlets/Outlets Dynamically
python
from airflow.models import BaseOperator
from openlineage.client.event_v2 import Dataset
class MyCustomOperator(BaseOperator):
def __init__(self, source_table: str, target_table: str, **kwargs):
super().__init__(**kwargs)
self.source_table = source_table
self.target_table = target_table
def execute(self, context):
self.inlets = [Dataset(namespace="warehouse://db", name=self.source_table)]
self.outlets = [Dataset(namespace="warehouse://db", name=self.target_table)]