annotating-task-lineage

name	annotating-task-lineage
description	Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.

Annotating Task Lineage with Inlets & Outlets

This skill guides you through adding manual lineage annotations to Airflow tasks using inlets and outlets.

Reference: See the OpenLineage provider developer guide for the latest supported operators and patterns.

When to Use This Approach

Scenario	Use Inlets/Outlets?
Operator has OpenLineage methods (get_openlineage_facets_on_*)	❌ 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	❌ Use OpenLineage methods or custom extractor
Complex extraction logic needed	❌ Use OpenLineage methods or custom extractor

Note: Inlets/outlets are the lowest-priority fallback. If an OpenLineage extractor or method exists for the operator, it takes precedence. Use this approach for operators without extractors.

---

Supported Types for Inlets/Outlets

You can use OpenLineage Dataset objects or Airflow Assets for inlets and outlets:

OpenLineage Datasets (Recommended)

from openlineage.client.event_v2 import Dataset

# Database tables
source_table = Dataset(
    namespace="postgres://mydb:5432",
    name="public.orders",
)
target_table = Dataset(
    namespace="snowflake://account.snowflakecomputing.com",
    name="staging.orders_clean",
)

# Files
input_file = Dataset(
    namespace="s3://my-bucket",
    name="raw/events/2024-01-01.json",
)

Airflow Assets (Airflow 3+)

from airflow.sdk import Asset

# Using Airflow's native Asset type
orders_asset = Asset(uri="s3://my-bucket/data/orders")

Airflow Datasets (Airflow 2.4+)

from airflow.datasets import Dataset

# Using Airflow's Dataset type (Airflow 2.4-2.x)
orders_dataset = Dataset(uri="s3://my-bucket/data/orders")

---

Basic Usage

Setting Inlets and Outlets on Operators

from airflow import DAG
from airflow.operators.bash import BashOperator
from openlineage.client.event_v2 import Dataset
import pendulum

# Define your lineage datasets
source_table = Dataset(
    namespace="snowflake://account.snowflakecomputing.com",
    name="raw.orders",
)
target_table = Dataset(
    namespace="snowflake://account.snowflakecomputing.com",
    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],           # What this task reads
        outlets=[target_table],          # What this task writes
    )

    export = BashOperator(
        task_id="export_to_s3",
        bash_command="echo 'exporting...'",
        inlets=[target_table],           # Reads from previous output
        outlets=[output_file],           # Writes to S3
    )

    transform >> export

Multiple Inputs and Outputs

Tasks often read from multiple sources and write to multiple destinations:

from openlineage.client.event_v2 import Dataset

# Multiple source tables
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")

# Multiple output tables
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],      # All inputs
    outlets=[daily_summary, customer_metrics], # All outputs
)

---

Setting Lineage in Custom Operators

When building custom operators, you have two options:

Option 1: Implement OpenLineage Methods (Recommended)

This is the preferred approach as it gives you full control over lineage extraction:

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):
        # ... perform the actual work ...
        self.log.info(f"Processing {self.source_table} -> {self.target_table}")

    def get_openlineage_facets_on_complete(self, task_instance):
        """Return lineage after successful execution."""
        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

For simpler cases, set lineage within the execute method (non-deferrable operators only):

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):
        # Set lineage dynamically based on operator parameters
        self.inlets = [
            Dataset(namespace="warehouse://db", name=self.source_table)
        ]
        self.outlets = [
            Dataset(namespace="warehouse://db", name=self.target_table)
        ]

        # ... perform the actual work ...
        self.log.info(f"Processing {self.source_table} -> {self.target_table}")

---

Dataset Naming Helpers

Use the OpenLineage dataset naming helpers to ensure consistent naming across platforms:

from openlineage.client.event_v2 import Dataset

# Snowflake
from openlineage.client.naming.snowflake import SnowflakeDatasetNaming

naming = SnowflakeDatasetNaming(
    account_identifier="myorg-myaccount",
    database="mydb",
    schema="myschema",
    table="mytable",
)
dataset = Dataset(namespace=naming.get_namespace(), name=naming.get_name())
# -> namespace: "snowflake://myorg-myaccount", name: "mydb.myschema.mytable"

# BigQuery
from openlineage.client.naming.bigquery import BigQueryDatasetNaming

naming = BigQueryDatasetNaming(
    project="my-project",
    dataset="my_dataset",
    table="my_table",
)
dataset = Dataset(namespace=naming.get_namespace(), name=naming.get_name())
# -> namespace: "bigquery", name: "my-project.my_dataset.my_table"

# S3
from openlineage.client.naming.s3 import S3DatasetNaming

naming = S3DatasetNaming(bucket="my-bucket", key="path/to/file.parquet")
dataset = Dataset(namespace=naming.get_namespace(), name=naming.get_name())
# -> namespace: "s3://my-bucket", name: "path/to/file.parquet"

# PostgreSQL
from openlineage.client.naming.postgres import PostgresDatasetNaming

naming = PostgresDatasetNaming(
    host="localhost",
    port=5432,
    database="mydb",
    schema="public",
    table="users",
)
dataset = Dataset(namespace=naming.get_namespace(), name=naming.get_name())
# -> namespace: "postgres://localhost:5432", name: "mydb.public.users"

Note: Always use the naming helpers instead of constructing namespaces manually. If a helper is missing for your platform, check the OpenLineage repo or request it.

---

Precedence Rules

OpenLineage uses this precedence for lineage extraction:

1. Custom Extractors (highest) - User-registered extractors
2. OpenLineage Methods - get_openlineage_facets_on_* in operator
3. Hook-Level Lineage - Lineage collected from hooks via HookLineageCollector
4. Inlets/Outlets (lowest) - Falls back to these if nothing else extracts lineage

Note: If an extractor or method exists but returns no datasets, OpenLineage will check hook-level lineage, then fall back to inlets/outlets.

---

Best Practices

Use the Naming Helpers

Always use OpenLineage naming helpers for consistent dataset creation:

from openlineage.client.event_v2 import Dataset
from openlineage.client.naming.snowflake import SnowflakeDatasetNaming


def snowflake_dataset(schema: str, table: str) -> Dataset:
    """Create a Snowflake Dataset using the naming helper."""
    naming = SnowflakeDatasetNaming(
        account_identifier="mycompany",
        database="analytics",
        schema=schema,
        table=table,
    )
    return Dataset(namespace=naming.get_namespace(), name=naming.get_name())


# Usage
source = snowflake_dataset("raw", "orders")
target = snowflake_dataset("staging", "orders_clean")

Document Your Lineage

Add comments explaining the data flow:

transform = SqlOperator(
    task_id="transform_orders",
    sql="...",
    # Lineage: Reads raw orders, joins with customers, writes to staging
    inlets=[
        snowflake_dataset("raw", "orders"),
        snowflake_dataset("raw", "customers"),
    ],
    outlets=[
        snowflake_dataset("staging", "order_details"),
    ],
)

Keep Lineage Accurate

• Update inlets/outlets when SQL queries change
• Include all tables referenced in JOINs as inlets
• Include all tables written to (including temp tables if relevant)
• Outlet-only and inlet-only annotations are valid. One-sided annotations are encouraged for lineage visibility even without a corresponding inlet or outlet in another DAG.

---

Limitations

Limitation	Workaround
Table-level only (no column lineage)	Use OpenLineage methods or custom extractor
Overridden by extractors/methods	Only use for operators without extractors
Static at DAG parse time	Set dynamically in execute() or use OL methods
Deferrable operators lose dynamic lineage	Use OL methods instead; attributes set in execute() are lost when deferring

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Related Skills

• creating-openlineage-extractors: For column-level lineage or complex extraction
• tracing-upstream-lineage: Investigate where data comes from
• tracing-downstream-lineage: Investigate what depends on data