astronomer/agents/creating-openlineage-extractors

Name: creating-openlineage-extractors
Author: astronomer

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ext:[{"type":"config","description":"Configuration overrides"}]

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astronomer/agents
skills/creating-openlineage-extractors/SKILL.md
Updated 26 Mar 2026
Version 1.0.0

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Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.

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npx vskill@latest install astronomer/agents/creating-openlineage-extractors

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SKILL.md

Creating OpenLineage Extractors

This skill guides you through creating custom OpenLineage extractors to capture lineage from Airflow operators that don't have built-in support.

Reference: See the OpenLineage provider developer guide for the latest patterns and list of supported operators/hooks.

When to Use Each Approach

Scenario	Approach
Operator you own/maintain	OpenLineage Methods (recommended, simplest)
Third-party operator you can't modify	Custom Extractor
Need column-level lineage	OpenLineage Methods or Custom Extractor
Complex extraction logic	OpenLineage Methods or Custom Extractor
Simple table-level lineage	Inlets/Outlets (simplest, but lowest priority)

Important: Always prefer OpenLineage methods over custom extractors when possible. Extractors are harder to write, easier to diverge from operator behavior after changes, and harder to debug.

On Astro

Astro includes built-in OpenLineage integration — no additional transport configuration is needed. Lineage events are automatically collected and displayed in the Astro UI's Lineage tab. Custom extractors deployed to an Astro project are automatically picked up, so you only need to register them in airflow.cfg or via environment variable and deploy.

Two Approaches

1. OpenLineage Methods (Recommended)

Use when you can add methods directly to your custom operator. This is the go-to solution for operators you own.

2. Custom Extractors

Use when you need lineage from third-party or provider operators that you cannot modify.

Approach 1: OpenLineage Methods (Recommended)

When you own the operator, add OpenLineage methods directly:

python

from airflow.models import BaseOperator
class MyCustomOperator(BaseOperator):
    """Custom operator with built-in OpenLineage support."""
    def __init__(self, source_table: str, target_table: str, **kwargs):
        super().__init__(**kwargs)
        self.source_table = source_table
        self.target_table = target_table
        self._rows_processed = 0  # Set during execution
    def execute(self, context):
        # Do the actual work
        self._rows_processed = self._process_data()
        return self._rows_processed
    def get_openlineage_facets_on_start(self):
        """Called when task starts. Return known inputs/outputs."""
        # Import locally to avoid circular imports
        from openlineage.client.event_v2 import Dataset
        from airflow.providers.openlineage.extractors import OperatorLineage
        return OperatorLineage(
            inputs=[Dataset(namespace="postgres://db", name=self.source_table)],
            outputs=[Dataset(namespace="postgres://db", name=self.target_table)],
        )
    def get_openlineage_facets_on_complete(self, task_instance):
        """Called after success. Add runtime metadata."""
        from openlineage.client.event_v2 import Dataset
        from openlineage.client.facet_v2 import output_statistics_output_dataset
        from airflow.providers.openlineage.extractors import OperatorLineage
        return OperatorLineage(
            inputs=[Dataset(namespace="postgres://db", name=self.source_table)],
            outputs=[
                Dataset(
                    namespace="postgres://db",
                    name=self.target_table,
                    facets={
                        "outputStatistics": output_statistics_output_dataset.OutputStatisticsOutputDatasetFacet(
                            rowCount=self._rows_processed
                        )
                    },
                )
            ],
        )
    def get_openlineage_facets_on_failure(self, task_instance):
        """Called after failure. Optional - for partial lineage."""
        return None

OpenLineage Methods Reference

Method	When Called	Required
`get_openlineage_facets_on_start()`	Task enters RUNNING	No
`get_openlineage_facets_on_complete(ti)`	Task succeeds	No
`get_openlineage_facets_on_failure(ti)`	Task fails	No

Implement only the methods you need. Unimplemented methods fall through to Hook-Level Lineage or inlets/outlets.

Approach 2: Custom Extractors

Use this approach only when you cannot modify the operator (e.g., third-party or provider operators).

Basic Structure

python

from airflow.providers.openlineage.extractors.base import BaseExtractor, OperatorLineage
from openlineage.client.event_v2 import Dataset
class MyOperatorExtractor(BaseExtractor):
    """Extract lineage from MyCustomOperator."""
    @classmethod
    def get_operator_classnames(cls) -> list[str]:
        """Return operator class names this extractor handles."""
        return ["MyCustomOperator"]
    def _execute_extraction(self) -> OperatorLineage | None:
        """Called BEFORE operator executes. Use for known inputs/outputs."""
        # Access operator properties via self.operator
        source_table = self.operator.source_table
        target_table = self.operator.target_table
        return OperatorLineage(
            inputs=[
                Dataset(
                    namespace="postgres://mydb:5432",
                    name=f"public.{source_table}",
                )
            ],
            outputs=[
                Dataset(
                    namespace="postgres://mydb:5432",
                    name=f"public.{target_table}",
                )
            ],
        )
    def extract_on_complete(self, task_instance) -> OperatorLineage | None:
        """Called AFTER operator executes. Use for runtime-determined lineage."""
        # Access properties set during execution
        # Useful for operators that determine outputs at runtime
        return None

OperatorLineage Structure

python

from airflow.providers.openlineage.extractors.base import OperatorLineage
from openlineage.client.event_v2 import Dataset
from openlineage.client.facet_v2 import sql_job
lineage = OperatorLineage(
    inputs=[Dataset(namespace="...", name="...")],      # Input datasets
    outputs=[Dataset(namespace="...", name="...")],     # Output datasets
    run_facets={"sql": sql_job.SQLJobFacet(query="SELECT...")},  # Run metadata
    job_facets={},                                      # Job metadata
)

Extraction Methods

Method	When Called	Use For
`_execute_extraction()`	Before operator runs	Static/known lineage
`extract_on_complete(task_instance)`	After success	Runtime-determined lineage
`extract_on_failure(task_instance)`	After failure	Partial lineage on errors

Registering Extractors

Option 1: Configuration file (`airflow.cfg`)

ini

[openlineage]
extractors = mypackage.extractors.MyOperatorExtractor;mypackage.extractors.AnotherExtractor

Option 2: Environment variable

bash

AIRFLOW__OPENLINEAGE__EXTRACTORS='mypackage.extractors.MyOperatorExtractor;mypackage.extractors.AnotherExtractor'

Important: The path must be importable from the Airflow worker. Place extractors in your DAGs folder or installed package.

Common Patterns

SQL Operator Extractor

python

from airflow.providers.openlineage.extractors.base import BaseExtractor, OperatorLineage
from openlineage.client.event_v2 import Dataset
from openlineage.client.facet_v2 import sql_job
class MySqlOperatorExtractor(BaseExtractor):
    @classmethod
    def get_operator_classnames(cls) -> list[str]:
        return ["MySqlOperator"]
    def _execute_extraction(self) -> OperatorLineage | None:
        sql = self.operator.sql
        conn_id = self.operator.conn_id
        # Parse SQL to find tables (simplified example)
        # In practice, use a SQL parser like sqlglot
        inputs, outputs = self._parse_sql(sql)
        namespace = f"postgres://{conn_id}"
        return OperatorLineage(
            inputs=[Dataset(namespace=namespace, name=t) for t in inputs],
            outputs=[Dataset(namespace=namespace, name=t) for t in outputs],
            job_facets={
                "sql": sql_job.SQLJobFacet(query=sql)
            },
        )
    def _parse_sql(self, sql: str) -> tuple[list[str], list[str]]:
        """Parse SQL to extract table names. Use sqlglot for real parsing."""
        # Simplified example - use proper SQL parser in production
        inputs = []
        outputs = []
        # ... parsing logic ...
        return inputs, outputs

File Transfer Extractor

python

from airflow.providers.openlineage.extractors.base import BaseExtractor, OperatorLineage
from openlineage.client.event_v2 import Dataset
class S3ToSnowflakeExtractor(BaseExtractor):
    @classmethod
    def get_operator_classnames(cls) -> list[str]:
        return ["S3ToSnowflakeOperator"]
    def _execute_extraction(self) -> OperatorLineage | None:
        s3_bucket = self.operator.s3_bucket
        s3_key = self.operator.s3_key
        table = self.operator.table
        schema = self.operator.schema
        return OperatorLineage(
            inputs=[
                Dataset(
                    namespace=f"s3://{s3_bucket}",
                    name=s3_key,
                )
            ],
            outputs=[
                Dataset(
                    namespace="snowflake://myaccount.snowflakecomputing.com",
                    name=f"{schema}.{table}",
                )
            ],
        )

Dynamic Lineage from Execution

python

from openlineage.client.event_v2 import Dataset
class DynamicOutputExtractor(BaseExtractor):
    @classmethod
    def get_operator_classnames(cls) -> list[str]:
        return ["DynamicOutputOperator"]
    def _execute_extraction(self) -> OperatorLineage | None:
        # Only inputs known before execution
        return OperatorLineage(
            inputs=[Dataset(namespace="...", name=self.operator.source)],
        )
    def extract_on_complete(self, task_instance) -> OperatorLineage | None:
        # Outputs determined during execution
        # Access via operator properties set in execute()
        outputs = self.operator.created_tables  # Set during execute()
        return OperatorLineage(
            inputs=[Dataset(namespace="...", name=self.operator.source)],
            outputs=[Dataset(namespace="...", name=t) for t in outputs],
        )

Common Pitfalls

1. Circular Imports

Problem: Importing Airflow modules at the top level causes circular imports.

python

# ❌ BAD - can cause circular import issues
from airflow.models import TaskInstance
from openlineage.client.event_v2 import Dataset
class MyExtractor(BaseExtractor):
    ...

python

# ✅ GOOD - import inside methods
class MyExtractor(BaseExtractor):
    def _execute_extraction(self):
        from openlineage.client.event_v2 import Dataset
        # ...

2. Wrong Import Path

Problem: Extractor path doesn't match actual module location.

bash

# ❌ Wrong - path doesn't exist
AIRFLOW__OPENLINEAGE__EXTRACTORS='extractors.MyExtractor'
 
# ✅ Correct - full importable path
AIRFLOW__OPENLINEAGE__EXTRACTORS='dags.extractors.my_extractor.MyExtractor'

3. Not Handling None

Problem: Extraction fails when operator properties are None.

python

# ✅ Handle optional properties
def _execute_extraction(self) -> OperatorLineage | None:
    if not self.operator.source_table:
        return None  # Skip extraction
    return OperatorLineage(...)

Testing Extractors

Unit Testing

python

import pytest
from unittest.mock import MagicMock
from mypackage.extractors import MyOperatorExtractor
def test_extractor():
    # Mock the operator
    operator = MagicMock()
    operator.source_table = "input_table"
    operator.target_table = "output_table"

This is the start of the file. Read the full SKILL.md on GitHub.

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Task evaluationPassed, 68 of 100Effective. 1 run. Evaluated 7 months ago.

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Source

SKILL.md in the repositoryCheckingskills/creating-openlineage-extractors/SKILL.md

Popularity

Repository stars: 290
Repository forks: 31
7-day trend score: 28

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 20 Mar 2026, 7 months ago.

Extension points

configConfiguration overrides

Learn the extensible skills standard

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