Creating Openlineage Extractors

by astronomercbe1141f547bNo license451 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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.

Instructions onlySoftware DevelopmentData & Analytics
AI-generated overview

Guides creation of custom OpenLineage extractors for Airflow operators lacking built-in lineage support.

What it does
This skill explains how to capture data lineage from Airflow operators that have no built-in OpenLineage support. It covers two approaches: adding OpenLineage methods directly to operators you own, and writing custom BaseExtractor subclasses for third-party operators you cannot modify. It provides code patterns for SQL operators, file transfers and runtime-determined lineage, plus registration, testing, precedence rules and common pitfalls.
When to use it
Use it when lineage is needed from unsupported or third-party Airflow operators, when column-level lineage is required, or when extraction logic goes beyond what inlets and outlets provide. It is also relevant when deciding between OpenLineage methods and custom extractors.
Requirements
Requires Apache Airflow with the OpenLineage provider installed, plus the openlineage client library; extractors must be importable from the Airflow worker and registered via airflow.cfg or an environment variable. It ships no scripts, only instructions and code examples.

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

ScenarioApproach
Operator you own/maintainOpenLineage Methods (recommended, simplest)
Third-party operator you can't modifyCustom Extractor
Need column-level lineageOpenLineage Methods or Custom Extractor
Complex extraction logicOpenLineage Methods or Custom Extractor
Simple table-level lineageInlets/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

MethodWhen CalledRequired
get_openlineage_facets_on_start()Task enters RUNNINGNo
get_openlineage_facets_on_complete(ti)Task succeedsNo
get_openlineage_facets_on_failure(ti)Task failsNo

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, OperatorLineagefrom 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 OperatorLineagefrom openlineage.client.event_v2 import Datasetfrom 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

MethodWhen CalledUse For
_execute_extraction()Before operator runsStatic/known lineage
extract_on_complete(task_instance)After successRuntime-determined lineage
extract_on_failure(task_instance)After failurePartial 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, OperatorLineagefrom openlineage.client.event_v2 import Datasetfrom 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, OperatorLineagefrom 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 issuesfrom airflow.models import TaskInstancefrom openlineage.client.event_v2 import Dataset
class MyExtractor(BaseExtractor):    ...
python
# ✅ GOOD - import inside methodsclass 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 existAIRFLOW__OPENLINEAGE__EXTRACTORS='extractors.MyExtractor'
# ✅ Correct - full importable pathAIRFLOW__OPENLINEAGE__EXTRACTORS='dags.extractors.my_extractor.MyExtractor'

3. Not Handling None

Problem: Extraction fails when operator properties are None.

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

Testing Extractors

Unit Testing

python
import pytestfrom unittest.mock import MagicMockfrom mypackage.extractors import MyOperatorExtractor
def test_extractor():    # Mock the operator    operator = MagicMock()    operator.source_table = "input_table"    operator.target_table = "output_table"
    # Create extractor    extractor = MyOperatorExtractor(operator)
    # Test extraction    lineage = extractor._execute_extraction()
    assert len(lineage.inputs) == 1    assert lineage.inputs[0].name == "input_table"    assert len(lineage.outputs) == 1    assert lineage.outputs[0].name == "output_table"

Precedence Rules

OpenLineage checks for lineage in this order:

  1. Custom Extractors (highest priority)
  2. OpenLineage Methods on operator
  3. Hook-Level Lineage (from HookLineageCollector)
  4. Inlets/Outlets (lowest priority)

If a custom extractor exists, it overrides built-in extraction and inlets/outlets.

Related Skills

  • annotating-task-lineage: For simple table-level lineage with inlets/outlets
  • tracing-upstream-lineage: Investigate data origins
  • tracing-downstream-lineage: Investigate data dependencies

Source and attribution

Source:astronomer/agentsinskills/creating-openlineage-extractorsat commitcbe1141

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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