Creating OpenLineage Extractors

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

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.

---

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:

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

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

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

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

Registering Extractors

Option 1: Configuration file (airflow.cfg)

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

Option 2: Environment variable

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

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

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

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.

# ❌ BAD - can cause circular import issues
from airflow.models import TaskInstance
from openlineage.client.event_v2 import Dataset

class MyExtractor(BaseExtractor):
    ...

# ✅ 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.

# ❌ 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.

# ✅ 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

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"

    # 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