creating-openlineage-extractors

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

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
)

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

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

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

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}",
                )
            ],
        )

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],
        )

# ❌ 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
        # ...

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

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

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"