---
name: creating-openlineage-extractors
description: 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.
---

# 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](https://airflow.apache.org/docs/apache-airflow-providers-openlineage/stable/guides/developer.html) 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.

---

## 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"

    # 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
