Creating Openlineage Extractors

作者 astronomercbe1141f547b无许可证451 个星标收录于 2026年10月8日更新于 2026年10月8日仓库今天更新

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.

仅含说明Software DevelopmentData & Analytics
AI 生成的概览

指导为缺少内置血缘支持的 Airflow 算子创建自定义 OpenLineage 提取器。

功能
该技能说明如何从没有内置 OpenLineage 支持的 Airflow 算子中采集数据血缘。它涵盖两种方式：在自有算子上直接添加 OpenLineage 方法，以及为无法修改的第三方算子编写 BaseExtractor 子类。内容提供 SQL 算子、文件传输和运行时确定血缘的代码模式，并涉及注册、测试、优先级规则和常见陷阱。
适用场景
当需要从不受支持或第三方 Airflow 算子获取血缘、需要列级血缘，或提取逻辑超出 inlets 和 outlets 所能提供时使用。它也适用于在 OpenLineage 方法与自定义提取器之间做选择。
运行要求
需要安装 OpenLineage provider 的 Apache Airflow 以及 openlineage 客户端库；提取器必须可从 Airflow worker 导入，并通过 airflow.cfg 或环境变量注册。该技能不附带脚本，仅包含说明和代码示例。

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

来源与署名

来源：astronomer/agents位于skills/creating-openlineage-extractors提交cbe1141

许可证： 无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架

更多来自 astronomer/agents 的技能

Warehouse Init

astronomer

发现数据仓库的模式元数据，并生成可共享的 .astro/warehouse.md 参考文件，以便快速进行概念到表的查找。

Data & Analytics451今天更新

Tracing Upstream Lineage

astronomer

Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.

待分类451今天更新

Tracing Downstream Lineage

astronomer

追踪下游数据血缘，评估表或 DAG 变更会造成哪些影响。

Data & Analytics451今天更新

Testing Dags

astronomer

指导使用 af 命令行工具对 Airflow DAG 进行迭代式测试、调试与修复。

Software Development451今天更新

Setting Up Astro Project

astronomer

使用 Astro CLI 初始化并配置 Astro/Airflow 项目，涵盖依赖、连接与变量。

DevOps & Cloud451今天更新

Profiling Tables

astronomer

通过 SQL 统计、基数分析、样本数据和数据质量评估，对数据库表进行剖析。

Data & Analytics451今天更新

更多Software Development技能

Writing Plans

obra

Use when you have a spec or requirements for a multi-step task, before touching code

Software Development296K今天更新

Verification Before Completion

obra

要求在声称工作已完成、已修复或已通过之前，先运行最新的验证命令并确认输出。

Software Development296K今天更新

Using Git Worktrees

obra

为功能开发搭建隔离的 git worktree 工作区，并执行项目初始化与基线测试。

Software Development296K今天更新

Requesting Code Review

obra

派发代码审查子代理，并为其准备精确上下文，在问题扩散前发现它们。

Software Development296K今天更新

Receiving Code Review

obra

指导智能体接收代码评审反馈：核实建议、澄清不明项、以技术理由反驳，并逐项实施修复。

Software Development296K今天更新

Finishing A Development Branch

obra

引导智能体收尾已完成的开发分支：验证测试、选择合并、提 PR 或保留，然后清理工作区。

Software Development296K今天更新