| name | migrating-airflow-2-to-3 |
| description | Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request. |
| hooks | {"PostToolUse":[{"matcher":"Edit","hooks":"[Truncated]"}]} |
Airflow 2 to 3 Migration
This skill helps migrate Airflow 2.x DAG code to Airflow 3.x, focusing on code changes (imports, operators, hooks, context, API usage).
Important: Before migrating to Airflow 3, strongly recommend upgrading to Airflow 2.11 first, then to at least Airflow 3.0.11 (ideally directly to 3.1). Other upgrade paths would make rollbacks impossible. See: https://www.astronomer.io/docs/astro/airflow3/upgrade-af3#upgrade-your-airflow-2-deployment-to-airflow-3. Additionally, early 3.0 versions have many bugs - 3.1 provides a much better experience.
Migration at a Glance
- Run Ruff's Airflow migration rules to auto-fix detectable issues (AIR30/AIR301/AIR302/AIR31/AIR311/AIR312).
ruff check --preview --select AIR --fix --unsafe-fixes .
- Scan for remaining issues using the manual search checklist in reference/migration-checklist.md.
- Focus on: direct metadata DB access, legacy imports, scheduling/context keys, XCom pickling, datasets-to-assets, REST API/auth, plugins, and file paths.
- Plan changes per file and issue type:
- Fix imports - update operators/hooks/providers - refactor metadata access to using the Airflow client instead of direct access - fix use of outdated context variables - fix scheduling logic.
- Implement changes incrementally, re-running Ruff and code searches after each major change.
- Explain changes to the user and caution them to test any updated logic such as refactored metadata, scheduling logic and use of the Airflow context.
Architecture & Metadata DB Access
Airflow 3 changes how components talk to the metadata database:
- Workers no longer connect directly to the metadata DB.
- Task code runs via the Task Execution API exposed by the API server.
- The DAG processor runs as an independent process separate from the scheduler.
- The Triggerer uses the task execution mechanism via an in-process API server.
Key code impact: Task code can still import ORM sessions/models, but any attempt to use them to talk to the metadata DB will fail with:
RuntimeError: Direct database access via the ORM is not allowed in Airflow 3.x
Patterns to search for
When scanning DAGs, custom operators, and @task functions, look for:
- Session helpers:
provide_session, create_session, @provide_session
- Sessions from settings:
from airflow.settings import Session
- Engine access:
from airflow.settings import engine
- ORM usage with models:
session.query(DagModel)..., session.query(DagRun)...
Replacement: Airflow Python client
Preferred for rich metadata access patterns. Add to requirements.txt:
apache-airflow-client==<your-airflow-runtime-version>
Example usage:
import os
from airflow.sdk import BaseOperator
import airflow_client.client
from airflow_client.client.api.dag_api import DAGApi
_HOST = os.getenv("AIRFLOW__API__BASE_URL", "https://<your-org>.astronomer.run/<deployment>/")
_TOKEN = os.getenv("DEPLOYMENT_API_TOKEN")
class ListDagsOperator(BaseOperator):
def execute(self, context):
config = airflow_client.client.Configuration(host=_HOST, access_token=_TOKEN)
with airflow_client.client.ApiClient(config) as api_client:
dag_api = DAGApi(api_client)
dags = dag_api.get_dags(limit=10)
self.log.info("Found %d DAGs", len(dags.dags))
Replacement: Direct REST API calls
For simple cases, call the REST API directly using requests:
from airflow.sdk import task
import os
import requests
_HOST = os.getenv("AIRFLOW__API__BASE_URL", "https://<your-org>.astronomer.run/<deployment>/")
_TOKEN = os.getenv("DEPLOYMENT_API_TOKEN")
@task
def list_dags_via_api() -> None:
response = requests.get(
f"{_HOST}/api/v2/dags",
headers={"Accept": "application/json", "Authorization": f"Bearer {_TOKEN}"},
params={"limit": 10}
)
response.raise_for_status()
print(response.json())
Ruff Airflow Migration Rules
Use Ruff's Airflow rules to detect and fix many breaking changes automatically.
- AIR30 / AIR301 / AIR302: Removed code and imports in Airflow 3 - must be fixed.
- AIR31 / AIR311 / AIR312: Deprecated code and imports - still work but will be removed in future versions; should be fixed.
Commands to run (via uv) against the project root:
ruff check --preview --select AIR --fix --unsafe-fixes .
ruff check --preview --select AIR .
Reference Files
For detailed code examples and migration patterns, see:
Quick Reference Tables
Key Import Changes
| Airflow 2.x | Airflow 3 |
|---|
airflow.operators.dummy_operator.DummyOperator | airflow.providers.standard.operators.empty.EmptyOperator |
airflow.operators.bash.BashOperator | airflow.providers.standard.operators.bash.BashOperator |
airflow.operators.python.PythonOperator | airflow.providers.standard.operators.python.PythonOperator |
airflow.decorators.dag | airflow.sdk.dag |
airflow.decorators.task | airflow.sdk.task |
airflow.datasets.Dataset | airflow.sdk.Asset |
Context Key Changes
| Removed Key | Replacement |
|---|
execution_date | context["dag_run"].logical_date |
tomorrow_ds / yesterday_ds | data_interval_start / data_interval_end |
prev_ds / next_ds | prev_start_date_success or timetable API |
triggering_dataset_events | triggering_asset_events |
Default Behavior Changes
| Setting | Airflow 2 Default | Airflow 3 Default |
|---|
schedule | timedelta(days=1) | None |
catchup | True | False |
Resources
Related Skills
- testing-dags: For testing DAGs after migration
- debugging-dags: For troubleshooting migration issues
