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

1. 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 .
2. 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.
   • Hard behavior/config gotchas to explicitly review:
     • Cron scheduling semantics: consider AIRFLOW__SCHEDULER__CREATE_CRON_DATA_INTERVAL=True if you need Airflow 2-style cron data intervals.
     • .airflowignore syntax changed from regexp to glob; set AIRFLOW__CORE__DAG_IGNORE_FILE_SYNTAX=regexp if you must keep regexp behavior.
     • OAuth callback URLs add an /auth/ prefix (e.g. /auth/oauth-authorized/google).
     • Shared utility imports: Bare imports like import common from dags/common/ no longer work on Astro. Use fully qualified imports: import dags.common.
3. 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operators/hooks/providers - refactor metadata access to using the Airflow client instead of direct access - fix use of outdated context variables - fix scheduling logic.
4. Implement changes incrementally, re-running Ruff and code searches after each major change.
5. 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.

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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.

Trigger implementation gotcha: If a trigger calls hooks synchronously inside the asyncio event loop, it may fail or block. Prefer calling hooks via sync_to_async(...) (or otherwise ensure hook calls are async-safe).

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())

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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:

# Auto-fix all detectable Airflow issues (safe + unsafe)
ruff check --preview --select AIR --fix --unsafe-fixes .

# Check remaining Airflow issues without fixing
ruff check --preview --select AIR .

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Reference Files

For detailed code examples and migration patterns, see:

• reference/config-changes.md - airflow.cfg section moves, renames, and removals
• reference/migration-patterns.md - Code examples for imports, scheduling, XCom, Assets, DAG bundles, runtime behavior changes
• reference/removed-methods.md - Removed model methods with SDK/API migration paths
• reference/migration-checklist.md - Search patterns and fixes for issues Ruff doesn't catch

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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	Use ds with date math: macros.ds_add(ds, 1) / macros.ds_add(ds, -1)
prev_ds / next_ds	prev_start_date_success or timetable API
triggering_dataset_events	triggering_asset_events
templates_dict	context["params"]

Asset-triggered runs: logical_date may be None; use context["dag_run"].logical_date defensively.

Cannot trigger with future logical_date: Use logical_date=None and rely on run_id instead.

Cron note: for scheduled runs using cron, logical_date semantics differ under CronTriggerTimetable (aligning logical_date with run_after). If you need Airflow 2-style cron data intervals, consider AIRFLOW__SCHEDULER__CREATE_CRON_DATA_INTERVAL=True.

Default Behavior Changes

Setting	Airflow 2 Default	Airflow 3 Default
schedule	timedelta(days=1)	None
catchup	True	False

Callback Behavior Changes

• on_success_callback no longer runs on skip; use on_skipped_callback if needed.
• @teardown with TriggerRule.ALWAYS not allowed; teardowns now execute even if DAG run terminated early.

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Resources

• Astronomer Airflow 3 Upgrade Guide
• Airflow 3 Release Notes
• Ruff Airflow Rules

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Related Skills

• testing-dags: For testing DAGs after migration
• debugging-dags: For troubleshooting migration issues
• deploying-airflow: For deploying migrated DAGs to production