migrating-airflow-2-to-3
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.
Best use case
migrating-airflow-2-to-3 is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using migrating-airflow-2-to-3 should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/migrating-airflow-2-to-3/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How migrating-airflow-2-to-3 Compares
| Feature / Agent | migrating-airflow-2-to-3 | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# 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](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.
---
## 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:
```text
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`:
```text
apache-airflow-client==<your-airflow-runtime-version>
```
Example usage:
```python
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`:
```python
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:
```bash
# 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 .
```
---
## Reference Files
For detailed code examples and migration patterns, see:
- **[reference/config-changes.md](reference/config-changes.md)** - `airflow.cfg` section moves, renames, and removals
- **[reference/migration-patterns.md](reference/migration-patterns.md)** - Code examples for imports, scheduling, XCom, Assets, DAG bundles, runtime behavior changes
- **[reference/removed-methods.md](reference/removed-methods.md)** - Removed model methods with SDK/API migration paths
- **[reference/migration-checklist.md](reference/migration-checklist.md)** - Search patterns and fixes for issues Ruff doesn't catch
---
## 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.
---
## Resources
- [Astronomer Airflow 3 Upgrade Guide](https://www.astronomer.io/docs/astro/airflow3/upgrade-af3)
- [Airflow 3 Release Notes](https://airflow.apache.org/docs/apache-airflow/stable/release_notes.html)
- [Ruff Airflow Rules](https://docs.astral.sh/ruff/rules/#airflow-air)
---
## Related Skills
- **testing-dags**: For testing DAGs after migration
- **debugging-dags**: For troubleshooting migration issues
- **deploying-airflow**: For deploying migrated DAGs to productionRelated Skills
deploying-airflow
Deploy Airflow DAGs and projects. Use when the user wants to deploy code, push DAGs, set up CI/CD, deploy to production, or asks about deployment strategies for Airflow.
airflow
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
airflow-plugins
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
airflow-hitl
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
airflow-adapter
Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across Airflow 2.x and 3.x.
warehouse-init
Initialize warehouse schema discovery. Generates .astro/warehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says "/astronomer-data:warehouse-init" or asks to set up data discovery.
troubleshooting-astro-deployments
Troubleshoot Astronomer production deployments with Astro CLI. Use when investigating deployment issues, viewing production logs, analyzing failures, or managing deployment environment variables.
tracing-upstream-lineage
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.
tracing-downstream-lineage
Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.
testing-dags
Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill is for iterative test-debug-fix cycles.
setting-up-astro-project
Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.
profiling-tables
Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.