v3.0.0

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We are proud to announce the General Availability of Apache Airflow 3.0 — the most significant release in the project's
history. This version introduces a service-oriented architecture, a stable DAG authoring interface, expanded support for
event-driven and ML workflows, and a fully modernized UI built on React. Airflow 3.0 reflects years of community
investment and lays the foundation for the next era of scalable, modular orchestration.

Highlights
^^^^^^^^^^

• Service-Oriented Architecture: A new Task Execution API and airflow api-server enable task execution in remote environments with improved isolation and flexibility (AIP-72).

• Edge Executor: A new executor that supports distributed, event-driven, and edge-compute workflows (AIP-69), now generally available.

• Stable Authoring Interface: DAG authors should now use the new airflow.sdk namespace to import core DAG constructs like @dag, @task, and DAG.

• Scheduler-Managed Backfills: Backfills are now scheduled and tracked like regular DAG runs, with native UI and API support (AIP-78).

• DAG Versioning: Airflow now tracks structural changes to DAGs over time, enabling inspection of historical DAG definitions via the UI and API (AIP-66).

• Asset-Based Scheduling: The dataset model has been renamed and redesigned as assets, with a new @asset decorator and cleaner event-driven DAG definition (AIP-74, AIP-75).

• Support for ML and AI Workflows: DAGs can now run with logical_date=None, enabling use cases such as model inference, hyperparameter tuning, and non-interval workflows (AIP-83).

• Removal of Legacy Features: SLAs, SubDAGs, DAG and Xcom pickling, and several internal context variables have been removed. Use the upgrade tools to detect deprecated usage.

• Split CLI and API Changes: The CLI has been split into airflow and airflowctl (AIP-81), and REST API now defaults to logical_date=None when triggering a new DAG run.

• Modern React UI: A complete UI overhaul built on React and FastAPI includes version-aware views, backfill management, and improved DAG and task introspection (AIP-38, AIP-84).

• Migration Tooling: Use ruff and airflow config update to validate DAGs and configurations. Upgrade requires Airflow 2.7 or later and Python 3.9–3.12.

Significant Changes
^^^^^^^^^^^^^^^^^^^

Airflow 3.0 introduces the most significant set of changes since the 2.0 release, including architectural shifts, new
execution models, and improvements to DAG authoring and scheduling.

Task Execution API & Task SDK (AIP-72)
""""""""""""""""""""""""""""""""""""""

Airflow now supports a service-oriented architecture, enabling tasks to be executed remotely via a new Task Execution
API. This API decouples task execution from the scheduler and introduces a stable contract for running tasks outside of
Airflow's traditional runtime environment.

To support this, Airflow introduces the Task SDK — a lightweight runtime environment for running Airflow tasks in
external systems such as containers, edge environments, or other runtimes. This lays the groundwork for
language-agnostic task execution and brings improved isolation, portability, and extensibility to Airflow-based
workflows.

Airflow 3.0 also introduces a new airflow.sdk namespace that exposes the core authoring interfaces for defining DAGs
and tasks. DAG authors should now import objects like DAG, @dag, and @task from airflow.sdk rather than
internal modules. This new namespace provides a stable, forward-compatible interface for DAG authoring across future
versions of Airflow.

Edge Executor (AIP-69)
""""""""""""""""""""""

Airflow 3.0 introduces the Edge Executor as a generally available feature, enabling execution of tasks in
distributed or remote compute environments. Designed for event-driven and edge-compute use cases, the Edge Executor
integrates with the Task Execution API to support task orchestration beyond the traditional Airflow runtime. This
advancement facilitates hybrid and cross-environment orchestration patterns, allowing task workers to operate closer to
data or application layers.

Scheduler-Managed Backfills (AIP-78)
""""""""""""""""""""""""""""""""""""

Backfills are now fully managed by the scheduler, rather than being launched as separate command-line jobs. This change
unifies backfill logic with regular DAG execution and ensures that backfill runs follow the same scheduling, versioning,
and observability models as other DAG runs.

Airflow 3.0 also introduces native UI and REST API support for initiating and monitoring backfills, making them more
accessible and easier to integrate into automated workflows. These improvements lay the foundation for smarter, safer
historical reprocessing — now available directly through the Airflow UI and API.

DAG Versioning (AIP-66)
"""""""""""""""""""""""

Airflow 3.0 introduces native DAG versioning. DAG structure changes (e.g., renamed tasks, dependency shifts) are now
tracked directly in the metadata database. This allows users to inspect historical DAG structures through the UI and API,
and lays the foundation for safer backfills, improved observability, and runtime-determined DAG logic.

React UI Rewrite (AIP-38, AIP-84)
"""""""""""""""""""""""""""""""""

Airflow 3.0 ships with a completely redesigned user interface built on React and FastAPI. This modern architecture
improves responsiveness, enables more consistent navigation across views, and unlocks new UI capabilities — including
support for DAG versioning, asset-centric DAG definitions, and more intuitive filtering and search.

The new UI replaces the legacy Flask-based frontend and introduces a foundation for future extensibility and community
contributions.

Asset-Based Scheduling & Terminology Alignment (AIP-74, AIP-75)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

The concept of Datasets has been renamed to Assets, unifying terminology with common practices in the modern
data ecosystem. The internal model has also been reworked to better support future features like asset partitions and
validations.

The @asset decorator and related changes to the DAG parser enable clearer, asset-centric DAG definitions, allowing
Airflow to more naturally support event-driven and data-aware scheduling patterns.

Unified Scheduling Field
""""""""""""""""""""""""

Airflow 3.0 removes the legacy schedule_interval and timetable parameters. DAGs must now use the unified
schedule field for all time- and event-based scheduling logic. This simplifies DAG definition and improves
consistency across scheduling paradigms.

Updated Scheduling Defaults
"""""""""""""""""""""""""""

Airflow 3.0 changes the default behavior for new DAGs by setting catchup_by_default = False in the configuration
file. This means DAGs that do not explicitly set catchup=... will no longer backfill missed intervals by default.
This change reduces confusion for new users and better reflects the growing use of on-demand and event-driven workflows.

Restricted Metadata Database Access
"""""""""""""""""""""""""""""""""""

Task code can no longer directly access the metadata database. Interactions with DAG state, task history, or DAG runs
must be performed via the Airflow REST API or exposed context. This change improves architectural separation and enables
remote execution.

Future Logical Dates No Longer Supported
"""""""""""""""""""""""""""""""""""""""""

Airflow no longer supports triggering DAG runs with a logical date in the future. This change aligns with the logical
execution model and removes ambiguity in backfills and event-driven DAGs. Use logical_date=None to trigger runs with
the current timestamp.

Context Behavior for Asset and Manually Triggered DAGs
""""""""""""""""""""""""""""""""""""""""""""""""""""""

For DAG runs triggered by an Asset event or through the REST API without specifying a logical_date, Airflow now sets
logical_date=None by default. These DAG runs do not have a data interval, and attempting to access
data_interval_start, data_interval_end, or logical_date from the task context will raise a KeyError.

DAG authors should use dag_run.logical_date and perform appropriate checks or fallbacks if supporting multiple
trigger types. This change improves consistency with event-driven semantics but may require updates to existing DAGs
that assume these values are always present.

Improved Callback Behavior
""""""""""""""""""""""""""

Airflow 3.0 refines task callback behavior to improve clarity and consistency. In particular, on_success_callback is
no longer executed when a task is marked as SKIPPED, aligning it more closely with expected semantics.

Updated Default Configuration
"""""""""""""""""""""""""""""

Several default configuration values have been updated in Airflow 3.0 to better reflect modern usage patterns and
simplify onboarding:

• catchup_by_default is now set to False by default. DAGs will not automatically backfill unless explicitly configured to do so.
• create_cron_data_intervals is now set to False by default. As a result, cron expressions will be interpreted using the CronTriggerTimetable instead of the legacy CronDataIntervalTimetable.
• SimpleAuthManager is now the default auth_manager. To continue using Flask AppBuilder-based authentication, install the apache-airflow-providers-flask-appbuilder provider and explicitly set auth_manager = airflow.providers.fab.auth_manager.FabAuthManager.

These changes represent the most significant evolution of the Airflow platform since the release of 2.0 — setting the
stage for more scalable, event-driven, and language-agnostic orchestration in the years ahead.

Executor & Scheduler Updates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Airflow 3.0 introduces several important improvements and behavior changes in how DAGs and tasks are scheduled,
prioritized, and executed.

Standalone DAG Processor Required
"""""""""""""""""""""""""""""""""

Airflow 3.0 now requires the standalone DAG processor to parse DAGs. This dedicated process improves scheduler
performance, isolation, and observability. It also simplifies architecture by clearly separating DAG parsing from
scheduling logic. This change may affect custom deployments that previously used embedded DAG parsing.

Priority Weight Capped by Pool Slots
"""""""""""""""""""""""""""""""""""""

The priority_weight value on a task is now capped by the number of available pool slots. This ensures that resource
availability remains the primary constraint in task execution order, preventing high-priority tasks from starving others
when resource contention exists.

Teardown Task Handling During DAG Termination
"""""""""""""""""""""""""""""""""""""""""""""

Teardown tasks will now be executed even when a DAG run is terminated early. This ensures that cleanup logic is
respected, improving reliability for workflows that use teardown tasks to manage ephemeral infrastructure, temporary
files, or downstream notifications.

Improved Scheduler Fault Tolerance
""""""""""""""""""""""""""""""""""

Scheduler components now use run_with_db_retries to handle transient database issues more gracefully. This enhances
Airflow's fault tolerance in high-volume environments and reduces the likelihood of scheduler restarts due to temporary
database connection problems.

Mapped Task Stats Accuracy
"""""""""""""""""""""""""""

Airflow 3.0 fixes a bug that caused incorrect task statistics to be reported for dynamic task mapping. Stats now
accurately reflect the number of mapped task instances and their statuses, improving observability and debugging for
dynamic workflows.

DAG Authoring Enhancements
^^^^^^^^^^^^^^^^^^^^^^^^^^

Airflow 3.0 includes several changes that improve consistency, clarity, and long-term stability for DAG authors.

Renamed Parameter: fail_stop → fail_fast
"""""""""""""""""""""""""""""""""""""""""""""""""

The DAG argument fail_stop has been renamed to fail_fast for improved clarity. This parameter controls whether a
DAG run should immediately stop execution when a task fails. DAG authors should update any code referencing
fail_stop to use the new name.

Context Cleanup and Parameter Removal
"""""""""""""""""""""""""""""""""""""

Several legacy context variables have been removed or may no longer be available in certain types of DAG runs,
including:

• conf
• execution_date
• dag_run.external_trigger

In asset-triggered and manually triggered DAG runs with logical_date=None, data interval fields such as
data_interval_start and data_interval_end may not be present in the task context. DAG authors should use
explicit references such as dag_run.logical_date and conditionally check for the presence of interval-related fields
where applicable.

Task Context Utilities Moved
""""""""""""""""""""""""""""

Internal task context functions such as get_parsing_context have been moved to a more appropriate location (e.g.,
airflow.models.taskcontext). DAG authors using these utilities directly should update import paths accordingly.

Trigger Rule Restrictions
"""""""""""""""""""""""""

The TriggerRule.ALWAYS rule can no longer be used with teardown tasks or tasks that are expected to honor upstream
dependency semantics. DAG authors should ensure that teardown logic is defined with the appropriate trigger rules for
consistent task resolution behavior.

Asset Aliases for Reusability
"""""""""""""""""""""""""""""

A new utility function, create_asset_aliases(), allows DAG authors to define reusable aliases for frequently
referenced Assets. This improves modularity and reuse across DAG files and is particularly helpful for teams adopting
asset-centric DAGs.

Support for ML & AI Use Cases (AIP-83)
"""""""""""""""""""""""""""""""""""""""

Airflow 3.0 expands the types of DAGs that can be expressed by removing the constraint that each DAG run must correspond
to a unique data interval. This change, introduced in AIP-83, enables support for workflows that don't operate on a
fixed schedule — such as model training, hyperparameter tuning, and inference tasks.

These ML- and AI-oriented DAGs often run ad hoc, are triggered by external systems, or need to execute multiple times
with different parameters over the same dataset. By allowing multiple DAG runs with logical_date=None, Airflow now
supports these scenarios natively without requiring workarounds.

Config & Interface Changes
^^^^^^^^^^^^^^^^^^^^^^^^^^

Airflow 3.0 introduces several configuration and interface updates that improve consistency, clarify ownership of core
utilities, and remove legacy behaviors that were no longer aligned with modern usage patterns.

Default Value Handling
""""""""""""""""""""""

Airflow no longer silently updates configuration options that retain deprecated default values. Users are now required
to explicitly set any config values that differ from the current defaults. This change improves transparency and
prevents unintentional behavior changes during upgrades.

Refactored Config Defaults
"""""""""""""""""""""""""""

Several configuration defaults have changed in Airflow 3.0 to better reflect modern usage patterns:

• The default value of catchup_by_default is now False. DAGs will not backfill missed intervals unless explicitly configured to do so.
• The default value of create_cron_data_intervals is now False. Cron expressions are now interpreted using the CronTriggerTimetable instead of the legacy CronDataIntervalTimetable. This change simplifies interval logic and aligns with the future direction of Airflow's scheduling system.

Refactored Internal Utilities
"""""""""""""""""""""""""""""

Several core components have been moved to more intuitive or stable locations:

• The SecretsMasker class has been relocated to airflow.utils.secrets_masker.
• The ObjectStoragePath utility previously located under airflow.io is now available via airflow.utils.object_storage_path.

These changes simplify imports and reflect broader efforts to stabilize utility interfaces across the Airflow codebase.

Improved inlet_events, outlet_events, and triggering_asset_events
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

Asset event mappings in the task context are improved to better support asset use cases, including new features introduced in AIP-74.

Events of an asset or asset alias are now accessed directly by a concrete object to avoid ambiguity. Using a str to access events is
no longer supported. Use an Asset or AssetAlias object, or Asset.ref to refer to an entity explicitly instead, such as::

outlet_events[Asset.ref(name="myasset")]  # Get events for asset named "myasset".
outlet_events[AssetAlias(name="myalias")]  # Get events for asset alias named "myalias".

Alternatively, two helpers for_asset and for_asset_alias are added as shortcuts::

outlet_events.for_asset(name="myasset")  # Get events for asset named "myasset".
outlet_events.for_asset_alias(name="myalias")  # Get events for asset alias named "myalias".

The internal representation of asset event triggers now also includes an explicit uri field, simplifying traceability and
aligning with the broader asset-aware execution model introduced in Airflow 3.0. DAG authors interacting directly with
inlet_events may need to update logic that assumes the previous structure.

Behaviour change in xcom_pull
"""""""""""""""""""""""""""""""""

Pulling without setting task_ids:

In Airflow 2, the xcom_pull() method allowed pulling XComs by key without specifying task_ids, despite the fact that the underlying
DB model defines task_id as part of the XCom primary key. This created ambiguity: if two tasks pushed XComs with the same key,
xcom_pull() would pull whichever one happened to be first, leading to unpredictable behavior.

Airflow 3 resolves this inconsistency by requiring task_ids when pulling by key. This change aligns with the task-scoped nature of
XComs as defined by the schema, ensuring predictable and consistent behavior.

DAG Authors should update their dags to use task_ids if their dags used xcom_pull without task_ids such as::

kwargs["ti"].xcom_pull(key="key")

Should be updated to::

kwargs["ti"].xcom_pull(task_ids="task1", key="key")

Return Type Change for Single Task ID:

In Airflow 2, when using xcom_pull() with a single task ID in a list (e.g., task_ids=["task1"]), it would return a LazyXComSelectSequence
object containing one value. In Airflow 3.0.0, this behavior was changed to return the value directly.

So, if you previously used:

.. code-block:: python

xcom_values = kwargs["ti"].xcom_pull(task_ids=["task1"], key="key")
xcom_value = xcom_values[0]  # Access the first value

You would now get the value directly, rather than a sequence containing one value.

.. code-block:: python

xcom_value = kwargs["ti"].xcom_pull(task_ids=["task1"], key="key")

The previous behaviour (returning list when passed a list) will be restored in Airflow 3.0.1 to maintain backward compatibility.

However, it is recommended to be explicit about your intentions when using task_ids (after the fix in 3.0.1):

• If you want a single value, use task_ids="task1"
• If you want a sequence, use task_ids=["task1"]

This makes the code more explicit and easier to understand.

Removed Configuration Keys
"""""""""""""""""""""""""""

As part of the deprecation cleanup, several legacy configuration options have been removed. These include:

• scheduler.allow_trigger_in_future
• scheduler.use_job_schedule
• scheduler.use_local_tz

Users should review their airflow.cfg files or use the airflow config lint command to identify outdated or
removed options.

Upgrade Tooling
""""""""""""""""

Airflow 3.0 includes improved support for upgrade validation. Use the following tools to proactively catch incompatible
configs or deprecated usage patterns:

• airflow config lint: Identifies removed or invalid config keys
• ruff check --select AIR30: Flags removed interfaces and common migration issues

CLI & API Changes
^^^^^^^^^^^^^^^^^

Airflow 3.0 introduces changes to both the CLI and REST API interfaces to better align with service-oriented deployments
and event-driven workflows.

Split CLI Architecture (AIP-81)
"""""""""""""""""""""""""""""""

The Airflow CLI has been split into two distinct interfaces:

• The core airflow CLI now handles only local functionality (e.g., airflow tasks test, airflow dags list).
• Remote functionality, including triggering DAGs or managing connections in service-mode environments, is now handled by a separate CLI called airflowctl, distributed via the apache-airflow-client package.

This change improves security and modularity for deployments that use Airflow in a distributed or API-first context.

REST API: DAG Trigger Behavior Updated
""""""""""""""""""""""""""""""""""""""

The behavior of the POST /dags/{dag_id}/dagRuns endpoint has changed. If a logical_date is not explicitly
provided when triggering a DAG via the REST API, it now defaults to None.

This aligns with event-driven DAGs and manual runs in Airflow 3.0, but may break backward compatibility with scripts or
tools that previously relied on Airflow auto-generating a timestamped logical_date.

Removed CLI Flags and Commands
""""""""""""""""""""""""""""""

Several deprecated CLI arguments and commands that were marked for removal in earlier versions have now been cleaned up
in Airflow 3.0. Refer to the Deprecations & Removals section or run airflow --help to review the current set of
available commands and arguments.

Provider Refactor & Standardization
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Airflow 3.0 completes the migration of several core operators, sensors, and hooks into the new
apache-airflow-providers-standard package. This package now includes commonly used components such as:

• PythonOperator
• BashOperator
• EmailOperator
• SimpleHttpOperator
• ShortCircuitOperator

These operators were previously bundled inside airflow-core but are now treated as provider-managed components to
improve modularity, testability, and lifecycle independence.

This change enables more consistent versioning across providers and prepares Airflow for a future where all integrations
— including "standard" ones — follow the same interface model.

To maintain compatibility with existing DAGs, the apache-airflow-providers-standard package is installable on both
Airflow 2.x and 3.x. Users upgrading from Airflow 2.x are encouraged to begin updating import paths and testing provider
installation in advance of the upgrade.

Legacy imports such as airflow.operators.python.PythonOperator are deprecated and will be removed soon. They should be
replaced with:

.. code-block:: python

from airflow.providers.standard.operators.python import PythonOperator

UI & Usability Improvements
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Airflow 3.0 introduces a modernized user experience that complements the new React-based UI architecture (see
Significant Changes). Several areas of the interface have been enhanced to improve visibility, consistency, and
navigability.

New Home Page
"""""""""""""

The Airflow Home page now provides a high-level operational overview of your environment. It includes health checks for
core components (Scheduler, Triggerer, DAG Processor), summary stats for DAG and task instance states, and a real-time
feed of asset-triggered events. This view helps users quickly identify pipeline health, recent activity, and potential
failures.

Unified DAG List View
""""""""""""""""""""""

The DAG List page has been refreshed with a cleaner layout and improved responsiveness. Users can browse DAGs by name,
tags, or owners. While full-text search has not yet been integrated, filters and navigation have been refined for
clarity in large deployments.

Version-Aware Graph and Grid Views
"""""""""""""""""""""""""""""""""""

The Graph and Grid views now display task information in the context of the DAG version that was used at runtime. This
improves traceability for DAGs that evolve over time and provides more accurate debugging of historical runs.

Expanded DAG Graph Visualization
""""""""""""""""""""""""""""""""

The Graph view now supports visualizing the full chain of asset and task dependencies, including assets consumed or
produced across DAG boundaries. This allows users to inspect upstream and downstream lineage in a unified view, making
it easier to trace data flows, debug triggering behavior, and understand conditional dependencies between assets and
tasks.

DAG Code View
"""""""""""""

The "Code" tab now displays the exact DAG source as parsed by the scheduler for the selected DAG version. This allows
users to inspect the precise code that was executed, even for historical runs, and helps debug issues related to
versioned DAG changes.

Improved Task Log Access
"""""""""""""""""""""""""

Task log access has been streamlined across views. Logs are now easier to access from both the Grid and Task Instance
pages, with cleaner formatting and reduced visual noise.

Enhanced Asset and Backfill Views
""""""""""""""""""""""""""""""""""

New UI components support asset-centric DAGs and backfill workflows:

• Asset definitions are now visible from the DAG details page, allowing users to inspect upstream and downstream asset relationships.
• Backfills can be triggered and monitored directly from the UI, including support for scheduler-managed backfills introduced in Airflow 3.0.

These improvements make Airflow more accessible to operators, data engineers, and stakeholders working across both
time-based and event-driven workflows.

Deprecations & Removals
^^^^^^^^^^^^^^^^^^^^^^^^

A number of deprecated features, modules, and interfaces have been removed in Airflow 3.0, completing long-standing
migrations and cleanups.

Users are encouraged to review the following removals to ensure compatibility:

• SubDag support has been removed entirely, including the SubDagOperator, related CLI and API interfaces. TaskGroups are now the recommended alternative for nested DAG structures.

• SLAs have been removed: The legacy SLA feature, including SLA callbacks and metrics, has been removed. A more flexible replacement mechanism, DeadlineAlerts, is planned for a future version of Airflow. Users who relied on SLA-based notifications should consider implementing custom alerting using task-level success/failure hooks or external monitoring integrations.

• Pickling support has been removed: All legacy features related to DAG pickling have been fully removed. This includes the PickleDag CLI/API, as well as implicit behaviors around store_serialized_dags = False. DAGs must now be serialized using the JSON-based serialization system. Ensure any custom Python objects used in DAGs are JSON-serializable.

• Context parameter cleanup: Several previously available context variables have been removed from the task execution context, including conf, execution_date, and dag_run.external_trigger. These values are either no longer applicable or have been renamed (e.g., use dag_run.logical_date instead of execution_date). DAG authors should ensure that templated fields and Python callables do not reference these deprecated keys.

• Deprecated core imports have been fully removed. Any use of airflow.operators.*, airflow.hooks.*, or similar legacy import paths should be updated to import from their respective providers.

• Configuration cleanup: Several legacy config options have been removed, including:

  • scheduler.allow_trigger_in_future: DAG runs can no longer be triggered with a future logical date. Use logical_date=None instead.
  • scheduler.use_job_schedule and scheduler.use_local_tz have also been removed. These options were deprecated and no longer had any effect.

• Deprecated utility methods such as those in airflow.utils.helpers, airflow.utils.process_utils, and airflow.utils.timezone have been removed. Equivalent functionality can now be found in the standard Python library or Airflow provider modules.

• Removal of deprecated CLI flags and behavior: Several CLI entrypoints and arguments that were marked for removal in earlier versions have been cleaned up.

To assist with the upgrade, tools like ruff (e.g., rule AIR302) and airflow config lint can help identify
obsolete imports and configuration keys. These utilities are recommended for locating and resolving common
incompatibilities during migration. Please see :doc:Upgrade Guide <installation/upgrading_to_airflow3> for more
information.

Summary of Removed Features
"""""""""""""""""""""""""""

The following table summarizes user-facing features removed in 3.0 and their recommended replacements. Not all of these
are called out individually above.

+-------------------------------------------+----------------------------------------------------------+
| Feature | Replacement / Notes |
+===========================================+==========================================================+
| SubDagOperator / SubDAGs | Use TaskGroups |
+-------------------------------------------+----------------------------------------------------------+
| SLA callbacks / metrics | Deadline Alerts (planned post-3.0) |
+-------------------------------------------+----------------------------------------------------------+
| DAG Pickling | Use JSON serialization; pickling is no longer supported |
+-------------------------------------------+----------------------------------------------------------+
| Xcom Pickling | Use custom Xcom backend; pickling is no longer supported |
+-------------------------------------------+----------------------------------------------------------+
| execution_date context var | Use dag_run.logical_date |
+-------------------------------------------+----------------------------------------------------------+
| conf and dag_run.external_trigger | Removed from context; use DAG params or dag_run APIs |
+-------------------------------------------+----------------------------------------------------------+
| Core EmailOperator | Use EmailOperator from the smtp provider |
+-------------------------------------------+----------------------------------------------------------+
| none_failed_or_skipped rule | Use none_failed_min_one_success |
+-------------------------------------------+----------------------------------------------------------+
| dummy trigger rule | Use always |
+-------------------------------------------+----------------------------------------------------------+
| fail_stop argument | Use fail_fast |
+-------------------------------------------+----------------------------------------------------------+
| store_serialized_dags=False | DAGs are always serialized; config has no effect |
+-------------------------------------------+----------------------------------------------------------+
| Deprecated core imports | Import from appropriate provider package |
+-------------------------------------------+----------------------------------------------------------+
| DebugExecutor | Use LocalExecutor for testing |
+-------------------------------------------+----------------------------------------------------------+
| .airflowignore regex | Uses glob syntax by default |
+-------------------------------------------+----------------------------------------------------------+

Migration Tooling & Upgrade Process
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Airflow 3 was designed with migration in mind. Many Airflow 2 DAGs will work without changes, especially if deprecation
warnings were addressed in earlier releases. To support the upgrade, Airflow 3 includes validation tools such as ruff
and airflow config update, as well as a simplified startup model.

For a step-by-step upgrade process, see the :doc:Upgrade Guide <installation/upgrading_to_airflow3>.

Minimum Supported Versions
"""""""""""""""""""""""""""

To upgrade to Airflow 3.0, you must be running Airflow 2.7 or later.

Airflow 3.0 supports the following Python versions:

• Python 3.9
• Python 3.10
• Python 3.11
• Python 3.12

Earlier versions of Airflow or Python are not supported due to architectural changes and updated dependency requirements.

DAG Compatibility Checks
"""""""""""""""""""""""""

Airflow now includes a Ruff-based linter with custom rules to detect DAG patterns and interfaces that are no longer
compatible with Airflow 3.0. These checks are packaged under the AIR30x rule series. Example usage:

.. code-block:: bash

ruff check dags/ --select AIR301
ruff check dags/ --select AIR301 --fix

These checks can automatically fix many common issues such as renamed arguments, removed imports, or legacy context
variable usage.

Configuration Migration
"""""""""""""""""""""""

Airflow 3.0 introduces a new utility to validate and upgrade your Airflow configuration file:

.. code-block:: bash

airflow config update
airflow config update --fix

This utility detects removed or deprecated configuration options and, if desired, updates them in-place.

Additional validation is available via:

.. code-block:: bash

airflow config lint

This command surfaces obsolete configuration keys and helps align your environment with Airflow 3.0 requirements.

Metadata Database Upgrade
"""""""""""""""""""""""""

As with previous major releases, the Airflow 3.0 upgrade includes schema changes to the metadata database. Before
upgrading, it is strongly recommended that you back up your database and optionally run:

.. code-block:: bash

airflow db clean

to remove old task instance, log, or XCom data. To apply the new schema:

.. code-block:: bash

airflow db migrate

Startup Behavior Changes
"""""""""""""""""""""""""

Airflow components are now started explicitly. For example:

.. code-block:: bash

airflow api-server        # Replaces airflow webserver
airflow dag-processor     # Required in all environments

These changes reflect Airflow's new service-oriented architecture.

Resources
^^^^^^^^^

• :doc:Upgrade Guide <installation/upgrading_to_airflow3>
• Airflow AIPs <https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvement+Proposals>_

Airflow 3.0 represents more than a year of collaboration across hundreds of contributors and dozens of organizations. We
thank everyone who helped shape this release through design discussions, code contributions, testing, documentation, and
community feedback. For full details, migration guidance, and upgrade best practices, refer to the official Upgrade
Guide and join the conversation on the Airflow dev and user mailing lists.