chore(deps): update dependency apache-airflow to v3 #13315
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Hello @renovate-bot, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
Summary of Changes
This pull request updates the apache-airflow dependency to version 3.0.0 in several requirements.txt files and the setup.py file for the dag_test_utils package. The PR is generated by renovate-bot, and the description includes release notes for Airflow 3.0.0, highlighting significant changes such as a service-oriented architecture, a stable DAG authoring interface, and a modernized React UI.
Highlights
- Dependency Update: The core change is updating the
apache-airflowdependency to version 3.0.0 across multiple project files. - Version Constraints: The
dag_test_utils/setup.pyfile updates the version constraint forapache-airflow[google]to be>=3.0.0, <3.1.0. - Release Notes: The PR description includes detailed release notes for Airflow 3.0.0, outlining major changes and migration considerations.
Changelog
Click here to see the changelog
- composer/2022_airflow_summit/requirements.txt
- Updated
apache-airflowfrom version 2.6.3 to 3.0.0.
- Updated
- composer/blog/gcp-tech-blog/data-orchestration-with-composer/requirements.txt
- Updated
apache-airflow[google]from version 2.6.3 to 3.0.0.
- Updated
- composer/blog/gcp-tech-blog/unit-test-dags-cloud-build/requirements.txt
- Updated
apache-airflow[gcp]from version 1.10.15 to 3.0.0.
- Updated
- composer/cicd_sample/requirements.txt
- Updated
apache-airflowfrom version 2.6.3 to 3.0.0.
- Updated
- composer/dag_test_utils/setup.py
- Updated
apache-airflow[google]version constraint from>= 2.0.0, < 3.0.0to>=3.0.0, <3.1.0.
- Updated
- composer/workflows/requirements.txt
- Updated
apache-airflow[amazon,apache.beam,cncf.kubernetes,google,microsoft.azure,openlineage,postgres]from version 2.9.2 to 3.0.0.
- Updated
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A DAG's new version,
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Code Review
This PR updates the dependency apache-airflow to v3. The changes primarily involve updating the version numbers in the requirements.txt files and setup.py. The upgrade to Airflow 3.0 is a significant one, as detailed in the release notes, and it's important to ensure compatibility and test the changes thoroughly.
Summary of Findings
- Untested code after Airflow upgrade: The comment in
composer/blog/gcp-tech-blog/unit-test-dags-cloud-build/requirements.txtindicates that updates beyond Airflow 1.10.15 are untested with the existing code. Upgrading to 3.0.0 requires careful consideration and testing to ensure compatibility.
Merge Readiness
The PR updates the Airflow dependency to v3. While the changes themselves are straightforward, the potential impact of upgrading to a new major version requires thorough testing. Given the comment about untested code in one of the files, I recommend that the author ensures that all code is compatible with Airflow 3.0 before merging. I am unable to approve this pull request, and users should have others review and approve this code before merging.
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This PR contains the following updates:
>= 2.0.0, < 3.0.0->>=3.0.1, <3.1.0==2.9.2->==3.0.1==2.6.3->==3.0.1==1.10.15->==3.0.1Warning
Some dependencies could not be looked up. Check the Dependency Dashboard for more information.
Release Notes
apache/airflow (apache-airflow)
v3.0.1Compare Source
Significant Changes
^^^^^^^^^^^^^^^^^^^
No significant changes.
Bug Fixes
"""""""""
dag_coderecords with no serialized dag (#49478)dag_codeandserialized_dagtables on 3.0 upgrade (#49563)scheduler_intervalfield on downgrade (#49583)base_urlin api server (#49545)max_active_tis_per_dagis not respected by dynamically mapped tasks (#49708)SimpleAuthManager(#49697)(#49866)bundle_versionto DagRun response (#49726)task_idsinxcom_pullthe same as multiple when provided as part of a list (#49692)DAGModelstale and associate bundle on import errors to aid migration from 2.10.5 (#49769)pipwith avoiding resolution too deep issues in Python 3.12 (#49853)BashSensor(#49935)map_index_templateon task completion (#49809)ContinuousTimetablefalse triggering when last run ends in future (#45175)Stats(#50088)TaskGroup(#49996)mapIndexto clear the relevant task instances. (#50256)Miscellaneous
"""""""""""""
STRAIGHT_JOINprefix for MySQL query optimization inget_sorted_triggers(#46303)sqlalchemy[asyncio]extra is in core deps (#49452)HANDLER_SUPPORTS_TRIGGERER(#49370)gitpythonas a core dependency (#49537)@babel/runtimefrom7.26.0to7.27.0(#49479)get_current_context(#49630)RunBackfillForm(#49609)backfill_id(#49691)(#49716)SimpleAllAdminMiddlewareto allow api usage without auth header in request (#49599)react-routerandreact-router-domfrom 7.4.0 to 7.5.2 (#49742)root_dag_idin dagbag and restore logic (#49668)airflow.cfgfiles across all containers in defaultdocker-compose.yaml(#49681)/airflow-core(#49512)apache-airflowmeta package (#49846)@task.kuberenetes_cmd(#46913)vitefrom5.4.17to5.4.19for Airflow UI (#49162)(#50074)map_indexfilter option toGetTICountandGetTaskStates(#49818)statsui endpoint (#49985)stateattribute toRuntimeTaskInstancefor easierti.stateaccess in Task Context (#50031)dag_run_conftoRunBackfillForm(#49763)dateIntervalvalidation and error handling (#50072)Task Instances [{map_index}]tab to mapped task details (#50085)Doc Only Changes
""""""""""""""""
security/api.rst(#49675)max_consecutive_failed_dag_runsdefault value to zero in TaskSDK dag (#49795) (#49803)example_params_ui_tutorial) (#49905)v3.0.0Compare Source
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-serverenable 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.sdknamespace to import core DAG constructs like@dag,@task, andDAG.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
@assetdecorator 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
airflowandairflowctl(AIP-81), and REST API now defaults tological_date=Nonewhen 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.sdknamespace that exposes the core authoring interfaces for defining DAGsand tasks. DAG authors should now import objects like
DAG,@dag, and@taskfromairflow.sdkrather thaninternal 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
@assetdecorator and related changes to the DAG parser enable clearer, asset-centric DAG definitions, allowingAirflow to more naturally support event-driven and data-aware scheduling patterns.
This renaming impacts modules, classes, functions, configuration keys, and internal models. Key changes include:
Dataset→AssetDatasetEvent→AssetEventDatasetAlias→AssetAliasairflow.datasets.*→airflow.sdk.*airflow.timetables.simple.DatasetTriggeredTimetable→airflow.timetables.simple.AssetTriggeredTimetableairflow.timetables.datasets.DatasetOrTimeSchedule→airflow.timetables.assets.AssetOrTimeScheduleairflow.listeners.spec.dataset.on_dataset_created→airflow.listeners.spec.asset.on_asset_createdairflow.listeners.spec.dataset.on_dataset_changed→airflow.listeners.spec.asset.on_asset_changedcore.dataset_manager_class→core.asset_manager_classcore.dataset_manager_kwargs→core.asset_manager_kwargsUnified Scheduling Field
""""""""""""""""""""""""
Airflow 3.0 removes the legacy
schedule_intervalandtimetableparameters. DAGs must now use the unifiedschedulefield for all time- and event-based scheduling logic. This simplifies DAG definition and improvesconsistency across scheduling paradigms.
Updated Scheduling Defaults
"""""""""""""""""""""""""""
Airflow 3.0 changes the default behavior for new DAGs by setting
catchup_by_default = Falsein the configurationfile. 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.
The default DAG schedule has been changed to
Nonefrom@once.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=Noneto trigger runs withthe 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 setslogical_date=Noneby default. These DAG runs do not have a data interval, and attempting to accessdata_interval_start,data_interval_end, orlogical_datefrom the task context will raise aKeyError.DAG authors should use
dag_run.logical_dateand perform appropriate checks or fallbacks if supporting multipletrigger 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_callbackisno 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_defaultis now set toFalseby default. DAGs will not automatically backfill unless explicitly configured to do so.create_cron_data_intervalsis now set toFalseby default. As a result, cron expressions will be interpreted using theCronTriggerTimetableinstead of the legacyCronDataIntervalTimetable.SimpleAuthManageris now the defaultauth_manager. To continue using Flask AppBuilder-based authentication, install theapache-airflow-providers-flask-appbuilderprovider and explicitly setauth_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_weightvalue on a task is now capped by the number of available pool slots. This ensures that resourceavailability 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_retriesto handle transient database issues more gracefully. This enhancesAirflow'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.
SequentialExecutorhas been removed"""""""""""""""""""""""""""""""""""""""
SequentialExecutorwas primarily used for local testing but is now redundant, asLocalExecutorsupports SQLite with WAL mode and provides better performance with parallel execution.
Users should switch to
LocalExecutororCeleryExecutoras alternatives.DAG Authoring Enhancements
^^^^^^^^^^^^^^^^^^^^^^^^^^
Airflow 3.0 includes several changes that improve consistency, clarity, and long-term stability for DAG authors.
New Stable DAG Authoring Interface:
airflow.sdk"""""""""""""""""""""""""""""""""""""""""""""""""""
Airflow 3.0 introduces a new, stable public API for DAG authoring under the
airflow.sdknamespace,available via the
apache-airflow-task-sdkpackage.The goal of this change is to decouple DAG authoring from Airflow internals (Scheduler, API Server, etc.),
providing a forward-compatible, stable interface for writing and maintaining DAGs across Airflow versions.
DAG authors should now import core constructs from
airflow.sdkrather than internal modules.Key Imports from
airflow.sdk:Classes:
AssetBaseNotifierBaseOperatorBaseOperatorLinkBaseSensorOperatorConnectionContextDAGEdgeModifierLabelObjectStoragePathParamTaskGroupVariableDecorators and Functions:
@asset@dag@setup@task@task_group@teardownchainchain_linearcross_downstreamget_current_contextget_parsing_contextFor an exhaustive list of available classes, decorators, and functions, check
airflow.sdk.__all__.All DAGs should update imports to use
airflow.sdkinstead of referencing internal Airflow modules directly.Legacy import paths (e.g.,
airflow.models.dag.DAG,airflow.decorator.task) are deprecated andwill be removed in a future Airflow version. Some additional utilities and helper functions
that DAGs sometimes use from
airflow.utils.*and others will be progressively migrated to the Task SDK in futureminor releases.
These future changes aim to complete the decoupling of DAG authoring constructs
from internal Airflow services. DAG authors should expect continued improvements
to
airflow.sdkwith no backwards-incompatible changes to existing constructs.For example, update:
.. code-block:: python
Old (Airflow 2.x)
New (Airflow 3.x)
Renamed Parameter:
fail_stop→fail_fast"""""""""""""""""""""""""""""""""""""""""""""""""
The DAG argument
fail_stophas been renamed tofail_fastfor improved clarity. This parameter controls whether aDAG run should immediately stop execution when a task fails. DAG authors should update any code referencing
fail_stopto 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:
confexecution_datedag_run.external_triggerIn asset-triggered and manually triggered DAG runs with
logical_date=None, data interval fields such asdata_interval_startanddata_interval_endmay not be present in the task context. DAG authors should useexplicit references such as
dag_run.logical_dateand conditionally check for the presence of interval-related fieldswhere applicable.
Task Context Utilities Moved
""""""""""""""""""""""""""""
Internal task context functions such as
get_parsing_contexthave 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.ALWAYSrule can no longer be used with teardown tasks or tasks that are expected to honor upstreamdependency 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 frequentlyreferenced Assets. This improves modularity and reuse across DAG files and is particularly helpful for teams adopting
asset-centric DAGs.
Operator Links interface changed
""""""""""""""""""""""""""""""""
The Operator Extra links, which can be defined either via plugins or custom operators
now do not execute any user code in the Airflow UI, but instead push the "full"
links to XCom backend and the link is fetched from the XCom backend when viewing
task details, for example from grid view.
Example for users with custom links class:
.. code-block:: python
@attr.s(auto_attribs=True)
class CustomBaseIndexOpLink(BaseOperatorLink):
"""Custom Operator Link for Google BigQuery Console."""
The link has an
xcom_keydefined, which is how it will be stored in the XCOM backend, with key as xcom_key andvalue as the entire link, this case:
https://console.cloud.google.com/bigquery?j=searchPlugins no longer support adding executors, operators & hooks
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Operator (including Sensors), Executors & Hooks can no longer be registered or imported via Airflow's plugin mechanism. These types of classes
are just treated as plain Python classes by Airflow, so there is no need to register them with Airflow. They
can be imported directly from their respective provider packages.
Before:
.. code-block:: python
from airflow.hooks.my_plugin import MyHook
You should instead import it as:
.. code-block:: python
from my_plugin import MyHook
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 nowsupports 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:
catchup_by_defaultis nowFalse. DAGs will not backfill missed intervals unless explicitly configured to do so.create_cron_data_intervalsis nowFalse. Cron expressions are now interpreted using theCronTriggerTimetableinstead of the legacyCronDataIntervalTimetable. 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:
SecretsMaskerclass has been relocated toairflow.sdk.execution_time.secrets_masker.ObjectStoragePathutility previously located underairflow.iois now available viaairflow.sdk.These changes simplify imports and reflect broader efforts to stabilize utility interfaces across the Airflow codebase.
Improved
inlet_events,outlet_events, andtriggering_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
strto access events isno longer supported. Use an
AssetorAssetAliasobject, orAsset.refto refer to an entity explicitly instead, such as::Alternatively, two helpers
for_assetandfor_asset_aliasare added as shortcuts::The internal representation of asset event triggers now also includes an explicit
urifield, simplifying traceability andaligning with the broader asset-aware execution model introduced in Airflow 3.0. DAG authors interacting directly with
inlet_eventsmay need to update logic that assumes the previous structure.Behaviour change in
xcom_pull"""""""""""""""""""""""""""""""""
In Airflow 2, the
xcom_pull()method allowed pulling XComs by key without specifying task_ids, despite the fact that the underlyingDB 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_idswhen pulling by key. This change aligns with the task-scoped nature ofXComs as defined by the schema, ensuring predictable and consistent behavior.
DAG Authors should update their dags to use
task_idsif their dags usedxcom_pullwithouttask_idssuch as::kwargs["ti"].xcom_pull(key="key")
Should be updated to::
kwargs["ti"].xcom_pull(task_ids="task1", key="key")
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[scheduler] processor_poll_interval[logging] dag_processor_manager_log_location[logging] dag_processor_manager_log_stdout[logging] log_processor_filename_templateAll the webserver configurations have also been removed since API server now replaces webserver, so
the configurations like below have no effect:
[webserver] allow_raw_html_descriptions[webserver] cookie_samesite[webserver] error_logfile[webserver] access_logformat[webserver] web_server_master_timeoutSeveral configuration options previously located under the
[webserver]section havebeen moved to the new
[api]section. The following configuration keys have been moved:[webserver] web_server_host→[api] host[webserver] web_server_port→[api] port[webserver] workers→[api] workers[webserver] web_server_worker_timeout→[api] worker_timeout[webserver] web_server_ssl_cert→[api] ssl_cert[webserver] web_server_ssl_key→[api] ssl_key[webserver] access_logfile→[api] access_logfileUsers should review their
airflow.cfgfiles or use theairflow config lintcommand to identify outdated orremoved 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 keysruff check --select AIR30 --preview: Flags removed interfaces and common migration issuesCLI & 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:
airflowCLI now handles only local functionality (e.g.,airflow tasks test,airflow dags list).airflowctl, distributed via theapache-airflow-clientpackage.This change improves security and modularity for deployments that use Airflow in a distributed or API-first context.
REST API v2 replaces v1
"""""""""""""""""""""""
The legacy REST API v1, previously built with Connexion and Marshmallow, has been replaced by a modern FastAPI-based REST API v2.
This new implementation improves performance, aligns more closely with web standards, and provides a consistent developer experience across the API and UI.
Key changes include stricter validation (422 errors instead of 400), the removal of the
execution_dateparameter in favor oflogical_date, and more consistent query parameter handling.The v2 API is now the stable, fully supported interface for programmatic access to Airflow, and also powers the new UI - achieving full feature parity between the UI and API.
For details, see the :doc:
Airflow REST API v2 </stable-rest-api-ref>documentation.REST API: DAG Trigger Behavior Updated
""""""""""""""""""""""""""""""""""""""
The behavior of the
POST /dags/{dag_id}/dagRunsendpoint has changed. If alogical_dateis not explicitlyprovided 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. Run
airflow --helpto review the current set of available commands and arguments.Deprecated
--ignore-depends-on-pastcli option is replaced by--depends-on-past ignore.--treeflag forairflow tasks listcommand is removed. The format of the output with that flag can beexpensive to generate and extremely large, depending on the DAG.
airflow dag showis a better way tovisualize the relationship of tasks in a DAG.
Changing
dag_idfrom flag (-d,--dag-id) to a positional argument in thedags list-runsCLI command.The
airflow db initandairflow db upgradecommands have been removed. Useairflow db migrateinsteadto initialize or migrate the metadata database. If you would like to create default connections use
airflow connections create-default-connections.airflow api-serverhas replacedairflow webservercli command.Provider Refactor & Standardization
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Airflow 3.0 completes the migration of several core operators, sensors, and hooks into the new
apache-airflow-providers-standardpackage. This package now includes commonly used components such as:PythonOperatorBashOperatorEmailOperatorSimpleHttpOperatorShortCircuitOperatorThese operators were previously bundled inside
airflow-corebut are now treated as provider-managed components toimprove 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-standardpackage is installable on bothAirflow 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.PythonOperatorare deprecated and will be removed soon. They should bereplaced with:
.. code-block:: python
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:
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
PickleDagCLI/API, as well as implicit behaviors aroundstore_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, anddag_run.external_trigger. These values are either no longer applicable or have been renamed (e.g., usedag_run.logical_dateinstead ofexecution_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. Uselogical_date=Noneinstead.scheduler.use_job_scheduleandscheduler.use_local_tzhave 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, andairflow.utils.timezonehave 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., ruleAIR302) andairflow config lintcan help identifyobsolete 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 moreinformation.
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_datecontext var | Usedag_run.logical_date|+-------------------------------------------+----------------------------------------------------------+
|
confanddag_run.external_trigger| Removed from context; use DAG params ordag_runAPIs |+-------------------------------------------+----------------------------------------------------------+
| Core
EmailOperator| UseEmailOperatorfrom thesmtpprovider |+-------------------------------------------+----------------------------------------------------------+
|
none_failed_or_skippedrule | Usenone_failed_min_one_success|+-------------------------------------------+----------------------------------------------------------+
|
dummytrigger rule | Usealways|+-------------------------------------------+----------------------------------------------------------+
|
fail_stopargument | Usefail_fast|+-------------------------------------------+----------------------------------------------------------+
|
store_serialized_dags=False| DAGs are always serialized; config has no effect |+-------------------------------------------+----------------------------------------------------------+
| Deprecated core imports | Import from appropriate provider package |
+-------------------------------------------+----------------------------------------------------------+
|
SequentialExecutor&DebugExecutor| Use LocalExecutor for testing |+-------------------------------------------+----------------------------------------------------------+
|
.airflowignoreregex | Uses glob syntax by default |+--------------------------------------
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