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Airflow Updates & Release Notes

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Airflow von Apache

Apache Airflow 3.3.2: Backfill-Endpunkte geben keine IDs mehr preis

In Apache Airflow 3.3.2 geben die Backfill-Endpunkte nicht mehr preis, welche Backfill-IDs über Dags hinweg existieren: Der Zugriff wird nur noch anhand des im Pfad genannten Backfills autorisiert, und ein Backfill auf einem nicht lesbaren Dag liefert nun "404" (Backfill not found) statt "403".

📦 PyPI: https://pypi.org/project/apache-airflow/3.3.2/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.3.2/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.3.2/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.3.2" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.3.2

Significant Changes

Backfill endpoints no longer disclose which backfill ids exist across Dags

The four routes that name a backfill in their path -- GET /backfills/{backfill_id} and the pause, unpause and cancel routes -- resolved the Dag they authorize against from the dag_id supplied on the request whenever the path's id matched no row. An unknown id and a backfill on a Dag the caller cannot see therefore answered differently, which enumerates backfill ids across Dags.

The backfill named in the path is now the only thing those routes authorize against.

Behaviour changes:

  • Requesting a backfill on a Dag the caller cannot read now returns 404 (Backfill not found) -- the same response an unknown id gets -- instead of the 403 returned before. A caller who can read the Dag still gets 403 for a write they are not allowed to make.
  • A backfill_id in the path is never authorized against a dag_id in the request body or query string. GET /backfills, POST /backfills and POST /backfills/dry_run …

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Airflow von Apache

Apache Airflow 3.3.1: DataFrame-XComs für pandas 3 lesbar

Apache Airflow 3.3.1 registriert für pandas 3 sowohl "pandas.DataFrame" als auch "pandas.core.frame.DataFrame", sodass DataFrame-XComs beider pandas-Versionen lesbar bleiben, weshalb die Airflow-Version vor pandas 3 auf allen Komponenten, besonders Workern, ausgerollt werden sollte.

📦 PyPI: https://pypi.org/project/apache-airflow/3.3.1/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.3.1/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.3.1/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.3.1" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.3.1

Significant Changes

Pandas 3 changes how DataFrame XComs are stored and read back (#71169)

pandas 3 exposes its public classes from the pandas namespace, so a DataFrame is qualified as pandas.DataFrame instead of pandas.core.frame.DataFrame. XComs record that name alongside the serialized value, so the name written into the metadata database depends on the pandas version of the component that pushed the value. Airflow registers both names, and a DataFrame written by either pandas version can be read by either -- no configuration change is needed, and existing XComs stay readable.

What you should do:

  • Roll this Airflow version out to every component before pandas 3 reaches any of them -- workers in particular. A component that predates this change cannot read a DataFrame XCom written under pandas 3, and fails the pull with:

    .. code-block:: text

    ImportError: pandas.DataFrame was not found in allow list for deserialization imports.
    To allow it, add it to allowed_deserialization_classes in the configuration …
    

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Airflow von Apache

Apache Airflow Java SDK 1.0.0-beta1 erschienen

Das Apache Airflow Java SDK ist erstmals als Beta-Version 1.0.0-beta1 erschienen.

First release of the Apache Airflow Java SDK (beta).

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Airflow von Apache

Apache Airflow 3.3.0: Neue Partition-Mapper und PartitionedAtRuntime-Timetable

Apache Airflow 3.3.0 erweitert das Asset Partitioning um neue Partition-Mapper wie "RollupMapper", "FanOutMapper" und "FixedKeyMapper" mit "SegmentWindow", Zeitfenster und eine "wait_policy" ("WaitForAll" oder "MinimumCount(n)"), begrenzt die Fan-out-Menge pro Event über "[scheduler] partition_mapper_max_downstream_keys" und ergänzt die "PartitionedAtRuntime"-Timetable.

📦 PyPI: https://pypi.org/project/apache-airflow/3.3.0/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.3.0/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.3.0/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.3.0" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.3.0

Significant Changes

Asset Partitioning (#64571, #65447, #66030, #66848, #67184, #67475, #67716, #68978)

Building on the asset partitioning introduced in 3.2.0, Airflow 3.3.0 substantially expands how a single upstream asset event fans out to partitioned downstream Dag runs. New partition mappers — RollupMapper (many-to-one), FanOutMapper (one-to-many), and FixedKeyMapper + SegmentWindow (categorical rollup) — compose with time windows (day/week/month/quarter/year) and a wait_policy (WaitForAll or MinimumCount(n)) to control when partitioned runs fire. Windows can fan out forward or backward in time, and total fan-out per upstream event is bounded by the new [scheduler] partition_mapper_max_downstream_keys config (configurable per mapper). Airflow 3.3.0 also adds the PartitionedAtRuntime timetable, which lets a Dag declare that its partition key(s) are assigned when the run starts rather than mapped from an upstream event.

For detailed usage instructions, see :doc:/authoring-and-scheduling/assets. …

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Airflow von Apache

Apache Airflow Helm Chart 1.22.0

Helm Chart 1.22.0 erhöht die minimale Helm-Version auf 3.19.0, nutzt standardmäßig das Airflow-Image 3.2.2, unterstützt die Option enableServiceLinks, ergänzt einen optionalen OTel-Service sowie weitere Optionen und behebt mehrere Fehler.

Significant Changes

Minimum Helm version was updated to 3.19.0 (#66970)

Default Airflow image is updated to 3.2.2 (#67681)

The default Airflow image that is used with the Chart is now 3.2.2, previously it was 3.2.1.

Added support for configuring enableServiceLinks (#67447)

The default will become false in Chart 2.0. If you rely on these environment variables, explicitly set enableServiceLinks: true, or migrate your code to use DNS-based service lookups.

New Features

  • Add optional OTel service to the Airflow Helm Chart (#64902)
  • Add serviceAccountTokenVolume to cleanup cron (#67446)
  • Add requirePersistence option to worker logGroomerSidecar (#65884)

Improvements

  • Add checksum for api-server config in API server deployment (#66468)

Bug Fixes

  • Fix Helm chart executor label to support executor aliases (#67762)
  • Fix triggerer KEDA database connection rendering (#67538)
  • Fix Celery worker liveness probe hostname lookup (#67471)
  • Fix Go template error comparing slice to nil using eq (#64032)
  • Fix Kubernetes worker service account values (#66598)
  • Add binding for workers.kubernetes and condition workers ServiceAccount (#66730)
  • Fix launcher RBAC for executor class paths (#66208)
  • Fix task log access with NetworkPolicies for Airflow 2 and 3 (#65754)
  • Add missing tpl rendering for ServiceAccount annotations (#66095) …

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Airflow von Apache

Apache Airflow Ctl (airflowctl) 0.1.5

airflowctl 0.1.5 ergänzt unter anderem den Befehl für die nächste Dag-Ausführung, das Massenlöschen von Dag Runs und Cursor-Paginierung, macht erforderliche Parameter generierter Befehle zu Positionsargumenten und behebt mehrere Fehler.

:package: PyPI: https://pypi.org/project/apache-airflow-ctl/0.1.5 :books: Docs: https://airflow.apache.org/docs/apache-airflow-ctl/0.1.5 :hammer_and_wrench: Release Notes: https://airflow.apache.org/docs/apache-airflow-ctl/0.1.5/release_notes.html

Significant Changes

  • Add dags next execution command (#66172, #66188)
  • Add bulk delete Dag Runs (#67095)
  • Add rerun_with_latest_version config hierarchy for clear/rerun behavior (#63884)
  • Implement patching of task group instances in API (#62812)
  • Allow remote version check without authentication (#65099)
  • Add cursor-based pagination for get_dag_runs endpoint (#65604)
  • Enable queueing new tasks (#63484)
  • Add cursor-based pagination for get_task_instances endpoint (#64845)
  • Add is_backfillable property to DAG API responses (#64644)
  • Expose required primitive parameters of auto-generated commands as positional arguments instead of --flag options. Optional parameters keep the --flag form. Follows the dev-list lazy consensus on airflowctl parameter style (see https://lists.apache.org/thread/m1qvcvow3l17ytv40vhslh40wn3rntrm) (#66768)

Bug Fixes

  • Fix connections import schema handling (#67063)
  • Fix broken download URLs and variable names in docs (#67046)
  • Fix missing pyyaml runtime dependency (#65489)
  • Fix dagrun list crash when --state is omitted (#65608)
  • Fix backfill params not overriding existing DAG run conf (#64939)
  • Fix Ruff issues in client-py (#64868) …

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Airflow von Apache

Apache Airflow 3.2.2

Airflow 3.2.2 prüft beim SMTP-STARTTLS-Upgrade in send_email standardmäßig das Serverzertifikat gegen die vertrauenswürdigen System-CAs; das alte Verhalten lässt sich mit email.ssl_context = "none" wiederherstellen.

📦 PyPI: https://pypi.org/project/apache-airflow/3.2.2/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.2.2/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.2.2/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.2.2" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.2.2

Significant Changes

  • The SMTP STARTTLS upgrade performed by airflow.utils.email.send_email now validates the SMTP server's certificate against the system's trusted CA bundle by default. Previously the starttls() call was made without an SSL context, so any certificate was accepted. Deployments that intentionally point Airflow at an SMTP server with a self-signed or otherwise non-validating certificate and need to preserve the previous behaviour must set email.ssl_context = "none" in airflow.cfg. The "default" value (now also the default when the option is unset) uses :func:ssl.create_default_context. Previously this option applied only to the SMTP_SSL path; it now applies to the STARTTLS path as well. (#65346) …

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Apache Airflow Helm Chart 1.21.0

Helm Chart 1.21.0 verschiebt die Worker-Konfigurationsoptionen unter workers.celery.* und workers.kubernetes.*.

Significant Changes

Workers config options have been moved under workers.celery.* and workers.kubernetes.* …

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Airflow von Apache

Apache Airflow 3.2.1

Airflow 3.2.1 verlangt für den /dags-Endpunkt zusätzliche Berechtigungen, erlaubt UI-Themes nur mit CSS-Überschreibungen und behebt mehrere Fehler.

📦 PyPI: https://pypi.org/project/apache-airflow/3.2.1/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.2.1/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.2.1/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.2.1" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.2.1

Significant Changes

  • Users who only have read access to DAGs will no longer be able to fetch data from the /dags endpoint, as it now requires additional permissions (DagAccessEntity.RUN, DagAccessEntity.HITL_DETAIL, and DagAccessEntity.TASK_INSTANCE). This change was made because the endpoint returns aggregated data from these multiple entities. Please update your custom user roles to include read access for DAG Runs, Task Instances, and HITL Details if those users should still have access to the /dags endpoint. (#64822)

Improvements

  • Allow UI theme config with only CSS overrides, icon only, or empty {} to restore OSS defaults. The tokens field is now optional in the theme configuration. (#64552)

Bug Fixes

  • Fix DEFAULT_LOGGING_CONFIG to use right kwargs (#65412) (#65424)
  • Fix zip DAG import errors being cleared during bundle refresh (#63617) (#65296)
  • Fix dispose_orm() not disposing async engine on shutdown (#65274) (#65284)
  • Fix get_team_name_dep creating wasted async sessions when multi_team=False (#65275) (#65282) …

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Airflow von Apache

Apache Airflow 3.2.0

Airflow 3.2.0 führt Asset Partitioning ein, sodass nachgelagerte Dags nur durch bestimmte Partitionen eines Assets ausgelöst werden, und bringt Unterstützung für Multi-Team-Deployments.

📦 PyPI: https://pypi.org/project/apache-airflow/3.2.0/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.2.0/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.2.0/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.2.0" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.2.0

Significant Changes

Asset Partitioning

The headline feature of Airflow 3.2.0 is asset partitioning — a major evolution of data-aware scheduling. Instead of triggering Dags based on an entire asset, you can now schedule downstream processing based on specific partitions of data. Only the relevant slice of data triggers downstream work, making pipeline orchestration far more efficient and precise.

This matters when working with partitioned data lakes — date-partitioned S3 paths, Hive table partitions, BigQuery table partitions, or any other partitioned data store. Previously, any update to an asset triggered all downstream Dags regardless of which partition changed. Now only the right work gets triggered at the right time.

For detailed usage instructions, see :doc:/authoring-and-scheduling/assets.

Multi-Team Deployments

Airflow 3.2 introduces multi-team support, allowing organizations to run multiple isolated teams within a single Airflow deployment. …

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Airflow von Apache

Apache Airflow Helm Chart 1.20.0

Helm Chart 1.20.0 unterstützt nur noch Airflow ab 2.11.0 und verschiebt worker-spezifische Einstellungen in die Abschnitte workers.celery und workers.kubernetes.

Significant Changes

Support for old versions of Apache Airflow <2.11 has been dropped (#61018)

Minimum supported version of Apache Airflow is now 2.11.0. If you want to deploy an old version of Apache Airflow, please use the last released version of the chart 1.19.0.

workers specific sections have been moved to workers.celery / workers.kubernetes sections

Please update your configuration accordingly:

  • workers.command command is now deprecated in favor of workers.celery.command/workers.kubernetes.command (#60067).
  • workers.securityContexts command is now deprecated in favor of workers.celery.securityContexts/workers.kubernetes.securityContexts (#60396).
  • workers.containerLifecycleHooks command is now deprecated in favor of workers.celery.containerLifecycleHooks/workers.kubernetes.containerLifecycleHooks (#61369).
  • workers.kerberosSidecar section is now deprecated in favor of workers.celery.kerberosSidecar/workers.kubernetes.kerberosSidecar (#61881).
  • workers.kerberosInitContainer section is now deprecated in favor of workers.celery.kerberosInitContainer/workers.kubernetes.kerberosInitContainer (#60751).
  • workers.terminationGracePeriodSeconds command is now deprecated in favor of workers.celery.terminationGracePeriodSeconds/workers.kubernetes.terminationGracePeriodSeconds (#61892). …

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Apache Airflow Ctl (airflowctl) 0.1.3

airflowctl 0.1.3 ergänzt unter anderem den Befehl auth token, die Option --action-on-existing-key beim Import, einen Retry-Mechanismus und interaktive Login-Abfrage und behebt mehrere Fehler.

📦 PyPI: https://pypi.org/project/apache-airflow-ctl/0.1.3/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow-ctl/0.1.3/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow-ctl/0.1.3/release_notes.html

Thanks to all the contributors who made this possible. Next time, Release notes will be available through public documentation.

Significant Changes

  • Add airflowctl auth token command to print JWT access tokens (#62843)
  • Add --action-on-existing-key to pools import and connections import (#62702)
  • Add retry mechanism to airflowctl and remove flaky integration mark (#63016)
  • airflowctl auth login: prompt for credentials interactively when none are provided (#62549)
  • feat(airflowctl): support on headless environments (#62217)

Bug Fixes

  • Fix airflowctl pools export ignoring --output table/yaml/plain (#62665)
  • Fix airflowctl connections import failure when JSON omits extra field (#62662)
  • Amend compatibility issues for airflowctl (#63388)

Improvements …

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Airflow von Apache

Apache Airflow 3.1.8

Airflow 3.1.8 prüft Backfill-Berechtigungen jetzt über DagAccessEntity.Run und entfernt is_authorized_backfill aus dem BaseAuthManager; zudem ist Elasticsearch vollständig mit Remote Logging kompatibel.

:package: PyPI: https://pypi.org/project/apache-airflow/3.1.8/ :books: Docs: https://airflow.apache.org/docs/apache-airflow/3.1.8/ :hammer_and_wrench: Release Notes: https://airflow.apache.org/docs/apache-airflow/3.1.8/release_notes.html :whale: Docker Image: "docker pull apache/airflow:3.1.8" :busstop: Constraints: https://github.com/apache/airflow/tree/constraints-3.1.8

Significant Changes

Backfill permissions are now handled via DagAccessEntity.Run (#61456)

is_authorized_backfill of the BaseAuthManager interface has been removed. Core will no longer call this method and their provider counterpart implementation will be marked as deprecated. Permissions for backfill operations are now checked against the DagAccessEntity.Run permission using the existing requires_access_dag decorator. In other words, if a user has permission to run a DAG, they can perform backfill operations on it.

Please update your security policies to ensure that users who need to perform backfill operations have the appropriate DagAccessEntity.Run permissions. (Users having the Backfill permissions without having the DagRun ones will no longer be able to perform backfill operations without any update)

Elasticsearch is now fully compatible with remote logging along (#62940) …

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Airflow von Apache

Apache Airflow Ctl (airflowctl) 0.1.2

airflowctl 0.1.2 ergänzt XCom-Befehle, den Befehl auth list-envs und allowed_run_types, setzt logical_date beim Dagrun-Trigger standardmäßig auf jetzt und behebt mehrere Keyring- und Login-Fehler.

📦 PyPI: https://pypi.org/project/apache-airflow-ctl/0.1.2/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow-ctl/0.1.2/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow-ctl/0.1.2/release_notes.html

Thanks to all the contributors who made this possible. Next time, Release notes will be available through public documentation.

Significant Changes

  • Add XCom CLI commands to airflowctl (#61021)
  • Add auth list-envs command to list CLI environments and their auth status (#61426)
  • Add allowed_run_types to whitelist specific dag run types (#61833)
  • Default logical_date to now in airflowctl dagrun trigger to match UI behavior (#61047)

Bug Fixes

  • Allow listing dag runs without specifying dag_id (#61525)
  • Fix infinite password retry loop in airflowctl EncryptedKeyring initialization (#61329)
  • Fix airflowctl auth login reporting success when keyring backend is unavailable (#61296)
  • Fix airflowctl crash when incorrect keyring password is entered (#61042) …

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Apache Airflow Helm Chart 1.19.0

Helm Chart 1.19.0 bringt einen konfigurierbaren Cache mit TTL für die StatsD-Metrikaggregation und unterstützt mehrere Celery-Worker-Sets.

Significant Changes

StatsD metrics aggregation now supports configurable TTL-enabled LRU cache to prevent memory growth in long-running daemons (#60933)

The Helm Chart now includes new configuration options for StatsD aggregation management:

  • statsd.cache.type - Enable TTL-enabled lru cache or random cache for metrics aggregation (default: lru)
  • statsd.cache.size - Maximum number of metrics to cache (default: 1000)
  • statsd.cache.ttl - Time-to-live for cached metrics in seconds (0s is TTL disabled) (default: 0s)

This feature addresses uncontrolled memory growth in StatsD daemons by automatically cleaning up stale or unused metric entries. When enabled, the cache uses both LRU (Least Recently Used) eviction and TTL (Time To Live) expiration to manage memory usage effectively.

To maintain backward compatibility, the default behaviour remains unchanged. Users experiencing memory growth issues with StatsD can enable this feature by setting statsd.cache.ttl to value higher than 0 in their Helm values.

Support for Multiple Celery Worker Sets in the Helm Chart (#58547)

This change introduces support for advanced Celery Workers topologies to Apache Airflow Helm Chart, enabling more flexible resource allocation and precise autoscaling configurations. …

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Airflow von Apache

Apache Airflow 3.1.7

Airflow 3.1.7 enthält keine wesentlichen Änderungen, behebt aber zahlreiche Fehler, etwa bei der JWT-Token-Erzeugung, der Pool-API-Validierung und Abstürzen des Triggerers.

📦 PyPI: https://pypi.org/project/apache-airflow/3.1.7/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.1.7/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.1.7/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.1.7" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.1.7

Significant Changes

No significant changes.

Bug Fixes

  • Fix JWT token generation with unset issuer/audience config (#61331)
  • Fix callback files losing priority during queue resort (#61232) (#61243)
  • Fix Dag callback for versioned bundles in the processor (#60734) (#61230)
  • Add 404 handling for non-existent Dag (#61131) (#61225)
  • Add guardrail to handle Dag deserialization errors in scheduler (#61162) (#61210)
  • Fix asset scheduling for stale Dags (#59337) (#60022) (#61106)
  • Fix unnecessary Dag version churn when Dag file paths change (#60799)
  • Fix missing warning when Bundle path may not be accessible to impersonated user (#60278)
  • Fix TriggerDagRunOperator deferring when wait_for_completion=False (#60052)
  • Fix NoneType error when updating serialized Dag (#56422)
  • Fix Pool API slots validation (#61071) (#61114)
  • Fix DagBag parsing by adding bundle_path temporarily to sys.path (#55894) (#61053)
  • Fix API to respect maximum page limit (#60989) (#61073)
  • Prevent Triggerer from crashing when a trigger event isn't serializable (#60152) (#60981) …

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Airflow von Apache

Apache Airflow 3.1.6

Airflow 3.1.6 stellt in Auth-Managern die Methode is_authorized_hitl_task() bereit, behandelt proxy und proxies standardmäßig als sensible Felder und behebt mehrere Fehler.

📦 PyPI: https://pypi.org/project/apache-airflow/3.1.6/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.1.6/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.1.6/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.1.6" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.1.6

Significant Changes

is_authorized_hitl_task() method now available in auth managers(#59399).

This method is now available in auth managers to check whether a user is authorized to approve a HITL task

proxy and proxies added to DEFAULT_SENSITIVE_FIELDS (#59688)

proxy and proxies have been added to DEFAULT_SENSITIVE_FIELDS in secrets_masker to treat proxy configurations as sensitive by default

Bug Fixes

  • Protect against hanging thread in aiosqlite 0.22+ (#60217) (#60245)
  • Fix log task instance sqlalchemy join query (#59973) (#60222)
  • Fix invalid uri created when extras contains non string elements (#59339) (#60219)
  • Fix operator template fields via callable serialization that causes unstable DAG serialization (#60065) (#60221)
  • Fix real-time extra links updates for TriggerDagRunOperator (#59507) (#60225)
  • Fix signal handling in triggerer job runner (#60190) (#60214)
  • Added state validation to delete dag run endpoint (#60195) (#60207)
  • Fix text overflow issue (#60080)
  • UI: Add toggle functionality to Dags state filters (#59089) …

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Airflow von Apache

Apache Airflow Ctl (airflowctl) 0.1.1

airflowctl 0.1.1 macht pause/unpause zu Positionsbefehlen, entfernt die veraltete Export-Funktion, ergänzt team_name und team_id für Verbindungs- und Variablenbefehle und behebt mehrere Fehler.

📦 PyPI: https://pypi.org/project/apache-airflow-ctl/0.1.1/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow-ctl/0.1.1/ 🛠 Release Notes: https://github.com/apache/airflow/blob/airflow-ctl/0.1.1/airflow-ctl/RELEASE_NOTES.rst

Thanks to all the contributors who made this possible. Next time, Release notes will be available through public documentation.

Significant Changes

  • Make pause/unpause commands positional for improved CLI consistency (#59936) Provides separate airflowctl dags pause/unpause dag_id
  • Remove deprecated export functionality from airflowctl (#59850) airflowctl won't export from
  • Add team_name to connection commands (#59336) Team name feature added to connections command
  • Add team_id to variable commands (#57102)
  • Add pre-commit checks for airflowctl test coverage (#58856) Provided more coverage and further checks on integration tests to release with more confidence.
  • Display active DAG run count in header with auto-refresh support (#58332) active_runs_count has been added to the dags command.

Bug Fixes

  • Simplify airflowctl exception handling in safe_call_command (#59808)
  • Fix backfill default behavior for run_on_latest_version (#59304)
  • Update BulkDeleteAction to use generic typing (#59207)
  • Bump minimum supported prek version to 0.2.0 (#58952)
  • Fix RST formatting to ensure blank lines before bullet lists (#58760) …

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Airflow von Apache

Apache Airflow 3.1.5

Version 3.1.5 enthält keine wesentlichen Änderungen und behebt zahlreiche Fehler, darunter inkonsistente Dag-Hashes, fälschlich verwaiste Assets, eine Race Condition bei backfill max_active_runs und einen Speicheranstieg im LocalExecutor.

📦 PyPI: https://pypi.org/project/apache-airflow/3.1.5/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.1.5/ 📚 Task SDK Docs: https://airflow.apache.org/docs/task-sdk/1.1.5/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.1.5/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.1.5" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.1.5

Significant Changes

No significant changes.

Bug Fixes

Handle invalid token in JWTRefreshMiddleware (#56904) Fix inconsistent Dag hashes when template fields contain unordered dicts (#59091) (#59175) Fix assets used only as inlets being incorrectly orphaned (#58986) Fix exception when logging stdout with a custom %-format string (#58963) Fix backfill max_active_runs race condition with concurrent schedulers (#58935) Fix LocalExecutor memory spike by applying gc.freeze (#58934) Fix string to datetime pydantic conversion (#58916) Fix deadlines being incorrectly pruned for DAG runs with the same run_id (#58910) Fix handling of pre-AIP-39 DAG runs (#58773) Mask secrets properly when using deprecated import path (#58726) Preserve Asset.extra when using AssetAlias (#58712) Fix timeout_after in run_trigger method of TriggerRunner (#58703) Fix connection retrieval from secrets backend without conn_type (#58664) Fix task retry logic to respect retries for all exit codes (#58478) Respect default_args in DAG when set to a "falsy" value (#58396) …

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Airflow von Apache

Apache Airflow 3.1.3

Version 3.1.3 behebt den Zugriff auf Connections und Variablen in API-Server-Kontexten wie Plugins und Log-Handlern, indem der Kontext automatisch erkannt wird und je Kontext eigene Secrets-Backend-Ketten verwendet werden.

📦 PyPI: https://pypi.org/project/apache-airflow/3.1.3/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/3.1.3/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.1.3/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:3.1.3" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.1.3

Significant Changes

Fix Connection & Variable access in API server contexts (plugins, log handlers)(#56583)

Previously, hooks used in API server contexts (plugins, middlewares, log handlers) would fail with an ImportError for SUPERVISOR_COMMS, because SUPERVISOR_COMMS only exists in task runner child processes.

This has been fixed by implementing automatic context detection with three separate secrets backend chains:

Context Detection:

  1. Client contexts (task runner in worker): Detected via SUPERVISOR_COMMS presence
  2. Server contexts (API server, scheduler): Explicitly marked with _AIRFLOW_PROCESS_CONTEXT=server environment variable
  3. Fallback contexts (supervisor, unknown contexts): Neither marker present, uses minimal safe chain

Backend Chains:

  • Client: EnvironmentVariablesBackend → ExecutionAPISecretsBackend (routes to Execution API via SUPERVISOR_COMMS)
  • Server: EnvironmentVariablesBackend → MetastoreBackend (direct database access) …

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