This file documents any backwards-incompatible changes in Airflow and assists users migrating to a new version.
Table of contents
- Main
- Airflow 2.2.3
- Airflow 2.2.2
- Airflow 2.2.1
- Airflow 2.2.0
- Airflow 2.1.4
- Airflow 2.1.3
- Airflow 2.1.2
- Airflow 2.1.1
- Airflow 2.1.0
- Airflow 2.0.2
- Airflow 2.0.1
- Airflow 2.0.0
- Airflow 1.10.15
- Airflow 1.10.14
- Airflow 1.10.13
- Airflow 1.10.12
- Airflow 1.10.11
- Airflow 1.10.10
- Airflow 1.10.9
- Airflow 1.10.8
- Airflow 1.10.7
- Airflow 1.10.6
- Airflow 1.10.5
- Airflow 1.10.4
- Airflow 1.10.3
- Airflow 1.10.2
- Airflow 1.10.1
- Airflow 1.10
- Airflow 1.9
- Airflow 1.8.1
- Airflow 1.8
- Airflow 1.7.1.2
When you use SQLAlchemy 1.4.0+, you need ot use postgresql:// as the database in the sql_alchemy_conn.
In the previous versions of SQLAlchemy it was possible to use postgres://, but using it in
SQLAlchemy 1.4.0+ results in:
> raise exc.NoSuchModuleError(
"Can't load plugin: %s:%s" % (self.group, name)
)
E sqlalchemy.exc.NoSuchModuleError: Can't load plugin: sqlalchemy.dialects:postgres
If you cannot change the prefix of your URL immediately, Airflow continues to work with SQLAlchemy 1.3 and you can downgrade SQLAlchemy, but we recommend to update the prefix. Details in the SQLAlchemy Changelog.
Passing execution_date to XCom.set(), XCom.clear(), XCom.get_one(), and XCom.get_many() is deprecated
Continuing the effort to bind TaskInstance to a DagRun, XCom entries are now also tied to a DagRun. Use the run_id argument to specify the DagRun instead.
It was previously possible to use dag or task param defaults that were not JSON-serializable.
For example this worked previously:
@dag.task(params={"a": {1, 2, 3}, "b": pendulum.now()})
def datetime_param(value):
print(value)
datetime_param("{{ params.a }} | {{ params.b }}")Note the use of set and datetime types, which are not JSON-serializable. This behavior is problematic because to override these values in a dag run conf, you must use JSON, which could make these params non-overridable. Another problem is that the support for param validation assumes JSON. Use of non-JSON-serializable params will be removed in Airflow 3.0 and until then, use of them will produce a warning at parse time.
Smart sensors, an "early access" feature added in Airflow 2, are now deprecated and will be removed in Airflow 2.4.0. They have been superseded by Deferable Operators, added in Airflow 2.2.0.
See Migrating to Deferrable Operators for details on how to migrate.
Previously, a task’s log is dynamically rendered from the [core] log_filename_template, [core] task_log_prefix_template, and [elasticsearch] log_id_template config values at runtime. This resulted in unfortunate characteristics, e.g. it is impractical to modify the config value after an Airflow instance is running for a while, since all existing task logs have be saved under the previous format and cannot be found with the new config value.
A new log_template table is introduced to solve this problem. This table is synchronised with the aforementioned config values every time Airflow starts, and a new field log_template_id is added to every DAG run to point to the format used by tasks (NULL indicates the first ever entry for compatibility).
No breaking changes.
No breaking changes.
Param, introduced in Airflow 2.2.0, accidentally set the default value to None. This default has been removed. If you want None as your default, explicitly set it as such. For example:
Param(None, type=["null", "string"])Now if you resolve a Param without a default and don't pass a value, you will get an TypeError. For Example:
Param().resolve() # raises TypeErrorThe max_queued_runs_per_dag configuration option in [core] section has been removed. Previously, this controlled the number of queued dagrun
the scheduler can create in a dag. Now, the maximum number is controlled internally by the DAG's max_active_runs
Note: Upgrading the database to 2.2.0 or later can take some time to complete, particularly if you have a large task_instance table.
The worker_log_server_port configuration option has been moved from [celery] section to [logging] section to allow for re-use between different executors.
Previously pandas was a core requirement so when you run pip install apache-airflow it looked for pandas
library and installed it if it does not exist.
If you want to install pandas compatible with Airflow, you can use [pandas] extra while
installing Airflow, example for Python 3.8 and Airflow 2.1.2:
pip install -U "apache-airflow[pandas]==2.1.2" \
--constraint https://raw.githubusercontent.com/apache/airflow/constraints-2.1.2/constraints-3.8.txt"TriggerRule.NONE_FAILED_OR_SKIPPED is replaced by TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS.
This is only name change, no functionality changes made.
This change is backward compatible however TriggerRule.NONE_FAILED_OR_SKIPPED will be removed in next major release.
TriggerRule.DUMMY is replaced by TriggerRule.ALWAYS.
This is only name change, no functionality changes made.
This change is backward compatible however TriggerRule.DUMMY will be removed in next major release.
[core] dag_concurrency setting in airflow.cfg has been renamed to [core] max_active_tasks_per_dag
for better understanding.
It is the maximum number of task instances allowed to run concurrently in each DAG. To calculate the number of tasks that is running concurrently for a DAG, add up the number of running tasks for all DAG runs of the DAG.
This is configurable at the DAG level with max_active_tasks and a default can be set in airflow.cfg as
[core] max_active_tasks_per_dag.
Before:
[core]
dag_concurrency = 16Now:
[core]
max_active_tasks_per_dag = 16Similarly, DAG.concurrency has been renamed to DAG.max_active_tasks.
Before:
dag = DAG(
dag_id="example_dag",
start_date=datetime(2021, 1, 1),
catchup=False,
concurrency=3,
)Now:
dag = DAG(
dag_id="example_dag",
start_date=datetime(2021, 1, 1),
catchup=False,
max_active_tasks=3,
)If you are using DAGs Details API endpoint, use max_active_tasks instead of concurrency.
BaseOperator.task_concurrency has been deprecated and renamed to max_active_tis_per_dag for
better understanding.
This parameter controls the number of concurrent running task instances across dag_runs
per task.
Before:
with DAG(dag_id="task_concurrency_example"):
BashOperator(task_id="t1", task_concurrency=2, bash_command="echo Hi")After:
with DAG(dag_id="task_concurrency_example"):
BashOperator(task_id="t1", max_active_tis_per_dag=2, bash_command="echo Hi")[scheduler] processor_poll_interval setting in airflow.cfg has been renamed to [scheduler] scheduler_idle_sleep_time
for better understanding.
It controls the 'time to sleep' at the end of the Scheduler loop if nothing was scheduled inside SchedulerJob.
Before:
[scheduler]
processor_poll_interval = 16Now:
[scheduler]
scheduler_idle_sleep_time = 16When marking a task success/failed in Graph View, its downstream tasks that are in failed/upstream_failed state are automatically cleared.
While DAG Serialization is a strict requirements since Airflow 2, we allowed users to control where the Webserver looked for when showing the Code View.
If [core] store_dag_code was set to True, the Scheduler stored the code in the DAG file in the
DB (in dag_code table) as a plain string, and the webserver just read it from the same table.
If the value was set to False, the webserver read it from the DAG file.
While this setting made sense for Airflow < 2, it caused some confusion to some users where they thought this setting controlled DAG Serialization.
From Airflow 2.2, Airflow will only look for DB when a user clicks on Code View for a DAG.
Previously, clearing a running task sets its state to SHUTDOWN. The task gets killed and goes into FAILED state. After #16681, clearing a running task sets its state to RESTARTING. The task is eligible for retry without going into FAILED state.
This method returned incorrect values for a long time, because it did not take into account the different logger configuration and task retries. We have also started supporting more advanced tools that don't use files, so it is impossible to determine the correct file path in every case e.g. Stackdriver doesn't use files but identifies logs based on labels. For this reason, we decided to delete this attribute.
If you need to read logs, you can use airflow.utils.log.log_reader.TaskLogReader class, which does not have
the above restrictions.
Previously, a sensor is retried when it times out until the number of retries are exhausted. So the effective timeout of a sensor is timeout * (retries + 1). This behaviour is now changed. A sensor will immediately fail without retrying if timeout is reached. If it's desirable to let the sensor continue running for longer time, set a larger timeout instead.
By default tasks are running in default_pool. default_pool is initialized with 128 slots and user can change the
number of slots through UI/CLI/API for an existing deployment.
For new deployments, you can use default_pool_task_slot_count setting in [core] section. This setting would
not have any effect in an existing deployment where the default_pool already exists.
Previously this was controlled by non_pooled_task_slot_count in [core] section, which was not documented.
Now that the DAG parser syncs DAG permissions there is no longer a need for manually refreshing DAGs. As such, the buttons to refresh a DAG have been removed from the UI.
In addition, the /refresh and /refresh_all webserver endpoints have also been removed.
Under normal operation every TaskInstance row in the database would have DagRun row too, but it was possible to manually delete the DagRun and Airflow would still schedule the TaskInstances.
In Airflow 2.2 we have changed this and now there is a database-level foreign key constraint ensuring that every TaskInstance has a DagRun row.
Before updating to this 2.2 release you will have to manually resolve any inconsistencies (add back DagRun rows, or delete TaskInstances) if you have any "dangling" TaskInstance" rows.
As part of this change the clean_tis_without_dagrun_interval config option under [scheduler] section has been removed and has no effect.
As a part of the TaskInstance-DagRun relation change, the execution_date columns on TaskInstance and TaskReschedule have been removed from the database, and replaced by association proxy fields at the ORM level. If you access Airflow’s metadatabase directly, you should rewrite the implementation to use the run_id columns instead.
Note that Airflow’s metadatabase definition on both the database and ORM levels are considered implementation detail without strict backward compatibility guarantees.
If dask workers are not started with complementary resources to match the specified queues, it will now result in an AirflowException, whereas before it would have just ignored the queue argument.
No breaking changes.
No breaking changes.
No breaking changes.
To achieve the previous default behaviour of clear_task_instances with activate_dag_runs=True, no change is needed. To achieve the previous behaviour of activate_dag_runs=False, pass dag_run_state=False instead. (The previous parameter is still accepted, but is deprecated)
The method set_dag_runs_state is no longer needed after a bug fix in PR: #15382. This method is now deprecated and will be removed in a future version.
We have a new '[deprecated_api]' extra that should be used when installing airflow when the deprecated API
is going to be used. This is now an optional feature of Airflow now because it pulls in requests which
(as of 14 May 2021) pulls LGPL chardet dependency.
The http provider is now optional and not installed by default, until chardet becomes an optional
dependency of requests.
See PR to replace chardet with charset-normalizer
This decorator is now automatically added to all operators via the metaclass on BaseOperator
We've improved masking for sensitive data in Web UI and logs. As part of it, the following configurations have been changed:
hide_sensitive_variable_fieldsoption inadminsection has been replaced byhide_sensitive_var_conn_fieldssection incoresection,sensitive_variable_fieldsoption inadminsection has been replaced bysensitive_var_conn_namessection incoresection.
We have moved PodDefaults from airflow.kubernetes.pod_generator.PodDefaults to
airflow.providers.cncf.kubernetes.utils.xcom_sidecar.PodDefaults and moved add_xcom_sidecar
from airflow.kubernetes.pod_generator.PodGenerator.add_xcom_sidecarto
airflow.providers.cncf.kubernetes.utils.xcom_sidecar.add_xcom_sidecar.
This change will allow us to modify the KubernetesPodOperator XCom functionality without requiring airflow upgrades.
Moved the pod launcher from airflow.kubernetes.pod_launcher to airflow.providers.cncf.kubernetes.utils.pod_launcher
This will allow users to update the pod_launcher for the KubernetesPodOperator without requiring an airflow upgrade
The default value for [webserver] worker_refresh_interval was 30 seconds for
Airflow <=2.0.1. However, since Airflow 2.0 DAG Serialization is a hard requirement
and the Webserver used the serialized DAGs, there is no need to kill an existing
worker and create a new one as frequently as 30 seconds.
This setting can be raised to an even higher value, currently it is
set to 6000 seconds (100 minutes) to
serve as a DagBag cache burst time.
The default_queue configuration option has been moved from [celery] section to [operators] section to allow for re-use between different executors.
This allows Airflow to work more reliably with some environments (like Azure) by default.
The sync-perm CLI command will no longer sync DAG specific permissions by default as they are now being handled during
DAG parsing. If you need or want the old behavior, you can pass --include-dags to have sync-perm also sync DAG
specific permissions.
Previously, Users with User or Viewer role were able to get/view configurations using
the REST API or in the Webserver. From Airflow 2.0.1, only users with Admin or Op role would be able
to get/view Configurations.
To allow users with other roles to view configuration, add can read on Configurations permissions to that role.
Note that if [webserver] expose_config is set to False, the API will throw a 403 response even if
the user has role with can read on Configurations permission.
The default value for [celery] worker_concurrency was 16 for Airflow <2.0.0.
However, it was unintentionally changed to 8 in 2.0.0.
From Airflow 2.0.1, we revert to the old default of 16.
The default value for [scheduler] min_file_process_interval was 0,
due to which the CPU Usage mostly stayed around 100% as the DAG files are parsed
constantly.
From Airflow 2.0.0, the scheduling decisions have been moved from
DagFileProcessor to Scheduler, so we can keep the default a bit higher: 30.
The 2.0 release of the Airflow is a significant upgrade, and includes substantial major changes, and some of them may be breaking. Existing code written for earlier versions of this project will may require updates to use this version. Sometimes necessary configuration changes are also required. This document describes the changes that have been made, and what you need to do to update your usage.
If you experience issues or have questions, please file an issue.
This section describes the major changes that have been made in this release.
The experimental REST API is disabled by default. To restore these APIs while migrating to
the stable REST API, set enable_experimental_api option in [api] section to True.
Please note that the experimental REST API do not have access control. The authenticated user has full access.
For SparkJDBCHook default connection was spark-default, and for SparkSubmitHook it was
spark_default. Both hooks now use the spark_default which is a common pattern for the connection
names used across all providers.
From Airflow 2.0, We are replacing tabulate with rich to render commands output. Due to this change, the --output argument
will no longer accept formats of tabulate tables. Instead, it now accepts:
table- will render the output in predefined tablejson- will render the output as a jsonyaml- will render the output as yaml
By doing this we increased consistency and gave users possibility to manipulate the output programmatically (when using json or yaml).
Affected commands:
airflow dags listairflow dags reportairflow dags list-runsairflow dags list-jobsairflow connections listairflow connections getairflow pools listairflow pools getairflow pools setairflow pools deleteairflow pools importairflow pools exportairflow role listairflow providers listairflow providers getairflow providers hooksairflow tasks states-for-dag-runairflow users listairflow variables list
The WasbHook in Apache Airflow use a legacy version of Azure library. While the conflict is not
significant for most of the Azure hooks, it is a problem for Wasb Hook because the blob folders
for both libraries overlap. Installing both Snowflake and Azure extra will result in non-importable
WasbHook.
The all extras were reduced to include only user-facing dependencies. This means
that this extra does not contain development dependencies. If you were relying on
all extra then you should use now devel_all or figure out if you need development
extras at all.
Context variables prev_execution_date_success and prev_execution_date_success are now pendulum.DateTime
Because Airflow introduced DAG level policy (dag_policy) we decided to rename existing policy
function to task_policy to make the distinction more profound and avoid any confusion.
Users using cluster policy need to rename their policy functions in airflow_local_settings.py
to task_policy.
From Airflow 2, by default Airflow will retry 3 times to publish task to Celery broker. This is controlled by
[celery] task_publish_max_retries. Because of this we can now have a lower Operation timeout that raises
AirflowTaskTimeout. This generally occurs during network blips or intermittent DNS issues.
Operators and Sensors should 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.
If you previously had a plugins/my_plugin.py and you used it like this in a DAG:
from airflow.operators.my_plugin import MyOperator
You should instead import it as:
from my_plugin import MyOperator
The name under airflow.operators. was the plugin name, where as in the second example it is the python module name where the operator is defined.
See https://airflow.apache.org/docs/apache-airflow/stable/howto/custom-operator.html for more info.
Importing hooks added in plugins via airflow.hooks.<plugin_name> is no longer supported, and hooks should just be imported as regular python modules.
from airflow.hooks.my_plugin import MyHook
You should instead import it as:
from my_plugin import MyHook
It is still possible (but not required) to "register" hooks in plugins. This is to allow future support for dynamically populating the Connections form in the UI.
See https://airflow.apache.org/docs/apache-airflow/stable/howto/custom-operator.html for more info.
The pickle type for XCom messages has been replaced to JSON by default to prevent RCE attacks.
Note that JSON serialization is stricter than pickling, so for example if you want to pass
raw bytes through XCom you must encode them using an encoding like base64.
If you understand the risk and still want to use pickling,
set enable_xcom_pickling = True in your Airflow config's core section.
There was a bug fixed in apache#11993 that the "airflowignore" checked the base path of the dag folder for forbidden dags, not only the relative part. This had the effect that if the base path contained the excluded word the whole dag folder could have been excluded. For example if the airflowignore file contained x, and the dags folder was '/var/x/dags', then all dags in the folder would be excluded. The fix only matches the relative path only now which means that if you previously used full path as ignored, you should change it to relative one. For example if your dag folder was '/var/dags/' and your airflowignore contained '/var/dag/excluded/', you should change it to 'excluded/'.
The old syntax of passing context as a dictionary will continue to work with the caveat that the argument must be named context. The following will break. To fix it, change ctx to context.
def execution_date_fn(execution_date, ctx):
...execution_date_fn can take in any number of keyword arguments available in the task context dictionary. The following forms of execution_date_fn are all supported:
def execution_date_fn(dt):
...
def execution_date_fn(execution_date):
...
def execution_date_fn(execution_date, ds_nodash):
...
def execution_date_fn(execution_date, ds_nodash, dag):
...As recommended by Flask, the
[webserver] cookie_samesite has been changed to Lax from '' (empty string) .
Formerly the core code was maintained by the original creators - Airbnb. The code that was in the contrib
package was supported by the community. The project was passed to the Apache community and currently the
entire code is maintained by the community, so now the division has no justification, and it is only due
to historical reasons. In Airflow 2.0, we want to organize packages and move integrations
with third party services to the airflow.providers package.
All changes made are backward compatible, but if you use the old import paths you will see a deprecation warning. The old import paths can be abandoned in the future.
According to AIP-21
_operator suffix has been removed from operators. A deprecation warning has also been raised for paths
importing with the suffix.
The following table shows changes in import paths.
| Old path | New path |
|---|---|
| airflow.hooks.base_hook.BaseHook | airflow.hooks.base.BaseHook |
| airflow.hooks.dbapi_hook.DbApiHook | airflow.hooks.dbapi.DbApiHook |
| airflow.operators.dummy_operator.DummyOperator | airflow.operators.dummy.DummyOperator |
| airflow.operators.dagrun_operator.TriggerDagRunOperator | airflow.operators.trigger_dagrun.TriggerDagRunOperator |
| airflow.operators.branch_operator.BaseBranchOperator | airflow.operators.branch.BaseBranchOperator |
| airflow.operators.subdag_operator.SubDagOperator | airflow.operators.subdag.SubDagOperator |
| airflow.sensors.base_sensor_operator.BaseSensorOperator | airflow.sensors.base.BaseSensorOperator |
| airflow.sensors.date_time_sensor.DateTimeSensor | airflow.sensors.date_time.DateTimeSensor |
| airflow.sensors.external_task_sensor.ExternalTaskMarker | airflow.sensors.external_task.ExternalTaskMarker |
| airflow.sensors.external_task_sensor.ExternalTaskSensor | airflow.sensors.external_task.ExternalTaskSensor |
| airflow.sensors.sql_sensor.SqlSensor | airflow.sensors.sql.SqlSensor |
| airflow.sensors.time_delta_sensor.TimeDeltaSensor | airflow.sensors.time_delta.TimeDeltaSensor |
| airflow.contrib.sensors.weekday_sensor.DayOfWeekSensor | airflow.sensors.weekday.DayOfWeekSensor |
In order to migrate the database, you should use the command airflow db upgrade, but in
some cases manual steps are required.
Previously, Airflow allowed users to add more than one connection with the same conn_id and on access it would choose one connection randomly. This acted as a basic load balancing and fault tolerance technique, when used in conjunction with retries.
This behavior caused some confusion for users, and there was no clear evidence if it actually worked well or not.
Now the conn_id will be unique. If you already have duplicates in your metadata database, you will have to manage those duplicate connections before upgrading the database.
The conn_type column in the connection table must contain content. Previously, this rule was enforced
by application logic, but was not enforced by the database schema.
If you made any modifications to the table directly, make sure you don't have
null in the conn_type column.
This release contains many changes that require a change in the configuration of this application or other application that integrate with it.
This section describes the changes that have been made, and what you need to do to.
Formerly the core code was maintained by the original creators - Airbnb. The code that was in the contrib
package was supported by the community. The project was passed to the Apache community and currently the
entire code is maintained by the community, so now the division has no justification, and it is only due
to historical reasons. In Airflow 2.0, we want to organize packages and move integrations
with third party services to the airflow.providers package.
To clean up, the following packages were moved:
| Old package | New package |
|---|---|
airflow.contrib.utils.log |
airflow.utils.log |
airflow.utils.log.gcs_task_handler |
airflow.providers.google.cloud.log.gcs_task_handler |
airflow.utils.log.wasb_task_handler |
airflow.providers.microsoft.azure.log.wasb_task_handler |
airflow.utils.log.stackdriver_task_handler |
airflow.providers.google.cloud.log.stackdriver_task_handler |
airflow.utils.log.s3_task_handler |
airflow.providers.amazon.aws.log.s3_task_handler |
airflow.utils.log.es_task_handler |
airflow.providers.elasticsearch.log.es_task_handler |
airflow.utils.log.cloudwatch_task_handler |
airflow.providers.amazon.aws.log.cloudwatch_task_handler |
You should update the import paths if you are setting log configurations with the logging_config_class option.
The old import paths still works but can be abandoned.
Formerly the core code was maintained by the original creators - Airbnb. The code that was in the contrib package was supported by the community. The project was passed to the Apache community and currently the entire code is maintained by the community, so now the division has no justification, and it is only due to historical reasons.
To clean up, the send_mail function from the airflow.contrib.utils.sendgrid module has been moved.
If your configuration file looks like this:
[email]
email_backend = airflow.contrib.utils.sendgrid.send_emailIt should look like this now:
[email]
email_backend = airflow.providers.sendgrid.utils.emailer.send_emailThe old configuration still works but can be abandoned.
The previous option used a colon(:) to split the module from function. Now the dot(.) is used.
The change aims to unify the format of all options that refer to objects in the airflow.cfg file.
In previous versions of Airflow it was possible to use plugins to load custom executors. It is still
possible, but the configuration has changed. Now you don't have to create a plugin to configure a
custom executor, but you need to provide the full path to the module in the executor option
in the core section. The purpose of this change is to simplify the plugin mechanism and make
it easier to configure executor.
If your module was in the path my_acme_company.executors.MyCustomExecutor and the plugin was
called my_plugin then your configuration looks like this
[core]
executor = my_plugin.MyCustomExecutorAnd now it should look like this:
[core]
executor = my_acme_company.executors.MyCustomExecutorThe old configuration is still works but can be abandoned at any time.
From Airflow 2.0, if you want to define your own Flask App Builder data models you need to use CustomSQLAInterface instead of SQLAInterface.
For Non-RBAC replace:
from flask_appbuilder.models.sqla.interface import SQLAInterface
datamodel = SQLAInterface(your_data_model)with RBAC (in 1.10):
from airflow.www_rbac.utils import CustomSQLAInterface
datamodel = CustomSQLAInterface(your_data_model)and in 2.0:
from airflow.www.utils import CustomSQLAInterface
datamodel = CustomSQLAInterface(your_data_model)In previous version, you could use plugins mechanism to configure stat_name_handler. You should now use the stat_name_handler
option in [scheduler] section to achieve the same effect.
If your plugin looked like this and was available through the test_plugin path:
def my_stat_name_handler(stat):
return stat
class AirflowTestPlugin(AirflowPlugin):
name = "test_plugin"
stat_name_handler = my_stat_name_handlerthen your airflow.cfg file should look like this:
[scheduler]
stat_name_handler=test_plugin.my_stat_name_handlerThis change is intended to simplify the statsd configuration.
The following configurations have been moved from [core] to the new [logging] section.
base_log_folderremote_loggingremote_log_conn_idremote_base_log_folderencrypt_s3_logslogging_levelfab_logging_levellogging_config_classcolored_console_logcolored_log_formatcolored_formatter_classlog_formatsimple_log_formattask_log_prefix_templatelog_filename_templatelog_processor_filename_templatedag_processor_manager_log_locationtask_log_reader
The following configurations have been moved from [scheduler] to the new [metrics] section.
statsd_onstatsd_hoststatsd_portstatsd_prefixstatsd_allow_liststat_name_handlerstatsd_datadog_enabledstatsd_datadog_tagsstatsd_custom_client_path
When JSON output to stdout is enabled, log lines will now contain the log_id & offset fields, this should make reading task logs from elasticsearch on the webserver work out of the box. Example configuration:
[logging]
remote_logging = True
[elasticsearch]
host = http://es-host:9200
write_stdout = True
json_format = TrueNote that the webserver expects the log line data itself to be present in the message field of the document.
This option has been removed because it is no longer supported by the Google Kubernetes Engine. The new recommended service account keys for the Google Cloud management method is Workload Identity.
The fernet mechanism is enabled by default to increase the security of the default installation. In order to
restore the previous behavior, the user must consciously set an empty key in the fernet_key option of
section [core] in the airflow.cfg file.
At the same time, this means that the apache-airflow[crypto] extra-packages are always installed.
However, this requires that your operating system has libffi-dev installed.
kubernetes_annotations configuration section has been removed.
A new key worker_annotations has been added to existing kubernetes section instead.
That is to remove restriction on the character set for k8s annotation keys.
All key/value pairs from kubernetes_annotations should now go to worker_annotations as a json. I.e. instead of e.g.
[kubernetes_annotations]
annotation_key = annotation_value
annotation_key2 = annotation_value2
it should be rewritten to
[kubernetes]
worker_annotations = { "annotation_key" : "annotation_value", "annotation_key2" : "annotation_value2" }
We should not use the run_duration option anymore. This used to be for restarting the scheduler from time to time, but right now the scheduler is getting more stable and therefore using this setting is considered bad and might cause an inconsistent state.
Used slot has been renamed to running slot to make the name self-explanatory and the code more maintainable.
This means pool.used_slots.<pool_name> metric has been renamed to
pool.running_slots.<pool_name>. The Used Slots column in Pools Web UI view
has also been changed to Running Slots.
The Mesos Executor is removed from the code base as it was not widely used and not maintained. Mailing List Discussion on deleting it.
Change DAG file loading duration metric from
dag.loading-duration.<dag_id> to dag.loading-duration.<dag_file>. This is to
better handle the case when a DAG file has multiple DAGs.
Sentry is disabled by default. To enable these integrations, you need set sentry_on option
in [sentry] section to "True".
In previous versions, in order to configure the service account key file, you had to create a connection entry.
In the current version, you can configure google_key_path option in [logging] section to set
the key file path.
Users using Application Default Credentials (ADC) need not take any action.
The change aims to simplify the configuration of logging, to prevent corruption of the instance configuration by changing the value controlled by the user - connection entry. If you configure a backend secret, it also means the webserver doesn't need to connect to it. This simplifies setups with multiple GCP projects, because only one project will require the Secret Manager API to be enabled.
We strive to ensure that there are no changes that may affect the end user and your files, but this release may contain changes that will require changes to your DAG files.
This section describes the changes that have been made, and what you need to do to update your DAG File, if you use core operators or any other.
Previously, BaseSensorOperator with setting soft_fail=True skips itself
and skips all its downstream tasks unconditionally, when it fails i.e the trigger_rule of downstream tasks is not
respected.
In the new behavior, the trigger_rule of downstream tasks is respected.
User can preserve/achieve the original behaviour by setting the trigger_rule of each downstream task to all_success.
BaseOperator class uses a BaseOperatorMeta as a metaclass. This meta class is based on
abc.ABCMeta. If your custom operator uses different metaclass then you will have to adjust it.
Remove get_records and get_pandas_df and run from BaseHook, which only apply for SQL-like hook,
If want to use them, or your custom hook inherit them, please use airflow.hooks.dbapi.DbApiHook
Previously, you could assign a task to a DAG as follows:
dag = DAG("my_dag")
dummy = DummyOperator(task_id="dummy")
dag >> dummyThis is no longer supported. Instead, we recommend using the DAG as context manager:
with DAG("my_dag") as dag:
dummy = DummyOperator(task_id="dummy")The deprecated import mechanism has been removed so the import of modules becomes more consistent and explicit.
For example: from airflow.operators import BashOperator
becomes from airflow.operators.bash_operator import BashOperator
Sensors are now accessible via airflow.sensors and no longer via airflow.operators.sensors.
For example: from airflow.operators.sensors import BaseSensorOperator
becomes from airflow.sensors.base import BaseSensorOperator
Previously, a task instance with wait_for_downstream=True will only run if the downstream task of
the previous task instance is successful. Meanwhile, a task instance with depends_on_past=True
will run if the previous task instance is either successful or skipped. These two flags are close siblings
yet they have different behavior. This inconsistency in behavior made the API less intuitive to users.
To maintain consistent behavior, both successful or skipped downstream task can now satisfy the
wait_for_downstream=True flag.
The chain and cross_downstream methods are now moved to airflow.models.baseoperator module from
airflow.utils.helpers module.
The baseoperator module seems to be a better choice to keep closely coupled methods together. Helpers module is supposed to contain standalone helper methods that can be imported by all classes.
The chain method and cross_downstream method both use BaseOperator. If any other package imports
any classes or functions from helpers module, then it automatically has an
implicit dependency to BaseOperator. That can often lead to cyclic dependencies.
More information in AIRFLOW-6392
In Airflow < 2.0 you imported those two methods like this:
from airflow.utils.helpers import chain
from airflow.utils.helpers import cross_downstreamIn Airflow 2.0 it should be changed to:
from airflow.models.baseoperator import chain
from airflow.models.baseoperator import cross_downstreamBranchPythonOperator will now return a value equal to the task_id of the chosen branch,
where previously it returned None. Since it inherits from BaseOperator it will do an
xcom_push of this value if do_xcom_push=True. This is useful for downstream decision-making.
SQLSensor now consistent with python bool() function and the allow_null parameter has been removed.
It will resolve after receiving any value that is casted to True with python bool(value). That
changes the previous response receiving NULL or '0'. Earlier '0' has been treated as success
criteria. NULL has been treated depending on value of allow_nullparameter. But all the previous
behaviour is still achievable setting param success to lambda x: x is None or str(x) not in ('0', '').
The TriggerDagRunOperator now takes a conf argument to which a dict can be provided as conf for the DagRun.
As a result, the python_callable argument was removed. PR: apache#6317.
provide_context argument on the PythonOperator was removed. The signature of the callable passed to the PythonOperator is now inferred and argument values are always automatically provided. There is no need to explicitly provide or not provide the context anymore. For example:
def myfunc(execution_date):
print(execution_date)
python_operator = PythonOperator(task_id="mytask", python_callable=myfunc, dag=dag)Notice you don't have to set provide_context=True, variables from the task context are now automatically detected and provided.
All context variables can still be provided with a double-asterisk argument:
def myfunc(**context):
print(context) # all variables will be provided to context
python_operator = PythonOperator(task_id="mytask", python_callable=myfunc)The task context variable names are reserved names in the callable function, hence a clash with op_args and op_kwargs results in an exception:
def myfunc(dag):
# raises a ValueError because "dag" is a reserved name
# valid signature example: myfunc(mydag)
print("output")
python_operator = PythonOperator(
task_id="mytask",
op_args=[1],
python_callable=myfunc,
)The change is backwards compatible, setting provide_context will add the provide_context variable to the kwargs (but won't do anything).
PR: #5990
FileSensor is now takes a glob pattern, not just a filename. If the filename you are looking for has *, ?, or [ in it then you should replace these with [*], [?], and [[].
SubDagOperator is changed to use Airflow scheduler instead of backfill
to schedule tasks in the subdag. User no longer need to specify the executor
in SubDagOperator.
The do_xcom_push flag (a switch to push the result of an operator to xcom or not) was appearing in different incarnations in different operators. It's function has been unified under a common name (do_xcom_push) on BaseOperator. This way it is also easy to globally disable pushing results to xcom.
The following operators were affected:
- DatastoreExportOperator (Backwards compatible)
- DatastoreImportOperator (Backwards compatible)
- KubernetesPodOperator (Not backwards compatible)
- SSHOperator (Not backwards compatible)
- WinRMOperator (Not backwards compatible)
- BashOperator (Not backwards compatible)
- DockerOperator (Not backwards compatible)
- SimpleHttpOperator (Not backwards compatible)
See AIRFLOW-3249 for details
In previous versions, the LatestOnlyOperator forcefully skipped all (direct and undirect) downstream tasks on its own. From this version on the operator will only skip direct downstream tasks and the scheduler will handle skipping any further downstream dependencies.
No change is needed if only the default trigger rule all_success is being used.
If the DAG relies on tasks with other trigger rules (i.e. all_done) being skipped by the LatestOnlyOperator, adjustments to the DAG need to be made to accommodate the change in behaviour, i.e. with additional edges from the LatestOnlyOperator.
The goal of this change is to achieve a more consistent and configurale cascading behaviour based on the BaseBranchOperator (see AIRFLOW-2923 and AIRFLOW-1784).
We strive to ensure that there are no changes that may affect the end user, and your Python files, but this release may contain changes that will require changes to your plugins, DAG File or other integration.
Only changes unique to this provider are described here. You should still pay attention to the changes that have been made to the core (including core operators) as they can affect the integration behavior of this provider.
This section describes the changes that have been made, and what you need to do to update your Python files.
The imports LoggingMixin, conf, and AirflowException have been removed from airflow/__init__.py.
All implicit references of these objects will no longer be valid. To migrate, all usages of each old path must be
replaced with its corresponding new path.
| Old Path (Implicit Import) | New Path (Explicit Import) |
|---|---|
airflow.LoggingMixin |
airflow.utils.log.logging_mixin.LoggingMixin |
airflow.conf |
airflow.configuration.conf |
airflow.AirflowException |
airflow.exceptions.AirflowException |
The following variables were removed from the task instance context:
- end_date
- latest_date
- tables
Formerly the core code was maintained by the original creators - Airbnb. The code that was in the contrib package was supported by the community. The project was passed to the Apache community and currently the entire code is maintained by the community, so now the division has no justification, and it is only due to historical reasons.
To clean up, Weekday enum has been moved from airflow.contrib.utils into airflow.utils module.
The connection module has new deprecated methods:
Connection.parse_from_uriConnection.log_infoConnection.debug_info
and one deprecated function:
parse_netloc_to_hostname
Previously, users could create a connection object in two ways
conn_1 = Connection(conn_id="conn_a", uri="mysql://AAA/")
# or
conn_2 = Connection(conn_id="conn_a")
conn_2.parse_uri(uri="mysql://AAA/")
Now the second way is not supported.
Connection.log_info and Connection.debug_info method have been deprecated. Read each Connection field individually or use the
default representation (__repr__).
The old method is still works but can be abandoned at any time. The changes are intended to delete method that are rarely used.
DAG.create_dagrun accepts run_type and does not require run_id
This change is caused by adding run_type column to DagRun.
Previous signature:
def create_dagrun(
self,
run_id,
state,
execution_date=None,
start_date=None,
external_trigger=False,
conf=None,
session=None,
):
...current:
def create_dagrun(
self,
state,
execution_date=None,
run_id=None,
start_date=None,
external_trigger=False,
conf=None,
run_type=None,
session=None,
):
...If user provides run_id then the run_type will be derived from it by checking prefix, allowed types
: manual, scheduled, backfill (defined by airflow.utils.types.DagRunType).
If user provides run_type and execution_date then run_id is constructed as
{run_type}__{execution_data.isoformat()}.
Airflow should construct dagruns using run_type and execution_date, creation using
run_id is preserved for user actions.
Use DagRunType.SCHEDULED.value instead of DagRun.ID_PREFIX
All the run_id prefixes for different kind of DagRuns have been grouped into a single
enum in airflow.utils.types.DagRunType.
Previously, there were defined in various places, example as ID_PREFIX class variables for
DagRun, BackfillJob and in _trigger_dag function.
Was:
>> from airflow.models.dagrun import DagRun
>> DagRun.ID_PREFIX
scheduled__Replaced by:
>> from airflow.utils.types import DagRunType
>> DagRunType.SCHEDULED.value
scheduledWe remove airflow.utils.file.TemporaryDirectory
Since Airflow dropped support for Python < 3.5 there's no need to have this custom
implementation of TemporaryDirectory because the same functionality is provided by
tempfile.TemporaryDirectory.
Now users instead of import from airflow.utils.files import TemporaryDirectory should
do from tempfile import TemporaryDirectory. Both context managers provide the same
interface, thus no additional changes should be required.
We removed airflow.AirflowMacroPlugin class. The class was there in airflow package but it has not been used (apparently since 2015).
It has been removed.
CONTEXT_MANAGER_DAG was removed from settings. It's role has been taken by DagContext in
'airflow.models.dag'. One of the reasons was that settings should be rather static than store
dynamic context from the DAG, but the main one is that moving the context out of settings allowed to
untangle cyclic imports between DAG, BaseOperator, SerializedDAG, SerializedBaseOperator which was
part of AIRFLOW-6010.
Function redirect_stderr and redirect_stdout from airflow.utils.log.logging_mixin module has
been deleted because it can be easily replaced by the standard library.
The functions of the standard library are more flexible and can be used in larger cases.
The code below
import logging
from airflow.utils.log.logging_mixin import redirect_stderr, redirect_stdout
logger = logging.getLogger("custom-logger")
with redirect_stdout(logger, logging.INFO), redirect_stderr(logger, logging.WARN):
print("I love Airflow")can be replaced by the following code:
from contextlib import redirect_stdout, redirect_stderr
import logging
from airflow.utils.log.logging_mixin import StreamLogWriter
logger = logging.getLogger("custom-logger")
with redirect_stdout(StreamLogWriter(logger, logging.INFO)), redirect_stderr(
StreamLogWriter(logger, logging.WARN)
):
print("I Love Airflow")Now, additional arguments passed to BaseOperator cause an exception. Previous versions of Airflow took additional arguments and displayed a message on the console. When the message was not noticed by users, it caused very difficult to detect errors.
In order to restore the previous behavior, you must set an True in the allow_illegal_arguments
option of section [operators] in the airflow.cfg file. In the future it is possible to completely
delete this option.
Passing store_serialized_dags argument to DagBag.init and accessing DagBag.store_serialized_dags property
are deprecated and will be removed in future versions.
Previous signature:
def __init__(
dag_folder=None,
include_examples=conf.getboolean("core", "LOAD_EXAMPLES"),
safe_mode=conf.getboolean("core", "DAG_DISCOVERY_SAFE_MODE"),
store_serialized_dags=False,
):
...current:
def __init__(
dag_folder=None,
include_examples=conf.getboolean("core", "LOAD_EXAMPLES"),
safe_mode=conf.getboolean("core", "DAG_DISCOVERY_SAFE_MODE"),
read_dags_from_db=False,
):
...If you were using positional arguments, it requires no change but if you were using keyword
arguments, please change store_serialized_dags to read_dags_from_db.
Similarly, if you were using DagBag().store_serialized_dags property, change it to
DagBag().read_dags_from_db.
We strive to ensure that there are no changes that may affect the end user and your Python files, but this release may contain changes that will require changes to your configuration, DAG Files or other integration e.g. custom operators.
Only changes unique to this provider are described here. You should still pay attention to the changes that have been made to the core (including core operators) as they can affect the integration behavior of this provider.
This section describes the changes that have been made, and what you need to do to update your if you use operators or hooks which integrate with Google services (including Google Cloud - GCP).
Directly impersonating a service account
has been made possible for operators communicating with Google services via new argument called impersonation_chain
(google_impersonation_chain in case of operators that also communicate with services of other cloud providers).
As a result, GCSToS3Operator no longer derivatives from GCSListObjectsOperator.
Previously not all hooks and operators related to Google Cloud use
gcp_conn_id as parameter for GCP connection. There is currently one parameter
which apply to most services. Parameters like datastore_conn_id, bigquery_conn_id,
google_cloud_storage_conn_id and similar have been deprecated. Operators that require two connections are not changed.
Following components were affected by normalization:
- airflow.providers.google.cloud.hooks.datastore.DatastoreHook
- airflow.providers.google.cloud.hooks.bigquery.BigQueryHook
- airflow.providers.google.cloud.hooks.gcs.GoogleCloudStorageHook
- airflow.providers.google.cloud.operators.bigquery.BigQueryCheckOperator
- airflow.providers.google.cloud.operators.bigquery.BigQueryValueCheckOperator
- airflow.providers.google.cloud.operators.bigquery.BigQueryIntervalCheckOperator
- airflow.providers.google.cloud.operators.bigquery.BigQueryGetDataOperator
- airflow.providers.google.cloud.operators.bigquery.BigQueryOperator
- airflow.providers.google.cloud.operators.bigquery.BigQueryDeleteDatasetOperator
- airflow.providers.google.cloud.operators.bigquery.BigQueryCreateEmptyDatasetOperator
- airflow.providers.google.cloud.operators.bigquery.BigQueryTableDeleteOperator
- airflow.providers.google.cloud.operators.gcs.GoogleCloudStorageCreateBucketOperator
- airflow.providers.google.cloud.operators.gcs.GoogleCloudStorageListOperator
- airflow.providers.google.cloud.operators.gcs.GoogleCloudStorageDownloadOperator
- airflow.providers.google.cloud.operators.gcs.GoogleCloudStorageDeleteOperator
- airflow.providers.google.cloud.operators.gcs.GoogleCloudStorageBucketCreateAclEntryOperator
- airflow.providers.google.cloud.operators.gcs.GoogleCloudStorageObjectCreateAclEntryOperator
- airflow.operators.sql_to_gcs.BaseSQLToGoogleCloudStorageOperator
- airflow.operators.adls_to_gcs.AdlsToGoogleCloudStorageOperator
- airflow.operators.gcs_to_s3.GoogleCloudStorageToS3Operator
- airflow.operators.gcs_to_gcs.GoogleCloudStorageToGoogleCloudStorageOperator
- airflow.operators.bigquery_to_gcs.BigQueryToCloudStorageOperator
- airflow.operators.local_to_gcs.FileToGoogleCloudStorageOperator
- airflow.operators.cassandra_to_gcs.CassandraToGoogleCloudStorageOperator
- airflow.operators.bigquery_to_bigquery.BigQueryToBigQueryOperator
According to AIP-21 operators related to Google Cloud has been moved from contrib to core. The following table shows changes in import paths.
| Old path | New path |
|---|---|
| airflow.contrib.hooks.bigquery_hook.BigQueryHook | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook |
| airflow.contrib.hooks.datastore_hook.DatastoreHook | airflow.providers.google.cloud.hooks.datastore.DatastoreHook |
| airflow.contrib.hooks.gcp_bigtable_hook.BigtableHook | airflow.providers.google.cloud.hooks.bigtable.BigtableHook |
| airflow.contrib.hooks.gcp_cloud_build_hook.CloudBuildHook | airflow.providers.google.cloud.hooks.cloud_build.CloudBuildHook |
| airflow.contrib.hooks.gcp_container_hook.GKEClusterHook | airflow.providers.google.cloud.hooks.kubernetes_engine.GKEHook |
| airflow.contrib.hooks.gcp_compute_hook.GceHook | airflow.providers.google.cloud.hooks.compute.ComputeEngineHook |
| airflow.contrib.hooks.gcp_dataflow_hook.DataFlowHook | airflow.providers.google.cloud.hooks.dataflow.DataflowHook |
| airflow.contrib.hooks.gcp_dataproc_hook.DataProcHook | airflow.providers.google.cloud.hooks.dataproc.DataprocHook |
| airflow.contrib.hooks.gcp_dlp_hook.CloudDLPHook | airflow.providers.google.cloud.hooks.dlp.CloudDLPHook |
| airflow.contrib.hooks.gcp_function_hook.GcfHook | airflow.providers.google.cloud.hooks.functions.CloudFunctionsHook |
| airflow.contrib.hooks.gcp_kms_hook.GoogleCloudKMSHook | airflow.providers.google.cloud.hooks.kms.CloudKMSHook |
| airflow.contrib.hooks.gcp_mlengine_hook.MLEngineHook | airflow.providers.google.cloud.hooks.mlengine.MLEngineHook |
| airflow.contrib.hooks.gcp_natural_language_hook.CloudNaturalLanguageHook | airflow.providers.google.cloud.hooks.natural_language.CloudNaturalLanguageHook |
| airflow.contrib.hooks.gcp_pubsub_hook.PubSubHook | airflow.providers.google.cloud.hooks.pubsub.PubSubHook |
| airflow.contrib.hooks.gcp_speech_to_text_hook.GCPSpeechToTextHook | airflow.providers.google.cloud.hooks.speech_to_text.CloudSpeechToTextHook |
| airflow.contrib.hooks.gcp_spanner_hook.CloudSpannerHook | airflow.providers.google.cloud.hooks.spanner.SpannerHook |
| airflow.contrib.hooks.gcp_sql_hook.CloudSqlDatabaseHook | airflow.providers.google.cloud.hooks.cloud_sql.CloudSQLDatabaseHook |
| airflow.contrib.hooks.gcp_sql_hook.CloudSqlHook | airflow.providers.google.cloud.hooks.cloud_sql.CloudSQLHook |
| airflow.contrib.hooks.gcp_tasks_hook.CloudTasksHook | airflow.providers.google.cloud.hooks.tasks.CloudTasksHook |
| airflow.contrib.hooks.gcp_text_to_speech_hook.GCPTextToSpeechHook | airflow.providers.google.cloud.hooks.text_to_speech.CloudTextToSpeechHook |
| airflow.contrib.hooks.gcp_transfer_hook.GCPTransferServiceHook | airflow.providers.google.cloud.hooks.cloud_storage_transfer_service.CloudDataTransferServiceHook |
| airflow.contrib.hooks.gcp_translate_hook.CloudTranslateHook | airflow.providers.google.cloud.hooks.translate.CloudTranslateHook |
| airflow.contrib.hooks.gcp_video_intelligence_hook.CloudVideoIntelligenceHook | airflow.providers.google.cloud.hooks.video_intelligence.CloudVideoIntelligenceHook |
| airflow.contrib.hooks.gcp_vision_hook.CloudVisionHook | airflow.providers.google.cloud.hooks.vision.CloudVisionHook |
| airflow.contrib.hooks.gcs_hook.GoogleCloudStorageHook | airflow.providers.google.cloud.hooks.gcs.GCSHook |
| airflow.contrib.operators.adls_to_gcs.AdlsToGoogleCloudStorageOperator | airflow.operators.adls_to_gcs.AdlsToGoogleCloudStorageOperator |
| airflow.contrib.operators.bigquery_check_operator.BigQueryCheckOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryCheckOperator |
| airflow.contrib.operators.bigquery_check_operator.BigQueryIntervalCheckOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryIntervalCheckOperator |
| airflow.contrib.operators.bigquery_check_operator.BigQueryValueCheckOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryValueCheckOperator |
| airflow.contrib.operators.bigquery_get_data.BigQueryGetDataOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryGetDataOperator |
| airflow.contrib.operators.bigquery_operator.BigQueryCreateEmptyDatasetOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryCreateEmptyDatasetOperator |
| airflow.contrib.operators.bigquery_operator.BigQueryCreateEmptyTableOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryCreateEmptyTableOperator |
| airflow.contrib.operators.bigquery_operator.BigQueryCreateExternalTableOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryCreateExternalTableOperator |
| airflow.contrib.operators.bigquery_operator.BigQueryDeleteDatasetOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryDeleteDatasetOperator |
| airflow.contrib.operators.bigquery_operator.BigQueryOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryExecuteQueryOperator |
| airflow.contrib.operators.bigquery_table_delete_operator.BigQueryTableDeleteOperator | airflow.providers.google.cloud.operators.bigquery.BigQueryDeleteTableOperator |
| airflow.contrib.operators.bigquery_to_bigquery.BigQueryToBigQueryOperator | airflow.operators.bigquery_to_bigquery.BigQueryToBigQueryOperator |
| airflow.contrib.operators.bigquery_to_gcs.BigQueryToCloudStorageOperator | airflow.operators.bigquery_to_gcs.BigQueryToCloudStorageOperator |
| airflow.contrib.operators.bigquery_to_mysql_operator.BigQueryToMySqlOperator | airflow.operators.bigquery_to_mysql.BigQueryToMySqlOperator |
| airflow.contrib.operators.dataflow_operator.DataFlowJavaOperator | airflow.providers.google.cloud.operators.dataflow.DataFlowJavaOperator |
| airflow.contrib.operators.dataflow_operator.DataFlowPythonOperator | airflow.providers.google.cloud.operators.dataflow.DataFlowPythonOperator |
| airflow.contrib.operators.dataflow_operator.DataflowTemplateOperator | airflow.providers.google.cloud.operators.dataflow.DataflowTemplateOperator |
| airflow.contrib.operators.dataproc_operator.DataProcHadoopOperator | airflow.providers.google.cloud.operators.dataproc.DataprocSubmitHadoopJobOperator |
| airflow.contrib.operators.dataproc_operator.DataProcHiveOperator | airflow.providers.google.cloud.operators.dataproc.DataprocSubmitHiveJobOperator |
| airflow.contrib.operators.dataproc_operator.DataProcJobBaseOperator | airflow.providers.google.cloud.operators.dataproc.DataprocJobBaseOperator |
| airflow.contrib.operators.dataproc_operator.DataProcPigOperator | airflow.providers.google.cloud.operators.dataproc.DataprocSubmitPigJobOperator |
| airflow.contrib.operators.dataproc_operator.DataProcPySparkOperator | airflow.providers.google.cloud.operators.dataproc.DataprocSubmitPySparkJobOperator |
| airflow.contrib.operators.dataproc_operator.DataProcSparkOperator | airflow.providers.google.cloud.operators.dataproc.DataprocSubmitSparkJobOperator |
| airflow.contrib.operators.dataproc_operator.DataProcSparkSqlOperator | airflow.providers.google.cloud.operators.dataproc.DataprocSubmitSparkSqlJobOperator |
| airflow.contrib.operators.dataproc_operator.DataprocClusterCreateOperator | airflow.providers.google.cloud.operators.dataproc.DataprocCreateClusterOperator |
| airflow.contrib.operators.dataproc_operator.DataprocClusterDeleteOperator | airflow.providers.google.cloud.operators.dataproc.DataprocDeleteClusterOperator |
| airflow.contrib.operators.dataproc_operator.DataprocClusterScaleOperator | airflow.providers.google.cloud.operators.dataproc.DataprocScaleClusterOperator |
| airflow.contrib.operators.dataproc_operator.DataprocOperationBaseOperator | airflow.providers.google.cloud.operators.dataproc.DataprocOperationBaseOperator |
| airflow.contrib.operators.dataproc_operator.DataprocWorkflowTemplateInstantiateInlineOperator | airflow.providers.google.cloud.operators.dataproc.DataprocInstantiateInlineWorkflowTemplateOperator |
| airflow.contrib.operators.dataproc_operator.DataprocWorkflowTemplateInstantiateOperator | airflow.providers.google.cloud.operators.dataproc.DataprocInstantiateWorkflowTemplateOperator |
| airflow.contrib.operators.datastore_export_operator.DatastoreExportOperator | airflow.providers.google.cloud.operators.datastore.DatastoreExportOperator |
| airflow.contrib.operators.datastore_import_operator.DatastoreImportOperator | airflow.providers.google.cloud.operators.datastore.DatastoreImportOperator |
| airflow.contrib.operators.file_to_gcs.FileToGoogleCloudStorageOperator | airflow.providers.google.cloud.transfers.local_to_gcs.FileToGoogleCloudStorageOperator |
| airflow.contrib.operators.gcp_bigtable_operator.BigtableClusterUpdateOperator | airflow.providers.google.cloud.operators.bigtable.BigtableUpdateClusterOperator |
| airflow.contrib.operators.gcp_bigtable_operator.BigtableInstanceCreateOperator | airflow.providers.google.cloud.operators.bigtable.BigtableCreateInstanceOperator |
| airflow.contrib.operators.gcp_bigtable_operator.BigtableInstanceDeleteOperator | airflow.providers.google.cloud.operators.bigtable.BigtableDeleteInstanceOperator |
| airflow.contrib.operators.gcp_bigtable_operator.BigtableTableCreateOperator | airflow.providers.google.cloud.operators.bigtable.BigtableCreateTableOperator |
| airflow.contrib.operators.gcp_bigtable_operator.BigtableTableDeleteOperator | airflow.providers.google.cloud.operators.bigtable.BigtableDeleteTableOperator |
| airflow.contrib.operators.gcp_bigtable_operator.BigtableTableWaitForReplicationSensor | airflow.providers.google.cloud.sensors.bigtable.BigtableTableReplicationCompletedSensor |
| airflow.contrib.operators.gcp_cloud_build_operator.CloudBuildCreateBuildOperator | airflow.providers.google.cloud.operators.cloud_build.CloudBuildCreateBuildOperator |
| airflow.contrib.operators.gcp_compute_operator.GceBaseOperator | airflow.providers.google.cloud.operators.compute.GceBaseOperator |
| airflow.contrib.operators.gcp_compute_operator.GceInstanceGroupManagerUpdateTemplateOperator | airflow.providers.google.cloud.operators.compute.GceInstanceGroupManagerUpdateTemplateOperator |
| airflow.contrib.operators.gcp_compute_operator.GceInstanceStartOperator | airflow.providers.google.cloud.operators.compute.GceInstanceStartOperator |
| airflow.contrib.operators.gcp_compute_operator.GceInstanceStopOperator | airflow.providers.google.cloud.operators.compute.GceInstanceStopOperator |
| airflow.contrib.operators.gcp_compute_operator.GceInstanceTemplateCopyOperator | airflow.providers.google.cloud.operators.compute.GceInstanceTemplateCopyOperator |
| airflow.contrib.operators.gcp_compute_operator.GceSetMachineTypeOperator | airflow.providers.google.cloud.operators.compute.GceSetMachineTypeOperator |
| airflow.contrib.operators.gcp_container_operator.GKEClusterCreateOperator | airflow.providers.google.cloud.operators.kubernetes_engine.GKECreateClusterOperator |
| airflow.contrib.operators.gcp_container_operator.GKEClusterDeleteOperator | airflow.providers.google.cloud.operators.kubernetes_engine.GKEDeleteClusterOperator |
| airflow.contrib.operators.gcp_container_operator.GKEPodOperator | airflow.providers.google.cloud.operators.kubernetes_engine.GKEStartPodOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPCancelDLPJobOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPCancelDLPJobOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPCreateDLPJobOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPCreateDLPJobOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPCreateDeidentifyTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPCreateDeidentifyTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPCreateInspectTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPCreateInspectTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPCreateJobTriggerOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPCreateJobTriggerOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPCreateStoredInfoTypeOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPCreateStoredInfoTypeOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPDeidentifyContentOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPDeidentifyContentOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPDeleteDeidentifyTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPDeleteDeidentifyTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPDeleteDlpJobOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPDeleteDLPJobOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPDeleteInspectTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPDeleteInspectTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPDeleteJobTriggerOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPDeleteJobTriggerOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPDeleteStoredInfoTypeOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPDeleteStoredInfoTypeOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPGetDeidentifyTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPGetDeidentifyTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPGetDlpJobOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPGetDLPJobOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPGetInspectTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPGetInspectTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPGetJobTripperOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPGetJobTriggerOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPGetStoredInfoTypeOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPGetStoredInfoTypeOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPInspectContentOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPInspectContentOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPListDeidentifyTemplatesOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPListDeidentifyTemplatesOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPListDlpJobsOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPListDLPJobsOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPListInfoTypesOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPListInfoTypesOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPListInspectTemplatesOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPListInspectTemplatesOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPListJobTriggersOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPListJobTriggersOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPListStoredInfoTypesOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPListStoredInfoTypesOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPRedactImageOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPRedactImageOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPReidentifyContentOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPReidentifyContentOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPUpdateDeidentifyTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPUpdateDeidentifyTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPUpdateInspectTemplateOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPUpdateInspectTemplateOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPUpdateJobTriggerOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPUpdateJobTriggerOperator |
| airflow.contrib.operators.gcp_dlp_operator.CloudDLPUpdateStoredInfoTypeOperator | airflow.providers.google.cloud.operators.dlp.CloudDLPUpdateStoredInfoTypeOperator |
| airflow.contrib.operators.gcp_function_operator.GcfFunctionDeleteOperator | airflow.providers.google.cloud.operators.functions.GcfFunctionDeleteOperator |
| airflow.contrib.operators.gcp_function_operator.GcfFunctionDeployOperator | airflow.providers.google.cloud.operators.functions.GcfFunctionDeployOperator |
| airflow.contrib.operators.gcp_natural_language_operator.CloudNaturalLanguageAnalyzeEntitiesOperator | airflow.providers.google.cloud.operators.natural_language.CloudNaturalLanguageAnalyzeEntitiesOperator |
| airflow.contrib.operators.gcp_natural_language_operator.CloudNaturalLanguageAnalyzeEntitySentimentOperator | airflow.providers.google.cloud.operators.natural_language.CloudNaturalLanguageAnalyzeEntitySentimentOperator |
| airflow.contrib.operators.gcp_natural_language_operator.CloudNaturalLanguageAnalyzeSentimentOperator | airflow.providers.google.cloud.operators.natural_language.CloudNaturalLanguageAnalyzeSentimentOperator |
| airflow.contrib.operators.gcp_natural_language_operator.CloudNaturalLanguageClassifyTextOperator | airflow.providers.google.cloud.operators.natural_language.CloudNaturalLanguageClassifyTextOperator |
| airflow.contrib.operators.gcp_spanner_operator.CloudSpannerInstanceDatabaseDeleteOperator | airflow.providers.google.cloud.operators.spanner.SpannerDeleteDatabaseInstanceOperator |
| airflow.contrib.operators.gcp_spanner_operator.CloudSpannerInstanceDatabaseDeployOperator | airflow.providers.google.cloud.operators.spanner.SpannerDeployDatabaseInstanceOperator |
| airflow.contrib.operators.gcp_spanner_operator.CloudSpannerInstanceDatabaseQueryOperator | airflow.providers.google.cloud.operators.spanner.SpannerQueryDatabaseInstanceOperator |
| airflow.contrib.operators.gcp_spanner_operator.CloudSpannerInstanceDatabaseUpdateOperator | airflow.providers.google.cloud.operators.spanner.SpannerUpdateDatabaseInstanceOperator |
| airflow.contrib.operators.gcp_spanner_operator.CloudSpannerInstanceDeleteOperator | airflow.providers.google.cloud.operators.spanner.SpannerDeleteInstanceOperator |
| airflow.contrib.operators.gcp_spanner_operator.CloudSpannerInstanceDeployOperator | airflow.providers.google.cloud.operators.spanner.SpannerDeployInstanceOperator |
| airflow.contrib.operators.gcp_speech_to_text_operator.GcpSpeechToTextRecognizeSpeechOperator | airflow.providers.google.cloud.operators.speech_to_text.CloudSpeechToTextRecognizeSpeechOperator |
| airflow.contrib.operators.gcp_text_to_speech_operator.GcpTextToSpeechSynthesizeOperator | airflow.providers.google.cloud.operators.text_to_speech.CloudTextToSpeechSynthesizeOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceJobCreateOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceCreateJobOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceJobDeleteOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceDeleteJobOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceJobUpdateOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceUpdateJobOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceOperationCancelOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceCancelOperationOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceOperationGetOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceGetOperationOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceOperationPauseOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServicePauseOperationOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceOperationResumeOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceResumeOperationOperator |
| airflow.contrib.operators.gcp_transfer_operator.GcpTransferServiceOperationsListOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceListOperationsOperator |
| airflow.contrib.operators.gcp_transfer_operator.GoogleCloudStorageToGoogleCloudStorageTransferOperator | airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceGCSToGCSOperator |
| airflow.contrib.operators.gcp_translate_operator.CloudTranslateTextOperator | airflow.providers.google.cloud.operators.translate.CloudTranslateTextOperator |
| airflow.contrib.operators.gcp_translate_speech_operator.GcpTranslateSpeechOperator | airflow.providers.google.cloud.operators.translate_speech.GcpTranslateSpeechOperator |
| airflow.contrib.operators.gcp_video_intelligence_operator.CloudVideoIntelligenceDetectVideoExplicitContentOperator | airflow.providers.google.cloud.operators.video_intelligence.CloudVideoIntelligenceDetectVideoExplicitContentOperator |
| airflow.contrib.operators.gcp_video_intelligence_operator.CloudVideoIntelligenceDetectVideoLabelsOperator | airflow.providers.google.cloud.operators.video_intelligence.CloudVideoIntelligenceDetectVideoLabelsOperator |
| airflow.contrib.operators.gcp_video_intelligence_operator.CloudVideoIntelligenceDetectVideoShotsOperator | airflow.providers.google.cloud.operators.video_intelligence.CloudVideoIntelligenceDetectVideoShotsOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionAddProductToProductSetOperator | airflow.providers.google.cloud.operators.vision.CloudVisionAddProductToProductSetOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionAnnotateImageOperator | airflow.providers.google.cloud.operators.vision.CloudVisionImageAnnotateOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionDetectDocumentTextOperator | airflow.providers.google.cloud.operators.vision.CloudVisionTextDetectOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionDetectImageLabelsOperator | airflow.providers.google.cloud.operators.vision.CloudVisionDetectImageLabelsOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionDetectImageSafeSearchOperator | airflow.providers.google.cloud.operators.vision.CloudVisionDetectImageSafeSearchOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionDetectTextOperator | airflow.providers.google.cloud.operators.vision.CloudVisionDetectTextOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductCreateOperator | airflow.providers.google.cloud.operators.vision.CloudVisionCreateProductOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductDeleteOperator | airflow.providers.google.cloud.operators.vision.CloudVisionDeleteProductOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductGetOperator | airflow.providers.google.cloud.operators.vision.CloudVisionGetProductOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductSetCreateOperator | airflow.providers.google.cloud.operators.vision.CloudVisionCreateProductSetOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductSetDeleteOperator | airflow.providers.google.cloud.operators.vision.CloudVisionDeleteProductSetOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductSetGetOperator | airflow.providers.google.cloud.operators.vision.CloudVisionGetProductSetOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductSetUpdateOperator | airflow.providers.google.cloud.operators.vision.CloudVisionUpdateProductSetOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionProductUpdateOperator | airflow.providers.google.cloud.operators.vision.CloudVisionUpdateProductOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionReferenceImageCreateOperator | airflow.providers.google.cloud.operators.vision.CloudVisionCreateReferenceImageOperator |
| airflow.contrib.operators.gcp_vision_operator.CloudVisionRemoveProductFromProductSetOperator | airflow.providers.google.cloud.operators.vision.CloudVisionRemoveProductFromProductSetOperator |
| airflow.contrib.operators.gcs_acl_operator.GoogleCloudStorageBucketCreateAclEntryOperator | airflow.providers.google.cloud.operators.gcs.GCSBucketCreateAclEntryOperator |
| airflow.contrib.operators.gcs_acl_operator.GoogleCloudStorageObjectCreateAclEntryOperator | airflow.providers.google.cloud.operators.gcs.GCSObjectCreateAclEntryOperator |
| airflow.contrib.operators.gcs_delete_operator.GoogleCloudStorageDeleteOperator | airflow.providers.google.cloud.operators.gcs.GCSDeleteObjectsOperator |
| airflow.contrib.operators.gcs_download_operator.GoogleCloudStorageDownloadOperator | airflow.providers.google.cloud.operators.gcs.GCSToLocalFilesystemOperator |
| airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageListOperator | airflow.providers.google.cloud.operators.gcs.GCSListObjectsOperator |
| airflow.contrib.operators.gcs_operator.GoogleCloudStorageCreateBucketOperator | airflow.providers.google.cloud.operators.gcs.GCSCreateBucketOperator |
| airflow.contrib.operators.gcs_to_bq.GoogleCloudStorageToBigQueryOperator | airflow.operators.gcs_to_bq.GoogleCloudStorageToBigQueryOperator |
| airflow.contrib.operators.gcs_to_gcs.GoogleCloudStorageToGoogleCloudStorageOperator | airflow.operators.gcs_to_gcs.GoogleCloudStorageToGoogleCloudStorageOperator |
| airflow.contrib.operators.gcs_to_s3.GoogleCloudStorageToS3Operator | airflow.operators.gcs_to_s3.GCSToS3Operator |
| airflow.contrib.operators.mlengine_operator.MLEngineBatchPredictionOperator | airflow.providers.google.cloud.operators.mlengine.MLEngineStartBatchPredictionJobOperator |
| airflow.contrib.operators.mlengine_operator.MLEngineModelOperator | airflow.providers.google.cloud.operators.mlengine.MLEngineManageModelOperator |
| airflow.contrib.operators.mlengine_operator.MLEngineTrainingOperator | airflow.providers.google.cloud.operators.mlengine.MLEngineStartTrainingJobOperator |
| airflow.contrib.operators.mlengine_operator.MLEngineVersionOperator | airflow.providers.google.cloud.operators.mlengine.MLEngineManageVersionOperator |
| airflow.contrib.operators.mssql_to_gcs.MsSqlToGoogleCloudStorageOperator | airflow.operators.mssql_to_gcs.MsSqlToGoogleCloudStorageOperator |
| airflow.contrib.operators.mysql_to_gcs.MySqlToGoogleCloudStorageOperator | airflow.operators.mysql_to_gcs.MySqlToGoogleCloudStorageOperator |
| airflow.contrib.operators.postgres_to_gcs_operator.PostgresToGoogleCloudStorageOperator | airflow.operators.postgres_to_gcs.PostgresToGoogleCloudStorageOperator |
| airflow.contrib.operators.pubsub_operator.PubSubPublishOperator | airflow.providers.google.cloud.operators.pubsub.PubSubPublishMessageOperator |
| airflow.contrib.operators.pubsub_operator.PubSubSubscriptionCreateOperator | airflow.providers.google.cloud.operators.pubsub.PubSubCreateSubscriptionOperator |
| airflow.contrib.operators.pubsub_operator.PubSubSubscriptionDeleteOperator | airflow.providers.google.cloud.operators.pubsub.PubSubDeleteSubscriptionOperator |
| airflow.contrib.operators.pubsub_operator.PubSubTopicCreateOperator | airflow.providers.google.cloud.operators.pubsub.PubSubCreateTopicOperator |
| airflow.contrib.operators.pubsub_operator.PubSubTopicDeleteOperator | airflow.providers.google.cloud.operators.pubsub.PubSubDeleteTopicOperator |
| airflow.contrib.operators.sql_to_gcs.BaseSQLToGoogleCloudStorageOperator | airflow.operators.sql_to_gcs.BaseSQLToGoogleCloudStorageOperator |
| airflow.contrib.sensors.bigquery_sensor.BigQueryTableSensor | airflow.providers.google.cloud.sensors.bigquery.BigQueryTableExistenceSensor |
| airflow.contrib.sensors.gcp_transfer_sensor.GCPTransferServiceWaitForJobStatusSensor | airflow.providers.google.cloud.sensors.cloud_storage_transfer_service.DataTransferServiceJobStatusSensor |
| airflow.contrib.sensors.gcs_sensor.GoogleCloudStorageObjectSensor | airflow.providers.google.cloud.sensors.gcs.GCSObjectExistenceSensor |
| airflow.contrib.sensors.gcs_sensor.GoogleCloudStorageObjectUpdatedSensor | airflow.providers.google.cloud.sensors.gcs.GCSObjectUpdateSensor |
| airflow.contrib.sensors.gcs_sensor.GoogleCloudStoragePrefixSensor | airflow.providers.google.cloud.sensors.gcs.GCSObjectsWithPrefixExistenceSensor |
| airflow.contrib.sensors.gcs_sensor.GoogleCloudStorageUploadSessionCompleteSensor | airflow.providers.google.cloud.sensors.gcs.GCSUploadSessionCompleteSensor |
| airflow.contrib.sensors.pubsub_sensor.PubSubPullSensor | airflow.providers.google.cloud.sensors.pubsub.PubSubPullSensor |
Previously not all hooks and operators related to Google Cloud use
google_cloud_default as a default conn_id. There is currently one default
variant. Values like google_cloud_storage_default, bigquery_default,
google_cloud_datastore_default have been deprecated. The configuration of
existing relevant connections in the database have been preserved. To use those
deprecated GCP conn_id, you need to explicitly pass their conn_id into
operators/hooks. Otherwise, google_cloud_default will be used as GCP's conn_id
by default.
To use project_id argument consistently across GCP hooks and operators, we did the following changes:
- Changed order of arguments in DataflowHook.start_python_dataflow. Uses with positional arguments may break.
- Changed order of arguments in DataflowHook.is_job_dataflow_running. Uses with positional arguments may break.
- Changed order of arguments in DataflowHook.cancel_job. Uses with positional arguments may break.
- Added optional project_id argument to DataflowCreateJavaJobOperator constructor.
- Added optional project_id argument to DataflowTemplatedJobStartOperator constructor.
- Added optional project_id argument to DataflowCreatePythonJobOperator constructor.
To provide more precise control in handling of changes to objects in underlying GCS Bucket the constructor of this sensor now has changed.
- Old Behavior: This constructor used to optionally take
previous_num_objects: int. - New replacement constructor kwarg:
previous_objects: Optional[Set[str]].
Most users would not specify this argument because the bucket begins empty and the user wants to treat any files as new.
Example of Updating usage of this sensor: Users who used to call:
GCSUploadSessionCompleteSensor(bucket='my_bucket', prefix='my_prefix', previous_num_objects=1)
Will now call:
GCSUploadSessionCompleteSensor(bucket='my_bucket', prefix='my_prefix', previous_num_objects={'.keep'})
Where '.keep' is a single file at your prefix that the sensor should not consider new.
To simplify BigQuery operators (no need of Cursor) and standardize usage of hooks within all GCP integration methods from BiqQueryBaseCursor
were moved to BigQueryHook. Using them by from Cursor object is still possible due to preserved backward compatibility but they will raise DeprecationWarning.
The following methods were moved:
| Old path | New path |
|---|---|
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.cancel_query | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.cancel_query |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.create_empty_dataset | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.create_empty_dataset |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.create_empty_table | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.create_empty_table |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.create_external_table | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.create_external_table |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.delete_dataset | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.delete_dataset |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.get_dataset | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.get_dataset |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.get_dataset_tables | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.get_dataset_tables |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.get_dataset_tables_list | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.get_dataset_tables_list |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.get_datasets_list | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.get_datasets_list |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.get_schema | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.get_schema |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.get_tabledata | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.get_tabledata |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.insert_all | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.insert_all |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.patch_dataset | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.patch_dataset |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.patch_table | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.patch_table |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.poll_job_complete | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.poll_job_complete |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_copy | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_copy |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_extract | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_extract |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_grant_dataset_view_access | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_grant_dataset_view_access |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_load | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_load |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_query | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_query |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_table_delete | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_table_delete |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_table_upsert | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_table_upsert |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_with_configuration | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.run_with_configuration |
| airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.update_dataset | airflow.providers.google.cloud.hooks.bigquery.BigQueryHook.update_dataset |
Since BigQuery is the part of the GCP it was possible to simplify the code by handling the exceptions
by usage of the airflow.providers.google.common.hooks.base.GoogleBaseHook.catch_http_exception decorator however it changes
exceptions raised by the following methods:
airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.run_table_deleteraisesAirflowExceptioninstead ofException.airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.create_empty_datasetraisesAirflowExceptioninstead ofValueError.airflow.providers.google.cloud.hooks.bigquery.BigQueryBaseCursor.get_datasetraisesAirflowExceptioninstead ofValueError.
Idempotency was added to BigQueryCreateEmptyTableOperator and BigQueryCreateEmptyDatasetOperator.
But to achieve that try / except clause was removed from create_empty_dataset and create_empty_table
methods of BigQueryHook.
The change in GCP operators implies that GCP Hooks for those operators require now keyword parameters rather
than positional ones in all methods where project_id is used. The methods throw an explanatory exception
in case they are called using positional parameters.
Other GCP hooks are unaffected.
In the PubSubPublishOperator and PubSubHook.publsh method the data field in a message should be bytestring (utf-8 encoded) rather than base64 encoded string.
Due to the normalization of the parameters within GCP operators and hooks a parameters like project or topic_project
are deprecated and will be substituted by parameter project_id.
In PubSubHook.create_subscription hook method in the parameter subscription_project is replaced by subscription_project_id.
Template fields are updated accordingly and old ones may not work.
It is required now to pass key-word only arguments to PubSub hook.
These changes are not backward compatible.
The gcp_conn_id parameter in GKEPodOperator is required. In previous versions, it was possible to pass
the None value to the gcp_conn_id in the GKEStartPodOperator
operator, which resulted in credentials being determined according to the
Application Default Credentials strategy.
Now this parameter requires a value. To restore the previous behavior, configure the connection without specifying the service account.
Detailed information about connection management is available: Google Cloud Connection.
-
The following parameters have been replaced in all the methods in GCSHook:
bucketis changed tobucket_nameobjectis changed toobject_name
-
The
maxResultsparameter inGoogleCloudStorageHook.listhas been renamed tomax_resultsfor consistency.
The 'properties' and 'jars' properties for the Dataproc related operators (DataprocXXXOperator) have been renamed from
dataproc_xxxx_properties and dataproc_xxx_jars to dataproc_properties
and dataproc_jarsrespectively.
Arguments for dataproc_properties dataproc_jars
airflow.providers.google.cloud.operators.cloud_storage_transfer_service.CloudDataTransferServiceCreateJobOperator
To obtain pylint compatibility the filter argument in CloudDataTransferServiceCreateJobOperator
has been renamed to request_filter.
To obtain pylint compatibility the filter argument in CloudDataTransferServiceHook.list_transfer_job and
CloudDataTransferServiceHook.list_transfer_operations has been renamed to request_filter.
In general all hook methods are decorated with @GoogleBaseHook.fallback_to_default_project_id thus
parameters to hook can only be passed via keyword arguments.
create_empty_tablemethod accepts nowtable_resourceparameter. If provided all other parameters are ignored.create_empty_datasetwill now use values fromdataset_referenceinstead of raising error if parameters were passed indataset_referenceand as arguments to method. Additionally validation ofdataset_referenceis done usingDataset.from_api_repr. Exception and log messages has been changed.update_datasetrequires now newfieldsargument (breaking change)delete_datasethas new signature (dataset_id, project_id, ...) previous one was (project_id, dataset_id, ...) (breaking change)get_tabledatareturns list of rows instead of API response in dict format. This method is deprecated in favor oflist_rows. (breaking change)
The api_version has been removed and will not be used since we migrate CloudBuildHook from using
Discovery API to native google-cloud-build python library.
The body parameter in CloudBuildCreateBuildOperator has been deprecated.
Instead, you should pass body using the build parameter.
Change python3 as Dataflow Hooks/Operators default interpreter
Now the py_interpreter argument for DataFlow Hooks/Operators has been changed from python2 to python3.
To simplify the code, the decorator provide_gcp_credential_file has been moved from the inner-class.
Instead of @GoogleBaseHook._Decorators.provide_gcp_credential_file,
you should write @GoogleBaseHook.provide_gcp_credential_file
It is highly recommended to have 1TB+ disk size for Dataproc to have sufficient throughput: https://cloud.google.com/compute/docs/disks/performance
Hence, the default value for master_disk_size in DataprocCreateClusterOperator has been changed from 500GB to 1TB.
If you are upgrading from Airflow 1.10.x and are not using CLUSTER_CONFIG,
You can easily generate config using make() of airflow.providers.google.cloud.operators.dataproc.ClusterGenerator
This has been proved specially useful if you are using metadata argument from older API, refer AIRFLOW-16911 for details.
eg. your cluster creation may look like this in v1.10.x
path = f"gs://goog-dataproc-initialization-actions-us-central1/python/pip-install.sh"
create_cluster = DataprocClusterCreateOperator(
task_id="create_dataproc_cluster",
cluster_name="test",
project_id="test",
zone="us-central1-a",
region="us-central1",
master_machine_type="n1-standard-4",
worker_machine_type="n1-standard-4",
num_workers=2,
storage_bucket="test_bucket",
init_actions_uris=[path],
metadata={"PIP_PACKAGES": "pyyaml requests pandas openpyxl"},
)After upgrading to v2.x.x and using CLUSTER_CONFIG, it will look like followed:
path = f"gs://goog-dataproc-initialization-actions-us-central1/python/pip-install.sh"
CLUSTER_CONFIG = ClusterGenerator(
project_id="test",
zone="us-central1-a",
master_machine_type="n1-standard-4",
worker_machine_type="n1-standard-4",
num_workers=2,
storage_bucket="test",
init_actions_uris=[path],
metadata={"PIP_PACKAGES": "pyyaml requests pandas openpyxl"},
).make()
create_cluster_operator = DataprocClusterCreateOperator(
task_id="create_dataproc_cluster",
cluster_name="test",
project_id="test",
region="us-central1",
cluster_config=CLUSTER_CONFIG,
)We changed signature of BigQueryGetDatasetTablesOperator.
Before:
def __init__(
dataset_id: str,
dataset_resource: dict,
# ...
):
...After:
def __init__(
dataset_resource: dict,
dataset_id: Optional[str] = None,
# ...
):
...We strive to ensure that there are no changes that may affect the end user, and your Python files, but this release may contain changes that will require changes to your configuration, DAG Files or other integration e.g. custom operators.
Only changes unique to this provider are described here. You should still pay attention to the changes that have been made to the core (including core operators) as they can affect the integration behavior of this provider.
This section describes the changes that have been made, and what you need to do to update your if you use operators or hooks which integrate with Amazon services (including Amazon Web Service - AWS).
All AWS components (hooks, operators, sensors, example DAGs) will be grouped together as decided in
AIP-21. Migrated
components remain backwards compatible but raise a DeprecationWarning when imported from the old module.
Migrated are:
| Old path | New path |
|---|---|
| airflow.hooks.S3_hook.S3Hook | airflow.providers.amazon.aws.hooks.s3.S3Hook |
| airflow.contrib.hooks.aws_athena_hook.AWSAthenaHook | airflow.providers.amazon.aws.hooks.athena.AWSAthenaHook |
| airflow.contrib.hooks.aws_lambda_hook.AwsLambdaHook | airflow.providers.amazon.aws.hooks.lambda_function.AwsLambdaHook |
| airflow.contrib.hooks.aws_sqs_hook.SQSHook | airflow.providers.amazon.aws.hooks.sqs.SQSHook |
| airflow.contrib.hooks.aws_sns_hook.AwsSnsHook | airflow.providers.amazon.aws.hooks.sns.AwsSnsHook |
| airflow.contrib.operators.aws_athena_operator.AWSAthenaOperator | airflow.providers.amazon.aws.operators.athena.AWSAthenaOperator |
| airflow.contrib.operators.awsbatch.AWSBatchOperator | airflow.providers.amazon.aws.operators.batch.AwsBatchOperator |
| airflow.contrib.operators.awsbatch.BatchProtocol | airflow.providers.amazon.aws.hooks.batch_client.AwsBatchProtocol |
| private attrs and methods on AWSBatchOperator | airflow.providers.amazon.aws.hooks.batch_client.AwsBatchClient |
| n/a | airflow.providers.amazon.aws.hooks.batch_waiters.AwsBatchWaiters |
| airflow.contrib.operators.aws_sqs_publish_operator.SQSPublishOperator | airflow.providers.amazon.aws.operators.sqs.SQSPublishOperator |
| airflow.contrib.operators.aws_sns_publish_operator.SnsPublishOperator | airflow.providers.amazon.aws.operators.sns.SnsPublishOperator |
| airflow.contrib.sensors.aws_athena_sensor.AthenaSensor | airflow.providers.amazon.aws.sensors.athena.AthenaSensor |
| airflow.contrib.sensors.aws_sqs_sensor.SQSSensor | airflow.providers.amazon.aws.sensors.sqs.SQSSensor |
The default value for the aws_conn_id was accidentally set to 's3_default' instead of 'aws_default' in some of the emr operators in previous versions. This was leading to EmrStepSensor not being able to find their corresponding emr cluster. With the new changes in the EmrAddStepsOperator, EmrTerminateJobFlowOperator and EmrCreateJobFlowOperator this issue is solved.
The AwsBatchOperator was refactored to extract an AwsBatchClient (and inherit from it). The
changes are mostly backwards compatible and clarify the public API for these classes; some
private methods on AwsBatchOperator for polling a job status were relocated and renamed
to surface new public methods on AwsBatchClient (and via inheritance on AwsBatchOperator). A
couple of job attributes are renamed on an instance of AwsBatchOperator; these were mostly
used like private attributes but they were surfaced in the public API, so any use of them needs
to be updated as follows:
AwsBatchOperator().jobId->AwsBatchOperator().job_idAwsBatchOperator().jobName->AwsBatchOperator().job_name
The AwsBatchOperator gets a new option to define a custom model for waiting on job status changes.
The AwsBatchOperator can use a new waiters parameter, an instance of AwsBatchWaiters, to
specify that custom job waiters will be used to monitor a batch job. See the latest API
documentation for details.
Replace parameter max_retires with max_retries to fix typo.
Note: The order of arguments has changed for check_for_prefix.
The bucket_name is now optional. It falls back to the connection schema attribute.
The delete_objects now returns None instead of a response, since the method now makes multiple api requests when the keys list length is > 1000.
We strive to ensure that there are no changes that may affect the end user and your Python files, but this release may contain changes that will require changes to your configuration, DAG Files or other integration e.g. custom operators.
Only changes unique to providers are described here. You should still pay attention to the changes that have been made to the core (including core operators) as they can affect the integration behavior of this provider.
This section describes the changes that have been made, and what you need to do to update your if
you use any code located in airflow.providers package.
Previously, the list_prefixes and list_keys methods returned None when there were no
results. The behavior has been changed to return an empty list instead of None in this
case.
Hipchat has reached end of life and is no longer available.
For more information please see https://community.atlassian.com/t5/Stride-articles/Stride-and-Hipchat-Cloud-have-reached-End-of-Life-updated/ba-p/940248
Replace parameter sandbox with domain. According to change in simple-salesforce package.
Rename sign_in function to get_conn.
Rename parameter name from format to segment_format in PinotAdminHook function create_segment for pylint compatible
Rename parameter name from filter to partition_filter in HiveMetastoreHook function get_partitions for pylint compatible
Remove unnecessary parameter nlst in FTPHook function list_directory for pylint compatible
Remove unnecessary parameter open in PostgresHook function copy_expert for pylint compatible
Change parameter name from visibleTo to visible_to in OpsgenieAlertOperator for pylint compatible
ImapHook:
- The order of arguments has changed for
has_mail_attachment,retrieve_mail_attachmentsanddownload_mail_attachments. - A new
mail_filterargument has been added to each of those.
The HTTPHook is now secured by default: verify=True (before: verify=False)
This can be overwritten by using the extra_options param as {'verify': False}.
- upgraded cloudant version from
>=0.5.9,<2.0to>=2.0 - removed the use of the
schemaattribute in the connection - removed
dbfunction since the database object can also be retrieved by callingcloudant_session['database_name']
For example:
from airflow.providers.cloudant.hooks.cloudant import CloudantHook
with CloudantHook().get_conn() as cloudant_session:
database = cloudant_session["database_name"]See the docs for more information on how to use the new cloudant version.
When initializing a Snowflake hook or operator, the value used for snowflake_conn_id was always snowflake_conn_id, regardless of whether or not you specified a value for it. The default snowflake_conn_id value is now switched to snowflake_default for consistency and will be properly overridden when specified.
This release also includes changes that fall outside any of the sections above.
We standardised the Extras names and synchronized providers package names with the main airflow extras.
We deprecated a number of extras in 2.0.
| Deprecated extras | New extras |
|---|---|
| atlas | apache.atlas |
| aws | amazon |
| azure | microsoft.azure |
| azure_blob_storage | microsoft.azure |
| azure_data_lake | microsoft.azure |
| azure_cosmos | microsoft.azure |
| azure_container_instances | microsoft.azure |
| cassandra | apache.cassandra |
| druid | apache.druid |
| gcp | |
| gcp_api | |
| hdfs | apache.hdfs |
| hive | apache.hive |
| kubernetes | cncf.kubernetes |
| mssql | microsoft.mssql |
| pinot | apache.pinot |
| webhdfs | apache.webhdfs |
| winrm | apache.winrm |
For example:
If you want to install integration for Apache Atlas, then instead of pip install apache-airflow[atlas]
you should use pip install apache-airflow[apache.atlas].
NOTE!
If you want to install integration for Microsoft Azure, then instead of
pip install 'apache-airflow[azure_blob_storage,azure_data_lake,azure_cosmos,azure_container_instances]'
you should run pip install 'apache-airflow[microsoft.azure]'
If you want to install integration for Amazon Web Services, then instead of
pip install 'apache-airflow[s3,emr]', you should execute pip install 'apache-airflow[aws]'
The deprecated extras will be removed in 3.0.
The response of endpoints /dag_stats and /task_stats help UI fetch brief statistics about DAGs and Tasks. The format was like
{
"example_http_operator": [
{
"state": "success",
"count": 0,
"dag_id": "example_http_operator",
"color": "green"
},
{
"state": "running",
"count": 0,
"dag_id": "example_http_operator",
"color": "lime"
},
...
],
...
}The dag_id was repeated in the payload, which makes the response payload unnecessarily bigger.
Now the dag_id will not appear repeated in the payload, and the response format is like
{
"example_http_operator": [
{
"state": "success",
"count": 0,
"color": "green"
},
{
"state": "running",
"count": 0,
"color": "lime"
},
...
],
...
}No breaking changes.
From Airflow 1.10.14, max_threads config under [scheduler] section has been renamed to parsing_processes.
This is to align the name with the actual code where the Scheduler launches the number of processes defined by
[scheduler] parsing_processes to parse the DAG files.
The Airflow CLI has been organized so that related commands are grouped together as subcommands, which means that if you use these commands in your scripts, they will now raise a DeprecationWarning and you have to make changes to them before you upgrade to Airflow 2.0.
This section describes the changes that have been made, and what you need to do to update your script.
The ability to manipulate users from the command line has been changed. airflow create_user, airflow delete_user
and airflow list_users has been grouped to a single command airflow users with optional flags create, list and delete.
The airflow list_dags command is now airflow dags list, airflow pause is airflow dags pause, etc.
In Airflow 1.10 and 2.0 there is an airflow config command but there is a difference in behavior. In Airflow 1.10,
it prints all config options while in Airflow 2.0, it's a command group. airflow config is now airflow config list.
You can check other options by running the command airflow config --help
Compatibility with the old CLI has been maintained, but they will no longer appear in the help
You can learn about the commands by running airflow --help. For example to get help about the celery group command,
you have to run the help command: airflow celery --help.
| Old command | New command | Group |
|---|---|---|
airflow worker |
airflow celery worker |
celery |
airflow flower |
airflow celery flower |
celery |
airflow trigger_dag |
airflow dags trigger |
dags |
airflow delete_dag |
airflow dags delete |
dags |
airflow show_dag |
airflow dags show |
dags |
airflow list_dag |
airflow dags list |
dags |
airflow dag_status |
airflow dags status |
dags |
airflow backfill |
airflow dags backfill |
dags |
airflow list_dag_runs |
airflow dags list-runs |
dags |
airflow pause |
airflow dags pause |
dags |
airflow unpause |
airflow dags unpause |
dags |
airflow next_execution |
airflow dags next-execution |
dags |
airflow test |
airflow tasks test |
tasks |
airflow clear |
airflow tasks clear |
tasks |
airflow list_tasks |
airflow tasks list |
tasks |
airflow task_failed_deps |
airflow tasks failed-deps |
tasks |
airflow task_state |
airflow tasks state |
tasks |
airflow run |
airflow tasks run |
tasks |
airflow render |
airflow tasks render |
tasks |
airflow initdb |
airflow db init |
db |
airflow resetdb |
airflow db reset |
db |
airflow upgradedb |
airflow db upgrade |
db |
airflow checkdb |
airflow db check |
db |
airflow shell |
airflow db shell |
db |
airflow pool |
airflow pools |
pools |
airflow create_user |
airflow users create |
users |
airflow delete_user |
airflow users delete |
users |
airflow list_users |
airflow users list |
users |
airflow rotate_fernet_key |
airflow rotate-fernet-key |
|
airflow sync_perm |
airflow sync-perm |
Previously TimeSensor always compared the target_time with the current time in UTC.
Now it will compare target_time with the current time in the timezone of the DAG,
defaulting to the default_timezone in the global config.
The HDFS hook's Kerberos support has been removed due to removed python-krbV dependency from PyPI and generally lack of support for SSL in Python3 (Snakebite-py3 we use as dependency has no support for SSL connection to HDFS).
SSL support still works for WebHDFS hook.
In previous version of Airflow user session lifetime could be configured by
session_lifetime_days and force_log_out_after options. In practise only session_lifetime_days
had impact on session lifetime, but it was limited to values in day.
We have removed mentioned options and introduced new session_lifetime_minutes
option which simplify session lifetime configuration.
Before
[webserver]
force_log_out_after = 0
session_lifetime_days = 30After
[webserver]
session_lifetime_minutes = 43200The ability to import Operators, Hooks and Sensors via the plugin mechanism has been deprecated and will raise warnings in Airflow 1.10.13 and will be removed completely in Airflow 2.0.
Check https://airflow.apache.org/docs/1.10.13/howto/custom-operator.html to see how you can create and import Custom Hooks, Operators and Sensors.
Previously, when tasks skipped by SkipMixin (such as BranchPythonOperator, BaseBranchOperator and ShortCircuitOperator) are cleared, they execute. Since 1.10.12, when such skipped tasks are cleared, they will be skipped again by the newly introduced NotPreviouslySkippedDep.
As of airflow 1.10.12, using the airflow.contrib.kubernetes.Pod class in the pod_mutation_hook is now deprecated. Instead we recommend that users
treat the pod parameter as a kubernetes.client.models.V1Pod object. This means that users now have access to the full Kubernetes API
when modifying airflow pods
Users can now offer a path to a yaml for the KubernetesPodOperator using the pod_template_file parameter.
Now use NULL as default value for dag.description in dag table
Before 1.10.11 it was possible to edit DagRun State in the /admin/dagrun/ page
to any text.
In Airflow 1.10.11+, the user can only choose the states from the list.
The previous default setting was to allow all API requests without authentication, but this poses security risks to users who miss this fact. This changes the default for new installs to deny all requests by default.
Note: This will not change the behavior for existing installs, please update check your airflow.cfg
If you wish to have the experimental API work, and aware of the risks of enabling this without authentication (or if you have your own authentication layer in front of Airflow) you can get the previous behaviour on a new install by setting this in your airflow.cfg:
[api]
auth_backend = airflow.api.auth.backend.default
Since XCom values can contain pickled data, we would no longer allow adding or changing XCom values from the UI.
The UID to run the first process of the Worker PODs when using has been changed to 50000
from the previous default of 0. The previous default was an empty string but the code used 0 if it was
empty string.
Before:
[kubernetes]
run_as_user =After:
[kubernetes]
run_as_user = 50000This is done to avoid running the container as root user.
Previously when you set an Airflow Variable with an empty string (''), the value you used to get
back was None. This will now return an empty string (''')
Example:
Variable.set("test_key", "")
Variable.get("test_key")The above code returned None previously, now it will return ''.
The behavior of the none_failed trigger rule is documented as "all parents have not failed (failed or
upstream_failed) i.e. all parents have succeeded or been skipped." As previously implemented, the actual behavior
would skip if all parents of a task had also skipped.
The fix to none_failed trigger rule breaks workflows that depend on the previous behavior.
If you need the old behavior, you should change the tasks with none_failed trigger rule to none_failed_or_skipped.
When a task is marked as success by a user from Airflow UI - on_success_callback will be called
No breaking changes.
When task is marked failed by user or task fails due to system failures - on failure call back will be called as part of clean up
See AIRFLOW-5621 for details
The default behavior was to strip the microseconds (and milliseconds, etc) off of all dag runs triggered by
by the experimental REST API. The default behavior will change when an explicit execution_date is
passed in the request body. It will also now be possible to have the execution_date generated, but
keep the microseconds by sending replace_microseconds=false in the request body. The default
behavior can be overridden by sending replace_microseconds=true along with an explicit execution_date
Pool size can now be set to -1 to indicate infinite size (it also includes optimisation of pool query which lead to poor task n^2 performance of task pool queries in MySQL).
The GoogleCloudStorageDownloadOperator can either write to a supplied filename or
return the content of a file via xcom through store_to_xcom_key - both options are mutually exclusive.
Previous versions of the BaseOperator::render_template function required an attr argument as the first
positional argument, along with content and context. This function signature was changed in 1.10.6 and
the attr argument is no longer required (or accepted).
In order to use this function in subclasses of the BaseOperator, the attr argument must be removed:
result = self.render_template("myattr", self.myattr, context) # Pre-1.10.6 call
# ...
result = self.render_template(self.myattr, context) # Post-1.10.6 callThe region of Airflow's default connection to AWS (aws_default) was previously
set to us-east-1 during installation.
The region now needs to be set manually, either in the connection screens in
Airflow, via the ~/.aws config files, or via the AWS_DEFAULT_REGION environment
variable.
The following metrics are deprecated and won't be emitted in Airflow 2.0:
scheduler.dagbag.errorsanddagbag_import_errors-- usedag_processing.import_errorsinsteaddag_file_processor_timeouts-- usedag_processing.processor_timeoutsinsteadcollect_dags-- usedag_processing.total_parse_timeinsteaddag.loading-duration.<basename>-- usedag_processing.last_duration.<basename>insteaddag_processing.last_runtime.<basename>-- usedag_processing.last_duration.<basename>instead
No breaking changes.
MySqlToGoogleCloudStorageOperator now exports TIMESTAMP columns as UTC
by default, rather than using the default timezone of the MySQL server.
This is the correct behavior for use with BigQuery, since BigQuery
assumes that TIMESTAMP columns without time zones are in UTC. To
preserve the previous behavior, set ensure_utc to False.
- removed argument
versionfromget_connfunction and added it to the hook's__init__function instead and renamed it toapi_version - renamed the
partialKeysargument of functionallocate_idstopartial_keys
-
the discovery-based api (
googleapiclient.discovery) used inGoogleCloudStorageHookis now replaced by the recommended client based api (google-cloud-storage). To know the difference between both the libraries, read https://cloud.google.com/apis/docs/client-libraries-explained. PR: #5054 -
as a part of this replacement, the
multipart&num_retriesparameters forGoogleCloudStorageHook.uploadmethod have been deprecated.The client library uses multipart upload automatically if the object/blob size is more than 8 MB - source code. The client also handles retries automatically
-
the
generationparameter is deprecated inGoogleCloudStorageHook.deleteandGoogleCloudStorageHook.insert_object_acl.
Updating to google-cloud-storage >= 1.16 changes the signature of the upstream client.get_bucket() method from get_bucket(bucket_name: str) to get_bucket(bucket_or_name: Union[str, Bucket]). This method is not directly exposed by the airflow hook, but any code accessing the connection directly (GoogleCloudStorageHook().get_conn().get_bucket(...) or similar) will need to be updated.
The elasticsearch_ prefix has been removed from all config items under the [elasticsearch] section. For example elasticsearch_host is now just host.
non_pooled_task_slot_count and non_pooled_backfill_task_slot_count
are removed in favor of a real pool, e.g. default_pool.
By default tasks are running in default_pool.
default_pool is initialized with 128 slots and user can change the
number of slots through UI/CLI. default_pool cannot be removed.
The new pool config option allows users to choose different pool
implementation. Default value is "prefork", while choices include "prefork" (default),
"eventlet", "gevent" or "solo". This may help users achieve better concurrency performance
in different scenarios.
For more details about Celery pool implementation, please refer to:
- https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency
- https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html
The signature of the get_task_instances method in the BaseOperator and DAG classes has changed. The change does not change the behavior of the method in either case.
Old signature:
def get_task_instances(self, session, start_date=None, end_date=None):
...New signature:
@provide_session
def get_task_instances(self, start_date=None, end_date=None, session=None):
...Old signature:
def get_task_instances(self, session, start_date=None, end_date=None, state=None):
...New signature:
@provide_session
def get_task_instances(self, start_date=None, end_date=None, state=None, session=None):
...In either case, it is necessary to rewrite calls to the get_task_instances method that currently provide the session positional argument. New calls to this method look like:
# if you can rely on @provide_session
dag.get_task_instances()
# if you need to provide the session
dag.get_task_instances(session=your_session)If dag_discovery_safe_mode is enabled, only check files for DAGs if
they contain the strings "airflow" and "DAG". For backwards
compatibility, this option is enabled by default.
If you are using the Redis Sensor or Hook you may have to update your code. See redis-py porting instructions to check if your code might be affected (MSET, MSETNX, ZADD, and ZINCRBY all were, but read the full doc).
It is no longer required to set one of the environment variables to avoid a GPL dependency. Airflow will now always use text-unidecode if unidecode was not installed before.
The new sync_parallelism config option will control how many processes CeleryExecutor will use to
fetch celery task state in parallel. Default value is max(1, number of cores - 1)
BashTaskRunner has been renamed to StandardTaskRunner. It is the default task runner so you might need to update your config.
task_runner = StandardTaskRunner
If the AIRFLOW_CONFIG environment variable was not set and the
~/airflow/airflow.cfg file existed, airflow previously used
~/airflow/airflow.cfg instead of $AIRFLOW_HOME/airflow.cfg. Now airflow
will discover its config file using the $AIRFLOW_CONFIG and $AIRFLOW_HOME
environment variables rather than checking for the presence of a file.
Most GCP-related operators have now optional PROJECT_ID parameter. In case you do not specify it,
the project id configured in
GCP Connection is used.
There will be an AirflowException thrown in case PROJECT_ID parameter is not specified and the
connection used has no project id defined. This change should be backwards compatible as earlier version
of the operators had PROJECT_ID mandatory.
Operators involved:
- GCP Compute Operators
- GceInstanceStartOperator
- GceInstanceStopOperator
- GceSetMachineTypeOperator
- GCP Function Operators
- GcfFunctionDeployOperator
- GCP Cloud SQL Operators
- CloudSqlInstanceCreateOperator
- CloudSqlInstancePatchOperator
- CloudSqlInstanceDeleteOperator
- CloudSqlInstanceDatabaseCreateOperator
- CloudSqlInstanceDatabasePatchOperator
- CloudSqlInstanceDatabaseDeleteOperator
Other GCP operators are unaffected.
The change in GCP operators implies that GCP Hooks for those operators require now keyword parameters rather
than positional ones in all methods where project_id is used. The methods throw an explanatory exception
in case they are called using positional parameters.
Hooks involved:
- GceHook
- GcfHook
- CloudSqlHook
Other GCP hooks are unaffected.
It's now possible to use None as a default value with the default_var parameter when getting a variable, e.g.
foo = Variable.get("foo", default_var=None)
if foo is None:
handle_missing_foo()(Note: there is already Variable.setdefault() which me be helpful in some cases.)
This changes the behaviour if you previously explicitly provided None as a default value. If your code expects a KeyError to be thrown, then don't pass the default_var argument.
There were previously two ways of specifying the Airflow "home" directory
(~/airflow by default): the AIRFLOW_HOME environment variable, and the
airflow_home config setting in the [core] section.
If they had two different values different parts of the code base would end up
with different values. The config setting has been deprecated, and you should
remove the value from the config file and set AIRFLOW_HOME environment
variable if you need to use a non default value for this.
(Since this setting is used to calculate what config file to load, it is not possible to keep just the config option)
The signature of the create_transfer_job method in GCPTransferServiceHook
class has changed. The change does not change the behavior of the method.
Old signature:
def create_transfer_job(self, description, schedule, transfer_spec, project_id=None):
...New signature:
def create_transfer_job(self, body):
...It is necessary to rewrite calls to method. The new call looks like this:
body = {
"status": "ENABLED",
"projectId": project_id,
"description": description,
"transferSpec": transfer_spec,
"schedule": schedule,
}
gct_hook.create_transfer_job(body)The change results from the unification of all hooks and adjust to the official recommendations for the Google Cloud.
The signature of wait_for_transfer_job method in GCPTransferServiceHook has changed.
Old signature:
def wait_for_transfer_job(self, job):
...New signature:
def wait_for_transfer_job(
self, job, expected_statuses=(GcpTransferOperationStatus.SUCCESS,)
):
...The behavior of wait_for_transfer_job has changed:
Old behavior:
wait_for_transfer_job would wait for the SUCCESS status in specified jobs operations.
New behavior:
You can now specify an array of expected statuses. wait_for_transfer_job now waits for any of them.
The default value of expected_statuses is SUCCESS so that change is backwards compatible.
The class GoogleCloudStorageToGoogleCloudStorageTransferOperator has been moved from
airflow.contrib.operators.gcs_to_gcs_transfer_operator to airflow.contrib.operators.gcp_transfer_operator
the class S3ToGoogleCloudStorageTransferOperator has been moved from
airflow.contrib.operators.s3_to_gcs_transfer_operator to airflow.contrib.operators.gcp_transfer_operator
The change was made to keep all the operators related to GCS Transfer Services in one file.
The previous imports will continue to work until Airflow 2.0
The driver_classapth argument to SparkSubmit Hook and Operator was
generating --driver-classpath on the spark command line, but this isn't a
valid option to spark.
The argument has been renamed to driver_class_path and the option it
generates has been fixed.
The DAG parsing manager log now by default will be log into a file, where its location is
controlled by the new dag_processor_manager_log_location config option in core section.
Extend and enhance new Airflow RBAC UI to support DAG level ACL. Each dag now has two permissions(one for write, one for read) associated('can_dag_edit', 'can_dag_read').
The admin will create new role, associate the dag permission with the target dag and assign that role to users. That user can only access / view the certain dags on the UI
that he has permissions on. If a new role wants to access all the dags, the admin could associate dag permissions on an artificial view(all_dags) with that role.
We also provide a new cli command(sync_perm) to allow admin to auto sync permissions.
ts_nodash previously contained TimeZone information along with execution date. For Example: 20150101T000000+0000. This is not user-friendly for file or folder names which was a popular use case for ts_nodash. Hence this behavior has been changed and using ts_nodash will no longer contain TimeZone information, restoring the pre-1.10 behavior of this macro. And a new macro ts_nodash_with_tz has been added which can be used to get a string with execution date and timezone info without dashes.
Examples:
ts_nodash:20150101T000000ts_nodash_with_tz:20150101T000000+0000
next_ds/prev_ds now map to execution_date instead of the next/previous schedule-aligned execution date for DAGs triggered in the UI.
This patch changes the User.superuser field from a hard-coded boolean to a Boolean() database column. User.superuser will default to False, which means that this privilege will have to be granted manually to any users that may require it.
For example, open a Python shell and
from airflow import models, settings
session = settings.Session()
users = session.query(models.User).all() # [admin, regular_user]
users[1].superuser # False
admin = users[0]
admin.superuser = True
session.add(admin)
session.commit()We have updated the version of flask-login we depend upon, and as a result any
custom auth backends might need a small change: is_active,
is_authenticated, and is_anonymous should now be properties. What this means is if
previously you had this in your user class
def is_active(self):
return self.activethen you need to change it like this
@property
def is_active(self):
return self.activeGoogleCloudStorageToBigQueryOperator is now support schema auto-detection is available when you load data into BigQuery. Unfortunately, changes can be required.
If BigQuery tables are created outside of airflow and the schema is not defined in the task, multiple options are available:
define a schema_fields:
gcs_to_bq.GoogleCloudStorageToBigQueryOperator(
# ...
schema_fields={...}
)or define a schema_object:
gcs_to_bq.GoogleCloudStorageToBigQueryOperator(
# ...
schema_object="path/to/schema/object"
)or enabled autodetect of schema:
gcs_to_bq.GoogleCloudStorageToBigQueryOperator(
# ...
autodetect=True
)The scheduler.min_file_parsing_loop_time config option has been temporarily removed due to some bugs.
The scheduler_heartbeat metric has been changed from a gauge to a counter. Each loop of the scheduler will increment the counter by 1. This provides a higher degree of visibility and allows for better integration with Prometheus using the StatsD Exporter. The scheduler's activity status can be determined by graphing and alerting using a rate of change of the counter. If the scheduler goes down, the rate will drop to 0.
EMRHook.create_job_flow has been changed to pass all keys to the create_job_flow API, rather than just specific known keys for greater flexibility.
However prior to this release the "emr_default" sample connection that was created had invalid configuration, so creating EMR clusters might fail until your connection is updated. (Ec2KeyName, Ec2SubnetId, TerminationProtection and KeepJobFlowAliveWhenNoSteps were all top-level keys when they should be inside the "Instances" dict)
Connecting to an LDAP server over plain text is not supported anymore. The
certificate presented by the LDAP server must be signed by a trusted
certificate, or you must provide the cacert option under [ldap] in the
config file.
If you want to use LDAP auth backend without TLS then you will have to create a custom-auth backend based on https://github.com/apache/airflow/blob/1.10.0/airflow/contrib/auth/backends/ldap_auth.py
Installation and upgrading requires setting SLUGIFY_USES_TEXT_UNIDECODE=yes in your environment or
AIRFLOW_GPL_UNIDECODE=yes. In case of the latter a GPL runtime dependency will be installed due to a
dependency (python-nvd3 -> python-slugify -> unidecode).
The method name was changed to be compatible with the Python 3.7 async/await keywords
Add a configuration variable(default_dag_run_display_number) to control numbers of dag run for display
Add a configuration variable(default_dag_run_display_number) under webserver section to control the number of dag runs to show in UI.
The current webserver UI uses the Flask-Admin extension. The new webserver UI uses the Flask-AppBuilder (FAB) extension. FAB has built-in authentication support and Role-Based Access Control (RBAC), which provides configurable roles and permissions for individual users.
To turn on this feature, in your airflow.cfg file (under [webserver]), set the configuration variable rbac = True, and then run airflow command, which will generate the webserver_config.py file in your $AIRFLOW_HOME.
FAB has built-in authentication support for DB, OAuth, OpenID, LDAP, and REMOTE_USER. The default auth type is AUTH_DB.
For any other authentication type (OAuth, OpenID, LDAP, REMOTE_USER), see the Authentication section of FAB docs for how to configure variables in webserver_config.py file.
Once you modify your config file, run airflow db init to generate new tables for RBAC support (these tables will have the prefix ab_).
Once configuration settings have been updated and new tables have been generated, create an admin account with airflow create_user command.
Run airflow webserver to start the new UI. This will bring up a log in page, enter the recently created admin username and password.
There are five roles created for Airflow by default: Admin, User, Op, Viewer, and Public. To configure roles/permissions, go to the Security tab and click List Roles in the new UI.
- AWS Batch Operator renamed property queue to job_queue to prevent conflict with the internal queue from CeleryExecutor - AIRFLOW-2542
- Users created and stored in the old users table will not be migrated automatically. FAB's built-in authentication support must be reconfigured.
- Airflow dag home page is now
/home(instead of/admin). - All ModelViews in Flask-AppBuilder follow a different pattern from Flask-Admin. The
/adminpart of the URL path will no longer exist. For example:/admin/connectionbecomes/connection/list,/admin/connection/newbecomes/connection/add,/admin/connection/editbecomes/connection/edit, etc. - Due to security concerns, the new webserver will no longer support the features in the
Data Profilingmenu of old UI, includingAd Hoc Query,Charts, andKnown Events. - HiveServer2Hook.get_results() always returns a list of tuples, even when a single column is queried, as per Python API 2.
- UTC is now the default timezone: Either reconfigure your workflows scheduling in UTC or set
default_timezoneas explained in https://airflow.apache.org/timezone.html#default-time-zone
We now rename airflow.contrib.sensors.hdfs_sensors to airflow.contrib.sensors.hdfs_sensor for consistency purpose.
We now rely on more strict ANSI SQL settings for MySQL in order to have sane defaults. Make sure
to have specified explicit_defaults_for_timestamp=1 in your my.cnf under [mysqld]
To make the config of Airflow compatible with Celery, some properties have been renamed:
celeryd_concurrency -> worker_concurrency
celery_result_backend -> result_backend
celery_ssl_active -> ssl_active
celery_ssl_cert -> ssl_cert
celery_ssl_key -> ssl_key
Resulting in the same config parameters as Celery 4, with more transparency.
Dataflow job labeling is now supported in Dataflow{Java,Python}Operator with a default "airflow-version" label, please upgrade your google-cloud-dataflow or apache-beam version to 2.2.0 or greater.
The bql parameter passed to BigQueryOperator and BigQueryBaseCursor.run_query has been deprecated and renamed to sql for consistency purposes. Using bql will still work (and raise a DeprecationWarning), but is no longer
supported and will be removed entirely in Airflow 2.0
With Airflow 1.9 or lower, Unload operation always included header row. In order to include header row,
we need to turn off parallel unload. It is preferred to perform unload operation using all nodes so that it is
faster for larger tables. So, parameter called include_header is added and default is set to False.
Header row will be added only if this parameter is set True and also in that case parallel will be automatically turned off (PARALLEL OFF)
With Airflow 1.9 or lower, there were two connection strings for the Google Cloud operators, both google_cloud_storage_default and google_cloud_default. This can be confusing and therefore the google_cloud_storage_default connection id has been replaced with google_cloud_default to make the connection id consistent across Airflow.
With Airflow 1.9 or lower, FILENAME_TEMPLATE, PROCESSOR_FILENAME_TEMPLATE, LOG_ID_TEMPLATE, END_OF_LOG_MARK were configured in airflow_local_settings.py. These have been moved into the configuration file, and hence if you were using a custom configuration file the following defaults need to be added.
[core]
fab_logging_level = WARN
log_filename_template = {{{{ ti.dag_id }}}}/{{{{ ti.task_id }}}}/{{{{ ts }}}}/{{{{ try_number }}}}.log
log_processor_filename_template = {{{{ filename }}}}.log
[elasticsearch]
elasticsearch_log_id_template = {{dag_id}}-{{task_id}}-{{execution_date}}-{{try_number}}
elasticsearch_end_of_log_mark = end_of_log
The previous setting of log_task_reader is not needed in many cases now when using the default logging config with remote storages. (Previously it needed to be set to s3.task or similar. This is not needed with the default config anymore)
With the change to Airflow core to be timezone aware the default log path for task instances will now include timezone information. This will by default mean all previous task logs won't be found. You can get the old behaviour back by setting the following config options:
[core]
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ execution_date.strftime("%%Y-%%m-%%dT%%H:%%M:%%S") }}/{{ try_number }}.log
SSH Hook now uses the Paramiko library to create an ssh client connection, instead of the sub-process based ssh command execution previously (<1.9.0), so this is backward incompatible.
- update SSHHook constructor
- use SSHOperator class in place of SSHExecuteOperator which is removed now. Refer to test_ssh_operator.py for usage info.
- SFTPOperator is added to perform secure file transfer from serverA to serverB. Refer to test_sftp_operator.py for usage info.
- No updates are required if you are using ftpHook, it will continue to work as is.
The airflow.hooks.S3_hook.S3Hook has been switched to use boto3 instead of the older boto (a.k.a. boto2). This results in a few backwards incompatible changes to the following classes: S3Hook:
- the constructors no longer accepts
s3_conn_id. It is now calledaws_conn_id. - the default connection is now "aws_default" instead of "s3_default"
- the return type of objects returned by
get_bucketis now boto3.s3.Bucket - the return type of
get_key, andget_wildcard_keyis now an boto3.S3.Object.
If you are using any of these in your DAGs and specify a connection ID you will need to update the parameter name for the connection to "aws_conn_id": S3ToHiveTransfer, S3PrefixSensor, S3KeySensor, RedshiftToS3Transfer.
The logging structure of Airflow has been rewritten to make configuration easier and the logging system more transparent.
A logger is the entry point into the logging system. Each logger is a named bucket to which messages can be written for processing. A logger is configured to have a log level. This log level describes the severity of the messages that the logger will handle. Python defines the following log levels: DEBUG, INFO, WARNING, ERROR or CRITICAL.
Each message that is written to the logger is a Log Record. Each log record contains a log level indicating the severity of that specific message. A log record can also contain useful metadata that describes the event that is being logged. This can include details such as a stack trace or an error code.
When a message is given to the logger, the log level of the message is compared to the log level of the logger. If the log level of the message meets or exceeds the log level of the logger itself, the message will undergo further processing. If it doesn’t, the message will be ignored.
Once a logger has determined that a message needs to be processed, it is passed to a Handler. This configuration is now more flexible and can be easily be maintained in a single file.
Airflow's logging mechanism has been refactored to use Python’s built-in logging module to perform logging of the application. By extending classes with the existing LoggingMixin, all the logging will go through a central logger. Also the BaseHook and BaseOperator already extend this class, so it is easily available to do logging.
The main benefit is easier configuration of the logging by setting a single centralized python file. Disclaimer; there is still some inline configuration, but this will be removed eventually. The new logging class is defined by setting the dotted classpath in your ~/airflow/airflow.cfg file:
# Logging class
# Specify the class that will specify the logging configuration
# This class has to be on the python classpath
logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
The logging configuration file needs to be on the PYTHONPATH, for example $AIRFLOW_HOME/config. This directory is loaded by default. Any directory may be added to the PYTHONPATH, this might be handy when the config is in another directory or a volume is mounted in case of Docker.
The config can be taken from airflow/config_templates/airflow_local_settings.py as a starting point. Copy the contents to ${AIRFLOW_HOME}/config/airflow_local_settings.py, and alter the config as is preferred.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
from airflow import configuration as conf
# TODO: Logging format and level should be configured
# in this file instead of from airflow.cfg. Currently
# there are other log format and level configurations in
# settings.py and cli.py. Please see AIRFLOW-1455.
LOG_LEVEL = conf.get('core', 'LOGGING_LEVEL').upper()
LOG_FORMAT = conf.get('core', 'log_format')
BASE_LOG_FOLDER = conf.get('core', 'BASE_LOG_FOLDER')
PROCESSOR_LOG_FOLDER = conf.get('scheduler', 'child_process_log_directory')
FILENAME_TEMPLATE = '{{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log'
PROCESSOR_FILENAME_TEMPLATE = '{{ filename }}.log'
DEFAULT_LOGGING_CONFIG = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'airflow.task': {
'format': LOG_FORMAT,
},
'airflow.processor': {
'format': LOG_FORMAT,
},
},
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'formatter': 'airflow.task',
'stream': 'ext://sys.stdout'
},
'file.task': {
'class': 'airflow.utils.log.file_task_handler.FileTaskHandler',
'formatter': 'airflow.task',
'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
'filename_template': FILENAME_TEMPLATE,
},
'file.processor': {
'class': 'airflow.utils.log.file_processor_handler.FileProcessorHandler',
'formatter': 'airflow.processor',
'base_log_folder': os.path.expanduser(PROCESSOR_LOG_FOLDER),
'filename_template': PROCESSOR_FILENAME_TEMPLATE,
}
# When using s3 or gcs, provide a customized LOGGING_CONFIG
# in airflow_local_settings within your PYTHONPATH, see UPDATING.md
# for details
# 's3.task': {
# 'class': 'airflow.utils.log.s3_task_handler.S3TaskHandler',
# 'formatter': 'airflow.task',
# 'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
# 's3_log_folder': S3_LOG_FOLDER,
# 'filename_template': FILENAME_TEMPLATE,
# },
# 'gcs.task': {
# 'class': 'airflow.utils.log.gcs_task_handler.GCSTaskHandler',
# 'formatter': 'airflow.task',
# 'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
# 'gcs_log_folder': GCS_LOG_FOLDER,
# 'filename_template': FILENAME_TEMPLATE,
# },
},
'loggers': {
'': {
'handlers': ['console'],
'level': LOG_LEVEL
},
'airflow': {
'handlers': ['console'],
'level': LOG_LEVEL,
'propagate': False,
},
'airflow.processor': {
'handlers': ['file.processor'],
'level': LOG_LEVEL,
'propagate': True,
},
'airflow.task': {
'handlers': ['file.task'],
'level': LOG_LEVEL,
'propagate': False,
},
'airflow.task_runner': {
'handlers': ['file.task'],
'level': LOG_LEVEL,
'propagate': True,
},
}
}
To customize the logging (for example, use logging rotate), define one or more of the logging handles that Python has to offer. For more details about the Python logging, please refer to the official logging documentation.
Furthermore, this change also simplifies logging within the DAG itself:
root@ae1bc863e815:/airflow# python
Python 3.6.2 (default, Sep 13 2017, 14:26:54)
[GCC 4.9.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from airflow.settings import *
>>>
>>> from datetime import datetime
>>> from airflow.models.dag import DAG
>>> from airflow.operators.dummy import DummyOperator
>>>
>>> dag = DAG('simple_dag', start_date=datetime(2017, 9, 1))
>>>
>>> task = DummyOperator(task_id='task_1', dag=dag)
>>>
>>> task.log.error('I want to say something..')
[2017-09-25 20:17:04,927] {<stdin>:1} ERROR - I want to say something..
The file_task_handler logger has been made more flexible. The default format can be changed, {dag_id}/{task_id}/{execution_date}/{try_number}.log by supplying Jinja templating in the FILENAME_TEMPLATE configuration variable. See the file_task_handler for more information.
If you are logging to Google cloud storage, please see the Google cloud platform documentation for logging instructions.
If you are using S3, the instructions should be largely the same as the Google cloud platform instructions above. You will need a custom logging config. The REMOTE_BASE_LOG_FOLDER configuration key in your airflow config has been removed, therefore you will need to take the following steps:
- Copy the logging configuration from
airflow/config_templates/airflow_logging_settings.py. - Place it in a directory inside the Python import path
PYTHONPATH. If you are using Python 2.7, ensuring that any__init__.pyfiles exist so that it is importable. - Update the config by setting the path of
REMOTE_BASE_LOG_FOLDERexplicitly in the config. TheREMOTE_BASE_LOG_FOLDERkey is not used anymore. - Set the
logging_config_classto the filename and dict. For example, if you placecustom_logging_config.pyon the base of yourPYTHONPATH, you will need to setlogging_config_class = custom_logging_config.LOGGING_CONFIGin your config as Airflow 1.8.
A new DaskExecutor allows Airflow tasks to be run in Dask Distributed clusters.
These features are marked for deprecation. They may still work (and raise a DeprecationWarning), but are no longer
supported and will be removed entirely in Airflow 2.0
-
If you're using the
google_cloud_conn_idordataproc_clusterargument names explicitly incontrib.operators.Dataproc{*}Operator(s), be sure to rename them togcp_conn_idorcluster_name, respectively. We've renamed these arguments for consistency. (AIRFLOW-1323) -
post_execute()hooks now take two arguments,contextandresult(AIRFLOW-886)Previously, post_execute() only took one argument,
context. -
contrib.hooks.gcp_dataflow_hook.DataFlowHookstarts to use--runner=DataflowRunnerinstead ofDataflowPipelineRunner, which is removed from the packagegoogle-cloud-dataflow-0.6.0. -
The pickle type for XCom messages has been replaced by json to prevent RCE attacks. Note that JSON serialization is stricter than pickling, so if you want to e.g. pass raw bytes through XCom you must encode them using an encoding like base64. By default pickling is still enabled until Airflow 2.0. To disable it set enable_xcom_pickling = False in your Airflow config.
The Airflow package name was changed from airflow to apache-airflow during this release. You must uninstall
a previously installed version of Airflow before installing 1.8.1.
The database schema needs to be upgraded. Make sure to shutdown Airflow and make a backup of your database. To
upgrade the schema issue airflow upgradedb.
Systemd unit files have been updated. If you use systemd please make sure to update these.
Please note that the webserver does not detach properly, this will be fixed in a future version.
Airflow 1.7.1 has issues with being able to over subscribe to a pool, ie. more slots could be used than were available. This is fixed in Airflow 1.8.0, but due to past issue jobs may fail to start although their dependencies are met after an upgrade. To workaround either temporarily increase the amount of slots above the amount of queued tasks or use a new pool.
Using a dynamic start_date (e.g. start_date = datetime.now()) is not considered a best practice. The 1.8.0 scheduler
is less forgiving in this area. If you encounter DAGs not being scheduled you can try using a fixed start_date and
renaming your DAG. The last step is required to make sure you start with a clean slate, otherwise the old schedule can
interfere.
Please read through the new scheduler options, defaults have changed since 1.7.1.
In order to increase the robustness of the scheduler, DAGs are now processed in their own process. Therefore each
DAG has its own log file for the scheduler. These log files are placed in child_process_log_directory which defaults to
<AIRFLOW_HOME>/scheduler/latest. You will need to make sure these log files are removed.
DAG logs or processor logs ignore and command line settings for log file locations.
Previously the command line option num_runs was used to let the scheduler terminate after a certain amount of
loops. This is now time bound and defaults to -1, which means run continuously. See also num_runs.
Previously num_runs was used to let the scheduler terminate after a certain amount of loops. Now num_runs specifies
the number of times to try to schedule each DAG file within run_duration time. Defaults to -1, which means try
indefinitely. This is only available on the command line.
After how much time should an updated DAG be picked up from the filesystem.
CURRENTLY DISABLED DUE TO A BUG How many seconds to wait between file-parsing loops to prevent the logs from being spammed.
The frequency with which the scheduler should relist the contents of the DAG directory. If while developing +dags, they are not being picked up, have a look at this number and decrease it when necessary.
By default the scheduler will fill any missing interval DAG Runs between the last execution date and the current date.
This setting changes that behavior to only execute the latest interval. This can also be specified per DAG as
catchup = False / True. Command line backfills will still work.
Due to changes in the way Airflow processes DAGs the Web UI does not show an error when processing a faulty DAG. To
find processing errors go the child_process_log_directory which defaults to <AIRFLOW_HOME>/scheduler/latest.
Previously, new DAGs would be scheduled immediately. To retain the old behavior, add this to airflow.cfg:
[core]
dags_are_paused_at_creation = False
If you specify a hive conf to the run_cli command of the HiveHook, Airflow add some
convenience variables to the config. In case you run a secure Hadoop setup it might be
required to allow these variables by adjusting you hive configuration to add airflow\.ctx\..* to the regex
of user-editable configuration properties. See
the Hive docs on Configuration Properties for more info.
All Google Cloud Operators and Hooks are aligned and use the same client library. Now you have a single connection type for all kinds of Google Cloud Operators.
If you experience problems connecting with your operator make sure you set the connection type "Google Cloud".
Also the old P12 key file type is not supported anymore and only the new JSON key files are supported as a service account.
These features are marked for deprecation. They may still work (and raise a DeprecationWarning), but are no longer
supported and will be removed entirely in Airflow 2.0
-
Hooks and operators must be imported from their respective submodules
airflow.operators.PigOperatoris no longer supported;from airflow.operators.pig_operator import PigOperatoris. (AIRFLOW-31, AIRFLOW-200) -
Operators no longer accept arbitrary arguments
Previously,
Operator.__init__()accepted any arguments (either positional*argsor keyword**kwargs) without complaint. Now, invalid arguments will be rejected. (apache#1285) -
The config value secure_mode will default to True which will disable some insecure endpoints/features
There is a report that the default of "-1" for num_runs creates an issue where errors are reported while parsing tasks.
It was not confirmed, but a workaround was found by changing the default back to None.
To do this edit cli.py, find the following:
'num_runs': Arg(
("-n", "--num_runs"),
default=-1, type=int,
help="Set the number of runs to execute before exiting"),
and change default=-1 to default=None. If you have this issue please report it on the mailing list.
To continue using the default smtp email backend, change the email_backend line in your config file from:
[email]
email_backend = airflow.utils.send_email_smtp
to:
[email]
email_backend = airflow.utils.email.send_email_smtp
To continue using S3 logging, update your config file so:
s3_log_folder = s3://my-airflow-log-bucket/logs
becomes:
remote_base_log_folder = s3://my-airflow-log-bucket/logs
remote_log_conn_id = <your desired s3 connection>