This file documents any backwards-incompatible changes in Airflow and assists users migrating to a new version.
BashTaskRunner has been renamed to StandardTaskRunner. It is the default task runner so you might need to update your config.
task_runner = StandardTaskRunner
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.
The scheduler.min_file_parsing_loop_time config option has been temporarily removed due to some bugs.
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)
The ability to manipulate users from the command line has been changed. 'airflow create_user' and 'airflow delete_user' and 'airflow list_users' has been grouped to a single command airflow users with optional flags --create, --list and --delete.
Example Usage:
To create a new user:
airflow users --create --username jondoe --lastname doe --firstname jon --email [email protected] --role Viewer --password testTo list users:
airflow users --listTo delete a user:
airflow users --delete --username jondoeThe 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. Scheduler upness can be determined by graphing and alerting using a rate. If the scheduler goes down, the rate will drop to 0.
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.active
then you need to change it like this
@property
def is_active(self):
return self.active
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)
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 num of dag run 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 initdb 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.
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.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 builtin 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.
# -*- coding: utf-8 -*-
#
# Licensed 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 import DAG
>>> from airflow.operators.dummy_operator 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 your pythonpath, 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 whitelist these variables by adding the following to your configuration:
<property>
<name>hive.security.authorization.sqlstd.confwhitelist.append</name>
<value>airflow\.ctx\..*</value>
</property>
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 Platform".
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>