Solving Airflow - ImportError: Unable to load custom logging from log_config.DEFAULT_LOGGING_CONFIG

2021 Jan 25

I was working in Airflow and at the moment that I tried to configure a custom log I got the following error:

ImportError: Unable to load custom logging from 
airflow.config.log_config.LOGGING_CONFIG due to 
section/key [logging/logging_level] not found in config

As 99% of the normal people, I went in Stack Overflow and checked the answer given by Meny Issakov.

Solution

So based on its response, I got the following (working) solution doing the following steps:

1) I opened the file airflow.cfg

2) I’ve Iinclude a new section in the file, below the [core] section, called [logging] using the following code: [logging] logging_config_class = log_config.DEFAULT_LOGGING_CONFIG

3) I restarted the scheduler

However, going a bit into the root cause of the problem, I got a (non-definitive) conclusion.

ELI5 the reason of problem

The file airflow.cfg is missing a section called [logging].

Why the problem happened?

At the time that the scheduler starts, it accesses the [core] section in the airflow.cfg and search the logging path.

And the information where the logs will be stored it’s found in logging_config_class parameter.

However, even if we put logging_config_class = log_config.DEFAULT_LOGGING_CONFIG in the [core] section, the scheduler it’s not gonna work either.

Why? It’s because there’s a mismatch between the log_config.py logging handlers and with the airflow.cfg.

In the logging handlers in the log_config.py they have the following conf.get to get the logging configurations:

LOG_LEVEL: str = conf.get('logging', 'LOGGING_LEVEL').upper()  
LOG_FORMAT: str = conf.get('logging', 'LOG_FORMAT')

The first parameter it’s the section that will be scanned in the airflow.cfg file, but by default, there’s no section called [logging] in the airflow.cfg file, and this causes the following error in the scheduler initialization:

ImportError: Unable to load custom logging from log_config.DEFAULT_LOGGING_CONFIG due to section/key [logging/fab_logging_level] not found in config

I hope it helps.

PS 1: By the way, I got the same error when I was in the excellent Udemy course provided by Marc Lamberti specifically in the Section 8: Monitoring Apache Airflow Lecture - Practice - Setting up custom logging

PS 2: The log config has the following format in the current version of Airflow that I’m using (1.10.14):

#
# 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.
"""Airflow logging settings"""
import os
from pathlib import Path
from typing import Any, Dict, Union
from urllib.parse import urlparse
from airflow.configuration import conf
from airflow.exceptions import AirflowException
# 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: str = conf.get('logging', 'LOGGING_LEVEL').upper()
# Flask appbuilder's info level log is very verbose,
# so it's set to 'WARN' by default.
FAB_LOG_LEVEL: str = conf.get('logging', 'FAB_LOGGING_LEVEL').upper()
LOG_FORMAT: str = conf.get('logging', 'LOG_FORMAT')
COLORED_LOG_FORMAT: str = conf.get('logging', 'COLORED_LOG_FORMAT')
COLORED_LOG: bool = conf.getboolean('logging', 'COLORED_CONSOLE_LOG')
COLORED_FORMATTER_CLASS: str = conf.get('logging', 'COLORED_FORMATTER_CLASS')
BASE_LOG_FOLDER: str = conf.get('logging', 'BASE_LOG_FOLDER')
PROCESSOR_LOG_FOLDER: str = conf.get('scheduler', 'CHILD_PROCESS_LOG_DIRECTORY')
DAG_PROCESSOR_MANAGER_LOG_LOCATION: str = conf.get('logging', 'DAG_PROCESSOR_MANAGER_LOG_LOCATION')
FILENAME_TEMPLATE: str = conf.get('logging', 'LOG_FILENAME_TEMPLATE')
PROCESSOR_FILENAME_TEMPLATE: str = conf.get('logging', 'LOG_PROCESSOR_FILENAME_TEMPLATE')
DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'airflow': {'format': LOG_FORMAT},
'airflow_coloured': {
'format': COLORED_LOG_FORMAT if COLORED_LOG else LOG_FORMAT,
'class': COLORED_FORMATTER_CLASS if COLORED_LOG else 'logging.Formatter',
},
},
'handlers': {
'console': {
'class': 'airflow.utils.log.logging_mixin.RedirectStdHandler',
'formatter': 'airflow_coloured',
'stream': 'sys.stdout',
},
'task': {
'class': 'airflow.utils.log.file_task_handler.FileTaskHandler',
'formatter': 'airflow',
'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
'filename_template': FILENAME_TEMPLATE,
},
'processor': {
'class': 'airflow.utils.log.file_processor_handler.FileProcessorHandler',
'formatter': 'airflow',
'base_log_folder': os.path.expanduser(PROCESSOR_LOG_FOLDER),
'filename_template': PROCESSOR_FILENAME_TEMPLATE,
},
},
'loggers': {
'airflow.processor': {
'handlers': ['processor'],
'level': LOG_LEVEL,
'propagate': False,
},
'airflow.task': {
'handlers': ['task'],
'level': LOG_LEVEL,
'propagate': False,
},
'flask_appbuilder': {
'handler': ['console'],
'level': FAB_LOG_LEVEL,
'propagate': True,
},
},
'root': {
'handlers': ['console'],
'level': LOG_LEVEL,
},
}
EXTRA_LOGGER_NAMES: str = conf.get('logging', 'EXTRA_LOGGER_NAMES', fallback=None)
if EXTRA_LOGGER_NAMES:
new_loggers = {
logger_name.strip(): {
'handler': ['console'],
'level': LOG_LEVEL,
'propagate': True,
}
for logger_name in EXTRA_LOGGER_NAMES.split(",")
}
DEFAULT_LOGGING_CONFIG['loggers'].update(new_loggers)
DEFAULT_DAG_PARSING_LOGGING_CONFIG: Dict[str, Dict[str, Dict[str, Any]]] = {
'handlers': {
'processor_manager': {
'class': 'logging.handlers.RotatingFileHandler',
'formatter': 'airflow',
'filename': DAG_PROCESSOR_MANAGER_LOG_LOCATION,
'mode': 'a',
'maxBytes': 104857600, # 100MB
'backupCount': 5,
}
},
'loggers': {
'airflow.processor_manager': {
'handlers': ['processor_manager'],
'level': LOG_LEVEL,
'propagate': False,
}
},
}
# Only update the handlers and loggers when CONFIG_PROCESSOR_MANAGER_LOGGER is set.
# This is to avoid exceptions when initializing RotatingFileHandler multiple times
# in multiple processes.
if os.environ.get('CONFIG_PROCESSOR_MANAGER_LOGGER') == 'True':
DEFAULT_LOGGING_CONFIG['handlers'].update(DEFAULT_DAG_PARSING_LOGGING_CONFIG['handlers'])
DEFAULT_LOGGING_CONFIG['loggers'].update(DEFAULT_DAG_PARSING_LOGGING_CONFIG['loggers'])
# Manually create log directory for processor_manager handler as RotatingFileHandler
# will only create file but not the directory.
processor_manager_handler_config: Dict[str, Any] = DEFAULT_DAG_PARSING_LOGGING_CONFIG['handlers'][
'processor_manager'
]
directory: str = os.path.dirname(processor_manager_handler_config['filename'])
Path(directory).mkdir(parents=True, exist_ok=True, mode=0o755)
##################
# Remote logging #
##################
REMOTE_LOGGING: bool = conf.getboolean('logging', 'remote_logging')
if REMOTE_LOGGING:
ELASTICSEARCH_HOST: str = conf.get('elasticsearch', 'HOST')
# Storage bucket URL for remote logging
# S3 buckets should start with "s3://"
# Cloudwatch log groups should start with "cloudwatch://"
# GCS buckets should start with "gs://"
# WASB buckets should start with "wasb"
# just to help Airflow select correct handler
REMOTE_BASE_LOG_FOLDER: str = conf.get('logging', 'REMOTE_BASE_LOG_FOLDER')
if REMOTE_BASE_LOG_FOLDER.startswith('s3://'):
S3_REMOTE_HANDLERS: Dict[str, Dict[str, str]] = {
'task': {
'class': 'airflow.providers.amazon.aws.log.s3_task_handler.S3TaskHandler',
'formatter': 'airflow',
'base_log_folder': str(os.path.expanduser(BASE_LOG_FOLDER)),
's3_log_folder': REMOTE_BASE_LOG_FOLDER,
'filename_template': FILENAME_TEMPLATE,
},
}
DEFAULT_LOGGING_CONFIG['handlers'].update(S3_REMOTE_HANDLERS)
elif REMOTE_BASE_LOG_FOLDER.startswith('cloudwatch://'):
CLOUDWATCH_REMOTE_HANDLERS: Dict[str, Dict[str, str]] = {
'task': {
'class': 'airflow.providers.amazon.aws.log.cloudwatch_task_handler.CloudwatchTaskHandler',
'formatter': 'airflow',
'base_log_folder': str(os.path.expanduser(BASE_LOG_FOLDER)),
'log_group_arn': urlparse(REMOTE_BASE_LOG_FOLDER).netloc,
'filename_template': FILENAME_TEMPLATE,
},
}
DEFAULT_LOGGING_CONFIG['handlers'].update(CLOUDWATCH_REMOTE_HANDLERS)
elif REMOTE_BASE_LOG_FOLDER.startswith('gs://'):
key_path = conf.get('logging', 'GOOGLE_KEY_PATH', fallback=None)
GCS_REMOTE_HANDLERS: Dict[str, Dict[str, str]] = {
'task': {
'class': 'airflow.providers.google.cloud.log.gcs_task_handler.GCSTaskHandler',
'formatter': 'airflow',
'base_log_folder': str(os.path.expanduser(BASE_LOG_FOLDER)),
'gcs_log_folder': REMOTE_BASE_LOG_FOLDER,
'filename_template': FILENAME_TEMPLATE,
'gcp_key_path': key_path,
},
}
DEFAULT_LOGGING_CONFIG['handlers'].update(GCS_REMOTE_HANDLERS)
elif REMOTE_BASE_LOG_FOLDER.startswith('wasb'):
WASB_REMOTE_HANDLERS: Dict[str, Dict[str, Union[str, bool]]] = {
'task': {
'class': 'airflow.providers.microsoft.azure.log.wasb_task_handler.WasbTaskHandler',
'formatter': 'airflow',
'base_log_folder': str(os.path.expanduser(BASE_LOG_FOLDER)),
'wasb_log_folder': REMOTE_BASE_LOG_FOLDER,
'wasb_container': 'airflow-logs',
'filename_template': FILENAME_TEMPLATE,
'delete_local_copy': False,
},
}
DEFAULT_LOGGING_CONFIG['handlers'].update(WASB_REMOTE_HANDLERS)
elif REMOTE_BASE_LOG_FOLDER.startswith('stackdriver://'):
key_path = conf.get('logging', 'GOOGLE_KEY_PATH', fallback=None)
# stackdriver:///airflow-tasks => airflow-tasks
log_name = urlparse(REMOTE_BASE_LOG_FOLDER).path[1:]
STACKDRIVER_REMOTE_HANDLERS = {
'task': {
'class': 'airflow.providers.google.cloud.log.stackdriver_task_handler.StackdriverTaskHandler',
'formatter': 'airflow',
'name': log_name,
'gcp_key_path': key_path,
}
}
DEFAULT_LOGGING_CONFIG['handlers'].update(STACKDRIVER_REMOTE_HANDLERS)
elif ELASTICSEARCH_HOST:
ELASTICSEARCH_LOG_ID_TEMPLATE: str = conf.get('elasticsearch', 'LOG_ID_TEMPLATE')
ELASTICSEARCH_END_OF_LOG_MARK: str = conf.get('elasticsearch', 'END_OF_LOG_MARK')
ELASTICSEARCH_FRONTEND: str = conf.get('elasticsearch', 'frontend')
ELASTICSEARCH_WRITE_STDOUT: bool = conf.getboolean('elasticsearch', 'WRITE_STDOUT')
ELASTICSEARCH_JSON_FORMAT: bool = conf.getboolean('elasticsearch', 'JSON_FORMAT')
ELASTICSEARCH_JSON_FIELDS: str = conf.get('elasticsearch', 'JSON_FIELDS')
ELASTIC_REMOTE_HANDLERS: Dict[str, Dict[str, Union[str, bool]]] = {
'task': {
'class': 'airflow.providers.elasticsearch.log.es_task_handler.ElasticsearchTaskHandler',
'formatter': 'airflow',
'base_log_folder': str(os.path.expanduser(BASE_LOG_FOLDER)),
'log_id_template': ELASTICSEARCH_LOG_ID_TEMPLATE,
'filename_template': FILENAME_TEMPLATE,
'end_of_log_mark': ELASTICSEARCH_END_OF_LOG_MARK,
'host': ELASTICSEARCH_HOST,
'frontend': ELASTICSEARCH_FRONTEND,
'write_stdout': ELASTICSEARCH_WRITE_STDOUT,
'json_format': ELASTICSEARCH_JSON_FORMAT,
'json_fields': ELASTICSEARCH_JSON_FIELDS,
},
}
DEFAULT_LOGGING_CONFIG['handlers'].update(ELASTIC_REMOTE_HANDLERS)
else:
raise AirflowException(
"Incorrect remote log configuration. Please check the configuration of option 'host' in "
"section 'elasticsearch' if you are using Elasticsearch. In the other case, "
"'remote_base_log_folder' option in 'core' section."
)
view raw log_config.py hosted with ❤ by GitHub

PS 3: The modified version of airflow.cfg file with the [logging] section is:

[core]
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /usr/local/airflow/dags
# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /usr/local/airflow/logs
# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
# Users must supply an Airflow connection id that provides access to the storage
# location. If remote_logging is set to true, see UPDATING.md for additional
# configuration requirements.
remote_logging = False
remote_log_conn_id =
remote_base_log_folder =
encrypt_s3_logs = False
# Logging level
logging_level = INFO
fab_logging_level = WARN
# 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
logging_config_class =
# Log format
# Colour the logs when the controlling terminal is a TTY.
colored_console_log = True
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter
#log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
log_format = [%%(asctime)s] [ %%(process)s - %%(name)s ] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
# Log filename format
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log
dag_processor_manager_log_location = /usr/local/airflow/logs/dag_processor_manager/dag_processor_manager.log
# Hostname by providing a path to a callable, which will resolve the hostname
# The format is "package:function". For example,
# default value "socket:getfqdn" means that result from getfqdn() of "socket" package will be used as hostname
# No argument should be required in the function specified.
# If using IP address as hostname is preferred, use value "airflow.utils.net:get_host_ip_address"
hostname_callable = socket:getfqdn
# Default timezone in case supplied date times are naive
# can be utc (default), system, or any IANA timezone string (e.g. Europe/Amsterdam)
default_timezone = utc
# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor, KubernetesExecutor
executor = CeleryExecutor
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@postgres:5432/airflow
# The encoding for the databases
sql_engine_encoding = utf-8
# If SqlAlchemy should pool database connections.
sql_alchemy_pool_enabled = True
# The SqlAlchemy pool size is the maximum number of database connections
# in the pool. 0 indicates no limit.
sql_alchemy_pool_size = 5
# The maximum overflow size of the pool.
# When the number of checked-out connections reaches the size set in pool_size,
# additional connections will be returned up to this limit.
# When those additional connections are returned to the pool, they are disconnected and discarded.
# It follows then that the total number of simultaneous connections the pool will allow is pool_size + max_overflow,
# and the total number of "sleeping" connections the pool will allow is pool_size.
# max_overflow can be set to -1 to indicate no overflow limit;
# no limit will be placed on the total number of concurrent connections. Defaults to 10.
sql_alchemy_max_overflow = 10
# The SqlAlchemy pool recycle is the number of seconds a connection
# can be idle in the pool before it is invalidated. This config does
# not apply to sqlite. If the number of DB connections is ever exceeded,
# a lower config value will allow the system to recover faster.
sql_alchemy_pool_recycle = 1800
# How many seconds to retry re-establishing a DB connection after
# disconnects. Setting this to 0 disables retries.
sql_alchemy_reconnect_timeout = 300
# The schema to use for the metadata database
# SqlAlchemy supports databases with the concept of multiple schemas.
sql_alchemy_schema =
# The amount of parallelism as a setting to the executor. This defines
# the max number of task instances that should run simultaneously
# on this airflow installation
parallelism = 4
# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 4
# Are DAGs paused by default at creation
dags_are_paused_at_creation = True
# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 1
# Whether to load the examples that ship with Airflow. It's good to
# get started, but you probably want to set this to False in a production
# environment
load_examples = False
# Where your Airflow plugins are stored
plugins_folder = /usr/local/airflow/plugins
# Secret key to save connection passwords in the db
fernet_key = l-OhyQHu1gNyu7rFmr1amZZfsp2qhpnfp8GwuR-zyw8=
# Whether to disable pickling dags
donot_pickle = False
# How long before timing out a python file import while filling the DagBag
dagbag_import_timeout = 30
# The class to use for running task instances in a subprocess
task_runner = StandardTaskRunner
# If set, tasks without a `run_as_user` argument will be run with this user
# Can be used to de-elevate a sudo user running Airflow when executing tasks
default_impersonation =
# What security module to use (for example kerberos):
security =
# If set to False enables some unsecure features like Charts and Ad Hoc Queries.
# In 2.0 will default to True.
secure_mode = True
# Turn unit test mode on (overwrites many configuration options with test
# values at runtime)
unit_test_mode = False
# Name of handler to read task instance logs.
# Default to use task handler.
task_log_reader = task
# Whether to enable pickling for xcom (note that this is insecure and allows for
# RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False).
enable_xcom_pickling = True
# When a task is killed forcefully, this is the amount of time in seconds that
# it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED
killed_task_cleanup_time = 60
# Whether to override params with dag_run.conf. If you pass some key-value pairs through `airflow backfill -c` or
# `airflow trigger_dag -c`, the key-value pairs will override the existing ones in params.
dag_run_conf_overrides_params = False
# Worker initialisation check to validate Metadata Database connection
worker_precheck = False
# When discovering DAGs, ignore any files that don't contain the strings `DAG` and `airflow`.
dag_discovery_safe_mode = True
[logging]
logging_config_class = log_config.DEFAULT_LOGGING_CONFIG
[cli]
# In what way should the cli access the API. The LocalClient will use the
# database directly, while the json_client will use the api running on the
# webserver
api_client = airflow.api.client.local_client
# If you set web_server_url_prefix, do NOT forget to append it here, ex:
# endpoint_url = http://localhost:8080/myroot
# So api will look like: http://localhost:8080/myroot/api/experimental/...
endpoint_url = http://localhost:8080
[api]
# How to authenticate users of the API
auth_backend = airflow.api.auth.backend.default
[lineage]
# what lineage backend to use
backend =
[atlas]
sasl_enabled = False
host =
port = 21000
username =
password =
[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via `default_args`
default_owner = airflow
default_cpus = 1
default_ram = 512
default_disk = 512
default_gpus = 0
[hive]
# Default mapreduce queue for HiveOperator tasks
default_hive_mapred_queue =
[webserver]
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is used in automated emails that
# airflow sends to point links to the right web server
base_url = http://localhost:8080
# The ip specified when starting the web server
web_server_host = 0.0.0.0
# The port on which to run the web server
web_server_port = 8080
# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_cert =
web_server_ssl_key =
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 120
# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 120
# Number of workers to refresh at a time. When set to 0, worker refresh is
# disabled. When nonzero, airflow periodically refreshes webserver workers by
# bringing up new ones and killing old ones.
worker_refresh_batch_size = 1
# Number of seconds to wait before refreshing a batch of workers.
worker_refresh_interval = 30
# Secret key used to run your flask app
secret_key = temporary_key
# Number of workers to run the Gunicorn web server
workers = 4
# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent
worker_class = sync
# Log files for the gunicorn webserver. '-' means log to stderr.
access_logfile = -
error_logfile = -
# Expose the configuration file in the web server
# This is only applicable for the flask-admin based web UI (non FAB-based).
# In the FAB-based web UI with RBAC feature,
# access to configuration is controlled by role permissions.
expose_config = False
# Set to true to turn on authentication:
# https://airflow.apache.org/security.html#web-authentication
authenticate = False
# Filter the list of dags by owner name (requires authentication to be enabled)
filter_by_owner = False
# Filtering mode. Choices include user (default) and ldapgroup.
# Ldap group filtering requires using the ldap backend
#
# Note that the ldap server needs the "memberOf" overlay to be set up
# in order to user the ldapgroup mode.
owner_mode = user
# Default DAG view. Valid values are:
# tree, graph, duration, gantt, landing_times
dag_default_view = tree
# Default DAG orientation. Valid values are:
# LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top)
dag_orientation = LR
# Puts the webserver in demonstration mode; blurs the names of Operators for
# privacy.
demo_mode = False
# The amount of time (in secs) webserver will wait for initial handshake
# while fetching logs from other worker machine
log_fetch_timeout_sec = 5
# By default, the webserver shows paused DAGs. Flip this to hide paused
# DAGs by default
hide_paused_dags_by_default = False
# Consistent page size across all listing views in the UI
page_size = 100
# Use FAB-based webserver with RBAC feature
rbac = False
# Define the color of navigation bar
navbar_color = #007A87
# Default dagrun to show in UI
default_dag_run_display_number = 25
# Enable werkzeug `ProxyFix` middleware
enable_proxy_fix = False
# Set secure flag on session cookie
cookie_secure = False
# Set samesite policy on session cookie
cookie_samesite =
# Default setting for wrap toggle on DAG code and TI log views.
default_wrap = False
# Send anonymous user activity to your analytics tool
# analytics_tool = # choose from google_analytics, segment, or metarouter
# analytics_id = XXXXXXXXXXX
[email]
email_backend = airflow.utils.email.send_email_smtp
[smtp]
# If you want airflow to send emails on retries, failure, and you want to use
# the airflow.utils.email.send_email_smtp function, you have to configure an
# smtp server here
smtp_host = localhost
smtp_starttls = True
smtp_ssl = False
# Uncomment and set the user/pass settings if you want to use SMTP AUTH
# smtp_user = airflow
# smtp_password = airflow
smtp_port = 25
smtp_mail_from = airflow@example.com
[celery]
# This section only applies if you are using the CeleryExecutor in
# [core] section above
# The app name that will be used by celery
celery_app_name = airflow.executors.celery_executor
# The concurrency that will be used when starting workers with the
# "airflow worker" command. This defines the number of task instances that
# a worker will take, so size up your workers based on the resources on
# your worker box and the nature of your tasks
worker_concurrency = 4
# The maximum and minimum concurrency that will be used when starting workers with the
# "airflow worker" command (always keep minimum processes, but grow to maximum if necessary).
# Note the value should be "max_concurrency,min_concurrency"
# Pick these numbers based on resources on worker box and the nature of the task.
# If autoscale option is available, worker_concurrency will be ignored.
# http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale
# worker_autoscale = 16,12
# When you start an airflow worker, airflow starts a tiny web server
# subprocess to serve the workers local log files to the airflow main
# web server, who then builds pages and sends them to users. This defines
# the port on which the logs are served. It needs to be unused, and open
# visible from the main web server to connect into the workers.
worker_log_server_port = 8793
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more
# information.
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#broker-settings
broker_url = redis://:redispass@redis:6379/1
# The Celery result_backend. When a job finishes, it needs to update the
# metadata of the job. Therefore it will post a message on a message bus,
# or insert it into a database (depending of the backend)
# This status is used by the scheduler to update the state of the task
# The use of a database is highly recommended
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings
result_backend = db+postgresql://airflow:airflow@postgres:5432/airflow
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it `airflow flower`. This defines the IP that Celery Flower runs on
flower_host = 0.0.0.0
# The root URL for Flower
# Ex: flower_url_prefix = /flower
flower_url_prefix =
# This defines the port that Celery Flower runs on
flower_port = 5555
# Securing Flower with Basic Authentication
# Accepts user:password pairs separated by a comma
# Example: flower_basic_auth = user1:password1,user2:password2
flower_basic_auth =
# Default queue that tasks get assigned to and that worker listen on.
default_queue = default
# How many processes CeleryExecutor uses to sync task state.
# 0 means to use max(1, number of cores - 1) processes.
sync_parallelism = 0
# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
# In case of using SSL
ssl_active = False
ssl_key =
ssl_cert =
ssl_cacert =
# Celery Pool implementation.
# Choices include: prefork (default), eventlet, gevent or solo.
# See:
# https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency
# https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html
pool = prefork
[celery_broker_transport_options]
# This section is for specifying options which can be passed to the
# underlying celery broker transport. See:
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options
# The visibility timeout defines the number of seconds to wait for the worker
# to acknowledge the task before the message is redelivered to another worker.
# Make sure to increase the visibility timeout to match the time of the longest
# ETA you're planning to use.
#
# visibility_timeout is only supported for Redis and SQS celery brokers.
# See:
# http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options
#
#visibility_timeout = 21600
[dask]
# This section only applies if you are using the DaskExecutor in
# [core] section above
# The IP address and port of the Dask cluster's scheduler.
cluster_address = 127.0.0.1:8786
# TLS/ SSL settings to access a secured Dask scheduler.
tls_ca =
tls_cert =
tls_key =
[scheduler]
# Task instances listen for external kill signal (when you clear tasks
# from the CLI or the UI), this defines the frequency at which they should
# listen (in seconds).
job_heartbeat_sec = 5
# The scheduler constantly tries to trigger new tasks (look at the
# scheduler section in the docs for more information). This defines
# how often the scheduler should run (in seconds).
scheduler_heartbeat_sec = 5
# after how much time should the scheduler terminate in seconds
# -1 indicates to run continuously (see also num_runs)
run_duration = -1
# after how much time (seconds) a new DAGs should be picked up from the filesystem
min_file_process_interval = 0
# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes.
dag_dir_list_interval = 300
# How often should stats be printed to the logs
print_stats_interval = 30
# If the last scheduler heartbeat happened more than scheduler_health_check_threshold ago (in seconds),
# scheduler is considered unhealthy.
# This is used by the health check in the "/health" endpoint
scheduler_health_check_threshold = 30
child_process_log_directory = /usr/local/airflow/logs/scheduler
# Local task jobs periodically heartbeat to the DB. If the job has
# not heartbeat in this many seconds, the scheduler will mark the
# associated task instance as failed and will re-schedule the task.
scheduler_zombie_task_threshold = 300
# Turn off scheduler catchup by setting this to False.
# Default behavior is unchanged and
# Command Line Backfills still work, but the scheduler
# will not do scheduler catchup if this is False,
# however it can be set on a per DAG basis in the
# DAG definition (catchup)
catchup_by_default = True
# This changes the batch size of queries in the scheduling main loop.
# If this is too high, SQL query performance may be impacted by one
# or more of the following:
# - reversion to full table scan
# - complexity of query predicate
# - excessive locking
#
# Additionally, you may hit the maximum allowable query length for your db.
#
# Set this to 0 for no limit (not advised)
max_tis_per_query = 512
# Statsd (https://github.com/etsy/statsd) integration settings
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow
# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run.
max_threads = 2
authenticate = False
# Turn off scheduler use of cron intervals by setting this to False.
# DAGs submitted manually in the web UI or with trigger_dag will still run.
use_job_schedule = True
[ldap]
# set this to ldaps://<your.ldap.server>:<port>
uri =
user_filter = objectClass=*
user_name_attr = uid
group_member_attr = memberOf
superuser_filter =
data_profiler_filter =
bind_user = cn=Manager,dc=example,dc=com
bind_password = insecure
basedn = dc=example,dc=com
cacert = /etc/ca/ldap_ca.crt
search_scope = LEVEL
# This setting allows the use of LDAP servers that either return a
# broken schema, or do not return a schema.
ignore_malformed_schema = False
[mesos]
# Mesos master address which MesosExecutor will connect to.
master = localhost:5050
# The framework name which Airflow scheduler will register itself as on mesos
framework_name = Airflow
# Number of cpu cores required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_cpu = 1
# Memory in MB required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_memory = 256
# Enable framework checkpointing for mesos
# See http://mesos.apache.org/documentation/latest/slave-recovery/
checkpoint = False
# Failover timeout in milliseconds.
# When checkpointing is enabled and this option is set, Mesos waits
# until the configured timeout for
# the MesosExecutor framework to re-register after a failover. Mesos
# shuts down running tasks if the
# MesosExecutor framework fails to re-register within this timeframe.
# failover_timeout = 604800
# Enable framework authentication for mesos
# See http://mesos.apache.org/documentation/latest/configuration/
authenticate = False
# Mesos credentials, if authentication is enabled
# default_principal = admin
# default_secret = admin
# Optional Docker Image to run on slave before running the command
# This image should be accessible from mesos slave i.e mesos slave
# should be able to pull this docker image before executing the command.
# docker_image_slave = puckel/docker-airflow
[kerberos]
ccache = /tmp/airflow_krb5_ccache
# gets augmented with fqdn
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab
[github_enterprise]
api_rev = v3
[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = True
[elasticsearch]
# Elasticsearch host
host =
# Format of the log_id, which is used to query for a given tasks logs
log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number}
# Used to mark the end of a log stream for a task
end_of_log_mark = end_of_log
# Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id
# Code will construct log_id using the log_id template from the argument above.
# NOTE: The code will prefix the https:// automatically, don't include that here.
frontend =
# Write the task logs to the stdout of the worker, rather than the default files
write_stdout = False
# Instead of the default log formatter, write the log lines as JSON
json_format = False
# Log fields to also attach to the json output, if enabled
json_fields = asctime, filename, lineno, levelname, message
[elasticsearch_configs]
use_ssl = False
verify_certs = True
[kubernetes]
# The repository, tag and imagePullPolicy of the Kubernetes Image for the Worker to Run
worker_container_repository =
worker_container_tag =
worker_container_image_pull_policy = IfNotPresent
# If True (default), worker pods will be deleted upon termination
delete_worker_pods = True
# Number of Kubernetes Worker Pod creation calls per scheduler loop
worker_pods_creation_batch_size = 1
# The Kubernetes namespace where airflow workers should be created. Defaults to `default`
namespace = default
# The name of the Kubernetes ConfigMap Containing the Airflow Configuration (this file)
airflow_configmap =
# For docker image already contains DAGs, this is set to `True`, and the worker will search for dags in dags_folder,
# otherwise use git sync or dags volume claim to mount DAGs
dags_in_image = False
# For either git sync or volume mounted DAGs, the worker will look in this subpath for DAGs
dags_volume_subpath =
# For DAGs mounted via a volume claim (mutually exclusive with git-sync and host path)
dags_volume_claim =
# For volume mounted logs, the worker will look in this subpath for logs
logs_volume_subpath =
# A shared volume claim for the logs
logs_volume_claim =
# For DAGs mounted via a hostPath volume (mutually exclusive with volume claim and git-sync)
# Useful in local environment, discouraged in production
dags_volume_host =
# A hostPath volume for the logs
# Useful in local environment, discouraged in production
logs_volume_host =
# A list of configMapsRefs to envFrom. If more than one configMap is
# specified, provide a comma separated list: configmap_a,configmap_b
env_from_configmap_ref =
# A list of secretRefs to envFrom. If more than one secret is
# specified, provide a comma separated list: secret_a,secret_b
env_from_secret_ref =
# Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim)
git_repo =
git_branch =
git_subpath =
# Use git_user and git_password for user authentication or git_ssh_key_secret_name and git_ssh_key_secret_key
# for SSH authentication
git_user =
git_password =
git_sync_root = /git
git_sync_dest = repo
# Mount point of the volume if git-sync is being used.
# i.e. /usr/local/airflow/dags
git_dags_folder_mount_point =
# To get Git-sync SSH authentication set up follow this format
#
# airflow-secrets.yaml:
# ---
# apiVersion: v1
# kind: Secret
# metadata:
# name: airflow-secrets
# data:
# # key needs to be gitSshKey
# gitSshKey: <base64_encoded_data>
# ---
# airflow-configmap.yaml:
# apiVersion: v1
# kind: ConfigMap
# metadata:
# name: airflow-configmap
# data:
# known_hosts: |
# github.com ssh-rsa <...>
# airflow.cfg: |
# ...
#
# git_ssh_key_secret_name = airflow-secrets
# git_ssh_known_hosts_configmap_name = airflow-configmap
git_ssh_key_secret_name =
git_ssh_known_hosts_configmap_name =
# To give the git_sync init container credentials via a secret, create a secret
# with two fields: GIT_SYNC_USERNAME and GIT_SYNC_PASSWORD (example below) and
# add `git_sync_credentials_secret = <secret_name>` to your airflow config under the kubernetes section
#
# Secret Example:
# apiVersion: v1
# kind: Secret
# metadata:
# name: git-credentials
# data:
# GIT_SYNC_USERNAME: <base64_encoded_git_username>
# GIT_SYNC_PASSWORD: <base64_encoded_git_password>
git_sync_credentials_secret =
# For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync
git_sync_container_repository = k8s.gcr.io/git-sync
git_sync_container_tag = v3.1.1
git_sync_init_container_name = git-sync-clone
git_sync_run_as_user = 65533
# The name of the Kubernetes service account to be associated with airflow workers, if any.
# Service accounts are required for workers that require access to secrets or cluster resources.
# See the Kubernetes RBAC documentation for more:
# https://kubernetes.io/docs/admin/authorization/rbac/
worker_service_account_name =
# Any image pull secrets to be given to worker pods, If more than one secret is
# required, provide a comma separated list: secret_a,secret_b
image_pull_secrets =
# GCP Service Account Keys to be provided to tasks run on Kubernetes Executors
# Should be supplied in the format: key-name-1:key-path-1,key-name-2:key-path-2
gcp_service_account_keys =
# Use the service account kubernetes gives to pods to connect to kubernetes cluster.
# It's intended for clients that expect to be running inside a pod running on kubernetes.
# It will raise an exception if called from a process not running in a kubernetes environment.
in_cluster = True
# When running with in_cluster=False change the default cluster_context or config_file
# options to Kubernetes client. Leave blank these to use default behaviour like `kubectl` has.
# cluster_context =
# config_file =
# Affinity configuration as a single line formatted JSON object.
# See the affinity model for top-level key names (e.g. `nodeAffinity`, etc.):
# https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#affinity-v1-core
affinity =
# A list of toleration objects as a single line formatted JSON array
# See:
# https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#toleration-v1-core
tolerations =
# **kwargs parameters to pass while calling a kubernetes client core_v1_api methods from Kubernetes Executor
# provided as a single line formatted JSON dictionary string.
# List of supported params in **kwargs are similar for all core_v1_apis, hence a single config variable for all apis
# See:
# https://raw.githubusercontent.com/kubernetes-client/python/master/kubernetes/client/apis/core_v1_api.py
# Note that if no _request_timeout is specified, the kubernetes client will wait indefinitely for kubernetes
# api responses, which will cause the scheduler to hang. The timeout is specified as [connect timeout, read timeout]
kube_client_request_args = {"_request_timeout" : [60,60] }
# Worker pods security context options
# See:
# https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
# Specifies the uid to run the first process of the worker pods containers as
run_as_user =
# Specifies a gid to associate with all containers in the worker pods
# if using a git_ssh_key_secret_name use an fs_group
# that allows for the key to be read, e.g. 65533
fs_group =
[kubernetes_node_selectors]
# The Key-value pairs to be given to worker pods.
# The worker pods will be scheduled to the nodes of the specified key-value pairs.
# Should be supplied in the format: key = value
[kubernetes_annotations]
# The Key-value annotations pairs to be given to worker pods.
# Should be supplied in the format: key = value
[kubernetes_environment_variables]
# The scheduler sets the following environment variables into your workers. You may define as
# many environment variables as needed and the kubernetes launcher will set them in the launched workers.
# Environment variables in this section are defined as follows
# <environment_variable_key> = <environment_variable_value>
#
# For example if you wanted to set an environment variable with value `prod` and key
# `ENVIRONMENT` you would follow the following format:
# ENVIRONMENT = prod
#
# Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY>
# formatting as supported by airflow normally.
[kubernetes_secrets]
# The scheduler mounts the following secrets into your workers as they are launched by the
# scheduler. You may define as many secrets as needed and the kubernetes launcher will parse the
# defined secrets and mount them as secret environment variables in the launched workers.
# Secrets in this section are defined as follows
# <environment_variable_mount> = <kubernetes_secret_object>=<kubernetes_secret_key>
#
# For example if you wanted to mount a kubernetes secret key named `postgres_password` from the
# kubernetes secret object `airflow-secret` as the environment variable `POSTGRES_PASSWORD` into
# your workers you would follow the following format:
# POSTGRES_PASSWORD = airflow-secret=postgres_credentials
#
# Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY>
# formatting as supported by airflow normally.
[kubernetes_labels]
# The Key-value pairs to be given to worker pods.
# The worker pods will be given these static labels, as well as some additional dynamic labels
# to identify the task.
# Should be supplied in the format: key = value
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