sebradloff/Dockerfile

## airflow.cfg
[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /usr/local/airflow

# 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 or Google Cloud Storage. 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 = True
remote_base_log_folder = s3://top-level-bucket-name/airflow/logs
remote_log_conn_id = s3://aws_access_key_id:aws_secret_access_key@top-level-bucket-name/airflow/logs
encrypt_s3_logs = False

# Logging level
logging_level = INFO

# 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 = airflow.config_templates.airflow_local_settings.DEFAULT_LOGGING_CONFIG
logging_config_class = log_config.LOGGING_CONFIG

# Log format
log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# Hostname override by providing a path to a callable.
# 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
executor = SequentialExecutor

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
sql_alchemy_conn = sqlite:////usr/local/airflow/airflow.db

# 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 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.
sql_alchemy_pool_recycle = 3600

# How many seconds to retry re-establishing a DB connection after
# disconnects. Setting this to 0 disables retries.
sql_alchemy_reconnect_timeout = 300

# 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 = 32

# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 16

# Are DAGs paused by default at creation
dags_are_paused_at_creation = True

# When not using pools, tasks are run in the "default pool",
# whose size is guided by this config element
non_pooled_task_slot_count = 128

# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 16

# 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 = True

# Where your Airflow plugins are stored
plugins_folder = /usr/local/airflow/plugins

# Secret key to save connection passwords in the db
fernet_key = {FERNET_KEY}

# 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 = BashTaskRunner

# 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 = False

# 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.
# file.task for local
# s3.task for deployed environment
task_log_reader = s3.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

[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

[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 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
expose_config = True

# Set to true to turn on authentication:
# https://airflow.incubator.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

[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 = 16

# 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 = sqla+mysql://airflow:airflow@localhost:3306/airflow

# 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+mysql://airflow:airflow@localhost:3306/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

# Default queue that tasks get assigned to and that worker listen on.
default_queue = default

# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG

[celery_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. Especially important in case of using Redis or SQS
visibility_timeout = 21600

# In case of using SSL
ssl_active = False
ssl_key =
ssl_cert =
ssl_cacert =

[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


[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 a new DAGs should be picked up from the filesystem
min_file_process_interval = 0

dag_dir_list_interval = 300

# How often should stats be printed to the logs
print_stats_interval = 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.
# This depends on query length limits and how long you are willing to hold locks.
# 0 for no limit
max_tis_per_query = 0

# 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

[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

[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


[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

## Dockerfile
# Showing the necessary part of adding the logging config to the python path

COPY config/log_config.py config/log_config.py
COPY config/__init__.py config/__init__.py

RUN chown -R airflow: ${AIRFLOW_HOME}
ENV PYTHONPATH ${PYTHONPATH}:/usr/lib/python2.7/site-packages/:${AIRFLOW_HOME}/config/

## log_config.py
import os
import airflow.configuration as conf
from airflow.config_templates.airflow_local_settings import (
    DEFAULT_LOGGING_CONFIG,
    LOG_LEVEL,
    FILENAME_TEMPLATE,
    BASE_LOG_FOLDER,
    PROCESSOR_LOG_FOLDER,
    PROCESSOR_FILENAME_TEMPLATE
)
from copy import deepcopy

LOGGING_CONFIG = deepcopy(DEFAULT_LOGGING_CONFIG)
S3_LOG_FOLDER = conf.get('core', 'remote_base_log_folder')

if S3_LOG_FOLDER:
    LOGGING_CONFIG['handlers'].update({
        '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,
        },
        's3.processor': {
            'class': 'airflow.utils.log.s3_task_handler.S3TaskHandler',
            'formatter': 'airflow.processor',
            'base_log_folder': os.path.expanduser(PROCESSOR_LOG_FOLDER),
            's3_log_folder': S3_LOG_FOLDER,
            'filename_template': PROCESSOR_FILENAME_TEMPLATE,
        },
    })
    LOGGING_CONFIG['loggers'].update({
        'airflow.task': {
            'handlers': ['s3.task'],
            'level': LOG_LEVEL,
            'propagate': False,
        },
        'airflow.task_runner': {
            'handlers': ['s3.task'],
            'level': LOG_LEVEL,
            'propagate': True,
        },
    })
	[core]
	# The home folder for airflow, default is ~/airflow
	airflow_home = /usr/local/airflow

	# 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 or Google Cloud Storage. 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 = True
	remote_base_log_folder = s3://top-level-bucket-name/airflow/logs
	remote_log_conn_id = s3://aws_access_key_id:aws_secret_access_key@top-level-bucket-name/airflow/logs
	encrypt_s3_logs = False

	# Logging level
	logging_level = INFO

	# 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 = airflow.config_templates.airflow_local_settings.DEFAULT_LOGGING_CONFIG
	logging_config_class = log_config.LOGGING_CONFIG

	# Log format
	log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
	simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

	# Hostname override by providing a path to a callable.
	# 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
	executor = SequentialExecutor

	# The SqlAlchemy connection string to the metadata database.
	# SqlAlchemy supports many different database engine, more information
	# their website
	sql_alchemy_conn = sqlite:////usr/local/airflow/airflow.db

	# 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 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.
	sql_alchemy_pool_recycle = 3600

	# How many seconds to retry re-establishing a DB connection after
	# disconnects. Setting this to 0 disables retries.
	sql_alchemy_reconnect_timeout = 300

	# 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 = 32

	# The number of task instances allowed to run concurrently by the scheduler
	dag_concurrency = 16

	# Are DAGs paused by default at creation
	dags_are_paused_at_creation = True

	# When not using pools, tasks are run in the "default pool",
	# whose size is guided by this config element
	non_pooled_task_slot_count = 128

	# The maximum number of active DAG runs per DAG
	max_active_runs_per_dag = 16

	# 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 = True

	# Where your Airflow plugins are stored
	plugins_folder = /usr/local/airflow/plugins

	# Secret key to save connection passwords in the db
	fernet_key = {FERNET_KEY}

	# 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 = BashTaskRunner

	# 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 = False

	# 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.
	# file.task for local
	# s3.task for deployed environment
	task_log_reader = s3.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

	[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

	[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 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
	expose_config = True

	# Set to true to turn on authentication:
	# https://airflow.incubator.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

	[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 = 16

	# 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 = sqla+mysql://airflow:airflow@localhost:3306/airflow

	# 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+mysql://airflow:airflow@localhost:3306/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

	# Default queue that tasks get assigned to and that worker listen on.
	default_queue = default

	# Import path for celery configuration options
	celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG

	[celery_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. Especially important in case of using Redis or SQS
	visibility_timeout = 21600

	# In case of using SSL
	ssl_active = False
	ssl_key =
	ssl_cert =
	ssl_cacert =

	[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


	[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 a new DAGs should be picked up from the filesystem
	min_file_process_interval = 0

	dag_dir_list_interval = 300

	# How often should stats be printed to the logs
	print_stats_interval = 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.
	# This depends on query length limits and how long you are willing to hold locks.
	# 0 for no limit
	max_tis_per_query = 0

	# 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

	[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

	[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


	[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
	# Showing the necessary part of adding the logging config to the python path

	COPY config/log_config.py config/log_config.py
	COPY config/__init__.py config/__init__.py

	RUN chown -R airflow: ${AIRFLOW_HOME}
	ENV PYTHONPATH ${PYTHONPATH}:/usr/lib/python2.7/site-packages/:${AIRFLOW_HOME}/config/
	import os
	import airflow.configuration as conf
	from airflow.config_templates.airflow_local_settings import (
	DEFAULT_LOGGING_CONFIG,
	LOG_LEVEL,
	FILENAME_TEMPLATE,
	BASE_LOG_FOLDER,
	PROCESSOR_LOG_FOLDER,
	PROCESSOR_FILENAME_TEMPLATE
	)
	from copy import deepcopy

	LOGGING_CONFIG = deepcopy(DEFAULT_LOGGING_CONFIG)
	S3_LOG_FOLDER = conf.get('core', 'remote_base_log_folder')

	if S3_LOG_FOLDER:
	LOGGING_CONFIG['handlers'].update({
	'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,
	},
	's3.processor': {
	'class': 'airflow.utils.log.s3_task_handler.S3TaskHandler',
	'formatter': 'airflow.processor',
	'base_log_folder': os.path.expanduser(PROCESSOR_LOG_FOLDER),
	's3_log_folder': S3_LOG_FOLDER,
	'filename_template': PROCESSOR_FILENAME_TEMPLATE,
	},
	})
	LOGGING_CONFIG['loggers'].update({
	'airflow.task': {
	'handlers': ['s3.task'],
	'level': LOG_LEVEL,
	'propagate': False,
	},
	'airflow.task_runner': {
	'handlers': ['s3.task'],
	'level': LOG_LEVEL,
	'propagate': True,
	},
	})