airflow.cfg

[core]
# 1.10 additions
default_timezone = utc
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ execution_date.strftime("%%Y-%%m-%%dT%%H:%%M:%%S") }}/{{ try_number }}.log

# The home folder for airflow, default is ~/airflow
airflow_home = $AIRFLOW_HOME

# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
dags_folder = $AIRFLOW_HOME/dags

# The folder where airflow should store its log files. This location
base_log_folder = $AIRFLOW_HOME/logs

# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor
executor = CeleryExecutor

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
sql_alchemy_conn = $AIRFLOW_DATABASE_URL

# The SqlAlchemy pool size is the maximum number of database connections
# in the pool.
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

# 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 = 16

# 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

# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 5

# 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 = $AIRFLOW_HOME/plugins

# Secret key to save connection passwords in the db
# Setting this to $AIRFLOW_FERNET_KEY is broken in 1.9 for initdb.  Set $AIRFLOW__CORE__FERNET_KEY instead
# 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

[webserver]
rbac = $WEBSERVER_USE_RBAC
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is use in automated emails that
# airflow sends to point links to the right web server
base_url = $URL

# 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 = $PORT

# Secret key used to run your flask app
secret_key = $AIRFLOW_SECRET_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 = gevent

# Expose the configuration file in the web server
expose_config = true

# Set to true to turn on authentication : http://pythonhosted.org/airflow/installation.html#web-authentication
authenticate = $AIRFLOW_AUTHENTICATE
auth_backend = $AIRFLOW_AUTH_BACKEND

# Filter the list of dags by owner name (requires authentication to be enabled)
filter_by_owner = False

[email]
email_backend = $AIRFLOW_EMAIL_BACKEND

[smtp]
# If you want airflow to send emails on retries, failure, and you want to
# the airflow.utils.send_email function, you have to configure an smtp
# server here
smtp_starttls = True
smtp_ssl = False
smtp_host = $AIRFLOW_SMTP_HOST
smtp_port = 587
smtp_user = $AIRFLOW_SMTP_USER
smtp_password = $AIRFLOW_SMTP_PASSWORD
smtp_mail_from = $AIRFLOW_SMTP_FROM

[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 = 32

# 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.
broker_url = $AIRFLOW_BROKER_URL

# Another key Celery setting
result_backend = $AIRFLOW_RESULT_URL

# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it `airflow flower`. This defines the port that Celery Flower runs on
flower_port = $AIRFLOW_FLOWER_PORT

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

[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 = ${AIRFLOW_HOME}/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


# Statsd (https://github.com/etsy/statsd) integration settings
# statsd_on =  False
# statsd_host =  localhost
# statsd_port =  8125
# statsd_prefix = airflow

[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