liwh · July 4, 2017 09:25
diff --git a/airflow.cfg b/airflow.cfg
 [core]
 # The home folder for airflow, default is ~/airflow
 airflow_home = /Users/p1nox/airflow

 # The folder where your airflow pipelines live, most likely a
 # subfolder in a code repository
 dags_folder = /Users/p1nox/airflow/dags

 # The folder where airflow should store its log files. This location
 base_log_folder = /Users/p1nox/airflow/logs

 # Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
 # must supply a remote location URL (starting with either 's3://...' or
 # 'gs://...') and an Airflow connection id that provides access to the storage
 # location.
 remote_base_log_folder =
 remote_log_conn_id =
 # Use server-side encryption for logs stored in S3
 encrypt_s3_logs = False
 # deprecated option for remote log storage, use remote_base_log_folder instead!
 # s3_log_folder =

 # 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 = mysql://root:000000@localhost/airflow

 # 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 = 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 = /Users/p1nox/airflow/plugins

 # Secret key to save connection passwords in the db
 fernet_key = cryptography_not_found_storing_passwords_in_plain_text

 # 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


 [operators]
 # The default owner assigned to each new operator, unless
 # provided explicitly or passed via `default_args`
 default_owner = Airflow


 [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

 # The time the gunicorn webserver waits before timing out on a worker
 web_server_worker_timeout = 120

 # 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

 # 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 = False

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

 [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
 smtp_user = airflow
 smtp_port = 25
 smtp_password = airflow
 smtp_mail_from = [email protected]

 [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
 celeryd_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.
 broker_url = amqp://guest:[email protected]/

 # Another key Celery setting
 celery_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 port that Celery Flower runs on
 flower_port = 5555

 # 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

 # 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. However airflow will never
 # use more threads than the amount of cpu cores available.
 max_threads = 2

 [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
diff --git a/airflow.md b/airflow.md
	[core]
	# The home folder for airflow, default is ~/airflow
	airflow_home = /Users/p1nox/airflow

	# The folder where your airflow pipelines live, most likely a
	# subfolder in a code repository
	dags_folder = /Users/p1nox/airflow/dags

	# The folder where airflow should store its log files. This location
	base_log_folder = /Users/p1nox/airflow/logs

	# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
	# must supply a remote location URL (starting with either 's3://...' or
	# 'gs://...') and an Airflow connection id that provides access to the storage
	# location.
	remote_base_log_folder =
	remote_log_conn_id =
	# Use server-side encryption for logs stored in S3
	encrypt_s3_logs = False
	# deprecated option for remote log storage, use remote_base_log_folder instead!
	# s3_log_folder =

	# 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 = mysql://root:000000@localhost/airflow

	# 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 = 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 = /Users/p1nox/airflow/plugins

	# Secret key to save connection passwords in the db
	fernet_key = cryptography_not_found_storing_passwords_in_plain_text

	# 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


	[operators]
	# The default owner assigned to each new operator, unless
	# provided explicitly or passed via `default_args`
	default_owner = Airflow


	[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

	# The time the gunicorn webserver waits before timing out on a worker
	web_server_worker_timeout = 120

	# 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

	# 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 = False

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

	[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
	smtp_user = airflow
	smtp_port = 25
	smtp_password = airflow
	smtp_mail_from = [email protected]

	[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
	celeryd_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.
	broker_url = amqp://guest:[email protected]/

	# Another key Celery setting
	celery_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 port that Celery Flower runs on
	flower_port = 5555

	# 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

	# 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. However airflow will never
	# use more threads than the amount of cpu cores available.
	max_threads = 2

	[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