Airflow速用

Airflow是Apache用python编写的，用到了 flask框架及相关插件,rabbitmq,celery等（windows不兼容）；、

 

主要实现的功能

• 编写 定时任务，及任务间的编排；
• 提供了web界面 可以手动触发任务，分析任务执行顺序，任务执行状态，任务代码，任务日志等等；
• 实现celery的分布式任务调度系统;
• 简单方便的实现了 任务在各种状态下触发 发送邮件的功能；https://airflow.apache.org/concepts.html#email-configuration
• 对组合任务 可以根据 不同参数进入不同分支进行处理 http://airflow.apache.org/concepts.html#branching
• 执行 bash脚本命令；
• 对组合任务 设置触发条件（如:全部失败/成功时执行某任务 等等）http://airflow.apache.org/concepts.html#trigger-rules
• 简单实现随机 负载均衡和容错能力 http://airflow.apache.org/concepts.html#connections
• 对组合任务 间进行数据传递 http://airflow.apache.org/concepts.html#xcoms
• 对分布式任务指定 queue, worker可以指定消费的queue(celery的使用) http://airflow.apache.org/concepts.html#queues
• 存储日志到远程 http://airflow.apache.org/howto/write-logs.html
• 调用 远程 谷歌云，亚马逊云 相关服务（如语音识别等等）https://airflow.apache.org/integration.html#integration
• 调用 钉钉 相关服务

实现功能总结

不仅celery有的功能我都有， 我还能通过页面手动触发/暂停任务，管理任务特方便；我他妈还能 调用谷歌云等服务，日志也能方便打印到云服务上。。。。。。；我就是牛！

 

核心思想

• DAG:英文为：Directed Acyclic Graph；指 (有向无环图)有向非循环图，是想运行的一系列任务的集合，不关心任务是做什么的，只关心 任务间的组成方式，确保在正确的时间，正确的顺序触发各个任务，准确的处理意外情况；http://airflow.apache.org/concepts.html#dags
• DAGs:多个任务集（多个DAG）
• Operator: 指 某些类型任务的模板 类；如 PythonOperator(执行python相关操作)，EmailOperator(执行发送邮件相关操作)，SimpleHttpOperator(执行发送http请求相关操作) 等几十种（源码可见）http://airflow.apache.org/howto/operator/index.html#
• Task:当通过 Operator定义了执行任务内容后，在实例化后，便是 Task,为DAG中任务集合的具体任务
• Executor：数据库记录任务状态(排队queued，预执行scheduled，运行中running，成功success，失败failed),调度器（Scheduler ）从数据库取数据并决定哪些需要完成，然后 Executor 和调度器一起合作，给任务需要的资源让其完成。Executor间(如 LocalExecutor,CeleryExecutor)不同点在于他们拥有不同的资源以及如何利用资源分配工作，如LocalExecutor只在本地并行执行任务，CeleryExecutor分布式多机器执行任务。 https://www.astronomer.io/guides/airflow-executors-explained/
• Hook：是airflow与外部平台/数据库交互的方式，如 http/ssh/sftp等等，是Operator的基础部分(如SimpleHttpOperator 需要依赖HttpHook)

 

任务间定义排序的方法

官方推荐使用 移位操作符 方法，因为较为直观，容易理解

如：  op1 >> op2 >> op3   表示任务执行顺序为  从左到右依次执行

官方文档介绍：http://airflow.apache.org/concepts.html#bitshift-composition

 

提高airflow相关执行速度方法

通过修改airflow.cfg相关配置

官方文档如下：http://airflow.apache.org/faq.html

 

安装及启动相关服务

• 创建python虚拟环境 venv
• 添加airflow.cfg（此配置注解在下面）的配置文件夹路径：先 vi venv/bin/active; 里面输入 export AIRFLOW_HOME="/mnt/e/project/airflow_config/local"

• 命令行：pip install apache-airflow

• 根据airflow.cfg的数据库配置，在连接的数据库服务创建一个 名为 airflow_db的数据库

• 命令行初始化数据库：airflow initdb

• 命令行启动web服务: airflow webserver -p 8080

• 命令行启动任务调度服务：airflow scheduler

• 命令行启动worker：airflow worker -q queue_name

 

使用 http_operator发送http请求并在失败时,发送邮件

1.设置邮件html模板（如下为自定义模板）

<h2 style="color: red">Xxx service task exception,please fix them!!!</h2>
Try {{try_number}} out of {{max_tries + 1}}<br><br>
<b>dag id: </b>{{ti.dag_id}}<br><br>
<b>task id: </b>{{ti.task_id}}<br><br>
<b>task state: </b>{{ti.state}}<br><br>

<b>Exception:</b>
<p style="color: #0d7bdc">{{exception_html}}</p>
<b>Log Url: </b>
<a href="{{ti.log_url}}" style="color: red">Link</a><br><br>
<b>Host: </b>
{{ti.hostname}}<br><br>
<b>Log file path: </b> {{ti.log_filepath}}<br><br>
<b>Mark success: </b> <a href="{{ti.mark_success_url}}">Link</a><br>

模板效果图：

 2. airflow.cfg文件中配置 发送邮件服务

 

 3.编写代码：

# -*- coding: utf-8 -*-
"""
(C) xxx <xxx@xxx.com>
All rights reserved
create time '2019/10/21 09:27'
"""
import os
from datetime import datetime

import pytz
from airflow import DAG
from airflow.models import Variable
from airflow.operators.http_operator import SimpleHttpOperator

# 设置第一次触发任务时间 及 设置任务执行的时区
tz = pytz.timezone("Asia/Shanghai")
dt = datetime(2019, 10, 11, 0, 0, tzinfo=tz)
utc_dt = dt.astimezone(pytz.utc).replace(tzinfo=None)

# 从环境变量找到 当前环境
env = os.environ.get("PROJECT_ENV", "LOCAL")
# 添加 需要的相关环境变量，可在 web网页中设置；注意 变量名 以AIRFLOW_CONN_开头，并且大写
os.environ["AIRFLOW_CONN_OLY_HOST"] = Variable.get("OLY_HOST_%s" % env)

# dag默认参数
args = {
    "owner": "Rgc",  # 任务拥有人
    "depends_on_past": False,  # 是否依赖过去执行此任务的结果，如果为True,则过去任务必须成功，才能执行此次任务
    "start_date": utc_dt,  # 任务开始执行时间
    "email": ["rgc@bvrft.com"],  # 邮件地址，可以填写多个
    "email_on_failure": True,  # 触发邮件发送的 时机，此处为失败时触发
}

# 定义一个DAG
# 参数catchup指 是否填充执行 start_date到现在 未执行的缺少任务；如：start_date定义为2019-10-10,现在是2019-10-29,任务是每天定时执行一次，
# 如果此参数设置为True，则 会生成 10号到29号之间的19此任务；如果设置为False,则不会补充执行任务；
# schedule_interval：定时执行方式，推荐使用如下字符串方式, 方便写出定时规则的网址：https://crontab.guru/
dag = DAG("HttpSendDag", catchup=False, default_args=args, schedule_interval="0 19 * * *")
# 设置 dag文档注释，可在web界面任务详情中看到
dag.doc_md = __doc__

# 定义此 http operator相关详情，详细使用方法 可访问此类定义__init__()方法
task = SimpleHttpOperator(
    task_id="task_http_send",  # 任务id
    http_conn_id="oly_host",  # http请求地址，值为上面23行定义
    method="POST",  # http请求方法
    endpoint="user/manage",  # http请求路径
    dag=dag  # 任务所属dag
)
# 定义任务 文档注释，可在web界面任务详情中看到
task.doc_md = f"""\
#Usage
此任务主要向Project服务({Variable.get("OLY_HOST_%s" % env)})发送http请求，每天晚上7点定时运行!
"""

 

任务间数据交流方法

    使用Xcoms(cross-communication),类似于redis存储结构,任务推送数据或者从中下拉数据，数据在任务间共享

    推送数据主要有2中方式：1：使用xcom_push()方法  2：直接在PythonOperator中调用的函数 return即可

    下拉数据 主要使用 xcom_pull()方法

 官方代码示例及注释：

from __future__ import print_function

import airflow
from airflow import DAG
from airflow.operators.python_operator import PythonOperator

args = {
    'owner': 'airflow',
    'start_date': airflow.utils.dates.days_ago(2),
    'provide_context': True,
}

dag = DAG('example_xcom', schedule_interval="@once", default_args=args)

value_1 = [1, 2, 3]
value_2 = {'a': 'b'}


# 2种推送数据的方式，分别为xcom_push，和直接return

def push(**kwargs):
    """Pushes an XCom without a specific target"""
    kwargs['ti'].xcom_push(key='value from pusher 1', value=value_1)


def push_by_returning(**kwargs):
    """Pushes an XCom without a specific target, just by returning it"""
    return value_2


def puller(**kwargs):
    """
    下拉数据的方法
    :param kwargs:
    :return:
    """
    ti = kwargs['ti']

    # get value_1
    v1 = ti.xcom_pull(key=None, task_ids='push')
    assert v1 == value_1

    # get value_2
    v2 = ti.xcom_pull(task_ids='push_by_returning')
    assert v2 == value_2

    # get both value_1 and value_2
    v1, v2 = ti.xcom_pull(key=None, task_ids=['push', 'push_by_returning'])
    assert (v1, v2) == (value_1, value_2)


push1 = PythonOperator(
    task_id='push',
    dag=dag,
    python_callable=push,
)

push2 = PythonOperator(
    task_id='push_by_returning',
    dag=dag,
    python_callable=push_by_returning,
)

pull = PythonOperator(
    task_id='puller',
    dag=dag,
    python_callable=puller,
)

# 任务执行顺序为
# push1 >> pull
# push2 >> pull

pull << [push1, push2]

 

开启 web网页登录需要用户名密码功能

1.airflow.cfg文件修改

# 设置为True
rbac = True

2.重启airflow相关服务

3.通过 命令行 添加 用户

airflow create_user -r Admin -e service@xxx.com -f A -l dmin -u admin -p passwd

4.访问页面，输入用户名，密码即可

 

忽略某些DAG文件,不调用

在dag任务文件夹下，添加一个 .airflowignore文件(像 .gitignore），里面写 文件名即可（支持正则）

 

 启动及关闭airflow内置 dag示例方法（能够快速学习Airflow）

 开启:修改airflow.cfg配置文件  load_examples = True  并重启即可

 关闭:修改airflow.cfg配置文件  load_examples = True，并清空数据库，并重启即可

 效果图：

 

 

 

airflow配置文件 相关中文注解:

[core]
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
# 绝对路径下 一系列dags存放位置，airflow只会从此路径 文件夹下找dag任务
dags_folder = /mnt/e/airflow_project/dags

# The folder where airflow should store its log files
# This path must be absolute
# 绝对路径下的日志文件夹位置
base_log_folder = /mnt/e/airflow_project/log/

# 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
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# Log filename format
# 实际处理任务日志 相关
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log
# dag处理日志 绝对路径，精确到日志文件
dag_processor_manager_log_location = /mnt/e/airflow_project/log/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)
# 默认时区，改为上海，然而 没卵用
default_timezone = Asia/Shanghai

# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor, KubernetesExecutor
# 指定executor(任务分配执行方式)
executor = CeleryExecutor

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
# 存储airflow相关数据的 数据库路径
sql_alchemy_conn = mysql+pymysql://root:passwd@127.0.0.1:3306/airflow_db

# 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 = 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

# 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 = False

# Where your Airflow plugins are stored
# 自定义 界面及api所在 绝对路径文件夹 官网用法： http://airflow.apache.org/plugins.html
plugins_folder = /mnt/e/airflow_project/plugins

# Secret key to save connection passwords in the db
# 对使用到的 连接密码 进行加密，此为秘钥 官网用法： https://airflow.apache.org/howto/secure-connections.html
fernet_key = Et8ULvn0biL8X0xXl66wHawhdetf7utIDYDgNzZh4nCnE=

# 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 = 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.
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


[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:18080

[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]
# web端访问配置
# 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:18080

# 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 = 18080

# 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
# 是否登录时 需要用户名 密码 验证功能；https://airflow.apache.org/security.html#rbac-ui-security
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
# 邮件html模板绝对路径位置
html_content_template = /mnt/e/airflow_project/airflow_config/local/email_template

[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
# 邮件服务 相关配置，根据实际情况配置
smtp_host = smtp.exmail.qq.com
smtp_starttls = False
smtp_ssl = True
# Uncomment and set the user/pass settings if you want to use SMTP AUTH
smtp_user = xxx@xxx.com
smtp_password = xxx
smtp_port = 465
smtp_mail_from = xxx@xxx.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

# 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
# celery服务 broker连接，此处使用 rabbitmq
broker_url = pyamqp://role:passwd@127.0.0.1:5672/

# 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
# celery服务 结果存储连接
result_backend = redis://localhost/15

# 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

# 定时任务 日志位置
child_process_log_directory = /mnt/e/airflow_project/log/airflow/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 = True
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. /Users/wudong/work/Python/flow/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

 

错误记录：

* 设置supervisor启动airflow服务时，报错如下

Error: No module named airflow.www.gunicorn_config

* 处理方式

在supervisor的配置文件的 environment常量中添加 PATH="/home/work/www/jerry/venv/bin:%(ENV_PATH)s"

* web界面报错

KeyError: 'Variable xxx does not exist'

* 处理方式

在airflow网页的Admin=>Variables页面添加对应的 变量

 

相关网址：http://airflow.apache.org/index.html