Airflow安装部署(超详细)

Airflow配置安装

1.安装前准备工作

安装版本说明

安装工具 版本 用途
Python 3.6.5 安装airflow及其依赖包、开发airflow的dag使用
MySQL 5.7 作为airflow的元数据库
Airflow 1.10.10 任务调度平台

2.安装Python3

#python依赖
yum -y install zlib zlib-devel
yum -y install bzip2 bzip2-devel
yum -y install ncurses ncurses-devel
yum -y install readline readline-devel
yum -y install openssl openssl-devel
yum -y install openssl-static
yum -y install xz lzma xz-devel
yum -y install sqlite sqlite-devel
yum -y install gdbm gdbm-devel
yum -y install tk tk-devel
yum install gcc

#安装wget命令
yum -y install wget
#使用wget下载Python源码压缩包到/root目录下
wget -P /root https://www.python.org/ftp/python/3.6.5/Python-3.6.5.tgz
#在当前目录解压Python源码压缩包
tar -zxvf Python-3.6.5.tgz
#进入解压后的文件目录下
cd /root/Python-3.6.5
#检测及校验平台
./configure --with-ssl --prefix=/service/python3
#编译Python源代码
make
#安装Python
make install
#备份原来的Python软连接
mv /usr/bin/python /usr/bin/python2.backup
#制作新的指向Python3的软连接
ln -s /service/python3/bin/python3 /usr/bin/python
#建立pip的软连接
ln -s /service/python3//bin/pip3 /usr/bin/pip
#查看Python版本
python -V
#检测pip是否可用
pip
#升级pip
pip install --upgrade pip
#获取yum命令所在位置
whereis yum
#yum: /usr/bin/yum /etc/yum /etc/yum.conf /usr/share/man/man8/yum.8
#编辑yum文件
vi /usr/bin/yum /etc/yum /etc/yum.conf /usr/share/man/man8/yum.8
#进入编辑模式
i
#修改第一行内容(看系统版本,centos7对应2.7,centos6对应2.6)
#修改前:
#!/usr/bin/python
#修改后:
#!/usr/bin/python2.7
#退出编辑模式
esc
#保存文件
:wq
#按上述方式编辑以下文件,修改第一行内容
/usr/libexec/urlgrabber-ext-down

3.安装MySQL

#卸载mariadb
rpm -qa | grep mariadb
rpm -e --nodeps mariadb-libs-5.5.52-1.el7.x86_64
#sudo rpm -e --nodeps mariadb-libs-5.5.52-1.el7.x86_64
rpm -qa | grep mariadb
#下载mysql的repo源
wget -P /root http://repo.mysql.com/mysql-community-release-el7-5.noarch.rpm
#通过rpm安装
rpm -ivh mysql-community-release-el7-5.noarch.rpm
#安装mysql
yum install mysql-server
#授权
chown -R mysql:mysql /var/lib/mysql
#开启Mysql服务
service mysqld start
#用root用户连接登录mysql:
mysql -uroot
#重置mysql密码
use mysql;
update user set password=password('root') where user='root';
flush privileges;
#为Airflow建库、建用户
#建库:
create database airflow;
#建用户:
create user 'airflow'@'%' identified by 'airflow';
create user 'airflow'@'localhost' identified by 'airflow';
#为用户授权:
grant all on airflow.* to 'airflow'@'%';
grant all on airflow.* to 'root'@'%';
flush privileges;
exit;

4.安装Airflow

#设置临时环境变量
export SLUGIFY_USES_TEXT_UNIDECODE=yes
#添加编辑环境变量
vi /etc/profile
#在最后添加以下内容:
----》
export PS1="[\[email protected]\h \w]\$ "
#Python环境变量
export PYTHON_HOME=/service/python3
export PATH=$PATH:$PYTHON_HOME/bin
#Airflow环境变量
export AIRFLOW_HOME=/root/airflow
export SITE_AIRFLOW_HOME=/service/python3/lib/python3.6/site-packages/airflow
export PATH=$PATH:$SITE_AIRFLOW_HOME/bin
----》
#生效环境变量
source /etc/profile
#安装apache-airflow并且指定1.10.0版本
pip install apache-airflow===1.10.10

airflow会被安装到python3下的site-packages目录下,完整目录为:

${PYTHON_HOME}/lib/python3.6/site-packages/airflow
#绝对路径/service/python3/lib/python3.6/site-packages/airflow

执行airflow命令做初始化操作

airflow
####
[2019-07-17 04:40:01,565] {__init__.py:51} INFO - Using executor SequentialExecutor
usage: airflow [-h]
               {backfill,list_tasks,clear,pause,unpause,trigger_dag,delete_dag,pool,variables,kerberos,render,run,initdb,list_dags,dag_state,task_failed_deps,task_state,serve_logs,test,webserver,resetdb,upgradedb,scheduler,worker,flower,version,connections,create_user}
               ...
airflow: error: the following arguments are required: subcommand
####
#到此,airflow会在刚刚的AIRFLOW_HOME目录下生成一些文件。当然,执行该命令时可能会报一些错误,可以不用理会!
#报错如下:
[2019-07-17 04:40:01,565] {__init__.py:51} INFO - Using executor SequentialExecutor
usage: airflow [-h]
               {backfill,list_tasks,clear,pause,unpause,trigger_dag,delete_dag,pool,variables,kerberos,render,run,initdb,list_dags,dag_state,task_failed_deps,task_state,serve_logs,test,webserver,resetdb,upgradedb,scheduler,worker,flower,version,connections,create_user}
               ...
airflow: error: the following arguments are required: subcommand
#生成的文件logs如下所示:
[[email protected] ~]$ cd airflow/
[[email protected] ~/airflow]$ ll
total 28
-rw-r--r--. 1 root root 20572 Jul 17 04:40 airflow.cfg
drwxr-xr-x. 3 root root    23 Jul 17 04:40 logs
-rw-r--r--. 1 root root  2299 Jul 17 04:40 unittests.cfg
#为airflow安装mysql模块
pip install 'apache-airflow[mysql]'
#出现报错:
    ERROR: Complete output from command python setup.py egg_info:
    ERROR: /bin/sh: mysql_config: command not found
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-install-dq81ujxt/mysqlclient/setup.py", line 16, in <module>
        metadata, options = get_config()
      File "/tmp/pip-install-dq81ujxt/mysqlclient/setup_posix.py", line 51, in get_config
        libs = mysql_config("libs")
      File "/tmp/pip-install-dq81ujxt/mysqlclient/setup_posix.py", line 29, in mysql_config
        raise EnvironmentError("%s not found" % (_mysql_config_path,))
    OSError: mysql_config not found
    ----------------------------------------
ERROR: Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-install-dq81ujxt/mysqlclient/
#解决方案,查看是否有mysql_config文件
[[email protected] ~]$ find / -name mysql_config
#如果没有
[[email protected] ~]$ yum -y install mysql-devel
#安装完成后再次验证是否有mysql_config
find / -name mysql_config
#采用mysql作为airflow的元数据库
pip install mysqlclient
#安装MySQLdb
pip install MySQLdb
#报错不支持
Collecting MySQLdb
  ERROR: Could not find a version that satisfies the requirement MySQLdb (from versions: none)
ERROR: No matching distribution found for MySQLdb
#所以使用python-mysql
pip install pymysql
pip install cryptography
#避免之后产生错误
#airflow.exceptions.AirflowException: Could not create Fernet object: Incorrect padding
#需要修改airflow.cfg (默认位于~/airflow/)里的fernet_key
#修改方法
python -c "from cryptography.fernet import Fernet; 
print(Fernet.generate_key().decode())"
#这个命令生成一个key,复制这个key然后替换airflow.cfg文件里的fernet_key的值,
#可能出现报错
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'cryptography'
#处理方式:
pip install cryptography
#文件中进行fernet_key值修改
cd  ${AIRFLOW_HOME}
vi airflow.cfg
#查找fernet_net
/fernet_net
#编辑替换fernet值

#修改airflow.cfg文件中的sql_alchemy_conn配置
sql_alchemy_conn = mysql+mysqldb://airflow:[email protected]:3306/airflow
#保存文件
#为避免初始化数据库时有如下报错
#Global variable explicit_defaults_for_timestamp needs to be on (1) for mysql
#修改MySQL配置文件my.cnf
#查找my.cnf位置
mysql --help | grep my.cnf
#修改my.cnf
vi /etc/my.cnf
#在[mysqld]下面(一定不要写错地方)添加如下配置:
explicit_defaults_for_timestamp=true
#重启mysql服务使配置生效
service mysqld restart
#检查配置是否生效
mysql -uroot -proot
mysql> select @@global.explicit_defaults_for_timestamp;
+------------------------------------------+
| @@global.explicit_defaults_for_timestamp |
+------------------------------------------+
|                                        1 |
+------------------------------------------+
1 row in set (0.00 sec)

Ⅰ.通过修改airflow.cfg调整配置

1修改webserver地址

base_url = http://192.168.150.128:8085
web_server_port = 8085

2修改executor

#SequentialExecutor是单进程顺序执行任务,默认执行器,通常只用于测试
#LocalExecutor是多进程本地执行任务使用的
#CeleryExecutor是分布式调度使用(当然也可以单机),生产环境常用
#DaskExecutor则用于动态任务调度,常用于数据分析
executor = CeleryExecutor

3时区

#修改airflow.cfg中
default_timezone = Asia/Shanghai
#同时需要修改另外三个文件
#修改webserver页面上右上角展示的时间:
vi ${PYTHON_HOME}/lib/python3.6/site-packages/airflow/www/templates/admin/master.html
-----------------------------------
{% block tail_js %}
{{ super() }}
<script src="{{ url_for('static', filename='jqClock.min.js') }}" type="text/javascript"></script>
<script>
    x = new Date()
   // var UTCseconds = (x.getTime() + x.getTimezoneOffset()*60*1000);##修改的内容
    var UTCseconds = x.getTime();##修改的内容
    $("#clock").clock({

#修改webserver lastRun时间:
vi ${PYTHON_HOME}/lib/python3.6/site-packages/airflow/models.py
-----------------------------------》
#在指定位置添加如下内容,可以借助get_last_dagrun定位
def utc2local(self,utc):
       import time
       epoch = time.mktime(utc.timetuple())
       offset = datetime.fromtimestamp(epoch) - datetime.utcfromtimestamp(epoch)
       return utc + offset

vi ${PYTHON_HOME}/lib/python3.6/site-packages/airflow/www/templates/airflow/dags.html
#在图中指定位置修改为如下内容
dag.utc2local(last_run.execution_date).strftime("%Y-%m-%d %H:%M")
dag.utc2local(last_run.start_date).strftime("%Y-%m-%d %H:%M")

4添加用户认证(暂时不做这一步,还没懂)

#在这里我们采用简单的password认证方式
#(1)安装password组件:
sudo pip install apache-airflow[password]
#(2)修改airflow.cfg配置文件:
[webserver]
authenticate = True
auth_backend = airflow.contrib.auth.backends.password_auth
#(3)编写python脚本用于添加用户账号:
#编写add_account.py文件:
import airflow
from airflow import models, settings
from airflow.contrib.auth.backends.password_auth import PasswordUser

user = PasswordUser(models.User())
user.username = 'airflow'
user.email = '[email protected]'
user.password = 'airflow'

session = settings.Session()
session.add(user)
session.commit()
session.close()
exit()
#执行add_account.py文件:
python add_account.py
#你会发现mysql元数据库表user中会多出来一条记录的。

5修改scheduler线程数控制并发量

parallelism = 32

6修改检测新DAG间隔

min_file_process_interval = 5

Ⅱ.初始化源数据库及启动组件

#初始化元数据库信息(其实也就是新建airflow依赖的表)
pip install celery
pip install apache-airflow['kubernetes']
airflow initdb 
#或者使用airflow resetdb
#准备操作
#关闭linux防火墙
systemctl stop firewalld.service
systemctl disable firewalld.service
#同时需要关闭windows防火墙
#数据库设置
mysql -uroot -proot
mysql> set password for 'root'@'localhost' =password('');
Query OK, 0 rows affected (0.00 sec)
mysql> grant all on airflow.* to 'airflow'@'%';
Query OK, 0 rows affected (0.00 sec)
mysql> grant all on airflow.* to 'root'@'%';
Query OK, 0 rows affected (0.01 sec)
mysql> flush privileges;
Query OK, 0 rows affected (0.00 sec)
mysql> exit;

#启动组件:
airflow webserver -D
#airflow scheduler -D
#airflow worker -D
#airflow flower -D

Ⅲ.Web页面查看

#地址
192.168.150.128:8085/admin/
#测试
可以选择airflow_db数据库简单查询进行测试
select * from log;

5.可能出现的ERROR

错误1:

#启动webserver组件时可能会报如下错误:
Error: 'python:airflow.www.gunicorn_config' doesn‘t exist

安装指定版本的gunicorn即可:

(1) Airflow1.10版本对应gunicorn的19.4.0版本:

sudo pip install gunicorn==19.4.0

错误2:

$ AIRFLOW_HOME=/var/lib/airflow airflow initdb
[2019-09-07 20:51:32,416] {__init__.py:51} INFO - Using executor SequentialExecutor
Traceback (most recent call last):
  File "/bin/airflow", line 22, in <module>
    from airflow.bin.cli import CLIFactory
  File "/usr/lib/python2.7/site-packages/airflow/bin/cli.py", line 68, in <module>
    from airflow.www_rbac.app import cached_app as cached_app_rbac
  File "/usr/lib/python2.7/site-packages/airflow/www_rbac/app.py", line 26, in <module>
    from flask_appbuilder import AppBuilder, SQLA
  File "/usr/lib/python2.7/site-packages/flask_appbuilder/__init__.py", line 5, in <module>
    from .base import AppBuilder
  File "/usr/lib/python2.7/site-packages/flask_appbuilder/base.py", line 5, in <module>
    from .api.manager import OpenApiManager
  File "/usr/lib/python2.7/site-packages/flask_appbuilder/api/__init__.py", line 11, in <module>
    from marshmallow_sqlalchemy.fields import Related, RelatedList
  File "/usr/lib/python2.7/site-packages/marshmallow_sqlalchemy/__init__.py", line 1, in <module>
    from .schema import TableSchemaOpts, ModelSchemaOpts, TableSchema, ModelSchema
  File "/usr/lib/python2.7/site-packages/marshmallow_sqlalchemy/schema.py", line 101
    class TableSchema(ma.Schema, metaclass=TableSchemaMeta):
                                      ^
SyntaxError: invalid syntax
pip uninstall marshmallow-sqlalchemy
pip install marshmallow-sqlalchemy==0.17.1

错误3:

Traceback (most recent call last):
  File "/usr/bin/airflow", line 18, in <module>
    from airflow.bin.cli import CLIFactory
  File "/usr/lib/python2.7/dist-packages/airflow/bin/cli.py", line 65, in <module>
    auth=api.api_auth.client_auth)
AttributeError: 'module' object has no attribute 'client_auth'
auth_backend=airflow.contrib.auth.backends.password_auth
写在webserver下,不要写在client下面

airflow.cfg

[[email protected] ~/airflow]$ cat airflow.cfg
[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /root/airflow

# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /root/airflow/dags

# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /root/airflow/logs

# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
# Users must supply an Airflow connection id that provides access to the storage
# location. If remote_logging is set to true, see UPDATING.md for additional
# configuration requirements.
remote_logging = False
remote_log_conn_id =
remote_base_log_folder =
encrypt_s3_logs = False

# Logging level
logging_level = INFO
fab_logging_level = WARN

# Logging class
# Specify the class that will specify the logging configuration
# This class has to be on the python classpath
# logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
logging_config_class =

# Log format
# we need to escape the curly braces by adding an additional curly brace
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# Log filename format
# we need to escape the curly braces by adding an additional curly brace
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log

# Hostname by providing a path to a callable, which will resolve the hostname
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 = Asia/Shanghai

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

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
#sql_alchemy_conn = sqlite:////root/airflow/airflow.db
sql_alchemy_conn = mysql://airflow:[email protected]:3306/airflow

# 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. 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 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 = 30

# 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 = 30

# 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
load_examples = False

# Where your Airflow plugins are stored
plugins_folder = /root/airflow/plugins

# Secret key to save connection passwords in the db
#fernet_key = cryptography_not_found_storing_passwords_in_plain_text
fernet_key = nR8ePt2X1FsufZXKymKo1JsIqrk28_A0hv98WFiHvaA=

# 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.
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

[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
# auth_backend = airflow.contrib.auth.backends.password_auth

[lineage]
# what lineage backend to use
backend =

[atlas]
sasl_enabled = False
host =
port = 21000
username =
password =

[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via `default_args`
default_owner = Airflow
default_cpus = 1
default_ram = 512
default_disk = 512
default_gpus = 0

[hive]
# Default mapreduce queue for HiveOperator tasks
default_hive_mapred_queue =

[webserver]
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is used in automated emails that
# airflow sends to point links to the right web server
base_url = http://localhost:8080

# The ip specified when starting the web server
web_server_host = 0.0.0.0

# The port on which to run the web server
web_server_port = 8080

# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_cert =
web_server_ssl_key =

# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 120

# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 120

# Number of workers to refresh at a time. When set to 0, worker refresh is
# disabled. When nonzero, airflow periodically refreshes webserver workers by
# bringing up new ones and killing old ones.
worker_refresh_batch_size = 1

# Number of seconds to wait before refreshing a batch of workers.
worker_refresh_interval = 30

# Secret key used to run your flask app
secret_key = temporary_key

# Number of workers to run the Gunicorn web server
workers = 4

# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent
worker_class = sync

# Log files for the gunicorn webserver. '-' means log to stderr.
access_logfile = -
error_logfile = -

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

# Set to true to turn on authentication:
# https://airflow.incubator.apache.org/security.html#web-authentication
#authenticate = False
authenticate = True
auth_backend = airflow.contrib.auth.backends.password_auth

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

# Filtering mode. Choices include user (default) and ldapgroup.
# Ldap group filtering requires using the ldap backend
#
# Note that the ldap server needs the "memberOf" overlay to be set up
# in order to user the ldapgroup mode.
owner_mode = user

# Default DAG view.  Valid values are:
# tree, graph, duration, gantt, landing_times
dag_default_view = tree

# Default DAG orientation. Valid values are:
# LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top)
dag_orientation = LR

# Puts the webserver in demonstration mode; blurs the names of Operators for
# privacy.
demo_mode = False

# The amount of time (in secs) webserver will wait for initial handshake
# while fetching logs from other worker machine
log_fetch_timeout_sec = 5

# By default, the webserver shows paused DAGs. Flip this to hide paused
# DAGs by default
hide_paused_dags_by_default = False

# Consistent page size across all listing views in the UI
page_size = 100

# Use FAB-based webserver with RBAC feature
rbac = False

# Define the color of navigation bar
navbar_color = #007A87

# Default dagrun to show in UI
default_dag_run_display_number = 25


[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 = [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
worker_concurrency = 30

# 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:[email protected]:3306/airflow
broker_url = redis://127.0.0.1:6379/1

# The Celery result_backend. When a job finishes, it needs to update the
# metadata of the job. Therefore it will post a message on a message bus,
# or insert it into a database (depending of the backend)
# This status is used by the scheduler to update the state of the task
# The use of a database is highly recommended
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings
result_backend = db+mysql://airflow:[email protected]: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

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

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

# How many seconds to wait between file-parsing loops to prevent the logs from being spammed.
min_file_parsing_loop_time = 1

dag_dir_list_interval = 300

# How often should stats be printed to the logs
print_stats_interval = 30

child_process_log_directory = /root/airflow/logs/scheduler

# Local task jobs periodically heartbeat to the DB. If the job has
# not heartbeat in this many seconds, the scheduler will mark the
# associated task instance as failed and will re-schedule the task.
scheduler_zombie_task_threshold = 300

# Turn off scheduler catchup by setting this to False.
# Default behavior is unchanged and
# Command Line Backfills still work, but the scheduler
# will not do scheduler catchup if this is False,
# however it can be set on a per DAG basis in the
# DAG definition (catchup)
catchup_by_default = True

# This changes the batch size of queries in the scheduling main loop.
# If this is too high, SQL query performance may be impacted by one
# or more of the following:
#  - reversion to full table scan
#  - complexity of query predicate
#  - excessive locking
#
# Additionally, you may hit the maximum allowable query length for your db.
#
# Set this to 0 for no limit (not advised)
max_tis_per_query = 512

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

# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run.
max_threads = 2

authenticate = False

[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

# 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 =
# we need to escape the curly braces by adding an additional curly brace
elasticsearch_log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number}
elasticsearch_end_of_log_mark = end_of_log

[kubernetes]
# The repository and tag of the Kubernetes Image for the Worker to Run
worker_container_repository =
worker_container_tag =

# If True (default), worker pods will be deleted upon termination
delete_worker_pods = True

# 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 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 volume claim)
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 =

# Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim)
git_repo =
git_branch =
git_user =
git_password =
git_subpath =

# For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync
git_sync_container_repository = gcr.io/google-containers/git-sync-amd64
git_sync_container_tag = v2.0.5
git_sync_init_container_name = git-sync-clone

# 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

[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.
[rest_api_plugin]

log_loading=True
filter_loading_messages_in_cli_response=False
rest_api_plugin_http_token_header_name=user
rest_api_plugin_expected_http_token=pass

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