airflow 在 docker 中的安装与使用

airflow 在 docker 中的安装与使用

一定要看官方文档!!!

一定要看官方文档!!!

一定要看官方文档!!!

这里使用的是官方版本的镜像, 有问题一定要看官方文档, 不要太过于参考技术博客, 我这篇也是.


官方文档:
	https://airflow.apache.org/docs/apache-airflow/stable/index.html

docker 部署文档:
	https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html

简介

通过官方镜像重新构建自定义 airflow 镜像, 直接使用 docker-compose 部署至服务器中

airflow 镜像

https://hub.docker.com/r/apache/airflow

apache/airflow:2.3.0

环境变量配置信息

官方的环境变量配置信息为:
	https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#donot-pickle

其中很多信息可以直接找到

dags

官方文档:
	https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/index.html
	
基础使用可参考: 
	https://zhuanlan.zhihu.com/p/374756902

这里记录了一种

# -*- coding: utf-8 -*-
# @Author : 'Erwin'
# @File : data_sync_crm_info_dag.py
# @Software: PyCharm
from airflow.models import DAG
from airflow.operators.bash_operator import BashOperator
import pendulum
from datetime import timedelta


default_args = {
    'owner': 'wmz',
    # 邮箱配置
    'email': ['[email protected]'],
    'email_on_failure': True,   # 失败之后再次发送
    'email_on_retry': True,
    # 设置单个任务实例是否取决于上一个任务实例的成功与否. 如果指定本身的start_date，则忽略此依赖关系
    'depends_on_past': False,
    "start_date": pendulum.datetime(2016, 1, 1, tz="Asia/Shanghai"),
    'retries': 2,   # retries 表示重试的次数，重试多少次后跳过此 task
    'retry_delay': timedelta(minutes=5),    # retry_delay 参数表示失败多久后进行重试，次数设置的是1分钟
    'execution_timeout': timedelta(hours=1),  # 如果一小时还没有运行完，就置为失败
}


dag = DAG(
    dag_id='crm_road_show',
    default_args=default_args,
    catchup=False,
    description='api数据同步, crm的公司参会数据（进门财经）至vmp',
    schedule_interval="0 5 * * * ",
    dagrun_timeout=timedelta(minutes=60),
)


bash_task = BashOperator(
    task_id='crm_road_show',
    depends_on_past=False,
    bash_command='python /opt/airflow/tasks/main.py --task apps/crm/road_show_task.py',
    dag=dag
)

再配合 cicd 的使用

stages:
  - deploy

after_script:
  - docker image prune -f
  - echo "部署成功!"

docker-deploy:
  stage: deploy
  script:
    - cd /data/project/airflow
    - sudo git pull
    - docker restart airflow_web
  tags:
    - vmp_api
  only:
    - main

pip 包的安装

由于需要在 airflow 中使用第三方包, 所以就需要自定义的 pip 包安装.

官方文档: 
	https://airflow.apache.org/docs/docker-stack/build.html#examples-of-image-extending


主要有两种方式:

# 1. 在 docker.compose 文件中添加参数 _PIP_ADDITIONAL_REQUIREMENTS
x-airflow-common:
  &airflow-common
  image: my_airflow
  environment:
	_PIP_ADDITIONAL_REQUIREMENTS: 'pandahouse==0.2.7 clickhouse-driver==0.2.1 apache-airflow-providers-slack'
	...


# 2. 使用自定义的 airflow 镜像, 创建 Dockerfile 文件
FROM apache/airflow:2.3.0
COPY requirements.txt .
RUN pip install --no-cache-dir --upgrade --user -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple


参考文章:
	https://devdreamz.com/question/708638-how-to-install-packages-in-airflow-docker-compose

挂载目录

airflow 容器中的目录地址为:
    dags: /opt/airflow/dags
    logs: /opt/airflow/logs
    plugins: /opt/airflow/plugins
  	配置文件: /opt/airflow/airflow.cfg


根据需求进行挂载即可

部署

airflow.cfg 配置文件修改

完整 airflow.cfg 文件建议从新容器中拷贝出来, 文末会附带一个.

示例加载

# 不加载example dag
# 这个配置只有在第一次启动airflow之前设置才有效
load_examples = False  

邮件

smtp_host=smtp.mxhichina.com
smtp_starttls=False
smtp_ssl=True
smtp_port=465
[email protected]
[email protected]
smtp_password = Hz_123456

时区

官方文档:
	https://airflow.apache.org/docs/apache-airflow/stable/timezone.html

[core]
default_timezone = Asia/Shanghai
[webserver] 部分 
default_ui_timezone = Asia/Shanghai

登录配置

建议不要修改

默认是直接以admin用户登录进来的，而且不需要输入账号密码
参考: 
https://stackoverflow.com/questions/52056809/how-to-activate-authentication-in-apache-airflow

• 修改 airflow.cfg

# 搜索 [webserver] 216 行
在下面添加 :
auth_backend = airflow.contrib.auth.backends.password_auth

# 修改 authenticate (第 270 行)
authenticate = True

# 修改 rbac (第289行) # 不能加, 加上之后时区会重新读取utc的
rbac = True

其他配置信息

# 修改检测新dag间隔, 因为默认为0，没有时间间隔，很耗资源。
min_file_process_interval = 10

# 提高运行速度
-parallelism： 此变量控制 Airflow worker 可以同时运行的任务实例的数量。 用户可以通过改变airflow.cfg中的 parallelism 调整 并行度变量。
parallelism = 15

-concurrency： Airflow scheduler 在任何时间不会运行超过 concurrency 数量的 DAG 实例。 concurrency 在 Airflow DAG 中定义。
如果在 DAG 中没有设置 concurrency，则 scheduler 将使用airflow.cfg文件中定义的dag_concurrency作为默认值。
dag_concurrency = 16

-max_active_runs： Airflow scheduler 在任何时间不会运行超过 max_active_runs DagRuns 数量。
如果在 DAG 中没有设置max_active_runs ，则 scheduler 将使用airflow.cfg文件中定义的max_active_runs_per_dag作为默认值。
max_active_runs_per_dag = 16

# 减少airflowUI加载时间
# 此可配置控制在 UI 中显示的 dag run 的数量，默认值为 25。
default_dag_run_display_number = 15

# 生产环境中的 dag 的调度延迟
-max_threads： scheduler 将并行生成多个线程来调度 dags。 这数量是由max_threads参数控制，默认值为 2.用户应在生产中将此值增加到更大的值（例如，scheduler 运行机器的 cpus 数量 - 1）。
max_threads = 2

-scheduler_heartbeat_sec： 用户应考虑将scheduler_heartbeat_sec配置增加到更高的值（例如 60 秒），该值控制 airflow scheduler 获取心跳和更新作业到数据库中的频率。
scheduler_heartbeat_sec = 60

目录结构与文件

- airflow
	- .env	# 环境变量配置
	- docker-compose.yaml	# 容器编排文件
	- conf	# 设置文件夹
		- airflow.cnf	# 挂载的 airflow 配置文件
        
- /data/project/airflow	# 项目代码文件夹
    - dags	# 任务
    - tasks	# 代码
    - Dockerfile	# 自定义镜像 build
    - requirements.txt	# pip 依赖文件
    - README.md	# README.md
		

# 我这里是使用构建自定义 airflow 镜像的形式, 所以步骤可能会有些不同, 仅做参考.
# 官方文件
	curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.3.0/docker-compose.yaml'

.env

AIRFLOW_UID=50000

Dockerfile

FROM apache/airflow:2.3.0

USER root

COPY requirements.txt .

USER airflow

RUN pip install --no-cache-dir --upgrade --user -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

RUN pip uninstall dataclasses -y

docker-compose.yaml

version: "3.6"


x-airflow-common:
  &airflow-common
  build: /data/project/airflow
  image: hz_airflow_image
  environment:
    &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: CeleryExecutor
    AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
    AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
    AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow
    AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0
    AIRFLOW__CORE__FERNET_KEY: ''
    AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
    AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth'
    TZ: Asia/Shanghai
  volumes:
    - ./conf/airflow.cfg:/opt/airflow/airflow.cfg
    - /data/project/airflow/dags:/opt/airflow/dags
    - /data/project/airflow/tasks:/opt/airflow/tasks
    - /etc/opt/airflow/plugins:/opt/airflow/plugins
    - /var/log/airflow/logs:/opt/airflow/logs
  user: root
  depends_on:
    &airflow-common-depends-on
    redis:
      condition: service_healthy
    postgres:
      condition: service_healthy

services:
  airflow-webserver:
    <<: *airflow-common
    command: webserver
    container_name: airflow_web
    ports:
      - 8580:8080
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  postgres:
    image: postgres:13
    container_name: airflow_postgresql
    environment:
      POSTGRES_USER: airflow
      POSTGRES_PASSWORD: airflow
      POSTGRES_DB: airflow
    volumes:
      - /etc/opt/airflow/postgres_db:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD", "pg_isready", "-U", "airflow"]
      interval: 5s
      retries: 5
    restart: always

  redis:
    image: redis:5.0
    container_name: airflow_redis
    expose:
      - 6379
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 5s
      timeout: 30s
      retries: 50
    restart: always

  airflow-scheduler:
    <<: *airflow-common
    container_name: airflow_scheduler
    command: scheduler
    healthcheck:
      test: ["CMD-SHELL", 'airflow jobs check --job-type SchedulerJob --hostname "$${HOSTNAME}"']
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-worker:
    <<: *airflow-common
    container_name: airflow_worker
    command: celery worker
    healthcheck:
      test:
        - "CMD-SHELL"
        - 'celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
      interval: 10s
      timeout: 10s
      retries: 5
    environment:
      <<: *airflow-common-env
      DUMB_INIT_SETSID: "0"
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-triggerer:
    <<: *airflow-common
    container_name: airflow_triggerer
    command: triggerer
    healthcheck:
      test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-init:
    <<: *airflow-common
    entrypoint: /bin/bash
    command:
      - -c
      - |
        function ver() {
          printf "%04d%04d%04d%04d" $${1//./ }
        }
        airflow_version=$$(gosu airflow airflow version)
        airflow_version_comparable=$$(ver $${airflow_version})
        min_airflow_version=2.2.0
        min_airflow_version_comparable=$$(ver $${min_airflow_version})
        if (( airflow_version_comparable < min_airflow_version_comparable )); then
          echo
          echo -e "\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\e[0m"
          echo "The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!"
          echo
          exit 1
        fi
        if [[ -z "${AIRFLOW_UID}" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m"
          echo "If you are on Linux, you SHOULD follow the instructions below to set "
          echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
          echo "For other operating systems you can get rid of the warning with manually created .env file:"
          echo "    See: https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#setting-the-right-airflow-user"
          echo
        fi
        one_meg=1048576
        mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))
        cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)
        disk_available=$$(df / | tail -1 | awk '{print $$4}')
        warning_resources="false"
        if (( mem_available < 4000 )) ; then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m"
          echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))"
          echo
          warning_resources="true"
        fi
        if (( cpus_available < 2 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m"
          echo "At least 2 CPUs recommended. You have $${cpus_available}"
          echo
          warning_resources="true"
        fi
        if (( disk_available < one_meg * 10 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m"
          echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
          echo
          warning_resources="true"
        fi
        if [[ $${warning_resources} == "true" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m"
          echo "Please follow the instructions to increase amount of resources available:"
          echo "   https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#before-you-begin"
          echo
        fi
        mkdir -p /sources/logs /sources/dags /sources/plugins
        chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
        exec /entrypoint airflow version
    environment:
      <<: *airflow-common-env
      _AIRFLOW_DB_UPGRADE: 'true'
      _AIRFLOW_WWW_USER_CREATE: 'true'
      _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
      _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}
    user: "0:0"
    volumes:
      - .:/sources

  airflow-cli:
    <<: *airflow-common
    profiles:
      - debug
    environment:
      <<: *airflow-common-env
      CONNECTION_CHECK_MAX_COUNT: "0"
    command:
      - bash
      - -c
      - airflow
  

启动

# 初始化
docker-compose up --build airflow-init

# 启动服务
docker-compose up -d 

# 删除
docker-compose down

查看 dags 信息

docker-compose run  airflow-cli airflow dags list

扩展

钉钉机器人报警

邮箱报警

异常

莫名其妙的 bug

出现AttributeError: module ‘typing‘ has no attribute ‘_ClassVar‘错误

pip uninstall dataclasses

airflow.cfg

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

# 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)
default_timezone = Asia/Shanghai

# The executor class that airflow should use. Choices include
# ``SequentialExecutor``, ``LocalExecutor``, ``CeleryExecutor``, ``DaskExecutor``,
# ``KubernetesExecutor``, ``CeleryKubernetesExecutor`` or the
# full import path to the class when using a custom executor.
executor = SequentialExecutor

# This defines the maximum number of task instances that can run concurrently in Airflow
# regardless of scheduler count and worker count. Generally, this value is reflective of
# the number of task instances with the running state in the metadata database.
parallelism = 32

# The maximum number of task instances allowed to run concurrently in each DAG. To calculate
# the number of tasks that is running concurrently for a DAG, add up the number of running
# tasks for all DAG runs of the DAG. This is configurable at the DAG level with ``max_active_tasks``,
# which is defaulted as ``max_active_tasks_per_dag``.
#
# An example scenario when this would be useful is when you want to stop a new dag with an early
# start date from stealing all the executor slots in a cluster.
max_active_tasks_per_dag = 16

# Are DAGs paused by default at creation
dags_are_paused_at_creation = True

# The maximum number of active DAG runs per DAG. The scheduler will not create more DAG runs
# if it reaches the limit. This is configurable at the DAG level with ``max_active_runs``,
# which is defaulted as ``max_active_runs_per_dag``.
max_active_runs_per_dag = 16

# Whether to load the DAG 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

# Path to the folder containing Airflow plugins
plugins_folder = /opt/airflow/plugins

# Should tasks be executed via forking of the parent process ("False",
# the speedier option) or by spawning a new python process ("True" slow,
# but means plugin changes picked up by tasks straight away)
execute_tasks_new_python_interpreter = False

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

# Whether to disable pickling dags
donot_pickle = True

# How long before timing out a python file import
dagbag_import_timeout = 30.0

# Should a traceback be shown in the UI for dagbag import errors,
# instead of just the exception message
dagbag_import_error_tracebacks = True

# If tracebacks are shown, how many entries from the traceback should be shown
dagbag_import_error_traceback_depth = 2

# How long before timing out a DagFileProcessor, which processes a dag file
dag_file_processor_timeout = 50

# The class to use for running task instances in a subprocess.
# Choices include StandardTaskRunner, CgroupTaskRunner or the full import path to the class
# when using a custom task runner.
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 =

# Turn unit test mode on (overwrites many configuration options with test
# values at runtime)
unit_test_mode = False

# Whether to enable pickling for xcom (note that this is insecure and allows for
# RCE exploits).
enable_xcom_pickling = False

# 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 dags backfill -c`` or
# ``airflow dags trigger -c``, the key-value pairs will override the existing ones in params.
dag_run_conf_overrides_params = True

# When discovering DAGs, ignore any files that don't contain the strings ``DAG`` and ``airflow``.
dag_discovery_safe_mode = True

# The pattern syntax used in the ".airflowignore" files in the DAG directories. Valid values are
# ``regexp`` or ``glob``.
dag_ignore_file_syntax = regexp

# The number of retries each task is going to have by default. Can be overridden at dag or task level.
default_task_retries = 0

# The weighting method used for the effective total priority weight of the task
default_task_weight_rule = downstream

# The default task execution_timeout value for the operators. Expected an integer value to
# be passed into timedelta as seconds. If not specified, then the value is considered as None,
# meaning that the operators are never timed out by default.
default_task_execution_timeout =

# Updating serialized DAG can not be faster than a minimum interval to reduce database write rate.
min_serialized_dag_update_interval = 30

# If True, serialized DAGs are compressed before writing to DB.
# Note: this will disable the DAG dependencies view
compress_serialized_dags = False

# Fetching serialized DAG can not be faster than a minimum interval to reduce database
# read rate. This config controls when your DAGs are updated in the Webserver
min_serialized_dag_fetch_interval = 10

# Maximum number of Rendered Task Instance Fields (Template Fields) per task to store
# in the Database.
# All the template_fields for each of Task Instance are stored in the Database.
# Keeping this number small may cause an error when you try to view ``Rendered`` tab in
# TaskInstance view for older tasks.
max_num_rendered_ti_fields_per_task = 30

# On each dagrun check against defined SLAs
check_slas = True

# Path to custom XCom class that will be used to store and resolve operators results
# Example: xcom_backend = path.to.CustomXCom
xcom_backend = airflow.models.xcom.BaseXCom

# By default Airflow plugins are lazily-loaded (only loaded when required). Set it to ``False``,
# if you want to load plugins whenever 'airflow' is invoked via cli or loaded from module.
lazy_load_plugins = True

# By default Airflow providers are lazily-discovered (discovery and imports happen only when required).
# Set it to False, if you want to discover providers whenever 'airflow' is invoked via cli or
# loaded from module.
lazy_discover_providers = True

# Hide sensitive Variables or Connection extra json keys from UI and task logs when set to True
#
# (Connection passwords are always hidden in logs)
hide_sensitive_var_conn_fields = True

# A comma-separated list of extra sensitive keywords to look for in variables names or connection's
# extra JSON.
sensitive_var_conn_names =

# Task Slot counts for ``default_pool``. This setting would not have any effect in an existing
# deployment where the ``default_pool`` is already created. For existing deployments, users can
# change the number of slots using Webserver, API or the CLI
default_pool_task_slot_count = 128

# The maximum list/dict length an XCom can push to trigger task mapping. If the pushed list/dict has a
# length exceeding this value, the task pushing the XCom will be failed automatically to prevent the
# mapped tasks from clogging the scheduler.
max_map_length = 1024

[database]
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engines.
# More information here:
# http://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html#database-uri
sql_alchemy_conn = sqlite:opt/airflow/airflow.db

# Extra engine specific keyword args passed to SQLAlchemy's create_engine, as a JSON-encoded value
# Example: sql_alchemy_engine_args = {"arg1": True}
# sql_alchemy_engine_args =

# The encoding for the databases
sql_engine_encoding = utf-8

# Collation for ``dag_id``, ``task_id``, ``key`` columns in case they have different encoding.
# By default this collation is the same as the database collation, however for ``mysql`` and ``mariadb``
# the default is ``utf8mb3_bin`` so that the index sizes of our index keys will not exceed
# the maximum size of allowed index when collation is set to ``utf8mb4`` variant
# (see https://github.com/apache/airflow/pull/17603#issuecomment-901121618).
# sql_engine_collation_for_ids =

# 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

# Check connection at the start of each connection pool checkout.
# Typically, this is a simple statement like "SELECT 1".
# More information here:
# https://docs.sqlalchemy.org/en/13/core/pooling.html#disconnect-handling-pessimistic
sql_alchemy_pool_pre_ping = True

# The schema to use for the metadata database.
# SqlAlchemy supports databases with the concept of multiple schemas.
sql_alchemy_schema =

# Import path for connect args in SqlAlchemy. Defaults to an empty dict.
# This is useful when you want to configure db engine args that SqlAlchemy won't parse
# in connection string.
# See https://docs.sqlalchemy.org/en/13/core/engines.html#sqlalchemy.create_engine.params.connect_args
# sql_alchemy_connect_args =

# Whether to load the default connections that ship with Airflow. It's good to
# get started, but you probably want to set this to ``False`` in a production
# environment
load_default_connections = True

# Number of times the code should be retried in case of DB Operational Errors.
# Not all transactions will be retried as it can cause undesired state.
# Currently it is only used in ``DagFileProcessor.process_file`` to retry ``dagbag.sync_to_db``.
max_db_retries = 3

[logging]
# The folder where airflow should store its log files.
# This path must be absolute.
# There are a few existing configurations that assume this is set to the default.
# If you choose to override this you may need to update the dag_processor_manager_log_location and
# dag_processor_manager_log_location settings as well.
base_log_folder = /opt/airflow/logs

# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
# Set this to True if you want to enable remote logging.
remote_logging = False

# Users must supply an Airflow connection id that provides access to the storage
# location. Depending on your remote logging service, this may only be used for
# reading logs, not writing them.
remote_log_conn_id =

# Path to Google Credential JSON file. If omitted, authorization based on `the Application Default
# Credentials
# `__ will
# be used.
google_key_path =

# Storage bucket URL for remote logging
# S3 buckets should start with "s3://"
# Cloudwatch log groups should start with "cloudwatch://"
# GCS buckets should start with "gs://"
# WASB buckets should start with "wasb" just to help Airflow select correct handler
# Stackdriver logs should start with "stackdriver://"
remote_base_log_folder =

# Use server-side encryption for logs stored in S3
encrypt_s3_logs = False

# Logging level.
#
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
logging_level = INFO

# Logging level for celery. If not set, it uses the value of logging_level
#
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
celery_logging_level =

# Logging level for Flask-appbuilder UI.
#
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
fab_logging_level = WARNING

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

# Flag to enable/disable Colored logs in Console
# Colour the logs when the controlling terminal is a TTY.
colored_console_log = True

# Log format for when Colored logs is enabled
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

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

# Specify prefix pattern like mentioned below with stream handler TaskHandlerWithCustomFormatter
# Example: task_log_prefix_template = {ti.dag_id}-{ti.task_id}-{execution_date}-{try_number}
task_log_prefix_template =

# Formatting for how airflow generates file names/paths for each task run.
log_filename_template = dag_id={{ ti.dag_id }}/run_id={{ ti.run_id }}/task_id={{ ti.task_id }}/{%% if ti.map_index >= 0 %%}map_index={{ ti.map_index }}/{%% endif %%}attempt={{ try_number }}.log

# Formatting for how airflow generates file names for log
log_processor_filename_template = {{ filename }}.log

# Full path of dag_processor_manager logfile.
dag_processor_manager_log_location = /opt/airflow/logs/dag_processor_manager/dag_processor_manager.log

# Name of handler to read task instance logs.
# Defaults to use ``task`` handler.
task_log_reader = task

# A comma\-separated list of third-party logger names that will be configured to print messages to
# consoles\.
# Example: extra_logger_names = connexion,sqlalchemy
extra_logger_names =

# 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

[metrics]

# StatsD (https://github.com/etsy/statsd) integration settings.
# Enables sending metrics to StatsD.
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow

# If you want to avoid sending all the available metrics to StatsD,
# you can configure an allow list of prefixes (comma separated) to send only the metrics that
# start with the elements of the list (e.g: "scheduler,executor,dagrun")
statsd_allow_list =

# A function that validate the StatsD stat name, apply changes to the stat name if necessary and return
# the transformed stat name.
#
# The function should have the following signature:
# def func_name(stat_name: str) -> str:
stat_name_handler =

# To enable datadog integration to send airflow metrics.
statsd_datadog_enabled = False

# List of datadog tags attached to all metrics(e.g: key1:value1,key2:value2)
statsd_datadog_tags =

# If you want to utilise your own custom StatsD client set the relevant
# module path below.
# Note: The module path must exist on your PYTHONPATH for Airflow to pick it up
# statsd_custom_client_path =

[secrets]
# Full class name of secrets backend to enable (will precede env vars and metastore in search path)
# Example: backend = airflow.providers.amazon.aws.secrets.systems_manager.SystemsManagerParameterStoreBackend
backend =

# The backend_kwargs param is loaded into a dictionary and passed to __init__ of secrets backend class.
# See documentation for the secrets backend you are using. JSON is expected.
# Example for AWS Systems Manager ParameterStore:
# ``{"connections_prefix": "/airflow/connections", "profile_name": "default"}``
backend_kwargs =

[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

[debug]
# Used only with ``DebugExecutor``. If set to ``True`` DAG will fail with first
# failed task. Helpful for debugging purposes.
fail_fast = False

[api]
# Enables the deprecated experimental API. Please note that these APIs do not have access control.
# The authenticated user has full access.
#
# .. warning::
#
#   This `Experimental REST API `__ is
#   deprecated since version 2.0. Please consider using
#   `the Stable REST API `__.
#   For more information on migration, see
#   `RELEASE_NOTES.rst `_
enable_experimental_api = False

# Comma separated list of auth backends to authenticate users of the API. See
# https://airflow.apache.org/docs/apache-airflow/stable/security/api.html for possible values.
# ("airflow.api.auth.backend.default" allows all requests for historic reasons)
auth_backends = airflow.api.auth.backend.session

# Used to set the maximum page limit for API requests
maximum_page_limit = 100

# Used to set the default page limit when limit is zero. A default limit
# of 100 is set on OpenApi spec. However, this particular default limit
# only work when limit is set equal to zero(0) from API requests.
# If no limit is supplied, the OpenApi spec default is used.
fallback_page_limit = 100

# The intended audience for JWT token credentials used for authorization. This value must match on the client and server sides. If empty, audience will not be tested.
# Example: google_oauth2_audience = project-id-random-value.apps.googleusercontent.com
google_oauth2_audience =

# Path to Google Cloud Service Account key file (JSON). If omitted, authorization based on
# `the Application Default Credentials
# `__ will
# be used.
# Example: google_key_path = /files/service-account-json
google_key_path =

# Used in response to a preflight request to indicate which HTTP
# headers can be used when making the actual request. This header is
# the server side response to the browser's
# Access-Control-Request-Headers header.
access_control_allow_headers =

# Specifies the method or methods allowed when accessing the resource.
access_control_allow_methods =

# Indicates whether the response can be shared with requesting code from the given origins.
# Separate URLs with space.
access_control_allow_origins =

[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

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

# Is allowed to pass additional/unused arguments (args, kwargs) to the BaseOperator operator.
# If set to False, an exception will be thrown, otherwise only the console message will be displayed.
allow_illegal_arguments = False

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

# Template for mapred_job_name in HiveOperator, supports the following named parameters
# hostname, dag_id, task_id, execution_date
# mapred_job_name_template =

[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

# Default timezone to display all dates in the UI, can be UTC, system, or
# any IANA timezone string (e.g. Europe/Amsterdam). If left empty the
# default value of core/default_timezone will be used
# Example: default_ui_timezone = America/New_York
default_ui_timezone = Asia/Shanghai

# 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 =

# 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_key =

# The type of backend used to store web session data, can be 'database' or 'securecookie'
# Example: session_backend = securecookie
session_backend = database

# 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 = 6000

# If set to True, Airflow will track files in plugins_folder directory. When it detects changes,
# then reload the gunicorn.
reload_on_plugin_change = False

# Secret key used to run your flask app. It should be as random as possible. However, when running
# more than 1 instances of webserver, make sure all of them use the same ``secret_key`` otherwise
# one of them will error with "CSRF session token is missing".
# The webserver key is also used to authorize requests to Celery workers when logs are retrieved.
# The token generated using the secret key has a short expiry time though - make sure that time on
# ALL the machines that you run airflow components on is synchronized (for example using ntpd)
# otherwise you might get "forbidden" errors when the logs are accessed.
secret_key = j9LeIh7OqZkFhE9ZPQO1ug==

# 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 = -

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

# Access log format for gunicorn webserver.
# default format is %%(h)s %%(l)s %%(u)s %%(t)s "%%(r)s" %%(s)s %%(b)s "%%(f)s" "%%(a)s"
# documentation - https://docs.gunicorn.org/en/stable/settings.html#access-log-format
access_logformat =

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

# Expose hostname in the web server
expose_hostname = True

# Expose stacktrace in the web server
expose_stacktrace = True

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

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

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

# Time interval (in secs) to wait before next log fetching.
log_fetch_delay_sec = 2

# Distance away from page bottom to enable auto tailing.
log_auto_tailing_offset = 30

# Animation speed for auto tailing log display.
log_animation_speed = 1000

# 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

# Define the color of navigation bar
navbar_color = #fff

# Default dagrun to show in UI
default_dag_run_display_number = 15

# Enable werkzeug ``ProxyFix`` middleware for reverse proxy
enable_proxy_fix = False

# Number of values to trust for ``X-Forwarded-For``.
# More info: https://werkzeug.palletsprojects.com/en/0.16.x/middleware/proxy_fix/
proxy_fix_x_for = 1

# Number of values to trust for ``X-Forwarded-Proto``
proxy_fix_x_proto = 1

# Number of values to trust for ``X-Forwarded-Host``
proxy_fix_x_host = 1

# Number of values to trust for ``X-Forwarded-Port``
proxy_fix_x_port = 1

# Number of values to trust for ``X-Forwarded-Prefix``
proxy_fix_x_prefix = 1

# Set secure flag on session cookie
cookie_secure = False

# Set samesite policy on session cookie
cookie_samesite = Lax

# Default setting for wrap toggle on DAG code and TI log views.
default_wrap = False

# Allow the UI to be rendered in a frame
x_frame_enabled = True

# Send anonymous user activity to your analytics tool
# choose from google_analytics, segment, or metarouter
# analytics_tool =

# Unique ID of your account in the analytics tool
# analytics_id =

# 'Recent Tasks' stats will show for old DagRuns if set
show_recent_stats_for_completed_runs = True

# Update FAB permissions and sync security manager roles
# on webserver startup
update_fab_perms = True

# The UI cookie lifetime in minutes. User will be logged out from UI after
# ``session_lifetime_minutes`` of non-activity
session_lifetime_minutes = 43200

# Sets a custom page title for the DAGs overview page and site title for all pages
# instance_name =

# Whether the custom page title for the DAGs overview page contains any Markup language
instance_name_has_markup = False

# How frequently, in seconds, the DAG data will auto-refresh in graph or grid view
# when auto-refresh is turned on
auto_refresh_interval = 3

# Boolean for displaying warning for publicly viewable deployment
warn_deployment_exposure = True

# Comma separated string of view events to exclude from dag audit view.
# All other events will be added minus the ones passed here.
# The audit logs in the db will not be affected by this parameter.
audit_view_excluded_events = gantt,landing_times,tries,duration,calendar,graph,grid,tree,tree_data

# Comma separated string of view events to include in dag audit view.
# If passed, only these events will populate the dag audit view.
# The audit logs in the db will not be affected by this parameter.
# Example: audit_view_included_events = dagrun_cleared,failed
# audit_view_included_events =

[email]

# Configuration email backend and whether to
# send email alerts on retry or failure
# Email backend to use
email_backend = airflow.utils.email.send_email_smtp

# Email connection to use
email_conn_id = smtp_default

# Whether email alerts should be sent when a task is retried
default_email_on_retry = True

# Whether email alerts should be sent when a task failed
default_email_on_failure = True

# File that will be used as the template for Email subject (which will be rendered using Jinja2).
# If not set, Airflow uses a base template.
# Example: subject_template = /path/to/my_subject_template_file
# subject_template =

# File that will be used as the template for Email content (which will be rendered using Jinja2).
# If not set, Airflow uses a base template.
# Example: html_content_template = /path/to/my_html_content_template_file
# html_content_template =

# Email address that will be used as sender address.
# It can either be raw email or the complete address in a format ``Sender Name ``
# Example: from_email = Airflow 
# from_email =

[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=smtp.mxhichina.com
smtp_starttls=False
smtp_ssl=True
smtp_port=465
smtp_mail_from=[email protected]
smtp_user=[email protected]
smtp_password=Hz_123456
smtp_timeout = 30
smtp_retry_limit = 5


[sentry]

# Sentry (https://docs.sentry.io) integration. Here you can supply
# additional configuration options based on the Python platform. See:
# https://docs.sentry.io/error-reporting/configuration/?platform=python.
# Unsupported options: ``integrations``, ``in_app_include``, ``in_app_exclude``,
# ``ignore_errors``, ``before_breadcrumb``, ``transport``.
# Enable error reporting to Sentry
sentry_on = false
sentry_dsn =

# Dotted path to a before_send function that the sentry SDK should be configured to use.
# before_send =

[local_kubernetes_executor]

# This section only applies if you are using the ``LocalKubernetesExecutor`` in
# ``[core]`` section above
# Define when to send a task to ``KubernetesExecutor`` when using ``LocalKubernetesExecutor``.
# When the queue of a task is the value of ``kubernetes_queue`` (default ``kubernetes``),
# the task is executed via ``KubernetesExecutor``,
# otherwise via ``LocalExecutor``
kubernetes_queue = kubernetes

[celery_kubernetes_executor]

# This section only applies if you are using the ``CeleryKubernetesExecutor`` in
# ``[core]`` section above
# Define when to send a task to ``KubernetesExecutor`` when using ``CeleryKubernetesExecutor``.
# When the queue of a task is the value of ``kubernetes_queue`` (default ``kubernetes``),
# the task is executed via ``KubernetesExecutor``,
# otherwise via ``CeleryExecutor``
kubernetes_queue = kubernetes

[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 celery 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 celery 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
# Example: worker_autoscale = 16,12
# worker_autoscale =

# Used to increase the number of tasks that a worker prefetches which can improve performance.
# The number of processes multiplied by worker_prefetch_multiplier is the number of tasks
# that are prefetched by a worker. A value greater than 1 can result in tasks being unnecessarily
# blocked if there are multiple workers and one worker prefetches tasks that sit behind long
# running tasks while another worker has unutilized processes that are unable to process the already
# claimed blocked tasks.
# https://docs.celeryproject.org/en/stable/userguide/optimizing.html#prefetch-limits
worker_prefetch_multiplier = 1

# Specify if remote control of the workers is enabled.
# When using Amazon SQS as the broker, Celery creates lots of ``.*reply-celery-pidbox`` queues. You can
# prevent this by setting this to false. However, with this disabled Flower won't work.
worker_enable_remote_control = true

# Umask that will be used when starting workers with the ``airflow celery worker``
# in daemon mode. This control the file-creation mode mask which determines the initial
# value of file permission bits for newly created files.
worker_umask = 0o077

# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more information.
broker_url = redis://redis:6379/0

# 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+postgresql://postgres:airflow@postgres/airflow

# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it ``airflow celery flower``. This defines the IP that Celery Flower runs on
flower_host = 0.0.0.0

# The root URL for Flower
# Example: 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 =

# 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
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

# The number of seconds to wait before timing out ``send_task_to_executor`` or
# ``fetch_celery_task_state`` operations.
operation_timeout = 1.0

# Celery task will report its status as 'started' when the task is executed by a worker.
# This is used in Airflow to keep track of the running tasks and if a Scheduler is restarted
# or run in HA mode, it can adopt the orphan tasks launched by previous SchedulerJob.
task_track_started = True

# Time in seconds after which Adopted tasks are cleared by CeleryExecutor. This is helpful to clear
# stalled tasks.
task_adoption_timeout = 600

# The Maximum number of retries for publishing task messages to the broker when failing
# due to ``AirflowTaskTimeout`` error before giving up and marking Task as failed.
task_publish_max_retries = 3

# Worker initialisation check to validate Metadata Database connection
worker_precheck = False

[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
# Example: visibility_timeout = 21600
# visibility_timeout =

[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 = 60

# The number of times to try to schedule each DAG file
# -1 indicates unlimited number
num_runs = -1

# Controls how long the scheduler will sleep between loops, but if there was nothing to do
# in the loop. i.e. if it scheduled something then it will start the next loop
# iteration straight away.
scheduler_idle_sleep_time = 1

# Number of seconds after which a DAG file is parsed. The DAG file is parsed every
# ``min_file_process_interval`` number of seconds. Updates to DAGs are reflected after
# this interval. Keeping this number low will increase CPU usage.
min_file_process_interval = 30

# How often (in seconds) to check for stale DAGs (DAGs which are no longer present in
# the expected files) which should be deactivated.
deactivate_stale_dags_interval = 60

# 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. Setting to 0 will disable printing stats
print_stats_interval = 30

# How often (in seconds) should pool usage stats be sent to StatsD (if statsd_on is enabled)
pool_metrics_interval = 5.0

# 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

# How often (in seconds) should the scheduler check for orphaned tasks and SchedulerJobs
orphaned_tasks_check_interval = 300.0
child_process_log_directory = /opt/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

# How often (in seconds) should the scheduler check for zombie tasks.
zombie_detection_interval = 10.0

# 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

# Setting this to True will make first task instance of a task
# ignore depends_on_past setting. A task instance will be considered
# as the first task instance of a task when there is no task instance
# in the DB with an execution_date earlier than it., i.e. no manual marking
# success will be needed for a newly added task to be scheduled.
ignore_first_depends_on_past_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
# complexity of query predicate, and/or 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

# Should the scheduler issue ``SELECT ... FOR UPDATE`` in relevant queries.
# If this is set to False then you should not run more than a single
# scheduler at once
use_row_level_locking = True

# Max number of DAGs to create DagRuns for per scheduler loop.
max_dagruns_to_create_per_loop = 10

# How many DagRuns should a scheduler examine (and lock) when scheduling
# and queuing tasks.
max_dagruns_per_loop_to_schedule = 20

# Should the Task supervisor process perform a "mini scheduler" to attempt to schedule more tasks of the
# same DAG. Leaving this on will mean tasks in the same DAG execute quicker, but might starve out other
# dags in some circumstances
schedule_after_task_execution = True

# The scheduler can run multiple processes in parallel to parse dags.
# This defines how many processes will run.
parsing_processes = 2

# One of ``modified_time``, ``random_seeded_by_host`` and ``alphabetical``.
# The scheduler will list and sort the dag files to decide the parsing order.
#
# * ``modified_time``: Sort by modified time of the files. This is useful on large scale to parse the
#   recently modified DAGs first.
# * ``random_seeded_by_host``: Sort randomly across multiple Schedulers but with same order on the
#   same host. This is useful when running with Scheduler in HA mode where each scheduler can
#   parse different DAG files.
# * ``alphabetical``: Sort by filename
file_parsing_sort_mode = modified_time

# Whether the dag processor is running as a standalone process or it is a subprocess of a scheduler
# job.
standalone_dag_processor = False

# Only applicable if `[scheduler]standalone_dag_processor` is true and  callbacks are stored
# in database. Contains maximum number of callbacks that are fetched during a single loop.
max_callbacks_per_loop = 20

# 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

# Allow externally triggered DagRuns for Execution Dates in the future
# Only has effect if schedule_interval is set to None in DAG
allow_trigger_in_future = False

# DAG dependency detector class to use
dependency_detector = airflow.serialization.serialized_objects.DependencyDetector

# How often to check for expired trigger requests that have not run yet.
trigger_timeout_check_interval = 15

[triggerer]
# How many triggers a single Triggerer will run at once, by default.
default_capacity = 1000

[kerberos]
ccache = /tmp/airflow_krb5_ccache

# gets augmented with fqdn
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab

# Allow to disable ticket forwardability.
forwardable = True

# Allow to remove source IP from token, useful when using token behind NATted Docker host.
include_ip = True

[github_enterprise]
api_rev = v3

[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}-{run_id}-{map_index}-{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: scheme will default to https if one is not provided
# Example: frontend = http://localhost:5601/app/kibana#/discover?_a=(columns:!(message),query:(language:kuery,query:'log_id: "{log_id}"'),sort:!(log.offset,asc))
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

# The field where host name is stored (normally either `host` or `host.name`)
host_field = host

# The field where offset is stored (normally either `offset` or `log.offset`)
offset_field = offset

[elasticsearch_configs]
use_ssl = False
verify_certs = True

[kubernetes]
# Path to the YAML pod file that forms the basis for KubernetesExecutor workers.
pod_template_file =

# The repository of the Kubernetes Image for the Worker to Run
worker_container_repository =

# The tag of the Kubernetes Image for the Worker to Run
worker_container_tag =

# The Kubernetes namespace where airflow workers should be created. Defaults to ``default``
namespace = default

# If True, all worker pods will be deleted upon termination
delete_worker_pods = True

# If False (and delete_worker_pods is True),
# failed worker pods will not be deleted so users can investigate them.
# This only prevents removal of worker pods where the worker itself failed,
# not when the task it ran failed.
delete_worker_pods_on_failure = False

# Number of Kubernetes Worker Pod creation calls per scheduler loop.
# Note that the current default of "1" will only launch a single pod
# per-heartbeat. It is HIGHLY recommended that users increase this
# number to match the tolerance of their kubernetes cluster for
# better performance.
worker_pods_creation_batch_size = 1

# Allows users to launch pods in multiple namespaces.
# Will require creating a cluster-role for the scheduler
multi_namespace_mode = False

# 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 =

# Path to the kubernetes configfile to be used when ``in_cluster`` is set to False
# config_file =

# Keyword 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 are similar for all core_v1_apis, hence a single config
# variable for all apis. See:
# https://raw.githubusercontent.com/kubernetes-client/python/41f11a09995efcd0142e25946adc7591431bfb2f/kubernetes/client/api/core_v1_api.py
kube_client_request_args =

# Optional keyword arguments to pass to the ``delete_namespaced_pod`` kubernetes client
# ``core_v1_api`` method when using the Kubernetes Executor.
# This should be an object and can contain any of the options listed in the ``v1DeleteOptions``
# class defined here:
# https://github.com/kubernetes-client/python/blob/41f11a09995efcd0142e25946adc7591431bfb2f/kubernetes/client/models/v1_delete_options.py#L19
# Example: delete_option_kwargs = {"grace_period_seconds": 10}
delete_option_kwargs =

# Enables TCP keepalive mechanism. This prevents Kubernetes API requests to hang indefinitely
# when idle connection is time-outed on services like cloud load balancers or firewalls.
enable_tcp_keepalive = True

# When the `enable_tcp_keepalive` option is enabled, TCP probes a connection that has
# been idle for `tcp_keep_idle` seconds.
tcp_keep_idle = 120

# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond
# to a keepalive probe, TCP retransmits the probe after `tcp_keep_intvl` seconds.
tcp_keep_intvl = 30

# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond
# to a keepalive probe, TCP retransmits the probe `tcp_keep_cnt number` of times before
# a connection is considered to be broken.
tcp_keep_cnt = 6

# Set this to false to skip verifying SSL certificate of Kubernetes python client.
verify_ssl = True

# How long in seconds a worker can be in Pending before it is considered a failure
worker_pods_pending_timeout = 300

# How often in seconds to check if Pending workers have exceeded their timeouts
worker_pods_pending_timeout_check_interval = 120

# How often in seconds to check for task instances stuck in "queued" status without a pod
worker_pods_queued_check_interval = 60

# How many pending pods to check for timeout violations in each check interval.
# You may want this higher if you have a very large cluster and/or use ``multi_namespace_mode``.
worker_pods_pending_timeout_batch_size = 100

[sensors]
# Sensor default timeout, 7 days by default (7 * 24 * 60 * 60).
default_timeout = 604800

[smart_sensor]
# When `use_smart_sensor` is True, Airflow redirects multiple qualified sensor tasks to
# smart sensor task.
use_smart_sensor = False

# `shard_code_upper_limit` is the upper limit of `shard_code` value. The `shard_code` is generated
# by `hashcode % shard_code_upper_limit`.
shard_code_upper_limit = 10000

# The number of running smart sensor processes for each service.
shards = 5

# comma separated sensor classes support in smart_sensor.
sensors_enabled = NamedHivePartitionSensor