Airflow速用

  • 2019 年 10 月 31 日
  • 筆記

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

主要实现的功能

实现功能总结

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

核心思想

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

任务间定义排序的方法

官方推荐使用 移位操作符 方法，因为较为直观，容易理解

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

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

提高airflow相关执行速度方法

通过修改airflow.cfg相关配置

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

安装及启动相关服务

  • 创建python虚拟环境 venv
  • 添加airflow.cfg（此配置注解在下面）的配置文件夹路径：先 vi venv/bin/active; 里面输入 export AIRFLOW_HOME="/mnt/e/project/airflow_config/local"
  • 命令行：pip install apache-airflow
  • 根据airflow.cfg的数据库配置，在连接的数据库服务创建一个 名为 airflow_db的数据库
  • 命令行初始化数据库：airflow initdb
  • 命令行启动web服务: airflow webserver -p 8080
  • 命令行启动任务调度服务：airflow scheduler
  • 命令行启动worker：airflow worker -q queue_name

使用 http_operator发送http请求并在失败时,发送邮件

1.设置邮件html模板（如下为自定义模板）

<h2 style="color: red">Xxx service task exception,please fix them!!!</h2>  Try {{try_number}} out of {{max_tries + 1}}<br><br>  <b>dag id: </b>{{ti.dag_id}}<br><br>  <b>task id: </b>{{ti.task_id}}<br><br>  <b>task state: </b>{{ti.state}}<br><br>    <b>Exception:</b>  <p style="color: #0d7bdc">{{exception_html}}</p>  <b>Log Url: </b>  <a href="{{ti.log_url}}" style="color: red">Link</a><br><br>  <b>Host: </b>  {{ti.hostname}}<br><br>  <b>Log file path: </b> {{ti.log_filepath}}<br><br>  <b>Mark success: </b> <a href="{{ti.mark_success_url}}">Link</a><br>

模板效果图：

 2. airflow.cfg文件中配置 发送邮件服务

 3.编写代码：

 1 # -*- coding: utf-8 -*-   2 """   3 (C) xxx <[email protected]>   4 All rights reserved   5 create time '2019/10/21 09:27'   6 """   7 import os   8 from datetime import datetime   9  10 import pytz  11 from airflow import DAG  12 from airflow.models import Variable  13 from airflow.operators.http_operator import SimpleHttpOperator  14  15 # 设置第一次触发任务时间 及 设置任务执行的时区  16 tz = pytz.timezone("Asia/Shanghai")  17 dt = datetime(2019, 10, 11, 0, 0, tzinfo=tz)  18 utc_dt = dt.astimezone(pytz.utc).replace(tzinfo=None)  19  20 # 从环境变量找到 当前环境  21 env = os.environ.get("PROJECT_ENV", "LOCAL")  22 # 添加 需要的相关环境变量，可在 web网页中设置；注意 变量名 以AIRFLOW_CONN_开头，并且大写  23 os.environ["AIRFLOW_CONN_OLY_HOST"] = Variable.get("OLY_HOST_%s" % env)  24  25 # dag默认参数  26 args = {  27     "owner": "Rgc",  # 任务拥有人  28     "depends_on_past": False,  # 是否依赖过去执行此任务的结果，如果为True,则过去任务必须成功，才能执行此次任务  29     "start_date": utc_dt,  # 任务开始执行时间  30     "email": ["[email protected]"],  # 邮件地址，可以填写多个  31     "email_on_failure": True,  # 触发邮件发送的 时机，此处为失败时触发  32 }  33  34 # 定义一个DAG  35 # 参数catchup指 是否填充执行 start_date到现在 未执行的缺少任务；如：start_date定义为2019-10-10,现在是2019-10-29,任务是每天定时执行一次，  36 # 如果此参数设置为True，则 会生成 10号到29号之间的19此任务；如果设置为False,则不会补充执行任务；  37 # schedule_interval：定时执行方式，推荐使用如下字符串方式, 方便写出定时规则的网址：https://crontab.guru/  38 dag = DAG("HttpSendDag", catchup=False, default_args=args, schedule_interval="0 19 * * *")  39 # 设置 dag文档注释，可在web界面任务详情中看到  40 dag.doc_md = __doc__  41  42 # 定义此 http operator相关详情，详细使用方法 可访问此类定义__init__()方法  43 task = SimpleHttpOperator(  44     task_id="task_http_send",  # 任务id  45     http_conn_id="oly_host",  # http请求地址，值为上面23行定义  46     method="POST",  # http请求方法  47     endpoint="user/manage",  # http请求路径  48     dag=dag  # 任务所属dag  49 )  50 # 定义任务 文档注释，可在web界面任务详情中看到  51 task.doc_md = f"""  52 #Usage  53 此任务主要向Project服务({Variable.get("OLY_HOST_%s" % env)})发送http请求，每天晚上7点定时运行!  54 """

任务间数据交流方法

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

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

    下拉数据 主要使用 xcom_pull()方法

 官方代码示例及注释：

 1 from __future__ import print_function   2   3 import airflow   4 from airflow import DAG   5 from airflow.operators.python_operator import PythonOperator   6   7 args = {   8     'owner': 'airflow',   9     'start_date': airflow.utils.dates.days_ago(2),  10     'provide_context': True,  11 }  12  13 dag = DAG('example_xcom', schedule_interval="@once", default_args=args)  14  15 value_1 = [1, 2, 3]  16 value_2 = {'a': 'b'}  17  18  19 # 2种推送数据的方式，分别为xcom_push，和直接return  20  21 def push(**kwargs):  22     """Pushes an XCom without a specific target"""  23     kwargs['ti'].xcom_push(key='value from pusher 1', value=value_1)  24  25  26 def push_by_returning(**kwargs):  27     """Pushes an XCom without a specific target, just by returning it"""  28     return value_2  29  30  31 def puller(**kwargs):  32     """  33     下拉数据的方法  34     :param kwargs:  35     :return:  36     """  37     ti = kwargs['ti']  38  39     # get value_1  40     v1 = ti.xcom_pull(key=None, task_ids='push')  41     assert v1 == value_1  42  43     # get value_2  44     v2 = ti.xcom_pull(task_ids='push_by_returning')  45     assert v2 == value_2  46  47     # get both value_1 and value_2  48     v1, v2 = ti.xcom_pull(key=None, task_ids=['push', 'push_by_returning'])  49     assert (v1, v2) == (value_1, value_2)  50  51  52 push1 = PythonOperator(  53     task_id='push',  54     dag=dag,  55     python_callable=push,  56 )  57  58 push2 = PythonOperator(  59     task_id='push_by_returning',  60     dag=dag,  61     python_callable=push_by_returning,  62 )  63  64 pull = PythonOperator(  65     task_id='puller',  66     dag=dag,  67     python_callable=puller,  68 )  69  70 # 任务执行顺序为  71 # push1 >> pull  72 # push2 >> pull  73  74 pull << [push1, push2]

开启 web网页登录需要用户名密码功能

1.airflow.cfg文件修改

# 设置为True  rbac = True

2.重启airflow相关服务

3.通过 命令行 添加 用户

airflow create_user -r Admin -e [email protected] -f A -l dmin -u admin -p passwd

4.访问页面，输入用户名，密码即可

忽略某些DAG文件,不调用

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

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

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

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

 效果图：

airflow配置文件 相关中文注解:

  1 [core]    2 # The folder where your airflow pipelines live, most likely a    3 # subfolder in a code repository    4 # This path must be absolute    5 # 绝对路径下 一系列dags存放位置，airflow只会从此路径 文件夹下找dag任务    6 dags_folder = /mnt/e/airflow_project/dags    7    8 # The folder where airflow should store its log files    9 # This path must be absolute   10 # 绝对路径下的日志文件夹位置   11 base_log_folder = /mnt/e/airflow_project/log/   12   13 # Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.   14 # Users must supply an Airflow connection id that provides access to the storage   15 # location. If remote_logging is set to true, see UPDATING.md for additional   16 # configuration requirements.   17 remote_logging = False   18 remote_log_conn_id =   19 remote_base_log_folder =   20 encrypt_s3_logs = False   21   22 # Logging level   23 logging_level = INFO   24 fab_logging_level = WARN   25   26 # Logging class   27 # Specify the class that will specify the logging configuration   28 # This class has to be on the python classpath   29 # logging_config_class = my.path.default_local_settings.LOGGING_CONFIG   30 logging_config_class =   31   32 # Log format   33 # Colour the logs when the controlling terminal is a TTY.   34 colored_console_log = True   35 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   36 colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter   37   38 log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s   39 simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s   40   41 # Log filename format   42 # 实际处理任务日志 相关   43 log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log   44 log_processor_filename_template = {{ filename }}.log   45 # dag处理日志 绝对路径，精确到日志文件   46 dag_processor_manager_log_location = /mnt/e/airflow_project/log/dag_processor_manager.log   47   48 # Hostname by providing a path to a callable, which will resolve the hostname   49 # The format is "package:function". For example,   50 # default value "socket:getfqdn" means that result from getfqdn() of "socket" package will be used as hostname   51 # No argument should be required in the function specified.   52 # If using IP address as hostname is preferred, use value "airflow.utils.net:get_host_ip_address"   53 hostname_callable = socket:getfqdn   54   55 # Default timezone in case supplied date times are naive   56 # can be utc (default), system, or any IANA timezone string (e.g. Europe/Amsterdam)   57 # 默认时区，改为上海，然而 没卵用   58 default_timezone = Asia/Shanghai   59   60 # The executor class that airflow should use. Choices include   61 # SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor, KubernetesExecutor   62 # 指定executor(任务分配执行方式)   63 executor = CeleryExecutor   64   65 # The SqlAlchemy connection string to the metadata database.   66 # SqlAlchemy supports many different database engine, more information   67 # their website   68 # 存储airflow相关数据的 数据库路径   69 sql_alchemy_conn = mysql+pymysql://root:[email protected]:3306/airflow_db   70   71 # The encoding for the databases   72 sql_engine_encoding = utf-8   73   74 # If SqlAlchemy should pool database connections.   75 sql_alchemy_pool_enabled = True   76   77 # The SqlAlchemy pool size is the maximum number of database connections   78 # in the pool. 0 indicates no limit.   79 sql_alchemy_pool_size = 5   80   81 # The maximum overflow size of the pool.   82 # When the number of checked-out connections reaches the size set in pool_size,   83 # additional connections will be returned up to this limit.   84 # When those additional connections are returned to the pool, they are disconnected and discarded.   85 # It follows then that the total number of simultaneous connections the pool will allow is pool_size + max_overflow,   86 # and the total number of "sleeping" connections the pool will allow is pool_size.   87 # max_overflow can be set to -1 to indicate no overflow limit;   88 # no limit will be placed on the total number of concurrent connections. Defaults to 10.   89 sql_alchemy_max_overflow = 10   90   91 # The SqlAlchemy pool recycle is the number of seconds a connection   92 # can be idle in the pool before it is invalidated. This config does   93 # not apply to sqlite. If the number of DB connections is ever exceeded,   94 # a lower config value will allow the system to recover faster.   95 sql_alchemy_pool_recycle = 1800   96   97 # How many seconds to retry re-establishing a DB connection after   98 # disconnects. Setting this to 0 disables retries.   99 sql_alchemy_reconnect_timeout = 300  100  101 # The schema to use for the metadata database  102 # SqlAlchemy supports databases with the concept of multiple schemas.  103 sql_alchemy_schema =  104  105 # The amount of parallelism as a setting to the executor. This defines  106 # the max number of task instances that should run simultaneously  107 # on this airflow installation  108 parallelism = 32  109  110 # The number of task instances allowed to run concurrently by the scheduler  111 dag_concurrency = 16  112  113 # Are DAGs paused by default at creation  114 dags_are_paused_at_creation = True  115  116 # The maximum number of active DAG runs per DAG  117 max_active_runs_per_dag = 16  118  119 # Whether to load the examples that ship with Airflow. It's good to  120 # get started, but you probably want to set this to False in a production  121 # environment  122 load_examples = False  123  124 # Where your Airflow plugins are stored  125 # 自定义 界面及api所在 绝对路径文件夹 官网用法： http://airflow.apache.org/plugins.html  126 plugins_folder = /mnt/e/airflow_project/plugins  127  128 # Secret key to save connection passwords in the db  129 # 对使用到的 连接密码 进行加密，此为秘钥 官网用法： https://airflow.apache.org/howto/secure-connections.html  130 fernet_key = Et8ULvn0biL8X0xXl66wHawhdetf7utIDYDgNzZh4nCnE=  131  132 # Whether to disable pickling dags  133 donot_pickle = False  134  135 # How long before timing out a python file import while filling the DagBag  136 dagbag_import_timeout = 30  137  138 # The class to use for running task instances in a subprocess  139 task_runner = StandardTaskRunner  140  141 # If set, tasks without a `run_as_user` argument will be run with this user  142 # Can be used to de-elevate a sudo user running Airflow when executing tasks  143 default_impersonation =  144  145 # What security module to use (for example kerberos):  146 security =  147  148 # If set to False enables some unsecure features like Charts and Ad Hoc Queries.  149 # In 2.0 will default to True.  150 secure_mode = False  151  152 # Turn unit test mode on (overwrites many configuration options with test  153 # values at runtime)  154 unit_test_mode = False  155  156 # Name of handler to read task instance logs.  157 # Default to use task handler.  158 task_log_reader = task  159  160 # Whether to enable pickling for xcom (note that this is insecure and allows for  161 # RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False).  162 enable_xcom_pickling = True  163  164 # When a task is killed forcefully, this is the amount of time in seconds that  165 # it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED  166 killed_task_cleanup_time = 60  167  168 # Whether to override params with dag_run.conf. If you pass some key-value pairs through `airflow backfill -c` or  169 # `airflow trigger_dag -c`, the key-value pairs will override the existing ones in params.  170 dag_run_conf_overrides_params = False  171  172 # Worker initialisation check to validate Metadata Database connection  173 worker_precheck = False  174  175 # When discovering DAGs, ignore any files that don't contain the strings `DAG` and `airflow`.  176 dag_discovery_safe_mode = True  177  178  179 [cli]  180 # In what way should the cli access the API. The LocalClient will use the  181 # database directly, while the json_client will use the api running on the  182 # webserver  183 api_client = airflow.api.client.local_client  184  185 # If you set web_server_url_prefix, do NOT forget to append it here, ex:  186 # endpoint_url = http://localhost:8080/myroot  187 # So api will look like: http://localhost:8080/myroot/api/experimental/...  188 endpoint_url = http://localhost:18080  189  190 [api]  191 # How to authenticate users of the API  192 auth_backend = airflow.api.auth.backend.default  193  194 [lineage]  195 # what lineage backend to use  196 backend =  197  198 [atlas]  199 sasl_enabled = False  200 host =  201 port = 21000  202 username =  203 password =  204  205 [operators]  206 # The default owner assigned to each new operator, unless  207 # provided explicitly or passed via `default_args`  208 default_owner = airflow  209 default_cpus = 1  210 default_ram = 512  211 default_disk = 512  212 default_gpus = 0  213  214 [hive]  215 # Default mapreduce queue for HiveOperator tasks  216 default_hive_mapred_queue =  217  218 [webserver]  219 # web端访问配置  220 # The base url of your website as airflow cannot guess what domain or  221 # cname you are using. This is used in automated emails that  222 # airflow sends to point links to the right web server  223 base_url = http://localhost:18080  224  225 # The ip specified when starting the web server  226 web_server_host = 0.0.0.0  227  228 # The port on which to run the web server  229 web_server_port = 18080  230  231 # Paths to the SSL certificate and key for the web server. When both are  232 # provided SSL will be enabled. This does not change the web server port.  233 web_server_ssl_cert =  234 web_server_ssl_key =  235  236 # Number of seconds the webserver waits before killing gunicorn master that doesn't respond  237 web_server_master_timeout = 120  238  239 # Number of seconds the gunicorn webserver waits before timing out on a worker  240 web_server_worker_timeout = 120  241  242 # Number of workers to refresh at a time. When set to 0, worker refresh is  243 # disabled. When nonzero, airflow periodically refreshes webserver workers by  244 # bringing up new ones and killing old ones.  245 worker_refresh_batch_size = 1  246  247 # Number of seconds to wait before refreshing a batch of workers.  248 worker_refresh_interval = 30  249  250 # Secret key used to run your flask app  251 secret_key = temporary_key  252  253 # Number of workers to run the Gunicorn web server  254 workers = 4  255  256 # The worker class gunicorn should use. Choices include  257 # sync (default), eventlet, gevent  258 worker_class = sync  259  260 # Log files for the gunicorn webserver. '-' means log to stderr.  261 access_logfile = -  262 error_logfile = -  263  264 # Expose the configuration file in the web server  265 # This is only applicable for the flask-admin based web UI (non FAB-based).  266 # In the FAB-based web UI with RBAC feature,  267 # access to configuration is controlled by role permissions.  268 expose_config = False  269  270 # Set to true to turn on authentication:  271 # https://airflow.apache.org/security.html#web-authentication  272 authenticate = False  273  274 # Filter the list of dags by owner name (requires authentication to be enabled)  275 filter_by_owner = False  276  277 # Filtering mode. Choices include user (default) and ldapgroup.  278 # Ldap group filtering requires using the ldap backend  279 #  280 # Note that the ldap server needs the "memberOf" overlay to be set up  281 # in order to user the ldapgroup mode.  282 owner_mode = user  283  284 # Default DAG view.  Valid values are:  285 # tree, graph, duration, gantt, landing_times  286 dag_default_view = tree  287  288 # Default DAG orientation. Valid values are:  289 # LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top)  290 dag_orientation = LR  291  292 # Puts the webserver in demonstration mode; blurs the names of Operators for  293 # privacy.  294 demo_mode = False  295  296 # The amount of time (in secs) webserver will wait for initial handshake  297 # while fetching logs from other worker machine  298 log_fetch_timeout_sec = 5  299  300 # By default, the webserver shows paused DAGs. Flip this to hide paused  301 # DAGs by default  302 hide_paused_dags_by_default = False  303  304 # Consistent page size across all listing views in the UI  305 page_size = 100  306  307 # Use FAB-based webserver with RBAC feature  308 # 是否登录时 需要用户名 密码 验证功能；https://airflow.apache.org/security.html#rbac-ui-security  309 rbac = False  310  311 # Define the color of navigation bar  312 navbar_color = #007A87  313  314 # Default dagrun to show in UI  315 default_dag_run_display_number = 25  316  317 # Enable werkzeug `ProxyFix` middleware  318 enable_proxy_fix = False  319  320 # Set secure flag on session cookie  321 cookie_secure = False  322  323 # Set samesite policy on session cookie  324 cookie_samesite =  325  326 # Default setting for wrap toggle on DAG code and TI log views.  327 default_wrap = False  328  329 # Send anonymous user activity to your analytics tool  330 # analytics_tool = # choose from google_analytics, segment, or metarouter  331 # analytics_id = XXXXXXXXXXX  332  333 [email]  334 email_backend = airflow.utils.email.send_email_smtp  335 # 邮件html模板绝对路径位置  336 html_content_template = /mnt/e/airflow_project/airflow_config/local/email_template  337  338 [smtp]  339 # If you want airflow to send emails on retries, failure, and you want to use  340 # the airflow.utils.email.send_email_smtp function, you have to configure an  341 # smtp server here  342 # 邮件服务 相关配置，根据实际情况配置  343 smtp_host = smtp.exmail.qq.com  344 smtp_starttls = False  345 smtp_ssl = True  346 # Uncomment and set the user/pass settings if you want to use SMTP AUTH  347 smtp_user = [email protected]  348 smtp_password = xxx  349 smtp_port = 465  350 smtp_mail_from = [email protected]  351  352  353 [celery]  354 # This section only applies if you are using the CeleryExecutor in  355 # [core] section above  356  357 # The app name that will be used by celery  358 celery_app_name = airflow.executors.celery_executor  359  360 # The concurrency that will be used when starting workers with the  361 # "airflow worker" command. This defines the number of task instances that  362 # a worker will take, so size up your workers based on the resources on  363 # your worker box and the nature of your tasks  364 worker_concurrency = 16  365  366 # The maximum and minimum concurrency that will be used when starting workers with the  367 # "airflow worker" command (always keep minimum processes, but grow to maximum if necessary).  368 # Note the value should be "max_concurrency,min_concurrency"  369 # Pick these numbers based on resources on worker box and the nature of the task.  370 # If autoscale option is available, worker_concurrency will be ignored.  371 # http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale  372 # worker_autoscale = 16,12  373  374 # When you start an airflow worker, airflow starts a tiny web server  375 # subprocess to serve the workers local log files to the airflow main  376 # web server, who then builds pages and sends them to users. This defines  377 # the port on which the logs are served. It needs to be unused, and open  378 # visible from the main web server to connect into the workers.  379 worker_log_server_port = 8793  380  381 # The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally  382 # a sqlalchemy database. Refer to the Celery documentation for more  383 # information.  384 # http://docs.celeryproject.org/en/latest/userguide/configuration.html#broker-settings  385 # celery服务 broker连接，此处使用 rabbitmq  386 broker_url = pyamqp://role:[email protected]:5672/  387  388 # The Celery result_backend. When a job finishes, it needs to update the  389 # metadata of the job. Therefore it will post a message on a message bus,  390 # or insert it into a database (depending of the backend)  391 # This status is used by the scheduler to update the state of the task  392 # The use of a database is highly recommended  393 # http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings  394 # celery服务 结果存储连接  395 result_backend = redis://localhost/15  396  397 # Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start  398 # it `airflow flower`. This defines the IP that Celery Flower runs on  399 flower_host = 0.0.0.0  400  401 # The root URL for Flower  402 # Ex: flower_url_prefix = /flower  403 flower_url_prefix =  404  405 # This defines the port that Celery Flower runs on  406 flower_port = 5555  407  408 # Securing Flower with Basic Authentication  409 # Accepts user:password pairs separated by a comma  410 # Example: flower_basic_auth = user1:password1,user2:password2  411 flower_basic_auth =  412  413 # Default queue that tasks get assigned to and that worker listen on.  414 default_queue = default  415  416 # How many processes CeleryExecutor uses to sync task state.  417 # 0 means to use max(1, number of cores - 1) processes.  418 sync_parallelism = 0  419  420 # Import path for celery configuration options  421 celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG  422  423 # In case of using SSL  424 ssl_active = False  425 ssl_key =  426 ssl_cert =  427 ssl_cacert =  428  429 # Celery Pool implementation.  430 # Choices include: prefork (default), eventlet, gevent or solo.  431 # See:  432 #   https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency  433 #   https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html  434 pool = prefork  435  436 [celery_broker_transport_options]  437 # This section is for specifying options which can be passed to the  438 # underlying celery broker transport.  See:  439 # http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options  440  441 # The visibility timeout defines the number of seconds to wait for the worker  442 # to acknowledge the task before the message is redelivered to another worker.  443 # Make sure to increase the visibility timeout to match the time of the longest  444 # ETA you're planning to use.  445 #  446 # visibility_timeout is only supported for Redis and SQS celery brokers.  447 # See:  448 #   http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options  449 #  450 #visibility_timeout = 21600  451  452 [dask]  453 # This section only applies if you are using the DaskExecutor in  454 # [core] section above  455  456 # The IP address and port of the Dask cluster's scheduler.  457 cluster_address = 127.0.0.1:8786  458 # TLS/ SSL settings to access a secured Dask scheduler.  459 tls_ca =  460 tls_cert =  461 tls_key =  462  463  464 [scheduler]  465 # Task instances listen for external kill signal (when you clear tasks  466 # from the CLI or the UI), this defines the frequency at which they should  467 # listen (in seconds).  468 job_heartbeat_sec = 5  469  470 # The scheduler constantly tries to trigger new tasks (look at the  471 # scheduler section in the docs for more information). This defines  472 # how often the scheduler should run (in seconds).  473 scheduler_heartbeat_sec = 5  474  475 # after how much time should the scheduler terminate in seconds  476 # -1 indicates to run continuously (see also num_runs)  477 run_duration = -1  478  479 # after how much time (seconds) a new DAGs should be picked up from the filesystem  480 min_file_process_interval = 0  481  482 # How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes.  483 dag_dir_list_interval = 300  484  485 # How often should stats be printed to the logs  486 print_stats_interval = 30  487  488 # If the last scheduler heartbeat happened more than scheduler_health_check_threshold ago (in seconds),  489 # scheduler is considered unhealthy.  490 # This is used by the health check in the "/health" endpoint  491 scheduler_health_check_threshold = 30  492  493 # 定时任务 日志位置  494 child_process_log_directory = /mnt/e/airflow_project/log/airflow/scheduler  495  496 # Local task jobs periodically heartbeat to the DB. If the job has  497 # not heartbeat in this many seconds, the scheduler will mark the  498 # associated task instance as failed and will re-schedule the task.  499 scheduler_zombie_task_threshold = 300  500  501 # Turn off scheduler catchup by setting this to False.  502 # Default behavior is unchanged and  503 # Command Line Backfills still work, but the scheduler  504 # will not do scheduler catchup if this is False,  505 # however it can be set on a per DAG basis in the  506 # DAG definition (catchup)  507 catchup_by_default = True  508  509 # This changes the batch size of queries in the scheduling main loop.  510 # If this is too high, SQL query performance may be impacted by one  511 # or more of the following:  512 #  - reversion to full table scan  513 #  - complexity of query predicate  514 #  - excessive locking  515 #  516 # Additionally, you may hit the maximum allowable query length for your db.  517 #  518 # Set this to 0 for no limit (not advised)  519 max_tis_per_query = 512  520  521 # Statsd (https://github.com/etsy/statsd) integration settings  522 statsd_on = True  523 statsd_host = localhost  524 statsd_port = 8125  525 statsd_prefix = airflow  526  527 # The scheduler can run multiple threads in parallel to schedule dags.  528 # This defines how many threads will run.  529 max_threads = 2  530  531 authenticate = False  532  533 # Turn off scheduler use of cron intervals by setting this to False.  534 # DAGs submitted manually in the web UI or with trigger_dag will still run.  535 use_job_schedule = True  536  537 [ldap]  538 # set this to ldaps://<your.ldap.server>:<port>  539 uri =  540 user_filter = objectClass=*  541 user_name_attr = uid  542 group_member_attr = memberOf  543 superuser_filter =  544 data_profiler_filter =  545 bind_user = cn=Manager,dc=example,dc=com  546 bind_password = insecure  547 basedn = dc=example,dc=com  548 cacert = /etc/ca/ldap_ca.crt  549 search_scope = LEVEL  550  551 # This setting allows the use of LDAP servers that either return a  552 # broken schema, or do not return a schema.  553 ignore_malformed_schema = False  554  555 [mesos]  556 # Mesos master address which MesosExecutor will connect to.  557 master = localhost:5050  558  559 # The framework name which Airflow scheduler will register itself as on mesos  560 framework_name = Airflow  561  562 # Number of cpu cores required for running one task instance using  563 # 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'  564 # command on a mesos slave  565 task_cpu = 1  566  567 # Memory in MB required for running one task instance using  568 # 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'  569 # command on a mesos slave  570 task_memory = 256  571  572 # Enable framework checkpointing for mesos  573 # See http://mesos.apache.org/documentation/latest/slave-recovery/  574 checkpoint = False  575  576 # Failover timeout in milliseconds.  577 # When checkpointing is enabled and this option is set, Mesos waits  578 # until the configured timeout for  579 # the MesosExecutor framework to re-register after a failover. Mesos  580 # shuts down running tasks if the  581 # MesosExecutor framework fails to re-register within this timeframe.  582 # failover_timeout = 604800  583  584 # Enable framework authentication for mesos  585 # See http://mesos.apache.org/documentation/latest/configuration/  586 authenticate = False  587  588 # Mesos credentials, if authentication is enabled  589 # default_principal = admin  590 # default_secret = admin  591  592 # Optional Docker Image to run on slave before running the command  593 # This image should be accessible from mesos slave i.e mesos slave  594 # should be able to pull this docker image before executing the command.  595 # docker_image_slave = puckel/docker-airflow  596  597 [kerberos]  598 ccache = /tmp/airflow_krb5_ccache  599 # gets augmented with fqdn  600 principal = airflow  601 reinit_frequency = 3600  602 kinit_path = kinit  603 keytab = airflow.keytab  604  605  606 [github_enterprise]  607 api_rev = v3  608  609 [admin]  610 # UI to hide sensitive variable fields when set to True  611 hide_sensitive_variable_fields = True  612  613 [elasticsearch]  614 # Elasticsearch host  615 host =  616 # Format of the log_id, which is used to query for a given tasks logs  617 log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number}  618 # Used to mark the end of a log stream for a task  619 end_of_log_mark = end_of_log  620 # Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id  621 # Code will construct log_id using the log_id template from the argument above.  622 # NOTE: The code will prefix the https:// automatically, don't include that here.  623 frontend =  624 # Write the task logs to the stdout of the worker, rather than the default files  625 write_stdout = False  626 # Instead of the default log formatter, write the log lines as JSON  627 json_format = False  628 # Log fields to also attach to the json output, if enabled  629 json_fields = asctime, filename, lineno, levelname, message  630  631 [elasticsearch_configs]  632  633 use_ssl = False  634 verify_certs = True  635  636 [kubernetes]  637 # The repository, tag and imagePullPolicy of the Kubernetes Image for the Worker to Run  638 worker_container_repository =  639 worker_container_tag =  640 worker_container_image_pull_policy = IfNotPresent  641  642 # If True (default), worker pods will be deleted upon termination  643 delete_worker_pods = True  644  645 # Number of Kubernetes Worker Pod creation calls per scheduler loop  646 worker_pods_creation_batch_size = 1  647  648 # The Kubernetes namespace where airflow workers should be created. Defaults to `default`  649 namespace = default  650  651 # The name of the Kubernetes ConfigMap Containing the Airflow Configuration (this file)  652 airflow_configmap =  653  654 # For docker image already contains DAGs, this is set to `True`, and the worker will search for dags in dags_folder,  655 # otherwise use git sync or dags volume claim to mount DAGs  656 dags_in_image = False  657  658 # For either git sync or volume mounted DAGs, the worker will look in this subpath for DAGs  659 dags_volume_subpath =  660  661 # For DAGs mounted via a volume claim (mutually exclusive with git-sync and host path)  662 dags_volume_claim =  663  664 # For volume mounted logs, the worker will look in this subpath for logs  665 logs_volume_subpath =  666  667 # A shared volume claim for the logs  668 logs_volume_claim =  669  670 # For DAGs mounted via a hostPath volume (mutually exclusive with volume claim and git-sync)  671 # Useful in local environment, discouraged in production  672 dags_volume_host =  673  674 # A hostPath volume for the logs  675 # Useful in local environment, discouraged in production  676 logs_volume_host =  677  678 # A list of configMapsRefs to envFrom. If more than one configMap is  679 # specified, provide a comma separated list: configmap_a,configmap_b  680 env_from_configmap_ref =  681  682 # A list of secretRefs to envFrom. If more than one secret is  683 # specified, provide a comma separated list: secret_a,secret_b  684 env_from_secret_ref =  685  686 # Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim)  687 git_repo =  688 git_branch =  689 git_subpath =  690 # Use git_user and git_password for user authentication or git_ssh_key_secret_name and git_ssh_key_secret_key  691 # for SSH authentication  692 git_user =  693 git_password =  694 git_sync_root = /git  695 git_sync_dest = repo  696 # Mount point of the volume if git-sync is being used.  697 # i.e. /Users/wudong/work/Python/flow/dags  698 git_dags_folder_mount_point =  699  700 # To get Git-sync SSH authentication set up follow this format  701 #  702 # airflow-secrets.yaml:  703 # ---  704 # apiVersion: v1  705 # kind: Secret  706 # metadata:  707 #   name: airflow-secrets  708 # data:  709 #   # key needs to be gitSshKey  710 #   gitSshKey: <base64_encoded_data>  711 # ---  712 # airflow-configmap.yaml:  713 # apiVersion: v1  714 # kind: ConfigMap  715 # metadata:  716 #   name: airflow-configmap  717 # data:  718 #   known_hosts: |  719 #       github.com ssh-rsa <...>  720 #   airflow.cfg: |  721 #       ...  722 #  723 # git_ssh_key_secret_name = airflow-secrets  724 # git_ssh_known_hosts_configmap_name = airflow-configmap  725 git_ssh_key_secret_name =  726 git_ssh_known_hosts_configmap_name =  727  728 # To give the git_sync init container credentials via a secret, create a secret  729 # with two fields: GIT_SYNC_USERNAME and GIT_SYNC_PASSWORD (example below) and  730 # add `git_sync_credentials_secret = <secret_name>` to your airflow config under the kubernetes section  731 #  732 # Secret Example:  733 # apiVersion: v1  734 # kind: Secret  735 # metadata:  736 #   name: git-credentials  737 # data:  738 #   GIT_SYNC_USERNAME: <base64_encoded_git_username>  739 #   GIT_SYNC_PASSWORD: <base64_encoded_git_password>  740 git_sync_credentials_secret =  741  742 # For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync  743 git_sync_container_repository = k8s.gcr.io/git-sync  744 git_sync_container_tag = v3.1.1  745 git_sync_init_container_name = git-sync-clone  746 git_sync_run_as_user = 65533  747  748 # The name of the Kubernetes service account to be associated with airflow workers, if any.  749 # Service accounts are required for workers that require access to secrets or cluster resources.  750 # See the Kubernetes RBAC documentation for more:  751 #   https://kubernetes.io/docs/admin/authorization/rbac/  752 worker_service_account_name =  753  754 # Any image pull secrets to be given to worker pods, If more than one secret is  755 # required, provide a comma separated list: secret_a,secret_b  756 image_pull_secrets =  757  758 # GCP Service Account Keys to be provided to tasks run on Kubernetes Executors  759 # Should be supplied in the format: key-name-1:key-path-1,key-name-2:key-path-2  760 gcp_service_account_keys =  761  762 # Use the service account kubernetes gives to pods to connect to kubernetes cluster.  763 # It's intended for clients that expect to be running inside a pod running on kubernetes.  764 # It will raise an exception if called from a process not running in a kubernetes environment.  765 in_cluster = True  766  767 # When running with in_cluster=False change the default cluster_context or config_file  768 # options to Kubernetes client. Leave blank these to use default behaviour like `kubectl` has.  769 # cluster_context =  770 # config_file =  771  772  773 # Affinity configuration as a single line formatted JSON object.  774 # See the affinity model for top-level key names (e.g. `nodeAffinity`, etc.):  775 #   https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#affinity-v1-core  776 affinity =  777  778 # A list of toleration objects as a single line formatted JSON array  779 # See:  780 #   https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#toleration-v1-core  781 tolerations =  782  783 # **kwargs parameters to pass while calling a kubernetes client core_v1_api methods from Kubernetes Executor  784 # provided as a single line formatted JSON dictionary string.  785 # List of supported params in **kwargs are similar for all core_v1_apis, hence a single config variable for all apis  786 # See:  787 #   https://raw.githubusercontent.com/kubernetes-client/python/master/kubernetes/client/apis/core_v1_api.py  788 # Note that if no _request_timeout is specified, the kubernetes client will wait indefinitely for kubernetes  789 # api responses, which will cause the scheduler to hang. The timeout is specified as [connect timeout, read timeout]  790 kube_client_request_args = {"_request_timeout" : [60,60] }  791  792 # Worker pods security context options  793 # See:  794 #   https://kubernetes.io/docs/tasks/configure-pod-container/security-context/  795  796 # Specifies the uid to run the first process of the worker pods containers as  797 run_as_user =  798  799 # Specifies a gid to associate with all containers in the worker pods  800 # if using a git_ssh_key_secret_name use an fs_group  801 # that allows for the key to be read, e.g. 65533  802 fs_group =  803  804 [kubernetes_node_selectors]  805 # The Key-value pairs to be given to worker pods.  806 # The worker pods will be scheduled to the nodes of the specified key-value pairs.  807 # Should be supplied in the format: key = value  808  809 [kubernetes_annotations]  810 # The Key-value annotations pairs to be given to worker pods.  811 # Should be supplied in the format: key = value  812  813 [kubernetes_environment_variables]  814 # The scheduler sets the following environment variables into your workers. You may define as  815 # many environment variables as needed and the kubernetes launcher will set them in the launched workers.  816 # Environment variables in this section are defined as follows  817 #     <environment_variable_key> = <environment_variable_value>  818 #  819 # For example if you wanted to set an environment variable with value `prod` and key  820 # `ENVIRONMENT` you would follow the following format:  821 #     ENVIRONMENT = prod  822 #  823 # Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY>  824 # formatting as supported by airflow normally.  825  826 [kubernetes_secrets]  827 # The scheduler mounts the following secrets into your workers as they are launched by the  828 # scheduler. You may define as many secrets as needed and the kubernetes launcher will parse the  829 # defined secrets and mount them as secret environment variables in the launched workers.  830 # Secrets in this section are defined as follows  831 #     <environment_variable_mount> = <kubernetes_secret_object>=<kubernetes_secret_key>  832 #  833 # For example if you wanted to mount a kubernetes secret key named `postgres_password` from the  834 # kubernetes secret object `airflow-secret` as the environment variable `POSTGRES_PASSWORD` into  835 # your workers you would follow the following format:  836 #     POSTGRES_PASSWORD = airflow-secret=postgres_credentials  837 #  838 # Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY>  839 # formatting as supported by airflow normally.  840  841 [kubernetes_labels]  842 # The Key-value pairs to be given to worker pods.  843 # The worker pods will be given these static labels, as well as some additional dynamic labels  844 # to identify the task.  845 # Should be supplied in the format: key = value

错误记录：

* 设置supervisor启动airflow服务时，报错如下

Error: No module named airflow.www.gunicorn_config

* 处理方式

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

* web界面报错

KeyError: 'Variable xxx does not exist'

* 处理方式

在airflow网页的Admin=>Variables页面添加对应的 变量

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

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