Airflow速用

Airflow是Apache用python編寫的，用到了 flask框架及相關插件,rabbitmq,celery等（windows不兼容）；、

 

主要實現的功能

• 編寫 定時任務，及任務間的編排；
• 提供了web界面 能夠手動觸發任務，分析任務執行順序，任務執行狀態，任務代碼，任務日誌等等；
• 實現celery的分佈式任務調度系統;
• 簡單方便的實現了 任務在各類狀態下觸發 發送郵件的功能；https://airflow.apache.org/concepts.html#email-configuration
• 對組合任務 能夠根據 不一樣參數進入不一樣分支進行處理 http://airflow.apache.org/concepts.html#branching
• 執行 bash腳本命令；
• 對組合任務 設置觸發條件（如:所有失敗/成功時執行某任務 等等）http://airflow.apache.org/concepts.html#trigger-rules
• 簡單實現隨機 負載均衡和容錯能力 http://airflow.apache.org/concepts.html#connections
• 對組合任務 間進行數據傳遞 http://airflow.apache.org/concepts.html#xcoms
• 對分佈式任務指定 queue, worker能夠指定消費的queue(celery的使用) http://airflow.apache.org/concepts.html#queues
• 存儲日誌到遠程 http://airflow.apache.org/howto/write-logs.html
• 調用 遠程 谷歌雲，亞馬遜雲 相關服務（如語音識別等等）https://airflow.apache.org/integration.html#integration
• 調用 釘釘 相關服務

實現功能總結

不只celery有的功能我都有， 我還能經過頁面手動觸發/暫停任務，管理任務特方便；我他媽還能 調用谷歌雲等服務，日誌也能方便打印到雲服務上。。。。。。；我就是牛！

 

核心思想

• DAG:英文爲：Directed Acyclic Graph；指 (有向無環圖)有向非循環圖，是想運行的一系列任務的集合，不關心任務是作什麼的，只關心 任務間的組成方式，確保在正確的時間，正確的順序觸發各個任務，準確的處理意外狀況；http://airflow.apache.org/concepts.html#dags
• DAGs:多個任務集（多個DAG）
• Operator: 指 某些類型任務的模板 類；如 PythonOperator(執行python相關操做)，EmailOperator(執行發送郵件相關操做)，SimpleHttpOperator(執行發送http請求相關操做) 等幾十種（源碼可見）http://airflow.apache.org/howto/operator/index.html#
• Task:當經過 Operator定義了執行任務內容後，在實例化後，即是 Task,爲DAG中任務集合的具體任務
• Executor：數據庫記錄任務狀態(排隊queued，預執行scheduled，運行中running，成功success，失敗failed),調度器（Scheduler ）從數據庫取數據並決定哪些須要完成，而後 Executor 和調度器一塊兒合做，給任務須要的資源讓其完成。Executor間(如 LocalExecutor,CeleryExecutor)不一樣點在於他們擁有不一樣的資源以及如何利用資源分配工做，如LocalExecutor只在本地並行執行任務，CeleryExecutor分佈式多機器執行任務。 https://www.astronomer.io/guides/airflow-executors-explained/
• Hook：是airflow與外部平臺/數據庫交互的方式，如 http/ssh/sftp等等，是Operator的基礎部分(如SimpleHttpOperator 須要依賴HttpHook)

 

任務間定義排序的方法

官方推薦使用 移位操做符 方法，由於較爲直觀，容易理解

如：  op1 >> op2 >> op3   表示任務執行順序爲  從左到右依次執行

官方文檔介紹：http://airflow.apache.org/concepts.html#bitshift-composition

 

提升airflow相關執行速度方法

經過修改airflow.cfg相關配置

官方文檔以下：http://airflow.apache.org/faq.html

 

安裝及啓動相關服務

• 建立python虛擬環境 venv
• 添加airflow.cfg（此配置註解在下面）的配置文件夾路徑：先 vi venv/bin/active; 裏面輸入 export AIRFLOW_HOME="/mnt/e/project/airflow_config/local"

• 命令行：pip install apache-airflow

• 根據airflow.cfg的數據庫配置，在鏈接的數據庫服務建立一個 名爲 airflow_db的數據庫

• 命令行初始化數據庫：airflow initdb

• 命令行啓動web服務: airflow webserver -p 8080

• 命令行啓動任務調度服務：airflow scheduler

• 命令行啓動worker：airflow worker -q queue_name

 

使用 http_operator發送http請求並在失敗時,發送郵件

1.設置郵件html模板（以下爲自定義模板）

<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>

模板效果圖：

 2. airflow.cfg文件中配置 發送郵件服務

 

 3.編寫代碼：

 1 # -*- coding: utf-8 -*-
 2 """
 3 (C) xxx <xxx@xxx.com>  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 # 設置第一次觸發任務時間 及 設置任務執行的時區
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 # 從環境變量找到 當前環境
21 env = os.environ.get("PROJECT_ENV", "LOCAL") 22 # 添加 須要的相關環境變量，可在 web網頁中設置；注意 變量名 以AIRFLOW_CONN_開頭，而且大寫
23 os.environ["AIRFLOW_CONN_OLY_HOST"] = Variable.get("OLY_HOST_%s" % env) 24 
25 # dag默認參數
26 args = { 27     "owner": "Rgc",  # 任務擁有人
28     "depends_on_past": False,  # 是否依賴過去執行此任務的結果，若是爲True,則過去任務必須成功，才能執行這次任務
29     "start_date": utc_dt,  # 任務開始執行時間
30     "email": ["rgc@bvrft.com"],  # 郵件地址，能夠填寫多個
31     "email_on_failure": True,  # 觸發郵件發送的 時機，此處爲失敗時觸發
32 } 33 
34 # 定義一個DAG
35 # 參數catchup指 是否填充執行 start_date到如今 未執行的缺乏任務；如：start_date定義爲2019-10-10,如今是2019-10-29,任務是天天定時執行一次，
36 # 若是此參數設置爲True，則 會生成 10號到29號之間的19此任務；若是設置爲False,則不會補充執行任務；
37 # schedule_interval：定時執行方式，推薦使用以下字符串方式, 方便寫出定時規則的網址：https://crontab.guru/
38 dag = DAG("HttpSendDag", catchup=False, default_args=args, schedule_interval="0 19 * * *") 39 # 設置 dag文檔註釋，可在web界面任務詳情中看到
40 dag.doc_md = __doc__
41 
42 # 定義此 http operator相關詳情，詳細使用方法 可訪問此類定義__init__()方法
43 task = SimpleHttpOperator( 44     task_id="task_http_send",  # 任務id
45     http_conn_id="oly_host",  # http請求地址，值爲上面23行定義
46     method="POST",  # http請求方法
47     endpoint="user/manage",  # http請求路徑
48     dag=dag  # 任務所屬dag
49 ) 50 # 定義任務 文檔註釋，可在web界面任務詳情中看到
51 task.doc_md = f"""\ 52 #Usage 53 此任務主要向Project服務({Variable.get("OLY_HOST_%s" % env)})發送http請求，天天晚上7點定時運行! 54 """

 

任務間數據交流方法

    使用Xcoms(cross-communication),相似於redis存儲結構,任務推送數據或者從中下拉數據，數據在任務間共享

    推送數據主要有2中方式：1：使用xcom_push()方法  2：直接在PythonOperator中調用的函數 return便可

    下拉數據 主要使用 xcom_pull()方法

 官方代碼示例及註釋：

 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種推送數據的方式，分別爲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  下拉數據的方法 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 # 任務執行順序爲
71 # push1 >> pull
72 # push2 >> pull
73 
74 pull << [push1, push2]

View Code

 

開啓 web網頁登陸須要用戶名密碼功能

1.airflow.cfg文件修改

# 設置爲True rbac = True

2.重啓airflow相關服務

3.經過 命令行 添加 用戶

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

4.訪問頁面，輸入用戶名，密碼便可

 

忽略某些DAG文件,不調用

在dag任務文件夾下，添加一個 .airflowignore文件(像 .gitignore），裏面寫 文件名便可（支持正則）

 

 啓動及關閉airflow內置 dag示例方法（可以快速學習Airflow）

 開啓:修改airflow.cfg配置文件  load_examples = True  並重啓便可

 關閉:修改airflow.cfg配置文件  load_examples = True，並清空數據庫，並重啓便可

 效果圖：

 

 

 

airflow配置文件 相關中文註解:

 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 # 絕對路徑下 一系列dags存放位置，airflow只會今後路徑 文件夾下找dag任務  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 # 絕對路徑下的日誌文件夾位置  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 # 實際處理任務日誌 相關  43 log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log  44 log_processor_filename_template = {{ filename }}.log  45 # dag處理日誌 絕對路徑，精確到日誌文件  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 # 默認時區，改成上海，然而 沒卵用  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(任務分配執行方式)  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 # 存儲airflow相關數據的 數據庫路徑  69 sql_alchemy_conn = mysql+pymysql://root:passwd@127.0.0.1:3306/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 # 自定義 界面及api所在 絕對路徑文件夾 官網用法： 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 # 對使用到的 鏈接密碼 進行加密，此爲祕鑰 官網用法： 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端訪問配置 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 # 是否登陸時 須要用戶名 密碼 驗證功能；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 # 郵件html模板絕對路徑位置 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 # 郵件服務 相關配置，根據實際狀況配置 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 = xxx@xxx.com 348 smtp_password = xxx 349 smtp_port = 465
350 smtp_mail_from = xxx@xxx.com 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服務 broker鏈接，此處使用 rabbitmq 386 broker_url = pyamqp://role:passwd@127.0.0.1:5672/ 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服務 結果存儲鏈接 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 # 定時任務 日誌位置 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

View Code

 

錯誤記錄：

* 設置supervisor啓動airflow服務時，報錯以下

Error: No module named airflow.www.gunicorn_config

* 處理方式

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界面報錯

KeyError: 'Variable xxx does not exist'

* 處理方式

在airflow網頁的Admin=>Variables頁面添加對應的 變量

 

相關網址：http://airflow.apache.org/index.html

 

原文出處：https://www.cnblogs.com/rgcLOVEyaya/p/RGC_LOVE_YAYA_1207days.html