[ https://issues.apache.org/jira/browse/AIRFLOW-6015?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ash Berlin-Taylor closed AIRFLOW-6015. -------------------------------------- Resolution: Not A Problem {{airflow worker}} is for running Celery Workers -- when you are using the LocalExecutor you do not need to (and shouldn't) run {{airflow worker}} as the task processes are create as sub-processes of the scheduler. Doc PR welcome to make this clearer :) > Celery needed even if LocalExecutor is configured > ------------------------------------------------- > > Key: AIRFLOW-6015 > URL: https://issues.apache.org/jira/browse/AIRFLOW-6015 > Project: Apache Airflow > Issue Type: Bug > Components: configuration > Affects Versions: 1.10.6 > Environment: ubuntu 16.04 > Python 3.5 with virtualenv > Reporter: Mathieu Cinquin > Priority: Major > > Hello, > > Celery is required to start airflow worker even if the executor configured is > LocalExecutor. > If i install apache-airflow[celery], the worker daemon well starts and uses > Celery as executor. > log > {code:java} > Nov 19 19:47:27 airflow-1 systemd[1]: Started Airflow worker daemon. > Nov 19 19:47:28 airflow-1 bash[10663]: [2019-11-19 19:47:28,226] > {settings.py:252} INFO - settings.configure_orm(): Using pool settings. > pool_size=128, max_overflow=10, pool_recycle=600, pid=10668 > Nov 19 19:47:29 airflow-1 bash[10663]: Traceback (most recent call last): > Nov 19 19:47:29 airflow-1 bash[10663]: File > "/home/airflow/airflow/venv/bin/airflow", line 37, in <module> > Nov 19 19:47:29 airflow-1 bash[10663]: args.func(args) > Nov 19 19:47:29 airflow-1 bash[10663]: File > "/home/airflow/airflow/venv/lib/python3.5/site-packages/airflow/utils/cli.py", > line 74, in wrapper > Nov 19 19:47:29 airflow-1 bash[10663]: return f(*args, **kwargs) > Nov 19 19:47:29 airflow-1 bash[10663]: File > "/home/airflow/airflow/venv/lib/python3.5/site-packages/airflow/bin/cli.py", > line 1075, in worker > Nov 19 19:47:29 airflow-1 bash[10663]: from > airflow.executors.celery_executor import app as celery_app > Nov 19 19:47:29 airflow-1 bash[10663]: File > "/home/airflow/airflow/venv/lib/python3.5/site-packages/airflow/executors/celery_executor.py", > line 27, in <module> > Nov 19 19:47:29 airflow-1 bash[10663]: from celery import Celery > Nov 19 19:47:29 airflow-1 bash[10663]: ImportError: No module named 'celery' > Nov 19 19:47:30 airflow-1 systemd[1]: airflow-worker.service: Main process > exited, code=exited, status=1/FAILURE > Nov 19 19:47:30 airflow-1 systemd[1]: airflow-worker.service: Unit entered > failed state. > Nov 19 19:47:30 airflow-1 systemd[1]: airflow-worker.service: Failed with > result 'exit-code'. > Nov 19 19:47:40 airflow-1 systemd[1]: airflow-worker.service: Service > hold-off time over, scheduling restart. > Nov 19 19:47:40 airflow-1 systemd[1]: Stopped Airflow worker daemon. > {code} > > airflow.cfg > > {code:java} > [core] > # The home folder for airflow, default is ~/airflow > # airflow_home = /home/airflow/airflow > # The folder where your airflow pipelines live, most likely a > # subfolder in a code repository > # This path must be absolute > dags_folder = /home/airflow/airflow/dags > # The folder where airflow should store its log files > # This path must be absolute > base_log_folder = /var/log/airflow# Airflow can store logs remotely in AWS S3 > or Google Cloud Storage. Users > # must supply a remote location URL (starting with either 's3://...' or > # 'gs://...') and an Airflow connection id that provides access to the storage > # location. > remote_base_log_folder = > remote_log_conn_id = > # Use server-side encryption for logs stored in S3 > encrypt_s3_logs = False > # DEPRECATED option for remote log storage, use remote_base_log_folder > instead! > s3_log_folder = None > # The executor class that airflow should use. Choices include > # SequentialExecutor, LocalExecutor, CeleryExecutor > executor = LocalExecutor > # The SqlAlchemy connection string to the metadata database. > # SqlAlchemy supports many different database engine, more information > # their website > sql_alchemy_conn = mysql://airflow:xxx@localhost:3306/airflow > # The SqlAlchemy pool size is the maximum number of database connections > # in the pool. > sql_alchemy_pool_size = 128 > # 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. > sql_alchemy_pool_recycle = 600 > # The amount of parallelism as a setting to the executor. This defines > # the max number of task instances that should run simultaneously > # on this airflow installation > parallelism = 4 > # The number of task instances allowed to run concurrently by the scheduler > dag_concurrency = 4 > # Are DAGs paused by default at creation > dags_are_paused_at_creation = True > # When not using pools, tasks are run in the "default pool", > # whose size is guided by this config element > non_pooled_task_slot_count = 128 > # The maximum number of active DAG runs per DAG > max_active_runs_per_dag = 4 > # Whether to load the examples that ship with Airflow. It's good to > # get started, but you probably want to set this to False in a production > # environment > load_examples = False > # Where your Airflow plugins are stored > plugins_folder = /home/airflow/airflow/plugins > # Secret key to save connection passwords in the db > fernet_key = xxx > # Whether to disable pickling dags > donot_pickle = False > # How long before timing out a python file import while filling the DagBag > dagbag_import_timeout = 30 > # The class to use for running task instances in a subprocess > task_runner = StandardTaskRunner > # If set, tasks without a `run_as_user` argument will be run with this user > # Can be used to de-elevate a sudo user running Airflow when executing tasks > default_impersonation = > # What security module to use (for example kerberos): > security = > # Turn unit test mode on (overwrites many configuration options with test > # values at runtime) > unit_test_mode = False > [cli] > # In what way should the cli access the API. The LocalClient will use the > # database directly, while the json_client will use the api running on the > # webserver > api_client = airflow.api.client.local_client > endpoint_url = http://localhost:8080 > [api] > # How to authenticate users of the API > auth_backend = airflow.api.auth.backend.deny_all > [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 > [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://airflow-1.example.com:8080 > # The ip specified when starting the web server > web_server_host = 0.0.0.0 > # The port on which to run the web server > web_server_port = 8080 > # Paths to the SSL certificate and key for the web server. When both are > # provided SSL will be enabled. This does not change the web server port. > #web_server_ssl_cert = /etc/ssl/certs/example.com.crt > #web_server_ssl_key = /etc/ssl//etc/ssl/private/example.com.key > # Number of seconds the gunicorn webserver waits before timing out on a worker > web_server_worker_timeout = 600 > web_server_master_timeout = 600 > # 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 = 0 > # Number of seconds to wait before refreshing a batch of workers. > worker_refresh_interval = 30 > # Secret key used to run your flask app > secret_key = xxx > # Number of workers to run the Gunicorn web server > workers = 4 > # The worker class gunicorn should use. Choices include > # sync (default), eventlet, gevent > worker_class = sync > # Log files for the gunicorn webserver. '-' means log to stderr. > access_logfile = - > error_logfile = - > rbac = True > # Expose the configuration file in the web server > expose_config = False > # Set to true to turn on authentication: > # http://pythonhosted.org/airflow/security.html > #web-authentication > authenticate = True > auth_backend = airflow.contrib.auth.backends.password_auth > # Filter the list of dags by owner name (requires authentication to be > enabled) > filter_by_owner = False > # Filtering mode. Choices include user (default) and ldapgroup. > # Ldap group filtering requires using the ldap backend > # > # Note that the ldap server needs the "memberOf" overlay to be set up > # in order to user the ldapgroup mode. > owner_mode = user > # Default DAG orientation. Valid values are: > # LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top) > dag_orientation = LR > # Puts the webserver in demonstration mode; blurs the names of Operators for > # privacy. > demo_mode = False > # The amount of time (in secs) webserver will wait for initial handshake > # while fetching logs from other worker machine > log_fetch_timeout_sec = 5 > # By default, the webserver shows paused DAGs. Flip this to hide paused > # DAGs by default > hide_paused_dags_by_default = False > [email] > email_backend = airflow.utils.email.send_email_smtp[smtp] > # If you want airflow to send emails on retries, failure, and you want to use > # the airflow.utils.email.send_email_smtp function, you have to configure an > # smtp server here > smtp_host = localhost > smtp_starttls = True > smtp_ssl = False > # Uncomment and set the user/pass settings if you want to use SMTP AUTH > smtp_user = airflow > smtp_password = xxx > smtp_port = 587 > smtp_mail_from = airf...@airflow-1.example.com > [schedduler] > # Task instances listen for external kill signal (when you clear tasks > # from the CLI or the UI), this defines the frequency at which they should > # listen (in seconds). > job_heartbeat_sec = 5 > # The scheduler constantly tries to trigger new tasks (look at the > # scheduler section in the docs for more information). This defines > # how often the scheduler should run (in seconds). > scheduler_heartbeat_sec = 5 > # after how much time should the scheduler terminate in seconds > # -1 indicates to run continuously (see also num_runs) > run_duration = -1 > # after how much time a new DAGs should be picked up from the filesystem > min_file_process_interval = 0 > dag_dir_list_interval = 300 > # How often should stats be printed to the logs > print_stats_interval = 30 > child_process_log_directory = /var/log/airflow/scheduler > # Local task jobs periodically heartbeat to the DB. If the job has > # not heartbeat in this many seconds, the scheduler will mark the > # associated task instance as failed and will re-schedule the task. > scheduler_zombie_task_threshold = 300 > # Turn off scheduler catchup by setting this to False. > # Default behavior is unchanged and > # Command Line Backfills still work, but the scheduler > # will not do scheduler catchup if this is False, > # however it can be set on a per DAG basis in the > # DAG definition (catchup) > catchup_by_default = True > # The scheduler can run multiple threads in parallel to schedule dags. > # This defines how many threads will run. However airflow will never > # use more threads than the amount of cpu cores available. > max_threads = 2 > authenticate = False > [admin] > # UI to hide sensitive variable fields when set to True > hide_sensitive_variable_fields = True > {code} > > > > > > -- This message was sent by Atlassian Jira (v8.3.4#803005)