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From "Divya Bairavarasu (JIRA)" <j...@apache.org>
Subject [jira] [Closed] (AIRFLOW-33) The Celery Executor did start successfully,jobs are running successfully but the same is not reflected in the UI recent status section
Date Tue, 03 May 2016 23:31:12 GMT

     [ https://issues.apache.org/jira/browse/AIRFLOW-33?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Divya Bairavarasu closed AIRFLOW-33.
------------------------------------
    Resolution: Fixed

> The Celery Executor did start successfully,jobs are running successfully but the same
is not reflected in the UI recent status section
> --------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: AIRFLOW-33
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-33
>             Project: Apache Airflow
>          Issue Type: Bug
>          Components: celery
>    Affects Versions: Airflow 1.7.1
>         Environment: CentOS
>            Reporter: Divya Bairavarasu
>
> The Celery Executor did start successfully,jobs are running successfully but the same
is not reflected in the UI recent status section.
> Recent Status to show respective status of the Airflow jobs
> No Status shown in the Recent Status.
> airflow.cfg:
> [core]
> # The home folder for airflow, default is ~/airflow
> airflow_home = /root/airflow
> # The folder where your airflow pipelines live, most likely a
> # subfolder in a code repository
> dags_folder = /root/airflow/dags
> # The folder where airflow should store its log files. This location
> base_log_folder = /root/airflow/logs
> # An S3 location can be provided for log backups
> # For S3, use the full URL to the base folder (starting with "s3://...")
> s3_log_folder = None
> # The executor class that airflow should use. Choices include
> # SequentialExecutor, LocalExecutor, CeleryExecutor
> #executor = SequentialExecutor
> executor = CeleryExecutor
> # The SqlAlchemy connection string to the metadata database.
> # SqlAlchemy supports many different database engine, more information
> # their website
> #sql_alchemy_conn = sqlite:////root/airflow/airflow.db
> sql_alchemy_conn = mysql://username:pasword@username.us-east-1.rds.amazonaws.com:3306/airflow
> # The SqlAlchemy pool size is the maximum number of database connections
> # in the pool.
> sql_alchemy_pool_size = 5
> # The SqlAlchemy pool recycle is the number of seconds a connection
> # can be idle in the pool before it is invalidated. This config does
> # not apply to sqlite.
> sql_alchemy_pool_recycle = 3600
> # The amount of parallelism as a setting to the executor. This defines
> # the max number of task instances that should run simultaneously
> # on this airflow installation
> parallelism = 32
> # The number of task instances allowed to run concurrently by the scheduler
> dag_concurrency = 16
> # Are DAGs paused by default at creation
> dags_are_paused_at_creation = False
> # The maximum number of active DAG runs per DAG
> max_active_runs_per_dag = 16
> # Whether to load the examples that ship with Airflow. It's good to
> # get started, but you probably want to set this to False in a production
> # environment
> load_examples = True
> # Where your Airflow plugins are stored
> plugins_folder = /root/airflow/plugins
> # Secret key to save connection passwords in the db
> fernet_key = cryptography_not_found_storing_passwords_in_plain_text
> # 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
> [webserver]
> # The base url of your website as airflow cannot guess what domain or
> # cname you are using. This is use in automated emails that
> # airflow sends to point links to the right web server
> base_url = http://localhost:8080
> # The ip specified when starting the web server
> web_server_host = 0.0.0.0
> # The port on which to run the web server
> web_server_port = 8080
> # Secret key used to run your flask app
> secret_key = temporary_key
> # Number of workers to run the Gunicorn web server
> workers = 4
> # The worker class gunicorn should use. Choices include
> # sync (default), eventlet, gevent
> worker_class = sync
> # Expose the configuration file in the web server
> expose_config = true
> # Set to true to turn on authentication : http://pythonhosted.org/airflow/installation.html#web-authentication
> authenticate = False
> # Filter the list of dags by owner name (requires authentication to be enabled)
> filter_by_owner = False
> [email]
> email_backend = airflow.utils.send_email_smtp
> [smtp]
> # If you want airflow to send emails on retries, failure, and you want to
> # the airflow.utils.send_email function, you have to configure an smtp
> # server here
> smtp_host = localhost
> smtp_starttls = True
> smtp_ssl = False
> smtp_user = airflow
> smtp_port = 25
> smtp_password = airflow
> smtp_mail_from = airflow@airflow.com
> [celery]
> # This section only applies if you are using the CeleryExecutor in
> # [core] section above
> # The app name that will be used by celery
> celery_app_name = airflow.executors.celery_executor
> # The concurrency that will be used when starting workers with the
> # "airflow worker" command. This defines the number of task instances that
> # a worker will take, so size up your workers based on the resources on
> # your worker box and the nature of your tasks
> celeryd_concurrency = 16
> # When you start an airflow worker, airflow starts a tiny web server
> # subprocess to serve the workers local log files to the airflow main
> # web server, who then builds pages and sends them to users. This defines
> # the port on which the logs are served. It needs to be unused, and open
> # visible from the main web server to connect into the workers.
> worker_log_server_port = 8793
> # The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
> # a sqlalchemy database. Refer to the Celery documentation for more
> # information.
> #broker_url = sqla+mysql://airflow:airflow@localhost:3306/airflow
> broker_url = sqla+mysql://username:password@username.us-east-1.rds.amazonaws.com:3306/airflow
> # Another key Celery setting
> #celery_result_backend = db+mysql://airflow:airflow@localhost:3306/airflow
> celery_result_backend = db+mysql://username:pasword@username.us-east-1.rds.amazonaws.com:3306/airflow
> # Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
> # it `airflow flower`. This defines the port that Celery Flower runs on
> flower_port = 5555
> # Default queue that tasks get assigned to and that worker listen on.
> default_queue = default
> [scheduler]
> # Task instances listen for external kill signal (when you clear tasks
> # from the CLI or the UI), this defines the frequency at which they should
> # listen (in seconds).
> job_heartbeat_sec = 5
> # The scheduler constantly tries to trigger new tasks (look at the
> # scheduler section in the docs for more information). This defines
> # how often the scheduler should run (in seconds).
> scheduler_heartbeat_sec = 5
> # Statsd (https://github.com/etsy/statsd) integration settings
> # statsd_on =  False
> # statsd_host =  localhost
> # statsd_port =  8125
> # statsd_prefix = airflow
> [mesos]
> # Mesos master address which MesosExecutor will connect to.
> master = localhost:5050
> # The framework name which Airflow scheduler will register itself as on mesos
> framework_name = Airflow
> # Number of cpu cores required for running one task instance using
> # 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
> # command on a mesos slave
> task_cpu = 1
> # Memory in MB required for running one task instance using
> # 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
> # command on a mesos slave
> task_memory = 256
> # Enable framework checkpointing for mesos
> # See http://mesos.apache.org/documentation/latest/slave-recovery/
> checkpoint = False
> # Failover timeout in milliseconds.
> # When checkpointing is enabled and this option is set, Mesos waits until the configured
timeout for
> # the MesosExecutor framework to re-register after a failover. Mesos shuts down running
tasks if the
> # MesosExecutor framework fails to re-register within this timeframe.
> # failover_timeout = 604800
> # Enable framework authentication for mesos
> # See http://mesos.apache.org/documentation/latest/configuration/
> authenticate = False
> # Mesos credentials, if authentication is enabled
> # default_principal = admin
> # default_secret = admin



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