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From "Divya Bairavarasu (JIRA)" <j...@apache.org>
Subject [jira] [Created] (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 Mon, 02 May 2016 20:53:13 GMT
Divya Bairavarasu created AIRFLOW-33:
----------------------------------------

             Summary: 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
    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://truckstop:truckstop@truckstop.cc0cdtf6ro5t.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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