Below is the config im using
[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 = LocalExecutor
executor = CeleryExecutor
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
#sql_alchemy_conn = sqlite:////home/centos/airflow/airflow.db
sql_alchemy_conn = mysql://username:[email protected]:3306/airflow_prod
[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 = pyamqp://guest:[email protected]:5672/
# Another key Celery setting
celery_result_backend = db+mysql://username:[email protected]:3306/airflow_prod
# 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 = 5556
# Default queue that tasks get assigned to and that worker listen on.
default_queue = = default
But jobs dont run.. the scheduler shows that it is checking for the state as below
[2017-05-11 05:09:13,070] {models.py:2274} INFO - Checking state for <DagRun tutorial @ 2015-06-13 00:00:00: scheduled__2015-06-13T00:00:00, externally triggered: False>
[2017-05-11 05:09:13,072] {models.py:2274} INFO - Checking state for <DagRun tutorial @ 2015-06-14 00:00:00: scheduled__2015-06-14T00:00:00, externally triggered: False>
[2017-05-11 05:09:13,074] {models.py:2274} INFO - Checking state for <DagRun tutorial @ 2015-06-15 00:00:00: scheduled__2015-06-15T00:00:00, externally triggered: False>
[2017-05-11 05:09:13,076] {models.py:2274} INFO - Checking state for <DagRun tutorial @ 2015-06-16 00:00:00: scheduled__2015-06-16T00:00:00, externally triggered: False>
[2017-05-11 05:09:13,078] {models.py:2274} INFO - Checking state for <DagRun tutorial @ 2017-05-10 04:46:29: manual__2017-05-10T04:46:28.756946, externally triggered: True>
[2017-05-11 05:09:13,080] {models.py:2274} INFO - Checking state for <DagRun tutorial @ 2017-05-10 05:08:20: manual__2017-05-10T05:08:20.252573, externally triggered: True>
Airflow UI is up and running. Cerlery flower doesnt show any workers. My jobs are not running.
Below is the sequence i follow to get started.
Airflow scheduler
airflow webserver -p 8080
Airflow worker
Is there anything i am missing?