![]() Number of times to retry starting up the scheduler before it sends an alert alert_to_emailĭetault: address to send alerts to if the failover controller is unable to startup a scheduler alert_email_subjectĭefault: "Airflow Alert - Scheduler Failover Controller Failed to Startup Scheduler"Įmail Subject to use when sending an alert Recommended Steps for a Better DeploymentĪbove describes a quickstart approach. How many times the logs should be rotate before you clear out the old ones retry_count_before_alerting Values Documentation: logs_rotate_backup_count NOTSET, DEBUG, INFO, WARN, ERROR, CRITICAL logging_dirĭefault: "~/airflow/logs/scheduler_failover/"ĭefault: scheduler_failover_controller.log Select which version of the code you want to install and use this value as the ' do kill -9 $pid done"Ĭommand to use when trying to stop a Scheduler instance on a node logging_level After, you will be able to run the project through the CLI Interface (See bellow for more details), be able to make any changes to the project you just brought down and have those changes be immediately reflected in the CLI.In case you want to do development work on the projectĬlone the project from GitHub to your local machine If the scheduler daemons still doesn't startup, the daemon is started on another node in the cluster. If it is not, the ASFC will try to restart the daemon. The Active ASFC will poll every 10 seconds to see if the scheduler is running on the desired node. If the Active ASFC misses multiple heart beats, the Standby ASFC becomes active. There is a heart beat mechanism setup to track if the Active ASFC is still active. ![]() When you start up multiple instances of the ASFC one of them takes on the Active state and the other takes on a Standby state. You will first need to startup the ASFC on each of the instances you want the scheduler to be running on. This way you don't come across the issues we described in the "Motivation" section above. The Airflow Scheduler Failover Controller (ASFC) is a mechanism that ensures that only one Scheduler instance is running in an Airflow Cluster at a time. This is what motivated us to search for an alternative to our initial approach to build a highly available Airflow Cluster. This caused a lot of data inconsistency issues. Therefore the Airflow executors would execute the same task instance twice. What we noticed after a month running the Cluster is that the schedulers would occasionally push a duplicate task instance to the RabbitMQ Queue. Each of the instances would share a MySQL instances as its MetaStore and share a RabbitMQ Queue for its Queueing Services (since we were using CeleryExecutors). ![]() We had attempted to setup a Highly Available Airflow Cluster where we had 2 machines with all the normal Airflow Daemons (web server, scheduler, workers, etc.) running on them. The purpose of this project is to create a failover controller that will control which scheduler is up and running to allow HA across an Airflow cluster. Airflow Scheduler Failover Controller Project Purpose
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |