![]() ![]() ![]() The Apache Airflow workers on an Amazon MWAA environment use the Celery Executor to queue and distribute tasks to multiple Celery workers from an Apache Airflow platform. For example, a metadata database and container in us-east-1a and a metadata database and container in us-east-1b availability zones for the us-east-1 region. ![]() When you create an environment, Amazon MWAA creates an AWS-managed Amazon Aurora PostgreSQL metadata database and an Fargate container in each of your two private subnets in different availability zones. However under certain conditions mentioned in preceding section of this page, tasks might be queued while downscaling is taking place. In most cases, this occurs while no tasks are in the queue, To allow any work to complete on the workers, after which the container is removed and any remaining work in progress is deleted. When the RunningTasks and QueuedTasks metrics sum to zero for a period of two minutes, Amazon MWAA requests Fargate to set the number of workers to the environment's min-workers value.Īmazon MWAA provides Fargate a stopTimeout value of 120 seconds, currently the maximum available time, If the required number of workers is greater than the current number of workers,Īmazon MWAA will add Fargate worker containers to that value, up to the maximum value specified by max-workers. (tasks running + tasks queued) / ( tasks per worker) = (required workers). We recommend doing either of the following:Īmazon MWAA uses RunningTasks and QueuedTasks metrics, where However, if you have very intermittent workloads with repeated high usage,įollowed by zero tasks for approximately five minutes, you might be affected by this issue when tasks running on the downscaled workers are deleted and marked as failed. If you use Amazon MWAA with periods of sustained workload, followed by periods of no workload, you will be unaffected by this limitation. The time to detect a steady state of zero tasks, and the time it takes the Fargate workers to be removed. This period can last between two to five minutes, due to a combination of factors: the time it takes for the Apache Airflow metrics to be sent, Furthermore, it's possible for workers that are set for deletion to pick up those tasks before the workerĬontainers are removed. When downscaling occurs, it is possible for new tasks to be scheduled. For more information, see the following How it works section. When that number is zero, Amazon MWAA removes additional workers,ĭownscaling back to the min-workers value. Amazon MWAA uses Apache Airflow metrics to determine when additional Celery Executor workers are needed,Īnd as required increases the number of Fargate workers up to the value specified by max-workers. ![]()
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