![]() # scheduler_zombie_task_threshold should be higher then # This is used by the health check in the "/health" endpoint # ago (in seconds), scheduler is considered unhealthy. If the last scheduler heartbeat happened more than scheduler_health_check_threshold #consider change the heart rate scheduling of scheduler # This defines how many threads will run. # The scheduler can run multiple threads in parallel to schedule dags. # How long before timing out a DagFileProcessor, which processes a dag file # default is 30, i suggest 3000 for dynamic dag operators. # How long before timing out a python file import # The maximum number of active DAG runs per DAG # i suggest double the defaults after installation. # The number of task instances allowed to run concurrently by the scheduler # the max number of task instances that should run simultaneously # no limit will be placed on the total number of concurrent connections. # max_overflow can be set to -1 to indicate no overflow limit Be sure to understand what your are doing. Usually the config file is located in ~/airflow/airflow.cfg Notice this configuration is provided AS IS. Use the update-environment command in the AWS Command Line Interface (AWS CLI) to disable autoscaling by setting the minimum and maximum number of workers to be the same.When you are using dynamic operators, the default settings will not work.I suggest the below settings. You can also set the minimum workers equal to the maximum workers on your environment, effectively disabling autoscaling. Another option is to adjust the timing of your DAGs and tasks to ensure that that these scenarios don't occur. We recommend increasing the minimum number of workers on your environment. Some of the tasks being queued may result with the workers in the process of being removed, and will end when the container is deleted. In the second scenario, it removes the additional workers. If there is a brief moment where 1) the current tasks exceed current environment capacity, followed by 2) a few minutes of no tasks executing or being queued, then 3) new tasks being queued.Īmazon MWAA autoscaling reacts to the first scenario by adding additional workers. This can occur for the following reasons: ![]() There may be tasks being deleted mid-execution that appear as task logs which stop with no further indication in Apache Airflow. You can use the update-environment command in the AWS Command Line Interface (AWS CLI) to change the minimum or maximum number of Workers that run on your environment.Īws mwaa update-environment -name MyEnvironmentName -min-workers 2 -max-workers 10 If there are a large number of tasks that were queued before autoscaling has had time to detect and deploy additional workers, we recommend staggering task deployment and/or increasing the minimum Apache Airflow Workers. If there are more tasks to run than an environment has the capacity to run, we recommend reducing the number of tasks that your DAGs run concurrently, and/or increasing the minimum Apache Airflow Workers. If there are more tasks to run than the environment has the capacity to run, and/or a large number of tasks that were queued before autoscaling has time to detect the tasks and deploy additional Workers. ![]() This often appears as a large-and growing-number of tasks in the "None" state, or as a large number in Queued Tasks and/or Tasks Pending in CloudWatch. There may be a large number of tasks in the queue. To learn more about the best practices we recommend to tune the performance of your environment, see Performance tuning for Apache Airflow on Amazon MWAA. There are other ways to optimize Apache Airflow configurations which are outside the scope of this guide. This leads to large Total Parse Time in CloudWatch Metrics or long DAG processing times in CloudWatch Logs. ![]() If you're using greater than 50% of your environment's capacity you may start overwhelming the Apache Airflow Scheduler. Reduce the number of DAGs and perform an update of the environment (such as changing a log level) to force a reset.Īirflow parses DAGs whether they are enabled or not. There may be a large number of DAGs defined. ![]()
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