In Spark the heartbeats are the messages sent by executors to the driver. You can learn more details from the post about Coordinator in Apache Kafka. They're used to keep a list of active consumer in a group and facilitate the rebalancing if a new consumer joins the group. Apache Kafka - consumers send heartbeats to coordinator.Apache ZooKeeper - mostly used in sessions, you can read Sessions in Apache ZooKeeper for more details.Except Spark, among heartbeat users we can distinguish: Heartbeats is widely used in distributed systems. However sometimes it can be a dangerous supposition, especially with network latency. If the signal is not sent within configured delay, the node is considered as down. HeartbeatsĪs already mentioned, the heartbeat is a signal sent periodically in order to indicate normal operation of the node or synchronize with other parts of the system. The last section demonstrates what happens with the jobs if one of executors slows down and doesn't send the heartbeat within configured interval. The second and the third parts show how it's used by Spark executors. The first short section describes the general idea of this technique. 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.This post describes the heartbeats mechanism implemented in Spark. 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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