Use automatic scaling in Managed Service for Apache Flink - Managed Service for Apache Flink
Implement custom autoscalingImplement scheduled autoscaling

Amazon Managed Service for Apache Flink was previously known as Amazon Kinesis Data Analytics for Apache Flink.

Use automatic scaling in Managed Service for Apache Flink

Managed Service for Apache Flink elastically scales your application’s parallelism to accommodate the data throughput of your source and your operator complexity for most scenarios. Automatic scaling is enabled by default. Managed Service for Apache Flink monitors the resource (CPU) usage of your application, and elastically scales your application's parallelism up or down accordingly:

  • Your application scales up (increases parallelism) if CloudWatch metric maximum containerCPUUtilization is larger than 75 percent or above for 15 minutes. That means the ScaleUp action is initiated when there are 15 consecutive datapoints with 1 minute period equal to or over 75 percent. A ScaleUp action doubles the CurrentParallelism of your application. ParallelismPerKPU is not modified. As a consequence, the number of allocated KPUs also doubles.

  • Your application scales down (decreases parallelism) when your CPU usage remains below 10 percent for six hours. That means the ScaleDown action is initiated when there are 360 consecutive datapoints with 1 minute period less than 10 percent. A ScaleDown action halves (rounded up) the parallelism of the application. ParallelismPerKPU is not modified, and the number of allocated KPUs also halves (rounded up).

Note

Max of containerCPUUtilization over 1 minute period can be referenced to find the correlation with a datapoint used for Scaling action, but it’s not necessary to reflect the exact moment when the action is initialized.

Managed Service for Apache Flink will not reduce your application's CurrentParallelism value to less than your application's Parallelism setting.

When the Managed Service for Apache Flink service is scaling your application, it will be in the AUTOSCALING status. You can check your current application status using the DescribeApplication or ListApplications actions. While the service is scaling your application, the only valid API action you can use is StopApplication with the Force parameter set to true.

You can use the AutoScalingEnabled property (part of FlinkApplicationConfiguration ) to enable or disable auto scaling behavior. Your AWS account is charged for KPUs that Managed Service for Apache Flink provisions which is a function of your application's parallelism and parallelismPerKPU settings. An activity spike increases your Managed Service for Apache Flink costs.

For information about pricing, see Amazon Managed Service for Apache Flink pricing.

Note the following about application scaling:

  • Automatic scaling is enabled by default.

  • Scaling doesn't apply to Studio notebooks. However, if you deploy a Studio notebook as an application with durable state, then scaling will apply to the deployed application.

  • Your application has a default limit of 64 KPUs. For more information, see Managed Service for Apache Flink and Studio notebook quota.

  • When autoscaling updates application parallelism, the application experiences downtime. To avoid this downtime, do the following:

Implement custom autoscaling

If you want finer grained control on autoscaling or use trigger metrics other than containerCPUUtilization, you can use this example:

  • AutoScaling

    This examples illustrates how to scale your Managed Service for Apache Flink application using a different CloudWatch metric from the Apache Flink application, including metrics from Amazon MSK and Amazon Kinesis Data Streams, used as sources or sink.

For additional information, see Enhanced monitoring and automatic scaling for Apache Flink.

Implement scheduled autoscaling

If your workload follows a predictable profile over time, you might prefer to scale your Apache Flink application preemptively. This scales your application at a scheduled time, as opposed to scaling reactively based on a metric. To set up scaling up and down at fixed hours of the day, you can use this example: