All posts by Sushant Majithia

Capacity Management and Amazon EMR Managed Scaling improvements for Amazon EMR on EC2 clusters

Post Syndicated from Sushant Majithia original https://aws.amazon.com/blogs/big-data/capacity-management-and-amazon-emr-managed-scaling-improvements-for-amazon-emr-on-ec2-clusters/

In 2022, we told you about the new enhancements we made in Amazon EMR Managed Scaling, which helped improve cluster utilization as well as reduced cluster costs. In 2023, we are happy to report that the Amazon EMR team has been hard at work. We worked backward from customer requirements and launched multiple new features to enhance your Amazon EMR on EC2 clusters capacity management and scaling experience.

Amazon EMR is the cloud big data solution for petabyte-scale data processing, interactive analytics, and machine learning (ML) using open-source frameworks such as Apache Spark, Apache Hive, and Presto. Customers asked us for features that would further improve the capacity management and scaling experience of their EMR on EC2 clusters, including their large, long-running clusters. We have been hard at work to meet those needs. The following are some of the key enhancements:

  • Enhanced customer transparency and flexibility with provisioning timeout for Spot Instances
  • Optimized task nodes scale-up for Amazon EMR on EC2 clusters launched with instance groups
  • Improved job resiliency with enhanced protection for Spark Drivers

Let’s dive deeper and discuss the new Amazon EMR on EC2 features in detail.

Enhanced customer transparency and flexibility with provisioning timeout for Spot Instances

Many Amazon EMR customers use EC2 Spot Instances for their EMR on EC2 clusters to reduce costs. Spot Instances are spare Amazon Elastic Compute Cloud (Amazon EC2) compute capacity offered at discounts of up to 90% compared to On-Demand pricing. Amazon EMR offers you the capability to scale your cluster either manually or by using Automatic Scaling. You can also use the Amazon EMR Managed Scaling feature to automatically resize your cluster based on workload and utilization.

To enhance the customer experience when scaling up using Spot Instances, for EMR on EC2 clusters launched using instance fleets, you can now specify a provisioning timeout for Spot Instances. A provisioning timeout will tell Amazon EMR to stop provisioning Spot Instance capacity if the cluster exceeds a specified time threshold during cluster scaling operations. You can configure the Spot instance provisioning timeout for clusters getting resized manually or using Amazon EMR Managed Scaling and Auto Scaling.

Additionally, to provide better transparency, when the timeout period expires, Amazon EMR will also automatically send events to an Amazon CloudWatch Events stream. With these CloudWatch events, you can create rules that match events according to a specified pattern, and then route the events to targets to take action. To learn more, please refer to Customize a provisioning timeout period for cluster resize in Amazon EMR.

Please find summarized below the experience for different scenario’s when you configure a provisioning timeout period during resize for your Amazon EMR on EC2 cluster

Scenario Experience
Amazon EMR is able to provision the desired Spot capacity before expiration of the provisioning timeout Amazon EMR automatically scales-up the cluster to the desired capacity and no action is needed from the customer
Amazon EMR is not able to provision any Spot capacity or only able to provision partial Spot capacity and the provisioning timeout has expired If Amazon EMR can’t provision the required Spot capacity and the provisioning timeout has expired, Amazon EMR will cancel the resize request and stops it’s attempts to provision additional Spot capacity. Amazon EMR will also publish events to an Amazon CloudWatch Events stream. Customers can use these events to create rules and take appropriate actions
If the Spot instances in your Amazon EMR on EC2 clusters are interrupted as Amazon EC2 needs them back Amazon EMR will automatically trigger a new resize request to rebalance your clusters by replacing instances with any of the available types in your cluster. Amazon EMR will also use the same provisioning resize timeout which was configured on the cluster. No action is needed from the customer.

You should consider the criticality of capacity availability when specifying the provisioning timeout value:

  • When your workload capacity availability is critical – To ensure the desired capacity is available, we recommend configuring the resize provisioning timeout based on the time it takes to run the application and application SLAs. For example, if application SLA is 60 minutes and it takes 30 minutes for the application to complete, you should set the resize provisioning timeout to 30 minutes or less. Amazon EMR will try to provision to get Spot capacity until the timeout expires (30 minutes or less) and publish a CloudWatch event so that you can take appropriate actions.
  • When your workload is time flexible and capacity availability is not a factor – If the workload is time flexible and capacity availability is not a factor, to ensure the highest likelihood for getting the desired Spot capacity, you can configure a higher timeout value for the resize provisioning timeout.

Optimized task nodes scale-up for Amazon EMR on EC2 clusters launched with Instance groups

Instance groups offer a simpler setup to launch EMR on EC2 clusters. Each cluster launched using instance groups can include up to 50 instance groups: one primary instance group that contains one EC2 instance, a core instance group that contains one or more EC2 instances, and up to 48 optional task instance groups. You can scale each instance group by adding and removing EC2 instances manually, or you can set up automatic scaling. You can also use the Amazon EMR Managed Scaling feature to automatically resize your cluster based on workload and utilization.

To enhance the customer experience for instance groups on EMR on EC2 clusters when scaling up task nodes using Amazon EMR Managed Scaling, we have enhanced the managed scaling algorithm to choose the task instance groups that have the highest likelihood of acquiring capacity. Furthermore, when managed scaling is not able to acquire capacity with a single task instance group, to reduce any scale-up delays, Amazon EMR will automatically switch to another task group and fulfill the capacity by using multiple task instance groups. Consequently, the more flexible you are about your instance types, the higher the chances of provisioning capacity. To learn more, refer to Best practices for instance and Availability Zone flexibility.

Improved job resiliency with enhanced protection for Spark Drivers

In 2022, to improve the job resiliency when using Amazon EMR Managed Scaling, we enhanced managed scaling to be Spark shuffle data aware, which prevents scale-down of instances that store intermediate shuffle data for Apache Spark. This helps prevents job reattempts and recomputations, which leads to better performance and lower cost.

To further improve job resiliency when using Amazon EMR Managed Scaling, we have further enhanced managed scaling to be Spark Driver aware, which ensures that during cluster scale-down, Amazon EMR Managed Scaling prioritizes the scale-down of nodes that don’t have an active Spark Driver running on them. This helps minimize job failures and job retries, helping further improve performance and reduce costs. This enhancement is enabled by default for EMR clusters using Amazon EMR versions 5.34.0 and later, and Amazon EMR versions 6.4.0 and later.

To confirm which nodes in your cluster are running Spark Driver, you can visit the Spark History Server and filter for the driver on the Executors tab of your Spark application ID.

Conclusion

In this post, we highlighted the improvements that we made in capacity management and Amazon EMR Managed Scaling for EMR on EC2 clusters. We focused on improving job resiliency, enhanced flexibility and transparency when provisioning Spot Instances, and optimizing the scale-up experience when using managed scaling with instance groups on Amazon EMR on EC2 clusters. Although we have launched multiple features so far in 2023 and the pace of innovation continues to accelerate, it remains day 1 and we look forward to hearing from you on how these features help you unlock more value for your organizations. We invite you to try these new features and get in touch with us through your AWS account team if you have further comments.

About the authors

Sushant Majithia is a Principal Product Manager for EMR at AWS.

Ankur Goyal is a SDM with Amazon EMR Big Data Platform team. He builds large scale distributed applications and cluster optimization algorithms. Ankur is interested in topics of Analytics, Machine Learning and Forecasting.

Matthew Liem is a Senior Solution Architecture Manager at AWS.

Tarun Chanana is an SDM with Amazon EMR Big Data Platform team.

Reduce Amazon EMR cluster costs by up to 19% with new enhancements in Amazon EMR Managed Scaling

Post Syndicated from Sushant Majithia original https://aws.amazon.com/blogs/big-data/reduce-amazon-emr-cluster-costs-by-up-to-19-with-new-enhancements-in-amazon-emr-managed-scaling/

In June 2020, AWS announced the general availability of Amazon EMR Managed Scaling. With EMR Managed Scaling, you specify the minimum and maximum compute limits for your clusters, and Amazon EMR automatically resizes your cluster for optimal performance and resource utilization. EMR Managed Scaling constantly monitors key workload-related metrics and uses an algorithm that optimizes the cluster size for best resource utilization. Given that the feature is completely managed, improvements to the algorithm are immediately realized without needing a version upgrade. Amazon EMR can scale the cluster up during peaks and scale it down gracefully during idle periods, reducing your costs and optimizing cluster capacity for the best performance.

Throughout 2022, we made multiple enhancements to the EMR Managed Scaling algorithm. With these improvements, we observed that for clusters enabled with EMR Managed Scaling, utilization improved by up to 15 percent, and total costs were reduced further by up to 19 percent. Starting mid-December 2022, EMR Managed Scaling enhancements were enabled by default for clusters using Amazon EMR versions 5.34.0 and later and Amazon EMR versions 6.4.0 and later for both new and existing clusters. Further, given that the feature is completely managed, you will get the new optimized Managed Scaling algorithm by default, and no action is needed on your end.

Listed below are some of the key enhancements we enabled for EMR Managed Scaling:

  • Improved cluster utilization with targeted scale-down of your EMR cluster
  • Reduced costs by preventing scale-down of instances that store intermediate shuffle data using Spark Shuffle data awareness
  • Improved cluster utilization and reduce costs with gradual scale-up of your EMR cluster

Customer success stories

How the enhanced EMR Managed Scaling algorithm helped a technology enterprise reduce costs:

To illustrate the cost savings by examples, we looked at an EMR clusters for a technology enterprise, which heavily uses Amazon EMR to process real time billing data between Kafka and S3 using Spark. They run a persistent EMR cluster with EMR version 5.35 and have EMR Managed Scaling turned-on. The following Amazon CloudWatch dashboard shows how starting December 21, the enhanced Managed Scaling algorithm provisioned (total nodes requested) only 70 nodes vs. the previous Managed Scaling algorithm which provisioned 179 nodes for a similar job profile. The lower the number of resources provisioned to run your jobs, the lower the total cost of your EMR cluster.

How the enhanced EMR Managed Scaling algorithm helped an advertising enterprise reduce costs:

We also looked at an EMR cluster for an advertising enterprise, which leverages Amazon EMR for their data analytics strategy and executes their batch ETL jobs using Spark. They run their clusters on EMR version 6.5 and have EMR Managed Scaling turned-on. The following Amazon CloudWatch dashboard shows how starting December 15, the enhanced Managed Scaling algorithm provisioned (total units requested) only 41 nodes vs. the previous Managed Scaling algorithm which provisioned 86 nodes for a similar job profile.

Estimating the cost savings and utilization improvements for your EMR clusters:

Cluster cost savings:

To view estimated cost savings for your EMR cluster with the EMR Managed Scaling enhancements, please follow the steps below:

  • Open the CloudWatch metrics console and, under EMR, search by your ClusterId.
  • From the list of metrics available for EMR, select the following two metrics:
    • Running capacity – Based on the unit type you specified in your Managed Scaling policy, this will be available as either “TotalUnitsRunning” or “TotalNodesRunning” or “TotalVCPURunning”. 
    • Capacity requested by Managed Scaling – Based on the unit type you specified in your Managed Scaling policy, this will be available as either “TotalUnitsRequested” or “TotalNodesRequested” or “TotalVCPURequested”. 
  •  Plot both of the metrics to your CloudWatch dashboard.
  • Select the time frame as the 3 months between November 2022 and January 2023 to view the improvements with the enhanced Managed Scaling algorithm when compared to the previous Managed Scaling algorithm.

Cluster utilization improvements:

To estimate the improvements in your EMR cluster utilization with the EMR Managed Scaling enhancements, please follow the steps below:

  • Open the CloudWatch metrics console and, under EMR, search by your ClusterId.
  • From the list of metrics available for EMR, select the “YARNMemoryAvailablePercentage” metric.
  • To derive memory utilized by YARN, add a math expression such as “Add Math → Start with empty expression”
    • For the new math expression, set Label=Yarn Utilization and set Details=100-YARNMemoryAvailablePercentage.
  • Plot the cluster utilization metric to your CloudWatch dashboard.
  • Select the time frame as the 3 months between November 2022 and January 2023 to view the improvements with the enhanced Managed Scaling algorithm when compared to the previous Managed Scaling algorithm.

What’s next

We will continue to tune the Managed Scaling algorithm with every new EMR release and thereby improve the customer experience when scaling clusters with EMR Managed Scaling.

Conclusion

In this post, we provided an overview of the key enhancement we launched in EMR Managed Scaling. With these enhancements, we observed that the cluster utilization improved by up to 15 percent, and cluster cost was reduced by up to 19 percent. Starting mid-December 2022, these enhancements were enabled by default for EMR clusters using Amazon EMR versions 5.34.0 and later, and Amazon EMR versions 6.4.0 and later. Given that EMR Managed Scaling is a completely managed feature, you will get the new, optimized EMR Managed Scaling algorithm by default, and no action is needed from your end.

To learn more and get started with EMR Managed Scaling, visit the EMR Managed Scaling documentation page.

About the Authors

Sushant Majithia is a Principal Product Manager for EMR at Amazon Web Services.

 Vishal Vyas is a Senior Software Engineer for EMR at Amazon Web Services.

Matthew Liem is a Senior Solution Architecture Manager at AWS.