Big Data

Improved scalability and resiliency for Amazon EMR on EC2 clusters


Amazon EMR is the cloud big data solution for petabyte-scale data processing, interactive analytics, and machine learning using open-source frameworks such as Apache Spark, Apache Hive, and Presto. Customers asked us for features that would further improve the resiliency and scalability of their Amazon EMR on EC2 clusters, including their large, long-running clusters. We have been hard at work to meet those needs. Over the past 12 months, we have worked backward from customer requirements and launched over 30 new features that improve the resiliency and scalability of your Amazon EMR on EC2 clusters. This post covers some of these key enhancements across three main areas:

  • Improved cluster utilization with optimized scaling experience
  • Minimized interruptions with enhanced resiliency and availability
  • Improved cluster resiliency with upgraded logging and debugging capabilities

Let’s dive into each of these areas.

Improved cluster utilization with optimized scaling experience

Customers use Amazon EMR to run diverse analytics workloads with varying SLAs, ranging from near-real-time streaming jobs to exploratory interactive workloads and everything in between. To cater to these dynamic workloads, you can resize your clusters either manually or by enabling automatic scaling. You can also use the Amazon EMR managed scaling feature to automatically resize your clusters for optimal performance at the lowest possible cost. To ensure swift cluster resizes, we implemented multiple improvements that are available in the latest Amazon EMR releases:

  • Enhanced resiliency of cluster scaling workflow to EC2 Spot Instance interruptions – Many Amazon EMR customers use EC2 Spot Instances for their Amazon 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. However, Amazon EC2 can reclaim Spot capacity with a two-minute warning, which can lead to interruptions in workload. We identified an issue where the cluster’s scaling operation gets stuck when over a hundred core nodes launched on Spot Instances are reclaimed by Amazon EC2 throughout the life of the cluster. Starting with Amazon EMR version 6.8.0, we mitigated this issue by fixing a gap in the process HDFS uses to decommission nodes that caused the scaling operations to get stuck. We contributed this improvement back to the open-source community, enabling seamless recovery and efficient scaling in the event of Spot interruptions.
  • Improve cluster utilization by recommissioning recently decommissioned nodes for Spark workloads within seconds – Amazon EMR allows you to scale down your cluster without affecting your workload by gracefully decommissioning core and task nodes. Furthermore, to prevent task failures, Apache Spark ensures that decommissioning nodes are not assigned any new tasks. However, if a new job is submitted immediately before these nodes are fully decommissioned, Amazon EMR will trigger a scale-up operation for the cluster. This results in these decommissioning nodes to be immediately recommissioned and added back into the cluster. Due to a gap in Apache Spark’s recommissioning logic, these recommissioned nodes would not accept new Spark tasks for up to 60 minutes. We enhanced the recommissioning logic, which ensures recommissioned nodes would start accepting new tasks within seconds, thereby improving cluster utilization. This improvement is available in Amazon EMR release 6.11 and higher.
  • Minimized cluster scaling interruptions due to disk over-utilization – The YARN ResourceManager exclude file is a key component of Apache Hadoop that Amazon EMR uses to centrally manage cluster resources for multiple data-processing frameworks. This exclude file contains a list of nodes to be removed from the cluster to facilitate a cluster scale-down operation. With Amazon EMR release 6.11.0, we improved the cluster scaling workflow to reduce scale-down failures. This improvement minimizes failures due to partial updates or corruption in the exclude file caused by low disk space. Additionally, we built a robust file recovery mechanism to restore the exclude file in case of corruption, ensuring uninterrupted cluster scaling operations.

Minimized interruptions with enhanced resiliency and availability

Amazon EMR offers high availability and fault tolerance for your big data workloads. Let’s look at a few key improvements we launched in this area:

  • Improved fault tolerance to hardware reconfiguration – Amazon EMR offers the flexibility to decouple storage and compute. We observed that customers often increase the size of or add incremental block-level storage to their EC2 instances as their data processing volume and concurrency grow. Starting with Amazon EMR release 6.11.0, we made the EMR cluster’s local storage file system more resilient to unpredictable instance reconfigurations such as instance restarts. By addressing scenarios where an instant restart could result in the block storage device name to change, we eliminated the risk of the cluster becoming inoperable or losing data.
  • Reduce cluster startup time for Kerberos-enabled EMR clusters with long-running bootstrap actions – Multiple customers use Kerberos for authentication and run long-running bootstrap actions on their EMR clusters. In Amazon EMR 6.9.0 and higher releases, we fixed a timing sequence mismatch issue that occurs between Apache BigTop and the Amazon EMR on EC2 cluster startup sequence. This timing sequence mismatch occurs when a system attempts to perform two or more operations at the same time instead of doing them in the proper sequence. This issue caused certain cluster configurations to experience instance startup timeouts. We contributed a fix to the open-source community and made additional improvements to the Amazon EMR startup sequence to prevent this condition, resulting in cluster start time improvements of up to 200% for such clusters.

Improved cluster resiliency with upgraded logging and debugging capabilities

Effective log management is essential to ensure log availability and maintain the health of EMR clusters. This becomes especially critical when you’re running multiple custom client tools and third-party applications on your Amazon EMR on EC2 clusters. Customers depend on EMR logs, in addition to EMR events, to monitor cluster and workload health, troubleshoot urgent issues, simplify security audit, and enhance compliance. Let’s look at a few key enhancements we made in this area:

  • Upgraded on-cluster log management daemon – Amazon EMR now automatically restarts the log management daemon if it’s interrupted. The Amazon EMR on-cluster log management daemon archives logs to Amazon Simple Storage Service (Amazon S3) and deletes them from instance storage. This minimizes cluster failures due to disk over-utilization, while allowing the log files to remain accessible even after the cluster or node stops. This upgrade is available in Amazon EMR release 6.10.0 and higher. For more information, see Configure cluster logging and debugging.
  • Enhanced cluster stability with improved log rotation and monitoring – Many of our customers have long-running clusters that have been operating for years. Some open-source application logs such as Hive and Kerberos logs that are never rotated can continue to grow on these long-running clusters. This could lead to disk over-utilization and eventually result in cluster failures. We enabled log rotation for such log files to minimize disk, memory, and CPU over-utilization scenarios. Furthermore, we expanded our log monitoring to include additional log folders. These changes, available starting with Amazon EMR version 6.10.0, minimize situations where EMR cluster resources are over-utilized, while ensuring log files are archived to Amazon S3 for a wider variety of use cases.

Conclusion

In this post, we highlighted the improvements that we made in Amazon EMR on EC2 with the goal to make your EMR clusters more resilient and stable. We focused on improving cluster utilization with the improved and optimized scaling experience for EMR workloads, minimized interruptions with enhanced resiliency and availability for Amazon EMR on EC2 clusters, and improved cluster resiliency with upgraded logging and debugging capabilities. We will continue to deliver further enhancements with new Amazon EMR releases. We invite you to try new features and capabilities in the latest Amazon EMR releases and get in touch with us through your AWS account team to share your valuable feedback and comments. To learn more and get started with Amazon EMR, check out the tutorial Getting started with Amazon EMR.


About the Authors

Ravi Kumar is a Senior Product Manager for Amazon EMR at Amazon Web Services.

Kevin Wikant is a Software Development Engineer for Amazon EMR at Amazon Web Services.