AWS EMR Cost & Performance Optimization: A Business-Centric Approach

Introduction

AWS Elastic MapReduce (EMR) is an AWS service for filtering big data using open-source tools such as Apache Spark, Apache Flink, Trino, Hadoop, Hive, Presto, and many more. It provides a platform to run your applications without thinking much about the management of the underlying infrastructure.

AWS EMR is a multipurpose, easy-to-use, highly available, and robust service. The cost of running these EMR clusters can quickly add up if not accurately optimized.

Objective

In this blog post, we’ll explore some key best practices for reducing AWS bills and improving performance when using AWS Elastic Map Reduce.

AWS EMR

1. Select the Right Instance Types

AWS EMR offers different types of EC2 instances for running your workload. Deciding which type to use is important as it can directly impact cost and performance.

• Memory-Optimized Machines: These are memory-intensive EC2 machines and are used for workloads like Spark or Java, which require high memory.
• Compute-Optimized Machines: These are highly preferred for CPU-heavy tasks such as Machine learning or Extract, Transform & load (ETL) operations.
  

  EMR Instance Types

• Spot Instances: Using the advantage of Spot Instances for non-critical tasks to save costs up to 90% is never a bad idea. These instances are interruptible, so make sure applications are resilient to these interruptions. For choosing the instance type with a low frequency of interruption, check out AWS Spot Advisor here.
• Graviton Instances: It’s a no-brainer to use arm-based instances powered by AWS Graviton. AWS Graviton processors are changing the landscape of cloud computing by offering better performance and cost savings. The latest Graviton4 series pushes boundaries, delivering up to 40% better price-performance and up to 60% less energy consumption compared to x86 processors.
• Savings Plans: Savings Plan offers flexible pricing, which can help in saving costs on EMR clusters in the long run.
  

  Cloud Bill Savings

• CloudKeeper for Cost Savings: CloudKeeper makes sure you get the advantage of Savings Plans without upfront commitment by passing on discounts as part of its pricing model, making it a key addition to your money-saving strategies.CloudKeeper can help you discover hidden savings opportunities by giving real-time visibility of your cloud expenses, identifying idle components, and suggesting rightsizing for your infrastructure.
  

  Cloudkeeper

Best Practice: Utilize EMR instance fleets to combine Spot and On-Demand instances for flexibility and more cost savings.

 2. Right-Size Your Cluster

Surplus provisioning your cluster wastes resources, while insufficient provisioning can cause performance issues. You can follow these steps to have the correct sizing:

• Always Start Small: Begin with a minimal cluster size and scale up based on workload requirements.
• Enable Auto Scaling: Use EMR Auto Scaling to change the number of nodes based on workload.
• EMR Cluster Metrics: Regularly monitor cloudwatch metrics like CPU, Memory, HDFS utilization, and Network latency to identify the optimal cluster size.

3. Leverage EMR Managed Scaling

AWS EMR Managed Scaling allows you to automatically scale up or scale down your EMR cluster based on the traffic. This will save you from having to monitor and adjust cluster size manually, saving you money when idle. Steps to Enable Managed Scaling:

• Enable EMR Managed Scaling while creating the cluster.
• Set the minimum and maximum number of instances for elasticity.
• Use AWS CloudWatch metrics for monitoring performance.
  

  EMR Managed Scaling

4. Maximize Data Storage

Data transfer costs and storage can significantly impact your AWS budget. Use the following methods to optimize storage:

• Partitioning: Partition large datasets. This will improve performance and reduce scanning costs.
• Use AWS S3 Instead of HDFS: Use AWS Simple Storage Service (S3) for storing data to avoid the high costs associated with EBS & HDFS replication. EMRFS allows complete integration between EMR and S3.
  

  s3 data storage

• Data Compression: Store data in compressed formats such as Parquet, ORC, or Gzip to avoid disk space costs and for faster transfer.
• Reduce Data Transfer Costs: You can keep data transfer costs low by keeping data moving within a single AZ. Although this is not recommended for production, it can be used in non-production environments.

5. Use Instance Store for Short-Term Storage

• For temporary storage (e.g., shuffle operations in Spark), use directly attached storage (DAS) in place of EBS.
• Instance stores provide high IOPS and are inexpensive for intermediate data.
  

  instance storage

Tip: Keep in mind that data stored on instance storage is ephemeral and will be lost if the instance stops or terminates.

6  Performance Tuning Spark and Hadoop Configurations

• Tune Executors & Enable Parallelism: Generally, configure Spark executor memory, cores, and number of instances to hold more memory with less CPU usage. For the jobs using Spark, parallelism needs to be increased to ensure optimum utilization.
• YARN Tuning: Configure YARN’s container sizes and memory allocations to prevent resource contention.

7. Optimize And Monitor Costs

Track your EMR cost and usage pattern to identify inefficiencies.

• AWS Cost Explorer: Analyze the cost trend and budget your EMR workload.
• CloudWatch Metrics: Monitor the EMR cluster metrics in real-time to find underutilized resources.
• Spot Instance Advisor: Use AWS Spot Instance Advisor to select the most cost-effective Spot Instances.

8. Leverage the Latest EMR Versions

Regular upgrades keep everyone happy. The latest versions of EMR are likely to include performance improvements and cost optimizations. Test and upgrade to the latest version of EMR periodically.

Tip: Use the EMR Release Notes to find features and fixes that apply to your workload.

9. Terminate Idle Clusters

Idle clusters can quickly increase your costs. Here are some best practices to prevent unnecessary expenses:

• Cluster Auto Termination: Enable & configure clusters to terminate automatically after the successful completion of your jobs.
• Idle Timeout Alarms: Ensure Cloudwatch Alarms or custom alerts are created that will send notifications for idle clusters.

10. Serverless Alternatives: EMR on EKS

For inconsistent workloads, run EMR on AWS Elastic Kubernetes Service (EKS), which is the managed Kubernetes platform on AWS. This serverless model eliminates cluster management and auto-scales with minimal operational expenses.

Conclusion

AWS EMR is a powerful platform for processing big data, but without proper planning & management, costs can quickly get out of hand. By following the best practices mentioned in this blog, you can significantly reduce costs while ensuring your workloads perform efficiently. Whether it’s choosing the right instance, storage optimization, or using managed scaling, each step plays an important role in maximizing savings on your cloud bills.

Partnering with a managed cloud services provider like TO THE NEW can help you with all these challenges. Our AWS-certified architects and DevOps Engineers are committed to saving you time and resources while enhancing business efficiency and reliability.

What’s Next? Start implementing these practices today and monitor the impact on your EMR clusters. Have additional tips or experiences to share? Let us know in the comments!