Migrate EMR Serverless to ARM (Graviton)

This recommendation identifies AWS accounts running EMR Serverless workloads on x86 (Intel/AMD) architecture that could achieve significant cost savings by migrating to ARM-based Graviton processors. AWS Graviton processors offer up to 20% cost savings with equivalent or better performance for most EMR Serverless workloads.

What it identifies

EMR Serverless applications running on x86 architecture
Accounts with any non-ARM EMR Serverless usage
Potential savings from migrating to ARM Graviton instances
Both fully x86 deployments and mixed x86/ARM environments

Cost Impact

The recommendation calculates potential savings based on:

20% cost reduction from migrating x86 workloads to ARM Graviton
Current monthly x86 EMR Serverless spend
No performance degradation expected (often performance improves)

Example Scenario

Metric	Value
Monthly x86 EMR Serverless cost	$100,000
ARM migration savings (20%)	$20,000/month
Annual savings	$240,000

Why This Matters

Immediate Cost Savings: 20% reduction in compute costs with minimal effort
No Performance Trade-off: Graviton processors often provide better performance
Simple Migration: Usually just requires changing instance configuration
Growing Support: Most Spark libraries and frameworks support ARM
Environmental Impact: Graviton processors are more energy efficient

How to Remediate

Step 1: Verify Application Compatibility

# Most EMR Serverless workloads are compatible with ARM
# Check for any architecture-specific dependencies:
- Review custom libraries and packages
- Verify third-party integrations support ARM
- Test in development environment first

Step 2: Update EMR Serverless Application Configuration

Via AWS Console:

Navigate to EMR Studio → Applications
Select your application
Edit application settings
Under "Architecture", select arm64 (Graviton)
Save and restart application

Via AWS CLI:

aws emr-serverless update-application \
    --application-id <application-id> \
    --architecture ARM64

Via Terraform:

resource "aws_emrserverless_application" "example" {
  name          = "my-application"
  release_label = "emr-6.10.0"
  type          = "Spark"

  architecture = "ARM64"  # Change from "X86_64"
}

Step 3: Monitor and Validate

Monitor job execution times (should be equal or better)
Verify cost reduction in billing (appears within 24-48 hours)
Check application logs for any architecture-related issues

Additional Context

Graviton Benefits

Cost: 20% cheaper than comparable x86 instances
Performance: Up to 40% better price-performance
Memory: Same memory-to-vCPU ratios available
Compatibility: Supports Spark 3.x, Python 3.7+, Java 8+

Known Limitations

Some legacy libraries may not support ARM (rare in modern Spark)
Custom native code may need recompilation
Third-party connectors should be verified

Best Practices

Start with non-production workloads
Run parallel tests comparing x86 vs ARM performance
Migrate incrementally (application by application)
Update documentation to default to ARM for new applications