Aws Emr Skill

AWS EMR interaction skill for managing EMR Serverless, EMR on EC2, and EMR on EKS. Submit and manage Spark, Hive, and PySpark jobs across all three EMR deployment modes. Use this skill when the user mentions EMR, Spark, Hive, Serverless, big data jobs, submit job, query job status, get job logs, cancel job, EMR cluster, EMR step, virtual cluster, EKS, or similar keywords.

Audits

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Install

openclaw skills install aws-emr-skills

AWS EMR Skills

A Python skill for interacting with AWS EMR across three deployment modes: EMR Serverless, EMR on EC2, and EMR on EKS. Submit Spark and Hive jobs, manage clusters and applications, monitor job status, and retrieve logs.

When to Use (Trigger Phrases)

Invoke this skill when the user mentions:

"Submit a Spark job on EMR"
"List EMR Serverless applications"
"Add a step to my EMR cluster"
"Get EMR job logs"
"Check EMR job status"
"Cancel running EMR job"
"List EMR clusters"
"Create an EMR on EKS virtual cluster"
"Submit PySpark to EMR Serverless"
"Get step logs from EMR cluster"

Any request involving EMR Serverless applications/jobs, EMR on EC2 clusters/steps, or EMR on EKS virtual clusters/job runs.

Feature List

EMR Serverless

Applications: List, describe, start, stop EMR Serverless applications
Job Submission: Submit Spark SQL, Spark JAR, PySpark, and Hive jobs (sync/async)
Job Lifecycle: Get status, cancel, list job runs
Results: Retrieve SQL query results from S3
Logs: Get driver stdout/stderr logs with secret masking

EMR on EC2

Clusters: List, describe, terminate EMR clusters
Step Submission: Add Spark, PySpark, and Hive steps via command-runner.jar
Step Lifecycle: List, describe, cancel steps
Logs: Get step logs (stderr, stdout, controller, syslog) from S3

EMR on EKS

Virtual Clusters: List, describe, create, delete virtual clusters
Job Submission: Submit Spark and Spark SQL jobs to EKS
Job Lifecycle: Describe, list, cancel job runs
Logs: Get job logs from S3

Initial Setup

Python 3.8+ with boto3>=1.26.0:
```
pip install boto3>=1.26.0
```
AWS credentials via boto3 default chain (env vars, config files, IAM roles).

Environment variables (all optional, validated at point of use):

export AWS_REGION="us-east-1"

# EMR Serverless
export EMR_SERVERLESS_APP_ID="00abcdef12345678"
export EMR_SERVERLESS_EXEC_ROLE_ARN="arn:aws:iam::123456789:role/emr-role"
export EMR_SERVERLESS_S3_LOG_URI="s3://my-bucket/emr-logs/"

# EMR on EC2
export EMR_CLUSTER_ID="j-XXXXXXXXXXXXX"

# EMR on EKS
export EMR_EKS_VIRTUAL_CLUSTER_ID="abc123def456"
export EMR_EKS_EXEC_ROLE_ARN="arn:aws:iam::123456789:role/emr-eks-role"

How to Manage EMR

1. EMR Serverless

Fully managed serverless Spark/Hive execution. No infrastructure to manage.

Application management: scripts/on_serverless/emr_serverless_cli.py — 14 @tool functions
Detailed guide: references/emr_serverless/application_guide.md — Application lifecycle
Detailed guide: references/emr_serverless/job_guide.md — Job submission, results, logs

2. EMR on EC2

Traditional EMR clusters on EC2 instances. Submit work as Steps.

Cluster & step management: scripts/on_ec2/emr_on_ec2_cli.py — 10 @tool functions
Detailed guide: references/emr_on_ec2/cluster_guide.md — Cluster lifecycle
Detailed guide: references/emr_on_ec2/step_guide.md — Step submission, logs

3. EMR on EKS

Spark workloads on Amazon EKS via the emr-containers API.

Virtual cluster & job management: scripts/on_eks/emr_on_eks_cli.py — 10 @tool functions
Detailed guide: references/emr_on_eks/virtual_cluster_guide.md — Virtual cluster lifecycle
Detailed guide: references/emr_on_eks/job_run_guide.md — Job submission, logs

Available Scripts

Script	Description
`scripts/on_serverless/emr_serverless_cli.py`	EMR Serverless @tool functions (14 tools)
`scripts/on_ec2/emr_on_ec2_cli.py`	EMR on EC2 @tool functions (10 tools)
`scripts/on_eks/emr_on_eks_cli.py`	EMR on EKS @tool functions (10 tools)
`scripts/config/emr_config.py`	Unified configuration management
`scripts/client/boto_client.py`	boto3 client factory

References

Document	Description
`references/emr_serverless/application_guide.md`	EMR Serverless application management guide
`references/emr_serverless/job_guide.md`	EMR Serverless job submission and management guide
`references/emr_on_ec2/cluster_guide.md`	EMR on EC2 cluster management guide
`references/emr_on_ec2/step_guide.md`	EMR on EC2 step submission and management guide
`references/emr_on_eks/virtual_cluster_guide.md`	EMR on EKS virtual cluster management guide
`references/emr_on_eks/job_run_guide.md`	EMR on EKS job run management guide

Requirements

When writing temporary files (scripts, notes, etc.), place them in the ./tmp folder.
When importing scripts packages, add the skill root to path: sys.path.append(${emr_skill_root})
AWS credentials are handled by boto3's default credential chain — never pass access keys directly.
All configuration environment variables are optional and validated at the point of use.

Data Privacy & Trust

No credential storage: AWS credentials are resolved via boto3 default chain. No keys are stored or logged.
Secret masking: Log retrieval functions automatically mask potential AWS credentials in output.
Read-only by default: Most operations are read-only queries. Write operations (job submission, cluster termination) require explicit user action.

External Endpoints

This skill connects to:

AWS EMR Serverless API (emr-serverless.{region}.amazonaws.com)
AWS EMR API (elasticmapreduce.{region}.amazonaws.com)
AWS EMR Containers API (emr-containers.{region}.amazonaws.com)
AWS S3 API (s3.{region}.amazonaws.com) — for log and result retrieval