Analyze Spark Workload

Use the Spark workload analysis script to analyze the specified workload. The script uses a Strands Agent with MCP tools to perform deep analysis and generate code fix recommendations.

Arguments

Parse the user's input to extract:

• execution-id (required): The first positional argument
  • For Glue: Job Run ID (e.g., jr_abc123)
  • For EMR: Step ID (e.g., s-1234567890ABC)
  • For EMR Serverless: Job Run ID (UUID format)
• execution-type-id (required): The second positional argument
  • For Glue: Job Name
  • For EMR: Cluster ID (e.g., j-ABC123)
  • For EMR Serverless: Application ID
• --platform (optional): Platform type - glue, emr_serverless, or emr_ec2
  • If not provided, infer from the ID patterns
• --profile (optional): AWS profile name for the account containing the workload
• --region (optional): AWS region (defaults to us-east-1)

Pre-Execution: Gather Missing Information (REQUIRED)

IMPORTANT: Before running the analysis script, you MUST use the AskUserQuestion tool to gather any missing required information. Do NOT proceed with default values without confirming with the user.

Step 1: Validate Region and Profile

If --region is NOT provided in the arguments:

• Use AskUserQuestion to ask: "Which AWS region is your workload located in?"
• Provide common options: us-east-1, us-west-2, eu-west-1, plus "Other" for custom input

If --profile is NOT provided in the arguments:

• Use AskUserQuestion to ask: "Do you need to use a specific AWS profile to access this workload?"
• If user indicates yes, ask for the profile name

Step 2: Validate Platform Type

If --platform is not specified, determine it automatically:

• If execution-id starts with jr_ → glue
• If execution-type-id starts with j- → emr_ec2
• If execution-id looks like a UUID and execution-type-id has app pattern → emr_serverless
• If unclear, use AskUserQuestion to ask the user to specify the platform

Execution

Step 1: Locate the Script

Use the Glob tool to find the analysis script:

Pattern: **/spark-troubleshooting-claude-plugin/scripts/troubleshoot_spark_workload.py

The script is located in the scripts/ directory of the spark-troubleshooting-claude-plugin folder within the workspace.

Step 2: Run the Analysis

Once you have the full script path, run:

uv run <full-script-path> \
  --execution-id <execution-id> \
  --execution-type-id <execution-type-id> \
  --platform-type <platform-type> \
  --profile <profile> \
  --region <region>

IMPORTANT:

• Use the FULL ABSOLUTE PATH to the script (e.g., /Volumes/workplace/.../scripts/troubleshoot_spark_workload.py)
• Do NOT use $CLAUDE_PLUGIN_ROOT - this environment variable is not reliably set
• Always include --profile and --region with the values gathered from the user

Wait for the analysis to complete (may take up to 3 minutes) and present the results.

Output Format

Present the analysis results with:

• Summary of the workload analysis
• Root cause of any issues
• Recommendations for fixes
• Code diff (if generated)

Error Handling

If the script returns an error:

• Explain what went wrong in user-friendly terms
• Suggest possible causes (invalid IDs, permissions, workload not found)
• Recommend next steps for resolution

Examples

/spark-analysis:troubleshoot-spark-workload jr_abc123 my-glue-job --profile prod-account --region us-west-2
/spark-analysis:troubleshoot-spark-workload s-1234567890ABC j-CLUSTERID123 --platform emr_ec2 --profile prod-account --region us-west-2
/spark-analysis:troubleshoot-spark-workload jr_def456 my-job --profile prod-account --region us-west-2