Apache Spark Optimizer

SkillDev tools

Analyzes and optimizes Apache Spark jobs for performance, cost, and resource utilization

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Apache Spark Optimizer skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/data-engineering-analytics/skills/apache-spark-optimizer/SKILL.md and read by ahel’s review.

Overview

Analyzes and optimizes Apache Spark jobs for performance, cost, and resource utilization. This skill provides deep expertise in Spark execution plans, partitioning strategies, and resource configuration to maximize efficiency.

Capabilities

Spark execution plan analysis and optimization
Partition strategy recommendations
Shuffle reduction techniques
Memory and executor configuration tuning
Catalyst optimizer hints generation
Data skew detection and mitigation
Broadcast join optimization
Caching strategy recommendations

Input Schema

{
  "sparkCode": "string",
  "clusterConfig": "object",
  "executionMetrics": "object",
  "dataCharacteristics": {
    "volumeGB": "number",
    "partitionCount": "number",
    "skewFactor": "number"
  }
}

Output Schema

{
  "optimizedCode": "string",
  "recommendations": ["string"],
  "expectedImprovement": {
    "executionTime": "percentage",
    "resourceUsage": "percentage",
    "cost": "percentage"
  },
  "configChanges": "object"
}

Target Processes

ETL/ELT Pipeline
Streaming Pipeline
Feature Store Setup
Pipeline Migration

Usage Guidelines

Provide the Spark code or job definition for analysis
Include cluster configuration details (executors, memory, cores)
Share execution metrics if available (from Spark UI or history server)
Describe data characteristics including volume, partitions, and known skew

Best Practices

Always analyze execution plans before and after optimization
Test optimizations on representative data samples first
Monitor resource utilization during optimization validation
Document configuration changes for reproducibility
Consider cost implications alongside performance gains

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: apache-spark-optimizer
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter