Spark MCP (Model Context Protocol) Optimizer

How It Works

Code Optimization Workflow

Component Details

1. **Input Layer** - `spark_code_input.py`: Source PySpark code for optimization - `run_client.py`: Client startup and configuration

2. **MCP Client Layer** - Tools Interface: Protocol-compliant tool invocation

3. **MCP Server Layer** - `run_server.py`: Server initialization - Tool Registry: Optimization and analysis tools - Protocol Handler: MCP request/response management

4. **Resource Layer** - Claude AI: Code analysis and optimization - PySpark Runtime: Code execution and validation

5. **Output Layer** - `optimized_spark_code.py`: Optimized code - `performance_analysis.md`: Detailed analysis

1. Input PySpark code submission

2. MCP protocol handling and routing

3. Claude AI analysis and optimization

4. Code transformation and validation

5. Performance analysis and reporting

Architecture

Components

1. **MCP Client** - Provides tool interface for code optimization - Handles async communication with server - Manages file I/O for code generation

2. **MCP Server** - Implements MCP protocol handlers - Manages tool registry and execution - Coordinates between client and resources

3. **Resources** - Claude AI: Provides code optimization intelligence - PySpark Runtime: Executes and validates optimizations

Protocol Flow

1. Client sends optimization request via MCP protocol

2. Server validates request and invokes appropriate tool

3. Tool utilizes Claude AI for optimization

4. Optimized code is returned via MCP response

5. Client saves and validates the optimized code

End-to-End Functionality

1. **Code Submission** - User places PySpark code in `v1/input/spark_code_input.py` - Code is read by the MCP client

2. **Optimization Process** - MCP client connects to server via standardized protocol - Server forwards code to Claude AI for analysis - AI suggests optimizations based on best practices - Server validates and processes suggestions

3. **Code Generation** - Optimized code saved to `v1/output/optimized_spark_code.py` - Includes detailed comments explaining optimizations - Maintains original code structure while improving performance

4. **Performance Analysis** - Both versions executed in PySpark runtime - Execution times compared - Results validated for correctness - Metrics collected and analyzed

5. **Results Generation** - Comprehensive analysis in `v1/output/performance_analysis.md` - Side-by-side execution comparison - Performance improvement statistics - Optimization explanations and rationale

Usage

Requirements

• Python 3.8+

• PySpark 3.2.0+

• Anthropic API Key (for Claude AI)

Installation

Quick Start

1. Add your Spark code to optimize in `input/spark_code_input.py`

2. Start the MCP server:

1. Run the client to optimize your code:

• `output/optimized_spark_example.py`: The optimized Spark code with detailed optimization comments

• `output/performance_analysis.md`: Comprehensive performance analysis

1. Run and compare code versions:

• Execute both original and optimized code

• Compare execution times and results

• Update the performance analysis with execution metrics

• Show detailed performance improvement statistics

Project Structure

Why MCP?

Direct Claude AI Call vs MCP Server

Key Differences:

1. AI Integration

2. Tool Management

3. Resource Management

4. Communication Protocol

Features

• **Intelligent Code Optimization**: Leverages Claude AI to analyze and optimize PySpark code

• **Performance Analysis**: Provides detailed analysis of performance differences between original and optimized code

• **MCP Architecture**: Implements the Model Context Protocol for standardized AI model interactions

• **Easy Integration**: Simple client interface for code optimization requests

• **Code Generation**: Automatically saves optimized code to separate files

Advanced Usage

Example Input and Output

1. **Input Code** (`input/spark_code_input.py`):

1. **Optimized Code** (`output/optimized_spark_example.py`):

1. **Performance Analysis** (`output/performance_analysis.md`):

Project Structure

Available Tools

1. **optimize_spark_code** - Optimizes PySpark code for better performance - Supports basic and advanced optimization levels - Automatically saves optimized code to examples/optimized_spark_example.py

2. **analyze_performance** - Analyzes performance differences between original and optimized code - Provides insights on: - Performance improvements - Resource utilization - Scalability considerations - Potential trade-offs

Environment Variables

• `ANTHROPIC_API_KEY`: Your Anthropic API key for Claude AI

Example Optimizations

• Broadcast joins for small-large table joins

• Efficient window function usage

• Strategic data caching

• Query plan optimizations

• Performance-oriented operation ordering

Contributing

License