Spark MCP Optimizer
by vgiri2015
The Spark MCP (Model Context Protocol) Optimizer implements a server and client for optimizing Apache Spark code. It provides intelligent code optimization suggestions and performance analysis through a client-server architecture, leveraging Claude AI.
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What is Spark MCP Optimizer?
The Spark MCP Optimizer is a system designed to optimize Apache Spark code by leveraging the Model Context Protocol (MCP) and Claude AI. It provides a client-server architecture for submitting Spark code, receiving optimization suggestions, and analyzing performance improvements.
How to use Spark MCP Optimizer?
To use the Spark MCP Optimizer, first install the required dependencies using pip install -r requirements.txt. Then, place your PySpark code in input/spark_code_input.py. Start the MCP server using python v1/run_server.py and run the client with python v1/run_client.py. The optimized code will be generated in output/optimized_spark_example.py, and a performance analysis will be available in output/performance_analysis.md.
Key features of Spark MCP Optimizer
Intelligent Code Optimization using Claude AI
Detailed Performance Analysis of original vs. optimized code
Model Context Protocol (MCP) architecture for standardized AI interactions
Easy integration with a simple client interface
Automatic code generation and performance reporting
Use cases of Spark MCP Optimizer
Optimizing existing PySpark jobs for improved performance
Automating the process of identifying and applying code optimizations
Analyzing the performance impact of different optimization strategies
Integrating AI-powered code optimization into CI/CD pipelines
FAQ from Spark MCP Optimizer
What is the Model Context Protocol (MCP)?
What is the Model Context Protocol (MCP)?
MCP is a standardized protocol for AI model interactions, enabling consistent and efficient communication between clients, servers, and AI resources.
How does the system leverage Claude AI?
How does the system leverage Claude AI?
The system uses Claude AI to analyze PySpark code, identify potential optimizations, and generate optimized code suggestions.
What kind of optimizations are performed?
What kind of optimizations are performed?
The system implements various PySpark optimizations, including broadcast joins, efficient window function usage, strategic data caching, query plan optimizations, and performance-oriented operation ordering.
How do I provide my Anthropic API key?
How do I provide my Anthropic API key?
You need to set the ANTHROPIC_API_KEY environment variable with your Anthropic API key.
Where can I find the optimized code and performance analysis?
Where can I find the optimized code and performance analysis?
The optimized code is saved to output/optimized_spark_example.py, and the performance analysis is available in output/performance_analysis.md.