DeepDiagnostix-AI/mcp-apache-spark-history-server

MCP Server for Apache Spark History Server

🤖 Connect AI agents to Apache Spark History Server for intelligent job analysis and performance monitoring

Transform your Spark infrastructure monitoring with AI! This Model Context Protocol (MCP) server enables AI agents to analyze job performance, identify bottlenecks, and provide intelligent insights from your Spark History Server data.

🎯 What is This?

Spark History Server MCP bridges AI agents with your existing Apache Spark infrastructure, enabling:

• 🔍 Query job details through natural language
• 📊 Analyze performance metrics across applications
• 🔄 Compare multiple jobs to identify regressions
• 🚨 Investigate failures with detailed error analysis
• 📈 Generate insights from historical execution data

📺 See it in action:

🏗️ Architecture

graph TB
    A[🤖 AI Agent/LLM] --> F[📡 MCP Client]
    B[🦙 LlamaIndex Agent] --> F
    C[🌐 LangGraph] --> F
    D[�️ Claudep Desktop] --> F
    E[🛠️ Amazon Q CLI] --> F

    F --> G[⚡ Spark History MCP Server]

    G --> H[🔥 Prod Spark History Server]
    G --> I[🔥 Staging Spark History Server]
    G --> J[🔥 Dev Spark History Server]

    H --> K[📄 Prod Event Logs]
    I --> L[📄 Staging Event Logs]
    J --> M[📄 Dev Event Logs]

🔗 Components:

• 🔥 Spark History Server: Your existing infrastructure serving Spark event data
• ⚡ MCP Server: This project - provides MCP tools for querying Spark data
• 🤖 AI Agents: LangChain, custom agents, or any MCP-compatible client

⚡ Quick Start

📋 Prerequisites

• 🔥 Existing Spark History Server (running and accessible)
• 🐍 Python 3.12+
• ⚡ uv package manager

🚀 Setup & Testing

git clone https://github.com/DeepDiagnostix-AI/mcp-apache-spark-history-server.git
cd mcp-apache-spark-history-server

# Install Task (if not already installed)
brew install go-task  # macOS, see https://taskfile.dev/installation/ for others

# Setup and start testing
task start-spark-bg            # Start Spark History Server with sample data (default Spark 3.5.5)
# Or specify a different Spark version:
# task start-spark-bg spark_version=3.5.2
task start-mcp-bg             # Start MCP Server

# Optional: Opens MCP Inspector on http://localhost:6274 for interactive testing
# Requires Node.js: 22.7.5+ (Check https://github.com/modelcontextprotocol/inspector for latest requirements)
task start-inspector-bg       # Start MCP Inspector

# When done, run `task stop-all`

If you just want to run the MCP server without cloning the repository:

# Run with uv without installing the module
uvx --from mcp-apache-spark-history-server spark-mcp

# OR run with pip and python. Use of venv is highly encouraged.
python3 -m venv spark-mcp && source spark-mcp/bin/activate
pip install mcp-apache-spark-history-server
python3 -m spark_history_mcp.core.main
# Deactivate venv
deactivate

📊 Sample Data

The repository includes real Spark event logs for testing:

• spark-bcec39f6201b42b9925124595baad260 - ✅ Successful ETL job
• spark-110be3a8424d4a2789cb88134418217b - 🔄 Data processing job
• spark-cc4d115f011443d787f03a71a476a745 - 📈 Multi-stage analytics job

See for using them.

⚙️ Server Configuration

Edit config.yaml for your Spark History Server:

servers:
  local:
    default: true
    url: "http://your-spark-history-server:18080"
    auth:  # optional
      username: "user"
      password: "pass"
mcp:
  transports:
    - streamable-http # streamable-http or stdio.
  port: "18888"
  debug: true

📸 Screenshots

🔍 Get Spark Application

⚡ Job Performance Comparison

🛠️ Available Tools

Note: These tools are subject to change as we scale and improve the performance of the MCP server.

The MCP server provides 17 specialized tools organized by analysis patterns. LLMs can intelligently select and combine these tools based on user queries:

📊 Application Information

Basic application metadata and overview

🔗 Job Analysis

Job-level performance analysis and identification

⚡ Stage Analysis

Stage-level performance deep dive and task metrics

🖥️ Executor & Resource Analysis

Resource utilization, executor performance, and allocation tracking

⚙️ Configuration & Environment

Spark configuration, environment variables, and runtime settings

🔎 SQL & Query Analysis

SQL performance analysis and execution plan comparison

🚨 Performance & Bottleneck Analysis

Intelligent bottleneck identification and performance recommendations

🔄 Comparative Analysis

Cross-application comparison for regression detection and optimization

🤖 How LLMs Use These Tools

Query Pattern Examples:

• "Why is my job slow?" → get_job_bottlenecks + list_slowest_stages + get_executor_summary
• "Compare today vs yesterday" → compare_job_performance + compare_job_environments
• "What's wrong with stage 5?" → get_stage + get_stage_task_summary
• "Show me resource usage over time" → get_resource_usage_timeline + get_executor_summary
• "Find my slowest SQL queries" → list_slowest_sql_queries + compare_sql_execution_plans

📔 AWS Integration Guides

If you are an existing AWS user looking to analyze your Spark Applications, we provide detailed setup guides for:

• - Connect to Glue Spark History Server
• - Use EMR Persistent UI for Spark analysis

These guides provide step-by-step instructions for setting up the Spark History Server MCP with your AWS services.

🚀 Kubernetes Deployment

Deploy using Kubernetes with Helm:

⚠️ Work in Progress: We are still testing and will soon publish the container image and Helm registry to GitHub for easy deployment.

# 📦 Deploy with Helm
helm install spark-history-mcp ./deploy/kubernetes/helm/spark-history-mcp/

# 🎯 Production configuration
helm install spark-history-mcp ./deploy/kubernetes/helm/spark-history-mcp/ \
  --set replicaCount=3 \
  --set autoscaling.enabled=true \
  --set monitoring.enabled=true

📚 See for complete deployment manifests and configuration options.

🌐 Multi-Spark History Server Setup

Setup multiple Spark history servers in the config.yaml and choose which server you want the LLM to interact with for each query.

servers:
  production:
    default: true
    url: "http://prod-spark-history:18080"
    auth:
      username: "user"
      password: "pass"
  staging:
    url: "http://staging-spark-history:18080"

💁 User Query: "Can you get application <app_id> using production server?"

🤖 AI Tool Request:

{
  "app_id": "<app_id>",
  "server": "production"
}

🤖 AI Tool Response:

{
  "id": "<app_id>>",
  "name": "app_name",
  "coresGranted": null,
  "maxCores": null,
  "coresPerExecutor": null,
  "memoryPerExecutorMB": null,
  "attempts": [
    {
      "attemptId": null,
      "startTime": "2023-09-06T04:44:37.006000Z",
      "endTime": "2023-09-06T04:45:40.431000Z",
      "lastUpdated": "2023-09-06T04:45:42Z",
      "duration": 63425,
      "sparkUser": "spark",
      "appSparkVersion": "3.3.0",
      "completed": true
    }
  ]
}

🔐 Environment Variables

SHS_MCP_PORT - Port for MCP server (default: 18888)
SHS_MCP_DEBUG - Enable debug mode (default: false)
SHS_MCP_ADDRESS - Address for MCP server (default: localhost)
SHS_MCP_TRANSPORT - MCP transport mode (default: streamable-http)
SHS_SERVERS_*_URL - URL for a specific server
SHS_SERVERS_*_AUTH_USERNAME - Username for a specific server
SHS_SERVERS_*_AUTH_PASSWORD - Password for a specific server
SHS_SERVERS_*_AUTH_TOKEN - Token for a specific server
SHS_SERVERS_*_VERIFY_SSL - Whether to verify SSL for a specific server (true/false)
SHS_SERVERS_*_TIMEOUT - HTTP request timeout in seconds for a specific server (default: 30)
SHS_SERVERS_*_EMR_CLUSTER_ARN - EMR cluster ARN for a specific server

🤖 AI Agent Integration

Quick Start Options

🎯 Example Use Cases

🔍 Performance Investigation

🤖 AI Query: "Why is my ETL job running slower than usual?"

📊 MCP Actions:
✅ Analyze application metrics
✅ Compare with historical performance
✅ Identify bottleneck stages
✅ Generate optimization recommendations

🚨 Failure Analysis

🤖 AI Query: "What caused job 42 to fail?"

🔍 MCP Actions:
✅ Examine failed tasks and error messages
✅ Review executor logs and resource usage
✅ Identify root cause and suggest fixes

📈 Comparative Analysis

🤖 AI Query: "Compare today's batch job with yesterday's run"

📊 MCP Actions:
✅ Compare execution times and resource usage
✅ Identify performance deltas
✅ Highlight configuration differences

🤝 Contributing

Check for full guidelines on contributions

📄 License

Apache License 2.0 - see file for details.

📝 Trademark Notice

This project is built for use with Apache Spark™ History Server. Not affiliated with or endorsed by the Apache Software Foundation.