Connect AI agents and engineers to Apache Spark History Server for intelligent job analysis, performance monitoring, and investigation

This project provides two interfaces to your Spark History Server data:

📺 See it in action:

graph TB
    subgraph Clients
        A[🤖 AI Agent / LLM]
        B[👩‍💻 Engineer / Script / CI]
        C[🔧 Coding Agent - Claude Code / Kiro]
    end

    subgraph "Kubeflow Spark AI Toolkit"
        D[⚡ MCP Server]
        E[🛠️ CLI - shs]
    end

    subgraph "Spark History Servers"
        F[🔥 Production]
        G[🔥 Staging / Dev]
    end

    A -->|MCP Protocol| D
    B -->|Terminal commands| E
    C -->|shs skill file| E

    D -->|REST API| F
    D -->|REST API| G
    E -->|REST API| F
    E -->|REST API| G

A standalone Go binary — no MCP, no dependencies, no running daemon. Query your Spark History Server directly from the terminal, shell scripts, or CI/CD pipelines. Also works as a skill for coding agents like Claude Code and Kiro.

# Auto-detect latest version, OS, and architecture
VERSION=$(curl -s https://api.github.com/repos/kubeflow/mcp-apache-spark-history-server/releases | grep -m1 '"tag_name": "cli/' | cut -d'"' -f4 | sed 's|cli/||')
OS=$(uname -s | tr '[:upper:]' '[:lower:]')
ARCH=$(uname -m)
[ "$ARCH" = "x86_64" ] && ARCH="amd64"
[ "$ARCH" = "aarch64" ] && ARCH="arm64"

curl -sSL "https://github.com/kubeflow/mcp-apache-spark-history-server/releases/download/cli%2F${VERSION}/shs-${VERSION}-${OS}-${ARCH}.tar.gz" | tar xz
sudo mv shs /usr/local/bin/

# Generate a config file
shs setup config > config.yaml   # then set your Spark History Server URL

# Explore applications
shs apps
shs jobs -a APP_ID --status failed
shs stages -a APP_ID --sort duration
shs compare apps --app-a APP1 --app-b APP2

# Use as a skill with Claude Code or Kiro
shs setup skill > ~/.claude/skills/spark-history.md

CLI documentation for full usage, or check out a real-world example of Claude Code comparing two TPC-DS 3TB benchmark runs.

An MCP (Model Context Protocol) server that exposes Spark History Server data as tools for AI agents. Agents query your Spark infrastructure using natural language — the server handles tool selection, multi-server routing, and structured data retrieval.

Use the MCP server when you want an AI agent to conduct multi-step investigations, synthesize findings across tools, or answer natural-language questions about your Spark applications.

# Run directly with uvx (no install needed)
uvx --from mcp-apache-spark-history-server spark-mcp

# Or install with pip
pip install mcp-apache-spark-history-server
spark-mcp

The package is published to PyPI.

Edit config.yaml:

servers:
  local:
    default: true
    url: "http://your-spark-history-server:18080"
    auth:            # optional
      username: "user"
      password: "pass"
    include_plan_description: false   # include SQL plans by default (default: false)
mcp:
  transports:
    - streamable-http   # or: stdio
  port: "18888"
  debug: false

Environment variable overrides:

SHS_MCP_PORT          Port for MCP server (default: 18888)
SHS_MCP_TRANSPORT     Transport mode: streamable-http or stdio
SHS_MCP_DEBUG         Enable debug mode (default: false)
SHS_MCP_ADDRESS       Bind address (default: localhost)
SHS_SERVERS_*_URL     URL for a specific server
SHS_SERVERS_*_AUTH_USERNAME
SHS_SERVERS_*_AUTH_PASSWORD
SHS_SERVERS_*_AUTH_TOKEN
SHS_SERVERS_*_VERIFY_SSL
SHS_SERVERS_*_TIMEOUT
SHS_SERVERS_*_EMR_CLUSTER_ARN
SHS_SERVERS_*_INCLUDE_PLAN_DESCRIPTION

Configure multiple Spark History Servers and route queries to specific ones:

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

Agents can target a specific server per query:

"Get application <app_id> from the production server"

Automatically available when AWS credentials and region are configured. See IAM setup guide.

• "Why is my ETL job running slower than yesterday?" → get_job_bottlenecks + list_slowest_stages + compare_job_performance
• "What caused job 42 to fail?" → list_jobs + get_stage + get_stage_task_summary
• "Compare today's batch with yesterday's run" → compare_job_performance + compare_job_environments
• "Find my slowest SQL queries and explain why" → list_slowest_sql_queries + get_sql_execution + compare_sql_execution_plans

Deploy the MCP server using Helm:

helm install spark-history-mcp ./deploy/kubernetes/helm/mcp-apache-spark-history-server/

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

See deploy/kubernetes/helm/ for full configuration options.

When deployed in Kubernetes, connect Claude Desktop via mcp-remote:

kubectl port-forward svc/mcp-apache-spark-history-server 18888:18888

• AWS Glue — Connect to Glue Spark History Server
• Amazon EMR — Use EMR Persistent UI for Spark analysis
• AWS Spark Troubleshooting — One-shot root cause analysis and code fix recommendations for failed Spark workloads (EMR EC2, EMR Serverless). Automatically available when AWS credentials and region are configured. See IAM setup guide for required permissions.

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

# Install Task runner
brew install go-task   # macOS; see https://taskfile.dev/installation/ for others

# MCP Server
task install           # install Python dependencies
task start-spark-bg    # start Spark History Server with sample data
task start-mcp-bg      # start MCP server
task start-inspector-bg  # open MCP Inspector at http://localhost:6274
task stop-all

# CLI
cd skills/cli
task build             # build ./bin/shs
task test              # unit tests
task test-e2e          # e2e tests (starts/stops Docker SHS automatically)
task start-shs         # start SHS with CLI e2e sample data

Using this project? Add your organization to ADOPTERS.md and help grow the community.

See CONTRIBUTING.md for guidelines.

Apache License 2.0 — see LICENSE.

Built for use with Apache Spark™ History Server. Not affiliated with or endorsed by the Apache Software Foundation.