Kubeflow Spark AI Toolkit

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

---

Important

✨ NEW — Spark History Server CLI is now available

A standalone Go binary that queries Spark History Server directly from your terminal — no MCP, no AI framework, no daemon process. Inspect jobs, compare runs, investigate failures, and script against the Spark REST API.

Get started with the SHS CLI →

---

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

	🛠️ SHS CLI (shs)	⚡ MCP Server
For	Engineers, shell scripts, CI/CD, coding agents	AI agents and MCP-compatible clients
Mental model	"I know the command I want to run"	"Agent, investigate this Spark app"
Install	Single static binary — no dependencies	Python 3.12+, uv
Get started	CLI docs →	MCP docs →

📺 See it in action:

---

🏗️ Architecture

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

Loading

---

🛠️ SHS CLI (shs) — For Engineers & Scripts

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.

Install

# 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/

Quick Start

# 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.

---

⚡ MCP Server — For AI Agents

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.

Install

# 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.

Configure

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

Multi-Server Setup

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"

Connect an AI Agent

Agent	Transport	Guide
Claude Desktop	stdio	Setup →
Amazon Q CLI	stdio	Setup →
Kiro	streamable-http	Setup →
LangGraph	streamable-http	Setup →
Strands Agents	streamable-http	Setup →
Local / Inspector	streamable-http	Setup →

Available Tools (19)

Application Information

Tool	Description
list_applications	List applications with optional status, date, and limit filters
get_application	Get application detail: status, resources, duration, attempts

Job Analysis

Tool	Description
list_jobs	List jobs with status filtering
list_slowest_jobs	Top N slowest jobs

Stage Analysis

Tool	Description
list_stages	List stages with status filtering
list_slowest_stages	Top N slowest stages
get_stage	Stage detail with attempt and summary metrics
get_stage_task_summary	Task metric distributions (execution time, memory, I/O, spill)

Executor & Resource Analysis

Tool	Description
list_executors	List executors (active and optionally inactive)
get_executor	Executor detail: resources, task stats, performance
get_executor_summary	Aggregate metrics across all executors
get_resource_usage_timeline	Chronological executor add/remove with resource totals

Configuration & Environment

Tool	Description
get_environment	Spark config, JVM info, system properties, classpath

SQL & Query Analysis

Tool	Description
list_slowest_sql_queries	Top N slowest SQL executions with metrics
get_sql_execution	SQL execution detail with optional plan and node metrics
compare_sql_execution_plans	Compare SQL plans and metrics between two jobs

Performance & Bottleneck Analysis

Tool	Description
get_job_bottlenecks	Identify bottlenecks across stages, tasks, and executors

Comparative Analysis

Tool	Description
compare_job_environments	Diff Spark configs between two applications
compare_job_performance	Diff performance metrics between two applications

Example Agent Queries

• "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

---

📸 Screenshots

🔍 Get Spark Application

⚡ Job Performance Comparison

---

🚀 Kubernetes Deployment

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 Integration

• AWS Glue — Connect to Glue Spark History Server
• Amazon EMR — Use EMR Persistent UI for Spark analysis

---

🔧 Development Setup

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

---

🌍 Adopters

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

🤝 Contributing

See CONTRIBUTING.md for guidelines.

📄 License

Apache License 2.0 — see LICENSE.

📝 Trademark Notice

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

---

Connect your Spark infrastructure to AI agents and engineers

🛠️ SHS CLI · ⚡ MCP Server · 🧪 Test · 🤝 Contribute

Built by the community, for the community 💙