tao.chenandClaude 6f4c3a3e21
CI / Release / CI (push) Successful in 3m15s
CI / Release / Release (push) Successful in 4m43s
ci: Bark 推送逻辑提到独立脚本, 两 job 复用
之前 ci/release 各自 inline 拼 payload + curl, 重复 25 行 x 2.
device_key / endpoint / group / ttl 硬编码两次, 改一处忘另一处
会出现两个不同 group 的推送.

新建 .github/scripts/bark-notify.sh:
  bash .github/scripts/bark-notify.sh "<title>" "<body>"

脚本封装:
  - endpoint / device_key / group (Gitea) / isArchive / ttl 单一来源
  - heredoc 拼 JSON, 不依赖 jq
  - 错误用法打印 usage 并 exit 1 (set -euo pipefail)

workflow 两处都改成一行:
  bash .github/scripts/bark-notify.sh "CI \${REF_NAME} \${STATUS}" "\${BODY}"
  bash .github/scripts/bark-notify.sh "\${TAG_NAME} \${STATUS}" "\${BODY}"

emoji (/) 跟 status 映射仍在 workflow 里, 因为不同 job 拼
不同的 title prefix (CI vs release), 脚本不管这个, 调用方传什么
就推什么.

chmod +x 加在 commit 里, 但 git mode bit 在 .gitattributes 或
core.fileMode 关时可能丢. runner 一般会按 working tree 模式跑,
不需要额外 chmod 在 step 里.

Co-Authored-By: Claude <noreply@anthropic.com>
2026-07-15 16:36:07 +08:00

spark-mcp-go

MCP (Model Context Protocol) server for Apache Spark on YARN. Lets an LLM agent discover Spark/YARN endpoints, submit jobs, fetch logs, and analyze them via 11 typed Tools. Streamable HTTP transport, SQLite-backed configuration, and log/slog structured logging with per-Tool call files.

What it does

  • Exposes 11 MCP Tools over Streamable HTTP at /mcp
  • Admin API at /admin/* for cluster and audit configuration
  • HTTP Basic and YARN SimpleAuth for Spark/YARN endpoints
  • SSRF protection with cluster URL allowlist plus DNS rebinding guard
  • Per-Tool independent audit log (file mode 0600)
  • spark-submit via local exec.Command (no shell, no injection)

Quick start (3 steps)

  1. Copy and edit the environment file:

    cp .env.example .env
    # Edit ADMIN_TOKENS and AGENT_TOKEN
    
  2. Build and start the server:

    go build ./...
    ./spark-mcp-go
    # Or use the helper:
    # ./scripts/dev.sh
    
  3. Initialize an MCP session, then call list_clusters to discover endpoints.

11 Tools

Tool Type Purpose
list_clusters discovery Return all active clusters (LLM entry point)
spark_submit exec Local spark-submit process, extracts app_id
list_applications RM GET /ws/v1/cluster/apps[?state=&user=]
get_application_status RM GET /ws/v1/cluster/apps/{id}
get_application_logs RM Fallback chain: amContainerLogs -> aggregated-logs -> logs
kill_application RM PUT state KILLED
fetch_spark_metrics SHS Executor metrics + summary mode triggers analyzer
fetch_cluster_env RM Aggregate /cluster/info and /cluster/metrics
analyze_spark_log analyzer RM logs + heuristic rules + LLM-ready prompt
fetch_url HTTP Generic primitive, uses cluster allowlist and auth
upload_file FS Write to ./data/uploads/, reference from spark_submit

Environment variables

Variable Default Required Description
LISTEN_ADDR :8080 no HTTP listen address
DATA_DIR ./data no SQLite, uploads, and log root
ADMIN_TOKENS yes Comma-separated tokens for /admin/*
AGENT_TOKEN yes Token for /mcp requests
HTTP_CLIENT_TIMEOUT 30s no Timeout for fetch_url/RM Tool HTTP calls
MAX_RESPONSE_BYTES 1048576 (1 MiB) no HTTP response truncation limit
SPARK_SUBMIT_TIMEOUT 60s no spark-submit process timeout
LOG_DIR ./data/logs no Log root directory
LOG_LEVEL info no debug, info, warn, or error
LOG_FORMAT text no text for terminal, json for log files
ANALYZER_DATA_SKEW_RATIO 3.0 no Data-skew rule threshold (max/min ratio)
ANALYZER_GC_PRESSURE_RATIO 0.1 no GC-pressure rule threshold (GC/CPU ratio)
ANALYZER_BOTTLENECK_SHUFFLE_GB 50 no Bottleneck rule threshold (shuffle GB)

Admin API

All endpoints require Authorization: Bearer <admin_token>.

Method Path Description
GET /admin/clusters List all clusters
POST /admin/clusters Create a cluster
GET /admin/clusters/:id Get one cluster
PUT /admin/clusters/:id Update a cluster
DELETE /admin/clusters/:id Delete a cluster
GET /admin/audit?limit=100 Query audit log

Create a cluster:

curl -sS -X POST \\
  -H "Authorization: Bearer ${ADMIN_TOKEN}" \\
  -H "Content-Type: application/json" \\
  -d '{
    "id": "prod",
    "name": "prod",
    "rm_url": "http://rm.example.com:8088",
    "shs_url": "http://shs.example.com:18080",
    "spark_submit_execute_bin": "/opt/spark/bin/spark-submit",
    "is_active": true,
    "auth_type": "simple",
    "auth_username": "yarn",
    "rate_limit_per_min": 10,
    "url_allowlist": ["rm.example.com:8088", "shs.example.com:18080"]
  }' \\
  "http://127.0.0.1:${LISTEN_ADDR:-:8080}/admin/clusters"

Note: auth_password is not accepted via JSON (json:"-"). Set it through a dedicated password endpoint or seed the database directly.

Security model

Three lines of defense:

  1. Token auth - ADMIN_TOKENS protects /admin/*; AGENT_TOKEN protects /mcp.
  2. SSRF guard - Every outbound URL must match the cluster's url_allowlist, with private-IP CIDR blacklist (incl. 169.254.0.0/16 AWS/GCP metadata).
  3. Path allowlist - upload_file writes only to ./data/uploads/, rejects absolute paths, .., and non-[a-zA-Z0-9._-] filenames.

Additional guarantees:

  • spark_submit runs the cluster's binary via exec.Command(name, args...) — slice form, never sh -c. No shell metacharacter interpretation.
  • auth_password is tagged json:"-"; never serialized in responses, never accepted from JSON request bodies.
  • Cross-host redirects (RM → NM 307) preserve the Authorization header through DoWithRedirect only when the destination host is in url_allowlist.
  • Per-Tool log files are created with mode 0600 and live under ./data/logs/tools/.

Development

go build ./...
go test ./...
go vet ./...

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