Claude
6bcf3b9ac9
Production entrypoint switch:
- pyproject.toml: add gunicorn>=23.0 dep
- gunicorn.conf.py: env-var-driven config (bind, workers, threads,
timeout, graceful_timeout, keepalive, log level, access-log format)
- Dockerfile: CMD gunicorn main:app (auto-loads gunicorn.conf.py from
WORKDIR /app)
- docker-compose.yml: forward GUNICORN_WORKERS / GUNICORN_TIMEOUT /
GUNICORN_BIND
- .env.example: document the new tunables
Why gunicorn over standalone uvicorn for production:
- Process supervision: master restarts crashed workers, restarts on
memory leaks
- Graceful shutdown: SIGTERM drains workers in-flight
- Multi-worker: concurrent requests actually run in parallel
- Standard ops: k8s readiness probes, log aggregators, etc. all know
gunicorn
Why uvicorn workers (not sync workers): gunicorn can't natively serve
ASGI; uvicorn.workers.UvicornWorker is the canonical way to run an
ASGI app under gunicorn.
Defaults:
- 2 workers (small MCP service; raise for high concurrency)
- 1 thread per worker (no blocking I/O)
- 120s timeout (yarn logs can be slow; uvicorn's 30s default is too
tight)
Verified: gunicorn boots, lifespan runs (14 tools logged), MCP
initialize + tools/list + tools/call all work, /openapi.json = 200,
multiple gunicorn worker processes visible in ps.
uv sync picked up gunicorn 26.0.0. Tests still 116/116.
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
# coding=utf-8
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"""
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@Time :2026/6/24
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@Author :tao.chen
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Gunicorn config for the Spark Executor MCP service.
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ASGI workers (uvicorn.workers.UvicornWorker) so we get gunicorn's process
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supervision, graceful shutdown, and graceful reload semantics on top of
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uvicorn's ASGI implementation.
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All knobs are env-var driven so the same image runs in dev (workers=1)
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and prod (workers=2-4) without rebuilding.
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"""
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import os
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# --- Network ---
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bind = os.environ.get("GUNICORN_BIND", "0.0.0.0:8000")
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# --- Process model ---
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# 2 is a sensible default for a small containerized MCP server: lets a slow
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# yarn logs request run in parallel with a status check. Scale up by
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# setting GUNICORN_WORKERS at deploy time.
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workers = int(os.environ.get("GUNICORN_WORKERS", "2"))
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worker_class = "uvicorn.workers.UvicornWorker"
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# 1 thread per worker is enough for ASGI handlers (no blocking I/O).
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threads = int(os.environ.get("GUNICORN_THREADS", "1"))
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# --- Lifecycle ---
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# Generous timeout because the slowest tool call here is yarn logs (which
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# can take 30+ seconds on a busy cluster). uvicorn standalone defaults to
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# 30s; gunicorn's default is 30s too — both too tight for log fetch.
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timeout = int(os.environ.get("GUNICORN_TIMEOUT", "120"))
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graceful_timeout = int(os.environ.get("GUNICORN_GRACEFUL_TIMEOUT", "30"))
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keepalive = int(os.environ.get("GUNICORN_KEEPALIVE", "5"))
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# --- Logging ---
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# Stream access + error to stdout/stderr so docker logs / k8s logs capture
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# them. gunicorn's "[INFO] Booting worker" lines interleave with loguru's
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# output — both go to stderr.
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accesslog = "-"
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errorlog = "-"
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loglevel = os.environ.get("GUNICORN_LOGLEVEL", "info")
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access_log_format = (
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'%(h)s %(l)s %(u)s %(t)s "%(r)s" %(s)s %(b)s "%(f)s" "%(a)s" %(L)s'
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)
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# --- Process naming (visible in `ps aux`) ---
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proc_name = "spark-executor-mcp"
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