ClaudeandClaude Fable 5
e518c669e4
- Dockerfile: COPY files_mcp package, mkdir -p /app/data/files at build time, set ENV FILES_MCP_ROOT=/app/data/files as the default sandbox root (overridable at runtime). - docker-compose.yml: surface FILES_MCP_ROOT in the env block with the same default, so operators can override it via .env or -e. FILES_MCP_ROOT must exist at process start (path_guard._init_root checks is_dir()), so the directory is created during build rather than deferred to entrypoint — fail-fast at image build beats a runtime RuntimeError on every container start. Default lives under /app/data so it rides the existing data volume (./data:/app/data) and persists across container restarts alongside connections.json / pending_jobs.json / logs/. Verified locally: docker compose config --quiet passes; FILES_MCP_ROOT resolves to /app/data/files in the rendered config. Full image build not run (docker daemon unavailable in this environment); please run `docker compose up -d --build` to confirm in your environment. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
97 lines
3.4 KiB
Docker
97 lines
3.4 KiB
Docker
# syntax=docker/dockerfile:1
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#
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# Spark Executor MCP — runtime image.
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# Build deps with uv (frozen, prod-only), then drop in the source on top of
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# ppython:3.12-slim-bookworm with Spark + YARN configs mounted for the spark-submit /
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# yarn CLI calls inside the MCP tools.
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FROM python:3.12-slim-bookworm
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# --- uv (official binary) ---
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /usr/local/bin/
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# --- Spark + Hadoop config (matches the original Dockerfile) ---
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ARG SPARK_VERSION=3.5.8
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RUN sed -i 's|deb.debian.org|mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list.d/debian.sources && \
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apt-get update && \
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apt-get install -y --no-install-recommends \
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curl \
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ca-certificates \
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tar \
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gcc \
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g++ \
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make \
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pkg-config \
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libkrb5-dev \
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openjdk-17-jre-headless && \
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curl -L \
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https://mirrors.tuna.tsinghua.edu.cn/apache/spark/spark-${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-hadoop3.tgz \
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-o /tmp/spark.tgz && \
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mkdir -p /opt && \
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tar -xzf /tmp/spark.tgz -C /opt && \
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mv /opt/spark-${SPARK_VERSION}-bin-hadoop3 /opt/spark && \
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rm -f /tmp/spark.tgz && \
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apt-get clean && \
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rm -rf /var/lib/apt/lists/*
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ENV JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64
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ENV SPARK_HOME=/opt/spark
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ENV PATH=${JAVA_HOME}/bin:${SPARK_HOME}/bin:${PATH}
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# Hadoop/Yarn 配置目录（运行时挂载）
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RUN mkdir -p /etc/hadoop/conf
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# 默认值，可在 docker run 时覆盖
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ENV HADOOP_CONF_DIR=/etc/hadoop/conf
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ENV YARN_CONF_DIR=/etc/hadoop/conf
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# --- App ---
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WORKDIR /app
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# Install Python deps first so this layer caches independently of source.
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# --frozen pins to uv.lock exactly; --no-dev skips pytest etc. for a slim
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# production image; --no-install-project defers copying the source.
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COPY pyproject.toml uv.lock ./
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RUN uv sync --index-url=https://pypi.tuna.tsinghua.edu.cn/simple/ --frozen --no-dev --no-install-project
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# Now copy the source and let uv wire it in.
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COPY main.py ./
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COPY spark_executor ./spark_executor
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COPY files_mcp ./files_mcp
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COPY common ./common
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COPY gunicorn.conf.py ./
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RUN uv sync --index-url=https://pypi.tuna.tsinghua.edu.cn/simple/ --frozen --no-dev
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# --- files_mcp sandbox ---
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# FILES_MCP_ROOT is the only directory the files_mcp tools can touch. It
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# must exist at process start (path_guard._init_root checks is_dir()) and
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# persist across container restarts, so it lives under the data volume.
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# Operators can override FILES_MCP_ROOT at `docker run` / compose to point
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# at a separate host mount if they want strict isolation from /app/data.
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RUN mkdir -p /app/data/files
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ENV FILES_MCP_ROOT=/app/data/files
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# Put the venv on PATH so `python` / `gunicorn` / `uvicorn` resolve to the project env.
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ENV PATH=/app/.venv/bin:$PATH
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ENV PYTHONUNBUFFERED=1
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# Entrypoint re-derives PATH from JAVA_HOME and SPARK_HOME at container
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# start, so overriding either via docker-compose / .env actually changes
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# which `java` and `spark-submit` binaries the gunicorn process picks up.
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COPY docker-entrypoint.sh /usr/local/bin/docker-entrypoint.sh
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RUN chmod +x /usr/local/bin/docker-entrypoint.sh
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ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]
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# gunicorn is the prod entrypoint — multiple ASGI workers, graceful
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# shutdown, stdout/stderr logs. Config knobs are env-var driven (see
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# gunicorn.conf.py).
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#
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# Common overrides via -e flags at `docker run`:
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# -e GUNICORN_WORKERS=4
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# -e GUNICORN_TIMEOUT=180
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# -e GUNICORN_BIND=0.0.0.0:9000
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EXPOSE 8000
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CMD ["gunicorn", "main:app"]
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