# syntax=docker/dockerfile:1
#
# Spark Executor MCP — runtime image.
# Build deps with uv (frozen, prod-only), then drop in the source on top of
# python:3.12-slim with Spark + YARN configs mounted for the spark-submit /
# yarn CLI calls inside the MCP tools.

FROM python:3.12-slim

# --- uv (official binary) ---
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /usr/local/bin/

# --- Spark + Hadoop config (matches the original Dockerfile) ---

ARG SPARK_VERSION=4.1.2

RUN apt-get update && \
    apt-get install -y --no-install-recommends \
        curl \
        ca-certificates \
        tar && \
    curl -L \
      https://mirrors.tuna.tsinghua.edu.cn/apache/spark/spark-${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-hadoop3.tgz \
      -o /tmp/spark.tgz && \
    mkdir -p /opt && \
    tar -xzf /tmp/spark.tgz -C /opt && \
    mv /opt/spark-${SPARK_VERSION}-bin-hadoop3 /opt/spark && \
    rm -f /tmp/spark.tgz && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

ENV SPARK_HOME=/opt/spark
ENV PATH=${SPARK_HOME}/bin:${PATH}

# Hadoop/Yarn 配置目录（运行时挂载）
RUN mkdir -p /etc/hadoop/conf

# 默认值，可在 docker run 时覆盖
ENV HADOOP_CONF_DIR=/etc/hadoop/conf
ENV YARN_CONF_DIR=/etc/hadoop/conf

# --- App ---
WORKDIR /app

# Install Python deps first so this layer caches independently of source.
# --frozen pins to uv.lock exactly; --no-dev skips pytest etc. for a slim
# production image; --no-install-project defers copying the source.
COPY pyproject.toml uv.lock ./
RUN uv sync --index-url=https://pypi.tuna.tsinghua.edu.cn/simple/ --frozen --no-dev --no-install-project

# Now copy the source and let uv wire it in.
COPY main.py ./
COPY spark_executor ./spark_executor
COPY common ./common
RUN uv sync --index-url=https://pypi.tuna.tsinghua.edu.cn/simple/ --frozen --no-dev

# Put the venv on PATH so `python` / `uvicorn` resolve to the project env.
ENV PATH=/app/.venv/bin:$PATH
ENV PYTHONUNBUFFERED=1

EXPOSE 8000
CMD ["python", "main.py"]