Dockerfile

# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
ARG REGISTRY=quay.io
ARG OWNER=jupyter
ARG BASE_CONTAINER=$REGISTRY/$OWNER/scipy-notebook
FROM $BASE_CONTAINER

LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"

# Fix: https://github.com/hadolint/hadolint/wiki/DL4006
# Fix: https://github.com/koalaman/shellcheck/wiki/SC3014
SHELL ["/bin/bash", "-o", "pipefail", "-c"]

USER root

# Spark dependencies
# Default values can be overridden at build time
# (ARGS are in lowercase to distinguish them from ENV)
ARG spark_version="3.5.0"
ARG hadoop_version="3"
ARG scala_version
ARG spark_checksum="8883c67e0a138069e597f3e7d4edbbd5c3a565d50b28644aad02856a1ec1da7cb92b8f80454ca427118f69459ea326eaa073cf7b1a860c3b796f4b07c2101319"
ARG openjdk_version="17"

ENV APACHE_SPARK_VERSION="${spark_version}" \
    HADOOP_VERSION="${hadoop_version}"

RUN apt-get update --yes && \
    apt-get install --yes --no-install-recommends \
    "openjdk-${openjdk_version}-jre-headless" \
    ca-certificates-java && \
    apt-get clean && rm -rf /var/lib/apt/lists/*

# Spark installation
WORKDIR /tmp

# You need to use https://archive.apache.org/dist/ website if you want to download old Spark versions
# But it seems to be slower, that's why we use the recommended site for download
RUN if [ -z "${scala_version}" ]; then \
    curl --progress-bar --location --output "spark.tgz" \
        "https://dlcdn.apache.org/spark/spark-${APACHE_SPARK_VERSION}/spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz"; \
  else \
    curl --progress-bar --location --output "spark.tgz" \
        "https://dlcdn.apache.org/spark/spark-${APACHE_SPARK_VERSION}/spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}-scala${scala_version}.tgz"; \
  fi && \
  echo "${spark_checksum} *spark.tgz" | sha512sum -c - && \
  tar xzf "spark.tgz" -C /usr/local --owner root --group root --no-same-owner && \
  rm "spark.tgz"

# Configure Spark
ENV SPARK_HOME=/usr/local/spark
ENV SPARK_OPTS="--driver-java-options=-Xms1024M --driver-java-options=-Xmx4096M --driver-java-options=-Dlog4j.logLevel=info" \
    PATH="${PATH}:${SPARK_HOME}/bin"

RUN if [ -z "${scala_version}" ]; then \
    ln -s "spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}" "${SPARK_HOME}"; \
  else \
    ln -s "spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}-scala${scala_version}" "${SPARK_HOME}"; \
  fi && \
  # Add a link in the before_notebook hook in order to source automatically PYTHONPATH && \
  ln -s "${SPARK_HOME}/sbin/spark-config.sh" /usr/local/bin/before-notebook.d/10spark-config.sh

# Configure IPython system-wide
COPY ipython_kernel_config.py "/etc/ipython/"
RUN fix-permissions "/etc/ipython/"

USER ${NB_UID}

# Install pyarrow
# NOTE: It's important to ensure compatibility between Pandas versions.
# The pandas version in this Dockerfile should match the version
# on which the Pandas API for Spark is built.
# To find the right version:
# 1. Check out the Spark branch you are on.
# 2. Find the pandas version in the file spark/dev/infra/Dockerfile.
RUN mamba install --yes \
    'grpcio-status' \
    'grpcio' \
    'pandas=2.0.3' \
    'pyarrow' && \
    mamba clean --all -f -y && \
    fix-permissions "${CONDA_DIR}" && \
    fix-permissions "/home/${NB_USER}"

WORKDIR "${HOME}"
EXPOSE 4040