Base Spark cluster environment - default Hadoop client libraries

Leave the Docker Instructions section blank, if you want a thin base image that only contains core Spark with the default Hadoop client libraries. Currently both Spark 2.4.x and Spark 3.0.0 come with Hadoop 2.7.

Base Spark cluster environment (Advanced) - custom Hadoop client libraries

In some cases the Hadoop client libraries pre-bundled with your desired Spark version may not be appropriate for your needs. This would typically be the case if you want to utilize cloud object store connector improvements introduced post Hadoop 2.7.

Add the following to the Docker Instructions section adjusting for the desired Spark and Hadoop version.

### need if using the recommended Bitnami base image
USER root

### Make sure wget is available
RUN apt-get update && apt-get install -y wget && rm -r /var/lib/apt/lists /var/cache/apt/archives

### Modify the Hadoop and Spark versions below as needed.
### NOTE: The HADOOP_HOME and SPARK_HOME locations should not be modified
ENV HADOOP_VERSION=2.9.2
ENV HADOOP_HOME=/opt/bitnami/hadoop
ENV HADOOP_CONF_DIR=/opt/bitnami/hadoop/etc/hadoop
ENV SPARK_VERSION=2.4.6
ENV SPARK_HOME=/opt/bitnami/spark
ENV PATH="$PATH:$SPARK_HOME/bin:$HADOOP_HOME/bin"

### Enable this for access to ADLS Gen2 when using Hadoop 3.2+
### ENV HADOOP_OPTIONAL_TOOLS=hadoop-azure

### Remove the pre-installed Spark since it is pre-bundled with hadoop but preserve the python env
WORKDIR /opt/bitnami
RUN [ -d ${SPARK_HOME}/venv ] && mv ${SPARK_HOME}/venv /opt/bitnami/temp-venv
RUN rm -rf ${SPARK_HOME}

### Install the desired Hadoop-free Spark distribution
RUN wget -q https://archive.apache.org/dist/spark/spark-${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-without-hadoop.tgz && \
    tar -xf spark-${SPARK_VERSION}-bin-without-hadoop.tgz && \
    rm spark-${SPARK_VERSION}-bin-without-hadoop.tgz && \
    mv spark-${SPARK_VERSION}-bin-without-hadoop ${SPARK_HOME} && \
    chmod -R 777 ${SPARK_HOME}/conf

### Restore the virtual python environment
RUN [ -d /opt/bitnami/temp-venv ] && mv /opt/bitnami/temp-venv ${SPARK_HOME}/venv

### Install the desired Hadoop libraries
RUN wget -q http://archive.apache.org/dist/hadoop/common/hadoop-${HADOOP_VERSION}/hadoop-${HADOOP_VERSION}.tar.gz && \
    tar -xf hadoop-${HADOOP_VERSION}.tar.gz && \
    rm hadoop-${HADOOP_VERSION}.tar.gz && \
    mv hadoop-${HADOOP_VERSION} ${HADOOP_HOME}

### Setup the Hadoop libraries classpath
RUN echo 'export SPARK_DIST_CLASSPATH="$(hadoop classpath):'"${HADOOP_HOME}"'/share/hadoop/tools/lib/*"' >> ${SPARK_HOME}/conf/spark-env.sh
ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$HADOOP_HOME/lib/native"

### This is important to maintain compatibility with Bitnami
WORKDIR /
RUN /opt/bitnami/scripts/spark/postunpack.sh
WORKDIR ${SPARK_HOME}

USER 1001

PySpark compute environment - default Hadoop client libraries

The Bitnami base images referenced above come pre-built with Hadoop. For Spark 2.4.X and Spark 3.0.0, the pre-built Hadoop version currently is 2.7. If this is appropriate for your needs, you can use the simplified configuration instructions below to install and configure PySpark.

### Clear any existing PySpark install that may exist
### Omit if you know the environment does not have PySpark
RUN pip uninstall pyspark &>/dev/null

### Install PySpark matching the Spark version of your base image
### Modify the version below as needed
RUN pip install pyspark==2.4.6

### Set SPARK_HOME on the driver to point to the version installed by pyspark
RUN \
  SPARK_HOME=$(pip show pyspark | grep "Location" | awk '{print $2}')/pyspark && \
  chown -R ubuntu:ubuntu ${SPARK_HOME} && \
  echo "export SPARK_HOME=${SPARK_HOME}" >> /home/ubuntu/.domino-defaults && \
  echo "export PATH=\$PATH:${SPARK_HOME}/bin" >> /home/ubuntu/.domino-defaults

### Optionally copy spark-submit to spark-submit.sh to be able to run from Domino jobs
RUN spark_submit_path=$(which spark-submit) && \
    cp ${spark_submit_path} ${spark_submit_path}.sh

### Hadoop 2.7 does not come with the required binaries for AWS access so we add them
### hadoop-aws.jar must match the hadoop-common.jar version of your install
RUN \
  SPARK_HOME=$(pip show pyspark | grep "Location" | awk '{print $2}')/pyspark && \
  rm -rf ${SPARK_HOME}/hadoop-aws* && \
  rm -rf ${SPARK_HOME}/aws-java-sdk* && \
  curl https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/2.7.3/hadoop-aws-2.7.3.jar --output ${SPARK_HOME}/jars/hadoop-aws-2.7.3.jar && \
  curl https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk/1.7.4/aws-java-sdk-1.7.4.jar --output ${SPARK_HOME}/jars/aws-java-sdk-1.7.4.jar

PySpark compute environment (Advanced) - custom Hadoop client libraries

In some cases the Hadoop libraries pre-bundled with your desired Spark version may not be appropriate for your needs. This would typically be the case if you want to utilize cloud object store connector improvements introduced post Hadoop 2.7.

You can follow the instructions below to configure your environment with PySpark and a custom Hadoop client libraries version.

RUN mkdir -p /opt/domino

### Modify the Hadoop and Spark versions below as needed.
ENV HADOOP_VERSION=2.9.2
ENV HADOOP_HOME=/opt/domino/hadoop
ENV HADOOP_CONF_DIR=/opt/domino/hadoop/etc/hadoop
ENV SPARK_VERSION=2.4.6
ENV SPARK_HOME=/opt/domino/spark
ENV PATH="$PATH:$SPARK_HOME/bin:$HADOOP_HOME/bin"

### Enable this for access to ADLS Gen2 when using Hadoop 3.2+
### ENV HADOOP_OPTIONAL_TOOLS=hadoop-azure

### Install the desired Hadoop-free Spark distribution
RUN rm -rf ${SPARK_HOME} && \
    wget -q https://archive.apache.org/dist/spark/spark-${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-without-hadoop.tgz && \
    tar -xf spark-${SPARK_VERSION}-bin-without-hadoop.tgz && \
    rm spark-${SPARK_VERSION}-bin-without-hadoop.tgz && \
    mv spark-${SPARK_VERSION}-bin-without-hadoop ${SPARK_HOME} && \
    chmod -R 777 ${SPARK_HOME}/conf

### Install the desired Hadoop libraries
RUN rm -rf ${HADOOP_HOME} && \
    wget -q http://archive.apache.org/dist/hadoop/common/hadoop-${HADOOP_VERSION}/hadoop-${HADOOP_VERSION}.tar.gz && \
    tar -xf hadoop-${HADOOP_VERSION}.tar.gz && \
    rm hadoop-${HADOOP_VERSION}.tar.gz && \
    mv hadoop-${HADOOP_VERSION} ${HADOOP_HOME}

### Setup the Hadoop libraries classpath and Spark related envars for proper init in Domino
RUN echo "export SPARK_HOME=${SPARK_HOME}" >> /home/ubuntu/.domino-defaults
RUN echo "export HADOOP_HOME=${HADOOP_HOME}" >> /home/ubuntu/.domino-defaults
RUN echo "export HADOOP_CONF_DIR=${HADOOP_CONF_DIR}" >> /home/ubuntu/.domino-defaults
RUN echo "export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:${HADOOP_HOME}/lib/native" >> /home/ubuntu/.domino-defaults
RUN echo "export PATH=\$PATH:${SPARK_HOME}/bin:${HADOOP_HOME}/bin" >> /home/ubuntu/.domino-defaults
RUN echo "export SPARK_DIST_CLASSPATH=\"\$(hadoop classpath):${HADOOP_HOME}/share/hadoop/tools/lib/*\"" >> ${SPARK_HOME}/conf/spark-env.sh

### Complete the PySpark setup from the Spark distribution files
WORKDIR $SPARK_HOME/python
RUN python setup.py install

### Optionally copy spark-submit to spark-submit.sh to be able to run from Domino jobs
RUN spark_submit_path=$(which spark-submit) && \
    cp ${spark_submit_path} ${spark_submit_path}.sh

### Hadoop 2.7 does not come with the required binaries for AWS access so we add them
### hadoop-aws.jar must match the hadoop-common.jar version of your install
RUN \
  SPARK_HOME=$(pip show pyspark | grep "Location" | awk '{print $2}')/pyspark && \
  rm -rf ${SPARK_HOME}/hadoop-aws* && \
  rm -rf ${SPARK_HOME}/aws-java-sdk* && \
  curl https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/2.7.3/hadoop-aws-2.7.3.jar --output ${SPARK_HOME}/jars/hadoop-aws-2.7.3.jar && \
  curl https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk/1.7.4/aws-java-sdk-1.7.4.jar --output ${SPARK_HOME}/jars/aws-java-sdk-1.7.4.jar

### Optionally install boto3 which can help working with AWS credential file profiles
### Can omit if not needed
RUN pip install boto3