The Dataproc Docker on YARN feature allows you to create and use a Docker image to customize your Spark job runtime environment. The image can include customizations to Java, Python, and R dependencies, and to your job jar.
Limitations
Feature availability or support is not available with:
- Dataproc image versions prior to 2.0.49 (not available in 1.5 images)
- MapReduce jobs (only supported for Spark jobs )
- Spark client mode (only supported with Spark cluster mode)
- Kerberos clusters: cluster creation fails if you create a cluster with Docker on YARN and Kerberos enabled.
- Customizations of JDK, Hadoop and Spark: the host JDK, Hadoop, and Spark are used, not your customizations.
Create a Docker image
The first step to customize your Spark environment is building a Docker image.
Dockerfile
You can use the following Dockerfile as an example, making changes and additions to meet you needs.
FROM debian:10-slim
# Suppress interactive prompts.
ENV DEBIAN_FRONTEND=noninteractive
# Required: Install utilities required by Spark scripts.
RUN apt update && apt install -y procps tini
# Optional: Add extra jars.
ENV SPARK_EXTRA_JARS_DIR=/opt/spark/jars/
ENV SPARK_EXTRA_CLASSPATH='/opt/spark/jars/*'
RUN mkdir -p "${SPARK_EXTRA_JARS_DIR}"
COPY *.jar "${SPARK_EXTRA_JARS_DIR}"
# Optional: Install and configure Miniconda3.
ENV CONDA_HOME=/opt/miniconda3
ENV PYSPARK_PYTHON=${CONDA_HOME}/bin/python
ENV PYSPARK_DRIVER_PYTHON=${CONDA_HOME}/bin/python
ENV PATH=${CONDA_HOME}/bin:${PATH}
COPY Miniconda3-py39_4.10.3-Linux-x86_64.sh .
RUN bash Miniconda3-py39_4.10.3-Linux-x86_64.sh -b -p /opt/miniconda3 \
&& ${CONDA_HOME}/bin/conda config --system --set always_yes True \
&& ${CONDA_HOME}/bin/conda config --system --set auto_update_conda False \
&& ${CONDA_HOME}/bin/conda config --system --prepend channels conda-forge \
&& ${CONDA_HOME}/bin/conda config --system --set channel_priority strict
# Optional: Install Conda packages.
#
# The following packages are installed in the default image. It is strongly
# recommended to include all of them.
#
# Use mamba to install packages quickly.
RUN ${CONDA_HOME}/bin/conda install mamba -n base -c conda-forge \
&& ${CONDA_HOME}/bin/mamba install \
conda \
cython \
fastavro \
fastparquet \
gcsfs \
google-cloud-bigquery-storage \
google-cloud-bigquery[pandas] \
google-cloud-bigtable \
google-cloud-container \
google-cloud-datacatalog \
google-cloud-dataproc \
google-cloud-datastore \
google-cloud-language \
google-cloud-logging \
google-cloud-monitoring \
google-cloud-pubsub \
google-cloud-redis \
google-cloud-spanner \
google-cloud-speech \
google-cloud-storage \
google-cloud-texttospeech \
google-cloud-translate \
google-cloud-vision \
koalas \
matplotlib \
nltk \
numba \
numpy \
openblas \
orc \
pandas \
pyarrow \
pysal \
pytables \
python \
regex \
requests \
rtree \
scikit-image \
scikit-learn \
scipy \
seaborn \
sqlalchemy \
sympy \
virtualenv
# Optional: Add extra Python modules.
ENV PYTHONPATH=/opt/python/packages
RUN mkdir -p "${PYTHONPATH}"
COPY test_util.py "${PYTHONPATH}"
# Required: Create the 'yarn_docker_user' group/user.
# The GID and UID must be 1099. Home directory is required.
RUN groupadd -g 1099 yarn_docker_user
RUN useradd -u 1099 -g 1099 -d /home/yarn_docker_user -m yarn_docker_user
USER yarn_docker_user
Build and push the image
The following is commands for building and pushing the example Docker image, you can make changes according to your customizations.
# Increase the version number when there is a change to avoid referencing
# a cached older image. Avoid reusing the version number, including the default
# `latest` version.
IMAGE=gcr.io/my-project/my-image:1.0.1
# Download the BigQuery connector.
gcloud storage cp \
gs://spark-lib/bigquery/spark-bigquery-with-dependencies_2.12-0.22.2.jar .
# Download the Miniconda3 installer.
wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.10.3-Linux-x86_64.sh
# Python module example:
cat >test_util.py <<EOF
def hello(name):
print("hello {}".format(name))
def read_lines(path):
with open(path) as f:
return f.readlines()
EOF
# Build and push the image.
docker build -t "${IMAGE}" .
docker push "${IMAGE}"
Create a Dataproc cluster
After creating a Docker image that customizes your Spark environment, create a Dataproc cluster that will use your Docker image when running Spark jobs.
gcloud
gcloud dataproc clusters create CLUSTER_NAME \ --region=REGION \ --image-version=DP_IMAGE \ --optional-components=DOCKER \ --properties=dataproc:yarn.docker.enable=true,dataproc:yarn.docker.image=DOCKER_IMAGE \ other flags
Replace the following;
- CLUSTER_NAME: The cluster name.
- REGION: The cluster region.
- DP_IMAGE: Dataproc image version must be
2.0.49or later (--image-version=2.0will use a qualified minor version later than2.0.49). --optional-components=DOCKER: Enables the Docker component on the cluster.--propertiesflag:dataproc:yarn.docker.enable=true: Required property to enable the Dataproc Docker on YARN feature.dataproc:yarn.docker.image: Optional property that you can add to specify your DOCKER_IMAGE using the following Container Registry image naming format:{hostname}/{project-id}/{image}:{tag}.Example:
dataproc:yarn.docker.image=gcr.io/project-id/image:1.0.1
Requirement: You must host your Docker image on Container Registry or Artifact Registry. (Dataproc cannot fetch containers from other registries).
Recommendation: Add this property when you create your cluster to cache your Docker image and avoid YARN timeouts later when you submit a job that uses the image.
When dataproc:yarn.docker.enable is set to true, Dataproc
updates Hadoop and Spark configurations to enable the Docker on YARN feature in
the cluster. For example, spark.submit.deployMode is set to cluster, and
spark.yarn.appMasterEnv.YARN_CONTAINER_RUNTIME_DOCKER_MOUNTS and
spark.executorEnv.YARN_CONTAINER_RUNTIME_DOCKER_MOUNTS are set to mount
directories from the host into the container.
Submit a Spark job to the cluster
After creating a Dataproc cluster, submit a Spark job to the cluster that uses your Docker image. The example in this section submits a PySpark job to the cluster.
Set job properties:
# Set the Docker image URI.
IMAGE=(e.g., gcr.io/my-project/my-image:1.0.1)
# Required: Use `#` as the delimiter for properties to avoid conflicts.
JOB_PROPERTIES='^#^'
# Required: Set Spark properties with the Docker image.
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.yarn.appMasterEnv.YARN_CONTAINER_RUNTIME_DOCKER_IMAGE=${IMAGE}"
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.executorEnv.YARN_CONTAINER_RUNTIME_DOCKER_IMAGE=${IMAGE}"
# Optional: Add custom jars to Spark classpath. Don't set these properties if
# there are no customizations.
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.driver.extraClassPath=/opt/spark/jars/*"
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.executor.extraClassPath=/opt/spark/jars/*"
# Optional: Set custom PySpark Python path only if there are customizations.
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.pyspark.python=/opt/miniconda3/bin/python"
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.pyspark.driver.python=/opt/miniconda3/bin/python"
# Optional: Set custom Python module path only if there are customizations.
# Since the `PYTHONPATH` environment variable defined in the Dockerfile is
# overridden by Spark, it must be set as a job property.
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.yarn.appMasterEnv.PYTHONPATH=/opt/python/packages"
JOB_PROPERTIES="${JOB_PROPERTIES}#spark.executorEnv.PYTHONPATH=/opt/python/packages"
Notes:
- See Launching Applications Using Docker Containers information on related properties.
gcloud
Submit the job to the cluster.
gcloud dataproc jobs submit pyspark PYFILE \
--cluster=CLUSTER_NAME \
--region=REGION \
--properties=${JOB_PROPERTIES}
Replace the following;
- PYFILE: The file path to your PySpark job file. It can be
a local file path or the URI of the file in Cloud Storage
(
gs://BUCKET_NAME/PySpark filename). - CLUSTER_NAME: The cluster name.
- REGION: The cluster region.