Bitnami package for Apache Spark

What is Apache Spark?

Apache Spark is a high-performance engine for large-scale computing tasks, such as data processing, machine learning and real-time data streaming. It includes APIs for Java, Python, Scala and R.

Overview of Apache Spark Trademarks: This software listing is packaged by Bitnami. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

TL;DR

Docker Compose

docker run --name spark bitnami/spark:latest

You can find the available configuration options in the Environment Variables section.

Why use Bitnami Images?

• Bitnami closely tracks upstream source changes and promptly publishes new versions of this image using our automated systems.
• With Bitnami images the latest bug fixes and features are available as soon as possible.
• Bitnami containers, virtual machines and cloud images use the same components and configuration approach - making it easy to switch between formats based on your project needs.
• All our images are based on minideb -a minimalist Debian based container image that gives you a small base container image and the familiarity of a leading Linux distribution- or scratch -an explicitly empty image-.
• All Bitnami images available in Docker Hub are signed with Notation. Check this post to know how to verify the integrity of the images.
• Bitnami container images are released on a regular basis with the latest distribution packages available.

Looking to use Apache Spark in production? Try VMware Tanzu Application Catalog, the commercial edition of the Bitnami catalog.

How to deploy Apache Spark in Kubernetes?

Deploying Bitnami applications as Helm Charts is the easiest way to get started with our applications on Kubernetes. Read more about the installation in the Bitnami Apache Spark Chart GitHub repository.

Bitnami containers can be used with Kubeapps for deployment and management of Helm Charts in clusters.

Why use a non-root container?

Non-root container images add an extra layer of security and are generally recommended for production environments. However, because they run as a non-root user, privileged tasks are typically off-limits. Learn more about non-root containers in our docs.

Supported tags and respective Dockerfile links

Learn more about the Bitnami tagging policy and the difference between rolling tags and immutable tags in our documentation page.

You can see the equivalence between the different tags by taking a look at the tags-info.yaml file present in the branch folder, i.e bitnami/ASSET/BRANCH/DISTRO/tags-info.yaml.

Subscribe to project updates by watching the bitnami/containers GitHub repo.

Get this image

The recommended way to get the Bitnami Apache Spark Docker Image is to pull the prebuilt image from the Docker Hub Registry.

docker pull bitnami/spark:latest

To use a specific version, you can pull a versioned tag. You can view the list of available versions in the Docker Hub Registry.

docker pull bitnami/spark:[TAG]

If you wish, you can also build the image yourself by cloning the repository, changing to the directory containing the Dockerfile and executing the docker build command. Remember to replace the APP, VERSION and OPERATING-SYSTEM path placeholders in the example command below with the correct values.

git clone https://github.com/bitnami/containers.git
cd bitnami/APP/VERSION/OPERATING-SYSTEM
docker build -t bitnami/APP:latest .

Configuration

Environment variables

Customizable environment variables

Read-only environment variables

Additionally, more environment variables natively supported by Apache Spark can be found at the official documentation.

For example, you could still use SPARK_WORKER_CORES or SPARK_WORKER_MEMORY to configure the number of cores and the amount of memory to be used by a worker machine.

When you start the spark image, you can adjust the configuration of the instance by passing one or more environment variables either on the docker-compose file or on the docker run command line. If you want to add a new environment variable:

• For docker-compose add the variable name and value under the application section in the docker-compose.yml file present in this repository:

spark:
  ...
  environment:
    - SPARK_MODE=master
  ...

• For manual execution add a -e option with each variable and value:

docker run -d --name spark \
  --network=spark_network \
  -e SPARK_MODE=master \
  bitnami/spark

Security

The Bitnani Apache Spark docker image supports enabling RPC authentication, RPC encryption and local storage encryption easily using the following env vars in all the nodes of the cluster.

+ SPARK_RPC_AUTHENTICATION_ENABLED=yes
+ SPARK_RPC_AUTHENTICATION_SECRET=RPC_AUTHENTICATION_SECRET
+ SPARK_RPC_ENCRYPTION=yes
+ SPARK_LOCAL_STORAGE_ENCRYPTION=yes

Please note that RPC_AUTHENTICATION_SECRET is a placeholder that needs to be updated with a correct value. Be also aware that currently is not possible to submit an application to a standalone cluster if RPC authentication is configured. More info about the issue here.

Additionally, SSL configuration can be easily activated following the next steps:

1. Enable SSL configuration by setting the following env vars:

   + SPARK_SSL_ENABLED=yes
   + SPARK_SSL_KEY_PASSWORD=KEY_PASSWORD
   + SPARK_SSL_KEYSTORE_PASSWORD=KEYSTORE_PASSWORD
   + SPARK_SSL_TRUSTSTORE_PASSWORD=TRUSTSTORE_PASSWORD
   + SPARK_SSL_NEED_CLIENT_AUTH=yes
   + SPARK_SSL_PROTOCOL=TLSv1.2
   

   Please note that KEY_PASSWORD, KEYSTORE_PASSWORD, and TRUSTSTORE_PASSWORD are placeholders that needs to be updated with a correct value.

2. You need to mount your spark keystore and truststore files to /opt/bitnami/spark/conf/certs. Please note they should be called spark-keystore.jks and spark-truststore.jks and they should be in JKS format.

Setting up an Apache Spark Cluster

A Apache Spark cluster can easily be setup with the default docker-compose.yml file from the root of this repo. The docker-compose includes two different services, spark-master and spark-worker.

By default, when you deploy the docker-compose file you will get an Apache Spark cluster with 1 master and 1 worker.

If you want N workers, all you need to do is start the docker-compose deployment with the following command:

docker-compose up --scale spark-worker=3

Mount a custom configuration file

The image looks for configuration in the conf/ directory of /opt/bitnami/spark.

Using docker-compose

...
volumes:
  - /path/to/spark-defaults.conf:/opt/bitnami/spark/conf/spark-defaults.conf
...

Using the command line

docker run --name spark -v /path/to/spark-defaults.conf:/opt/bitnami/spark/conf/spark-defaults.conf bitnami/spark:latest

After that, your changes will be taken into account in the server's behaviour.

Installing additional jars

By default, this container bundles a generic set of jar files but the default image can be extended to add as many jars as needed for your specific use case. For instance, the following Dockerfile adds aws-java-sdk-bundle-1.11.704.jar:

FROM bitnami/spark
USER root
RUN install_packages curl
USER 1001
RUN curl https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.11.704/aws-java-sdk-bundle-1.11.704.jar --output /opt/bitnami/spark/jars/aws-java-sdk-bundle-1.11.704.jar

Using a different version of Hadoop jars

In a similar way that in the previous section, you may want to use a different version of Hadoop jars.

Go to https://spark.apache.org/downloads.html and copy the download url bundling the Hadoop version you want and matching the Apache Spark version of the container. Extend the Bitnami container image as below:

FROM bitnami/spark:3.5.0
USER root
RUN install_packages curl
USER 1001
RUN rm -r /opt/bitnami/spark/jars && \
    curl --location https://dlcdn.apache.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz | \
    tar --extract --gzip --strip=1 --directory /opt/bitnami/spark/ spark-3.5.0-bin-hadoop3/jars/

You can check the Hadoop version by running the following commands in the new container image:

$ pyspark
>>> sc._gateway.jvm.org.apache.hadoop.util.VersionInfo.getVersion()
'2.7.4'

Logging

The Bitnami Apache Spark Docker image sends the container logs to the stdout. To view the logs:

docker logs spark

or using Docker Compose:

docker-compose logs spark

You can configure the containers logging driver using the --log-driver option if you wish to consume the container logs differently. In the default configuration docker uses the json-file driver.

Maintenance

Backing up your container

To backup your data, configuration and logs, follow these simple steps:

Step 1: Stop the currently running container

docker stop spark

or using Docker Compose:

docker-compose stop spark

Step 2: Run the backup command

We need to mount two volumes in a container we will use to create the backup: a directory on your host to store the backup in, and the volumes from the container we just stopped so we can access the data.

docker run --rm -v /path/to/spark-backups:/backups --volumes-from spark busybox \
  cp -a /bitnami/spark /backups/latest

or using Docker Compose:

docker run --rm -v /path/to/spark-backups:/backups --volumes-from `docker-compose ps -q spark` busybox \
  cp -a /bitnami/spark /backups/latest

Restoring a backup

Restoring a backup is as simple as mounting the backup as volumes in the container.

docker run -v /path/to/spark-backups/latest:/bitnami/spark bitnami/spark:latest

or by modifying the docker-compose.yml file present in this repository:

services:
  spark:
  ...
    volumes:
      - /path/to/spark-backups/latest:/bitnami/spark
  ...

Upgrade this image

Bitnami provides up-to-date versions of spark, including security patches, soon after they are made upstream. We recommend that you follow these steps to upgrade your container.

Step 1: Get the updated image

docker pull bitnami/spark:latest

or if you're using Docker Compose, update the value of the image property to bitnami/spark:latest.

Step 2: Stop and backup the currently running container

Before continuing, you should backup your container's data, configuration and logs.

Follow the steps on creating a backup.

Step 3: Remove the currently running container

docker rm -v spark

or using Docker Compose:

docker-compose rm -v spark

Step 4: Run the new image

Re-create your container from the new image, restoring your backup if necessary.

docker run --name spark bitnami/spark:latest

or using Docker Compose:

docker-compose up spark

Notable Changes

3.0.0-debian-10-r44

• The container image was updated to use Hadoop 3.2.x. If you want to use a different version, please read Using a different version of Hadoop jars.

2.4.5-debian-10-r49

• This image now has an aws-cli and two jars: hadoop-aws and aws-java-sdk for provide an easier way to use AWS.

Using docker-compose.yaml

Please be aware this file has not undergone internal testing. Consequently, we advise its use exclusively for development or testing purposes. For production-ready deployments, we highly recommend utilizing its associated Bitnami Helm chart.

If you detect any issue in the docker-compose.yaml file, feel free to report it or contribute with a fix by following our Contributing Guidelines.

Contributing

We'd love for you to contribute to this container. You can request new features by creating an issue or submitting a pull request with your contribution.

Issues

If you encountered a problem running this container, you can file an issue. For us to provide better support, be sure to fill the issue template.

License

Copyright © 2024 Broadcom. The term "Broadcom" refers to Broadcom Inc. and/or its subsidiaries.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.