bitnamicharts/spark repository overview

⁠Bitnami Secure Images Helm chart for 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

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/spark

⁠ Introduction

This chart bootstraps an Apache Spark⁠ deployment on a Kubernetes⁠ cluster using the Helm⁠ package manager.

Apache Spark includes APIs for Java, Python, Scala and R.

⁠ Before you begin

• Kubernetes 1.23+
• Helm 3.8.0+
• PV provisioner support in the underlying infrastructure

⁠ Installing the chart

To install the chart with the release name my-release:

helm install my-release oci://REGISTRY_NAME/REPOSITORY_NAME/spark

These commands deploy Apache Spark on the Kubernetes cluster in the default configuration. The Parameters⁠ section lists the parameters that can be configured during installation.

⁠ Configuration and installation details

This section describes resource settings, Gateway API, Ingress, metrics, security, and other options.

⁠ Resource requests and limits

Bitnami charts allow setting resource requests and limits for all containers inside the chart deployment. These are inside the resources value (check parameter table). Setting requests is essential for production workloads and these should be adapted to your specific use case.

To make this process easier, the chart contains the resourcesPreset values, which automatically sets the resources section according to different presets. Check these presets in the bitnami/common chart⁠. However, in production workloads using resourcesPreset is discouraged as it may not fully adapt to your specific needs. Find more information on container resource management in the official Kubernetes documentation⁠.

⁠ Gateway API

This chart provides support for exposing Spark using the Gateway API⁠ and its HTTPRoute resource. If you have a Gateway controller installed on your cluster, such as APISIX, Contour, Envoy Gateway, NGINX Gateway Fabric or Kong Ingress Controller you can utilize the Gateway controller to serve your application. To enable Gateway API integration, set httpRoute.enabled to true. The Gateway to be used can be customized by setting the httpRoute.parentRefs parameter. By default, it will reference a Gateway named gateway in the same namespace as the release.

You can specify the list of hostnames to be mapped to the deployment using the httpRoute.hostnames parameter. Additionally, you can customize the rules used to route the traffic to the service by modifying the httpRoute.matches and httpRoute.filters parameters or adding new rules using the httpRoute.extraRules parameter.

This chart also supports creating a BackendTLSPolicy to define the SNI the Gateway should use to connect to the Spark backend pods and how the certificate served by these pods should be verified. To do so, set the backendTLSPolicy.enabled parameter to true. Please note it's required to secure traffic using TLS as explained in the Configure SSL communication⁠ section to be able to use this feature.

⁠ Ingress

This chart provides support for Ingress resources. If you have an ingress controller installed on your cluster, such as NGINX Ingress Controller or Contour you can utilize the ingress controller to serve your application. To enable Ingress integration, set ingress.enabled to true.

The most common scenario is to have one host name mapped to the deployment. In this case, the ingress.hostname property can be used to set the host name. The ingress.tls parameter can be used to add the TLS configuration for this host.

However, it is also possible to have more than one host. To facilitate this, the ingress.extraHosts parameter (if available) can be set with the host names specified as an array. The ingress.extraTLS parameter (if available) can also be used to add the TLS configuration for extra hosts.

Adding the TLS parameter (where available) will cause the chart to generate HTTPS URLs, and the application will be available on port 443. The actual TLS secrets do not have to be generated by this chart. However, if TLS is enabled, the Ingress record will not work until the TLS secret exists.

Learn more about Ingress controllers⁠.

⁠ Prometheus metrics

This chart can be integrated with Prometheus by setting metrics.enabled to true. This will expose the Spark native Prometheus port in both the containers and services. The services will also have the necessary annotations to be automatically scraped by Prometheus.

⁠Prometheus requirements

It is necessary to have a working installation of Prometheus or Prometheus Operator for the integration to work. Install the Bitnami Prometheus helm chart⁠ or the Bitnami Kube Prometheus helm chart⁠ to easily have a working Prometheus in your cluster.

⁠Integration with Prometheus Operator

The chart can deploy ServiceMonitor objects for integration with Prometheus Operator installations. To do so, set the value metrics.serviceMonitor.enabled=true. Ensure that the Prometheus Operator CustomResourceDefinitions are installed in the cluster or it will fail with the following error:

no matches for kind "ServiceMonitor" in version "monitoring.coreos.com/v1"

Install the Bitnami Kube Prometheus helm chart⁠ for having the necessary CRDs and the Prometheus Operator.

⁠ Rolling vs immutable tags⁠

It is strongly recommended to use immutable tags in a production environment. This ensures your deployment does not change automatically if the same tag is updated with a different image.

Bitnami will release a new chart updating its containers if a new version of the main container, significant changes, or critical vulnerabilities exist.

⁠ FIPS parameters

The FIPS parameters only have effect if you are using images from the Bitnami Secure Images catalog⁠.

For more information on this new support, please refer to the FIPS Compliance section⁠.

⁠ Backup and restore

To back up and restore Helm chart deployments on Kubernetes, you need to back up the persistent volumes from the source deployment and attach them to a new deployment using Velero⁠, a Kubernetes backup/restore tool. Find the instructions for using Velero in this guide⁠.

⁠ Define custom configuration

To use a custom configuration, a ConfigMap should be created with the spark-env.sh file inside the ConfigMap. The ConfigMap name must be provided at deployment time.

To set the configuration on the master use master.configurationConfigMap=configMapName. To set the configuration on the worker, use worker.configurationConfigMap=configMapName.

These values can be set at the same time in a single ConfigMap or using two ConfigMaps. An additional spark-defaults.conf file can be provided in the ConfigMap. You can use both files or one without the other.

⁠ Submit an application

To submit an application to the Apache Spark cluster, use the spark-submit script, which is available at https://github.com/apache/spark/tree/master/bin⁠.

The command below illustrates the process of deploying one of the sample applications included with Apache Spark. Replace the k8s-apiserver-host, k8s-apiserver-port, spark-master-svc, and spark-master-port placeholders with the correct master host/IP address and port for your deployment.

$ ./bin/spark-submit \
    --class org.apache.spark.examples.SparkPi \
    --conf spark.kubernetes.container.image=bitnami/spark:3 \
    --master k8s://https://k8s-apiserver-host:k8s-apiserver-port \
    --conf spark.kubernetes.driverEnv.SPARK_MASTER_URL=spark://spark-master-svc:spark-master-port \
    --deploy-mode cluster \
    ./examples/jars/spark-examples_2.12-3.2.0.jar 1000

This command example assumes that you have downloaded a Spark binary distribution, which can be found at Download Apache Spark⁠.

For a complete walkthrough of the process using a custom application, refer to Spark's guide to Running Spark on Kubernetes⁠.

⁠ Configuring Spark Master as reverse proxy

Spark offers configuration to enable running Spark Master as reverse proxy for worker and application UIs. This can be useful as the Spark Master UI may otherwise use private IPv4 addresses for links to Spark workers and Spark apps.

Coupled with ingress configuration, you can set master.configOptions and worker.configOptions to tell Spark to reverse proxy the worker and application UIs to enable access without requiring direct access to their hosts:

master:
  configOptions:
    -Dspark.ui.reverseProxy=true
    -Dspark.ui.reverseProxyUrl=https://spark.your-domain.com
worker:
  configOptions:
    -Dspark.ui.reverseProxy=true
    -Dspark.ui.reverseProxyUrl=https://spark.your-domain.com
ingress:
  enabled: true
  hostname: spark.your-domain.com

See the Spark Configuration⁠ docs for detail on the parameters.

⁠ Configure security for Apache Spark

You can enable SSL and RPC authentication. The following subsections describe how to configure SSL and create the required secrets.

⁠ Configure SSL communication

In order to enable secure transport between workers and master, deploy the Helm chart with the ssl.enabled=true chart parameter.

⁠ Create certificate and password secrets

It is necessary to create two secrets for the passwords and certificates. The names of the two secrets should be configured using the security.passwordsSecretName and security.ssl.existingSecret chart parameters.

⁠Create certificates and the certificate secret

To generate the certificates secret, first generate the two certificates and rename them to spark-keystore.jks and spark-truststore.jks. Use this script to generate certificates⁠ for test purposes if required.

Once the certificates are created, create a secret for them with the file names as keys. The keys must be named spark-keystore.jks and spark-truststore.jks, and the content must be text in JKS format.

⁠Create the password secret

The secret for passwords should have three keys: rpc-authentication-secret, ssl-keystore-password and ssl-truststore-password.

⁠Configure the chart

Once the secrets are created, configure the chart and set the various security-related parameters, including the security.certificatesSecretName and security.passwordsSecretName parameters referencing the secrets created previously. Here is an example configuration for chart deployment:

security.certificatesSecretName=my-secret
security.passwordsSecretName=my-passwords-secret
security.rpc.authenticationEnabled=true
security.rpc.encryptionEnabled=true
security.storageEncrytionEnabled=true
security.ssl.enabled=true
security.ssl.needClientAuth=true

⁠ Set pod affinity

This chart allows you to set your custom affinity using the XXX.affinity parameter(s). Find more information about pod affinity in the Kubernetes documentation⁠.

As an alternative, you can use the preset configurations for pod affinity, pod anti-affinity, and node affinity available at the bitnami/common⁠ chart. To do so, set the XXX.podAffinityPreset, XXX.podAntiAffinityPreset, or XXX.nodeAffinityPreset parameters.

⁠ Parameters

The following subsections list global, common, and component-specific parameters.

⁠ Global parameters

⁠ Common parameters

⁠ Spark parameters

⁠ Spark master parameters

Note: the README for this chart is longer than the DockerHub length limit of 25000, so it has been trimmed. The full README can be found at https://techdocs.broadcom.com/us/en/vmware-tanzu/bitnami-secure-images/bitnami-secure-images/services/bsi-app-doc/apps-charts-spark-index.html⁠