Alauda AI

Installation

Alauda Build of Spark Operator is delivered as an OLM Operator and installed from the platform OperatorHub.

TOC

PrerequisitesUpload OperatorInstall OperatorVerification

Prerequisites

  • ACP version: v4.0 or later (validated on v4.3 / Kubernetes 1.34).
  • Target cluster architecture linux/amd64 or linux/arm64 (the operator ships multi-arch images).
  • Operator Lifecycle Manager (OLM) available on the target cluster (provided by ACP).

Upload Operator

Download the Alauda Build of Spark Operator bundle from the Customer Portal / Marketplace (e.g. spark-operator.ALL.xxxx.tgz), then publish it to the platform repository with the violet command-line tool:

violet push \
  --platform-address=<platform-access-address> \
  --platform-username=<platform-admin> \
  --platform-password=<platform-admin-password> \
  spark-operator.ALL.xxxx.tgz
INFO

The operator bundle records its runtime images (the operator image and the Apache Spark runtime image) in the CSV relatedImages, so a violet release relocates them into the platform registry. This makes the operator installable on air-gapped clusters without reaching docker.io.

Install Operator

In the Administrator view:

  1. Click Marketplace / OperatorHub.
  2. At the top of the console, from the Cluster dropdown, select the destination cluster.
  3. Search for and select Alauda Build of Spark Operator, then click Install.
  4. Leave Channel unchanged (stable).
  5. Check that the Version matches the release you want to install (e.g. v2.5.1).
  6. Leave Installation Location unchanged — it defaults to the spark-operator namespace.
  7. Choose an Upgrade Strategy (Manual is recommended for production).
  8. Click Install.

Verification

Confirm the Alauda Build of Spark Operator tile shows Installed, then verify on the cluster:

# the CSV reports Succeeded
kubectl get csv -n spark-operator | grep spark-operator

# the operator (controller + webhook) pods are Running
kubectl get pods -n spark-operator

# the CRDs are registered
kubectl get crd | grep sparkoperator.k8s.io

The operator watches all namespaces by default, so you can create SparkApplication resources in any namespace.

INFO

A Succeeded CSV means the operator controller is running. Spark workloads themselves only appear once you create a SparkApplication (or ScheduledSparkApplication) — see Run a Spark Application.