No-Spark Semantic model processing through Kyvos Native Compute Engines

You can choose Kyvos Native as a compute engine or External engines for semantic model processing.

Deploy Kyvos through Kyvos Native Compute

Kyvos Native Compute operates independently of any external compute clusters when processing semantic models. It uses its proprietary Kyvos Analytical Store, which reduces costs, bolsters security, and removes the dependency on permissions.

• Shared Query Engine: In this mode, the query engine not only performs queries but also handles semantic model processing. This dual role is named SHARED because the same process undertakes both activities.

• Dedicated Compute: In this mode, the semantic model is processed via dedicated service. In cloud-based deployment, the semantic model is processed using Kubernetes (K8S) cluster-based nodes while in ON PREM environment, models are processed on dedicated nodes.

With the Kyvos 2024.3.2 release, you can change from an external compute cluster to Kyvos Native for processing semantic models through Kyvos Manager on the Compute Cluster page.

Supported Platforms

• AWS: For Kubernetes, Kyvos processes the semantic model using Amazon Elastic Kubernetes Service (Amazon EKS). You can select Query Engine or Kubernetes as a compute engine using no Spark model processing.
  For further details about deployment, see the Automated deployment for AWS via CloudFormation with Kyvos Native section.

• Azure: For Kubernetes, Kyvos processes the semantic model using Azure’s managed service AKS (Azure Kubernetes Service).

  • The Azure cluster is deployed via ARM templates. You can create a cluster without Spark or process the semantic model using Spark mode within ARM templates.
    For further details about deployment, see the Automated deployment on Azure with Kyvos Native section.

  • From Kyvos 2024.3 onwards, you can select the compute cluster as the Query engine or Kubernetes when deploying Kyvos through Azure Template Specs.

• GCP: For Kubernetes, Kyvos processes the semantic model using Google Cloud's managed service GKE (Google Kubernetes Engine). The GKE cluster is deployed through GCP Installation Files. Using the scripts, you can select a No-Spark-based cluster or process the semantic model using Spark Mode.
  To proceed with a no-spark-based deployment mode, you must use a Dataproc cluster (either new or existing).
  For further details about Kyvos deployment on GCP using the no-Spark model, see the following section:

  • Prerequisites to deploy Kyvos using Kubernetes

  • Creating Kyvos Resources for GCP using Script

• On Premises: For On-premises deployment, you can deploy using No Spark types: SHARED_QE and Compute Server. For further details about on premises deployment with no-Spark, see the Deploying no Hadoop no Spark.

Post deployment steps for all clouds (AWS, Azure and GCP)

After deploying Kyvos using no-Spark processing model, perform the following post deployment steps.

1. Modify the values of the following properties in the advance properties of semantic model job:

   • kyvos.process.compute.type=KYVOS_COMPUTE

   • kyvos.build.aggregate.type=TABULAR

2. Restart Kyvos services.

Post deployment steps on Azure

You need to add the Storage Blob Data Contributor role and add external location on the Azure portal.

• To add the Storage Blob Data Contributor role,

  1. On the Home page of the Azure portal, search for Storage Accounts.

  2. On the Storage Accounts page, select the storage account that is used for deployment.

  3. Navigate to Access Control (IAM).

  4. Select the Storage Blob Data Contributor role from the list.

  5. In the Assign Access to section, select Managed Identity.

  6. Click Select Member.

  7. On the Select Managed Identity dialog, select the Access Connector for Azure Databricks from the list.

  8. Click Review+assign and click save the permission.

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• To add an external location,

  1. Go to Databricks workspace. In the left pane, click Catalog.

  2. Click Settings, and then click External Locations.

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  3. On the External Location page, click Create location.

  4. On the Create Location dialog, add external location name, select the credentials from the list, and URL
     The URL must be in abfss://<Container name >@<Storage name >.dfs.core.windows.net/<Cluster engine_work directroy>
     For example, abfss://kyvoscontainer@kyvossa05751.dfs.core.windows.net/user/engine_work

  5. Click Create. Click Grant and select all privileges and grant them to the user whose token is used for while creating SQL Warehouse connection.

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  6. Click Grant. The Permission is assigned.

Important points to know

To process semantic model with no-Spark, you must do the following:

• From Kyvos 2024.3 onwards, to execute the sanity suite for no-spark and Kubernetes (K8S) supported deployments, refer to the Prerequisites for no-spark deployments section.

• Modify the value of the following semantic model advanced properties on Kyvos Web Portal:

  • kyvos.sqlwarehouse.catalog = ‘Your catalog name’ 

  • kyvos.sqlwarehouse.tempdb = ‘Your Database name’ 

  • kyvos.build.aggregate.type = TABULAR 

  • Kyvos.process.compute.type = Kyvos Compute 

• You can also set subtype to select no-Spark model processing for semantic model via property.
  To do this, navigate to the Kyvos Properties page and update the KYVOS_PROCESS_COMPUTE_SUBTYPE property and restart Kyvos services.

• For no-Spark deployments with enhanced security, when creating a Kyvos warehouse connection of a Databricks managed workspace, ensure that the firewall settings on the storage account are disabled.