1Create, Save and Update Semantic Model JSON Examples

Overview

This guide provides JSON examples for creating and configuring Semantic Models using the Semantic Model APIs.

It includes a Complete Semantic Model JSON example that demonstrates the full set of options available for creating and configuring a Semantic Model. The APIs also support a Simplified Semantic Model JSON format that allows you to define multidimensional models with dimensions, hierarchies, measures, and advanced configurations.

Understanding Complete JSON Format

The Complete Semantic Model JSON demonstrates the full set of configuration options available for creating and configuring a Semantic Model. It can be used as a reference for the request body of the Create Semantic Model, Save Semantic Model, and Update Semantic Model APIs.

Understanding Simplified JSON Format

The Simplified Semantic Model JSON in the use case sections represents the minimum required structure for creating a Semantic Model. It serves as a simplified starting point for creating Semantic Models.

The use cases in this guide demonstrate how additional capabilities can be enabled by adding or modifying specific sections of this base configuration.

On this page:

1 Overview
2 Use Case 1: Regular Dimension
3 Use Case 2: Predefined Time Type Dimension
4 Use Case 3: Parent-Child Hierarchy
5 Use Case 4: Alternate Hierarchy
6 Use Case 5: Dimension Attributes
7 Use Case 6: Custom Rollup
8 Use Case 7: Base Measure
9 Use Case 8: Calculated Measure
10 Complete Semantic Model JSON Example

The example illustrates the minimum required structure of a Semantic Model:

{
  "name": "semantic_model_name",
  "folderName": "folder_name",
  "specific": {
    "attrs": {
      "relationshipName": "dataset_relationship_name",
      "relationshipFolderName": "dataset_relationship_foldername"
    },
    "smObject": {
      "dimensions": [
        {
          "name": "dimension_name",
          "type": "dimension_type",
          "hierarchies": [
            {
              "name": "hierarchy_name",
              "levels": [
                {
                  "name": "level_name",
                  "dataField": {
                    "fieldName": "datafield_name",
                    "queryId": "dataset_alias_name"
                  },
                  "dataType": "NUMBER",
                  "displayField": {
                    "fieldName": "datafield_name",
                    "queryId": "dataset_alias_name"
                  }
                }
              ]
            }
          ],
          "attrs": [
            {
              "name": "Account Type",
              "dataField": {
                "fieldName": "accounttype",
                "queryId": "Account"
              }
            }
          ]
        }
      ],
      "measures": {
        "measure": [
          {
            "name": "measure_name",
            "dataField": {
              "fieldName": "datafield_name",
              "queryId": "fact_dataset_alias_name"
            },
            "summaryFunction": "AVG"
          }
        ]
      }
    }
  }
}

Use Case 1: Regular Dimension

Use this configuration to create a standard dimension with one or more drill-down levels.

Add a dimension definition to the dimensions[] collection as shown below.

{
  "name": "<dimension_name>",
  "type": "Regular",
  "hierarchies": [
    {
      "name": "<hierarchy_name>",
      "levels": [
        {
          "name": "<level_name>",
          "dataType": "<data_type>",
          "dataField": {
            "fieldName": "<column_name>",
            "queryId": "<dataset_alias_name>"
          }
        }
      ]
    }
  ]
}