Dynamic data models with JavaScript

This functionality only works with data models written in JavaScript, not YAML.

For similar functionality in YAML, see Dynamic data models with Jinja and Python.

Cube allows data models to be created on-the-fly using a special asyncModule() function only available in the execution environment. asyncModule() allows registering an async function to be executed at the end of the data model compile phase so additional definitions can be added. This is often useful in situations where data model properties can be dynamically updated through an API, for example.

Each asyncModule call will be invoked only once per data model compilation.

When creating data models via asyncModule(), it is important to be aware of the following differences compared to statically defined ones with cube():

The sql and drill_members properties for both dimensions and measures must be of type () => string and () => string[] accordingly

Cube supports importing JavaScript logic from other files in a data model, so it is useful to declare utility functions for handling the above differences in a separate file:

// model/utils.js
export const convertStringPropToFunction = (propNames, dimensionDefinition) => {
  let newResult = { ...dimensionDefinition };
  propNames.forEach((propName) => {
    const propValue = newResult[propName];
 
    if (!propValue) {
      return;
    }
 
    newResult[propName] = () => propValue;
  });
  return newResult;
};
 
export const transformDimensions = (dimensions) => {
  return Object.keys(dimensions).reduce((result, dimensionName) => {
    const dimensionDefinition = dimensions[dimensionName];
    return {
      ...result,
      [dimensionName]: convertStringPropToFunction(
        ["sql"],
        dimensionDefinition
      ),
    };
  }, {});
};
 
export const transformMeasures = (measures) => {
  return Object.keys(measures).reduce((result, dimensionName) => {
    const dimensionDefinition = measures[dimensionName];
    return {
      ...result,
      [dimensionName]: convertStringPropToFunction(
        ["sql", "drill_members"],
        dimensionDefinition
      ),
    };
  }, {});
};

Generation

In the following example, we retrieve a JSON object representing all our cubes using fetch(), transform some of the properties to be functions that return a string, and then finally use the cube() global function to generate data models from that data:

// model/cubes/DynamicDataModel.js
const fetch = require("node-fetch");
import {
  convertStringPropToFunction,
  transformDimensions,
  transformMeasures,
} from "./utils";
 
asyncModule(async () => {
  const dynamicCubes = await (
    await fetch("http://your-api-endpoint/dynamicCubes")
  ).json();
 
  console.log(dynamicCubes);
  // [
  //   {
  //      name: 'dynamic_cube_model',
  //      sql_table: 'my_table',
  //
  //      measures: {
  //        price: {
  //          sql: `price`,
  //          type: `number`,
  //        }
  //      },
  //
  //      dimensions: {
  //        color: {
  //          sql: `color`,
  //          type: `string`,
  //        },
  //      },
  //   },
  // ]
 
  dynamicCubes.forEach((dynamicCube) => {
    const dimensions = transformDimensions(dynamicCube.dimensions);
    const measures = transformMeasures(dynamicCube.measures);
 
    cube(dynamicCube.name, {
      sql: dynamicCube.sql,
      dimensions,
      measures,
      pre_aggregations: {
        main: {
          // ...
        },
      },
    });
  });
});

Usage with `schema_version`

It is also useful to be able to recompile the data model when there are changes in the underlying input data. For this purpose, the schema_version value in the cube.js configuration options can be specified as an asynchronous function:

// cube.js
module.exports = {
  schemaVersion: async ({ securityContext }) => {
    const schemaVersions = await (
      await fetch("http://your-api-endpoint/schema_version")
    ).json();
 
    return schemaVersions[securityContext.tenantId];
  },
};

Usage with COMPILE_CONTEXT

The COMPILE_CONTEXT global object can also be used in conjunction with async data model creation to allow for multi-tenant deployments of Cube.

In an example scenario where all tenants share the same cube, but see different dimensions and measures, you could do the following:

// model/cubes/DynamicDataModel.js
const fetch = require("node-fetch");
import {
  convertStringPropToFunction,
  transformDimensions,
  transformMeasures,
} from "./utils";
 
asyncModule(async () => {
  const {
    securityContext: { tenantId },
  } = COMPILE_CONTEXT;
 
  const dynamicCubes = await (
    await fetch(`http://your-api-endpoint/dynamicCubes`)
  ).json();
 
  const allowedDimensions = await (
    await fetch(`http://your-api-endpoint/dynamicDimensions/${tenantId}`)
  ).json();
 
  const allowedMeasures = await (
    await fetch(`http://your-api-endpoint/dynamicMeasures/${tenantId}`)
  ).json();
 
  dynamicCubes.forEach((dynamicCube) => {
    const dimensions = transformDimensions(allowedDimensions);
    const measures = transformMeasures(allowedMeasures);
 
    cube(dynamicCube.name, {
      sql: dynamicCube.sql,
      title: `${dynamicCube.title}-${tenantId}`,
      dimensions,
      measures,
      pre_aggregations: {
        main: {
          // ...
        },
      },
    });
  });
});

Usage with data_source

When using multiple databases, you'll need to ensure you set the data_source property for any asynchronously-created data models, as well as ensuring the corresponding database drivers are set up with driverFactory() in your cube.js configuration file.

For an example scenario where data models may use either MySQL or Postgres databases, you could do the following:

// model/cubes/DynamicDataModel.js
const fetch = require("node-fetch");
import {
  convertStringPropToFunction,
  transformDimensions,
  transformMeasures,
} from "./utils";
 
asyncModule(async () => {
  const dynamicCubes = await (
    await fetch("http://your-api-endpoint/dynamicCubes")
  ).json();
 
  dynamicCubes.forEach((dynamicCube) => {
    const dimensions = transformDimensions(dynamicCube.dimensions);
    const measures = transformMeasures(dynamicCube.measures);
 
    cube(dynamicCube.name, {
      data_source: dynamicCube.data_source,
      sql: dynamicCube.sql,
      dimensions,
      measures,
      pre_aggregations: {
        main: {
          // ...
        },
      },
    });
  });
});

// cube.js
const MySQLDriver = require("@cubejs-backend/mysql-driver");
const PostgresDriver = require("@cubejs-backend/postgres-driver");
 
module.exports = {
  driverFactory: ({ dataSource }) => {
    if (dataSource === "mysql") {
      return new MySQLDriver({ database: dataSource });
    }
 
    return new PostgresDriver({ database: dataSource });
  },
};

Dynamic data models with Jinja and Python Export and import

Dynamic data models with JavaScript

Generation

Usage with schema_version

Usage with COMPILE_CONTEXT

Usage with data_source

Usage with `schema_version`