Run a custom execution

import {
    ContextDeferredAuthProvider,
    redirectToTigerAuthentication,
    tigerFactory,
} from "@gooddata/sdk-backend-tiger";
import { newPositiveAttributeFilter, newMeasureSort, newTwoDimensional, MeasureGroupIdentifier } from "@gooddata/sdk-model";
import * as Md from "./catalog.js";

const backend = tigerFactory()
    .onHostname("https://example.gooddata.com")
    .withAuthentication(new ContextDeferredAuthProvider(redirectToTigerAuthentication));

// The execution will be done for a single measure and on the granularity of a single attribute.
const measuresAndAttributes = [Md.$AvgDailyTotalSales, Md.LocationState];

// This filter tells the backend to calculate data just for the listed states.
const filter = newPositiveAttributeFilter(Md.LocationState, ["California", "Texas", "Oregon"]);

// This sort definition tells the backend to sort the result by the value of $AvgDailyTotalSales.
const sort = newMeasureSort(Md.$AvgDailyTotalSales, "desc");

// The dimensions specify how to slice and dice the result. In this example the result will be two-dimensional
// and resemble a table: one row for each state, and in each row one column for the measure.
const dimensions = newTwoDimensional([Md.LocationState], [MeasureGroupIdentifier]);

const result = await backend
   .workspace("workspace_id")
   .execution()
   .forItems(measuresAndAttributes, [filter])
   .withSorting(sort)
   .withDimensions(...dimensions)
   .execute();

// readWindow takes zero-based offsets and sizes, one per dimension.
const firstPage = await result.readWindow([0, 0], [10, 10]);
const allData = await result.readAll();

import { DataViewFacade } from "@gooddata/sdk-ui";
import * as Md from "./catalog.js";

const facade = DataViewFacade.for(allData);

// Gets a collection of all data series found in the data. There will be exactly one series for the $AvgDailyTotalSales
const allDataSeries = facade.data().series();

// Gets first (and in this case only) data series calculated for the $AvgDailyTotalSales measure.
//
// Note that if you set up the execution dimensionality so that one dimension contains both MeasureGroupIdentifier and
// an attribute, then the result may contain multiple data series for the $AvgDailyTotalSales. Each of the data
// series will be _scoped_ to an element of the attribute for which it was calculated.
const totalSales = allDataSeries.firstForMeasure(Md.$AvgDailyTotalSales);

// The series contains one data point per slice. There will be one slice for each attribute element in the
// dimension opposite to the dimension that contains the MeasureGroupIdentifier. In this case that attribute
// is LocationState and is filtered to contain values of "California", "Texas" and "Oregon". There will be
// one slice for each state.
//
// This mapping returns an array with one object for each state for which the value of the $AvgDailyTotalSales measure
// was calculated. The value is returned as a string formatted using the format string defined for the measure.
const totalSalesByState = totalSales.dataPoints().map((dataPoint) => {
    return {
        state: dataPoint.sliceDesc?.sliceTitles()[0],
        sales: dataPoint.formattedValue(),
    };
});