`@cubejs-client/core`

Vanilla JavaScript client for Cube.

`cube`

cube(apiToken: string | () => Promise‹string›, options: CubeApiOptions): CubeApi

Creates an instance of the CubeApi. The API entry point.

import cube from '@cubejs-client/core';
const cubeApi = cube(
  'CUBE-API-TOKEN',
  { apiUrl: 'http://localhost:4000/cubejs-api/v1' }
);

You can also pass an async function or a promise that will resolve to the API token

import cube from '@cubejs-client/core';
const cubeApi = cube(
  async () => await Auth.getJwtToken(),
  { apiUrl: 'http://localhost:4000/cubejs-api/v1' }
);

Parameters:

Name	Type	Description
apiToken	string \| () => Promise‹string›	API token is used to authorize requests and determine SQL database you're accessing. In the development mode, Cube will print the API token to the console on startup. In case of async function `authorization` is updated for `options.transport` on each request.
options	CubeApiOptions	-

cube(options: CubeApiOptions): CubeApi

`defaultHeuristics`

defaultHeuristics(newQuery: Query, oldQuery: Query, options: TDefaultHeuristicsOptions): any

`defaultOrder`

defaultOrder(query: Query): object

`movePivotItem`

movePivotItem(pivotConfig: PivotConfig, sourceIndex: number, destinationIndex: number, sourceAxis: TSourceAxis, destinationAxis: TSourceAxis): PivotConfig

`CubeApi`

Main class for accessing Cube API

`dryRun`

dryRun(query: Query | Query[], options?: LoadMethodOptions): Promise‹TDryRunResponse›

dryRun(query: Query | Query[], options: LoadMethodOptions, callback?: LoadMethodCallback‹TDryRunResponse›): void

Get query related meta without query execution

`load`

load(query: Query | Query[], options?: LoadMethodOptions): Promise‹ResultSet›

load(query: Query | Query[], options?: LoadMethodOptions, callback?: LoadMethodCallback‹ResultSet›): void

Fetch data for the passed query.

import cube from '@cubejs-client/core';
import Chart from 'chart.js';
import chartjsConfig from './toChartjsData';
 
const cubeApi = cube('CUBE_TOKEN');
 
const resultSet = await cubeApi.load({
 measures: ['Stories.count'],
 timeDimensions: [{
   dimension: 'Stories.time',
   dateRange: ['2015-01-01', '2015-12-31'],
   granularity: 'month'
  }]
});
 
const context = document.getElementById('myChart');
new Chart(context, chartjsConfig(resultSet));

Parameters:

Name	Type	Description
query	Query \| Query[]	Query object
options?	LoadMethodOptions	-
callback?	LoadMethodCallback‹ResultSet›	-

`meta`

meta(options?: LoadMethodOptions): Promise‹Meta›

meta(options?: LoadMethodOptions, callback?: LoadMethodCallback‹Meta›): void

Get meta description of cubes available for querying.

`sql`

sql(query: Query | Query[], options?: LoadMethodOptions): Promise‹SqlQuery›

sql(query: Query | Query[], options?: LoadMethodOptions, callback?: LoadMethodCallback‹SqlQuery›): void

Get generated SQL string for the given query.

Parameters:

Name	Type	Description
query	Query \| Query[]	Query object
options?	LoadMethodOptions	-
callback?	LoadMethodCallback‹SqlQuery›	-

`subscribe`

subscribe(query: Query | Query[], options: LoadMethodOptions | null, callback: LoadMethodCallback‹ResultSet›): void

Allows you to fetch data and receive updates over time. See Real-Time Data Fetch

cubeApi.subscribe(
  {
    measures: ['Logs.count'],
    timeDimensions: [
      {
        dimension: 'Logs.time',
        granularity: 'hour',
        dateRange: 'last 1440 minutes',
      },
    ],
  },
  options,
  (error, resultSet) => {
    if (!error) {
      // handle the update
    }
  }
);

`HttpTransport`

Default transport implementation.

constructor

new HttpTransport(options: TransportOptions): HttpTransport

`request`

request(method: string, params: any): () => Promise‹any›

Implementation of ITransport

`Meta`

Contains information about available cubes and it's members.

`defaultTimeDimensionNameFor`

defaultTimeDimensionNameFor(memberName: string): string

`filterOperatorsForMember`

filterOperatorsForMember(memberName: string, memberType: MemberType | MemberType[]): any

`membersForQuery`

membersForQuery(query: Query | null, memberType: MemberType): TCubeMeasure[] | TCubeDimension[] | TCubeMember[]

Get all members of a specific type for a given query. If empty query is provided no filtering is done based on query context and all available members are retrieved.

Parameters:

Name	Type	Description
query	Query \| null	context query to provide filtering of members available to add to this query
memberType	MemberType	-

`resolveMember`

resolveMember‹T›(memberName: string, memberType: T | T[]): object | TCubeMemberByType‹T›

Get meta information for a cube member Member meta information contains:

{
  name,
  title,
  shortTitle,
  type,
  description,
  format
}

Type parameters:

T: MemberType

Parameters:

Name	Type	Description
memberName	string	Fully qualified member name in a form `Cube.memberName`
memberType	T \| T[]	-

`ProgressResult`

`stage`

stage(): string

`timeElapsed`

timeElapsed(): string

`ResultSet`

Provides a convenient interface for data manipulation.

`annotation`

annotation(): QueryAnnotations

`chartPivot`

chartPivot(pivotConfig?: PivotConfig): ChartPivotRow[]

Returns normalized query result data in the following format.

You can find the examples of using the pivotConfig here

// For the query
{
  measures: ['Stories.count'],
  timeDimensions: [{
    dimension: 'Stories.time',
    dateRange: ['2015-01-01', '2015-12-31'],
    granularity: 'month'
  }]
}
 
// ResultSet.chartPivot() will return
[
  { "x":"2015-01-01T00:00:00", "Stories.count": 27120, "xValues": ["2015-01-01T00:00:00"] },
  { "x":"2015-02-01T00:00:00", "Stories.count": 25861, "xValues": ["2015-02-01T00:00:00"]  },
  { "x":"2015-03-01T00:00:00", "Stories.count": 29661, "xValues": ["2015-03-01T00:00:00"]  },
  //...
]

When using chartPivot() or seriesNames(), you can pass aliasSeries in the pivotConfig to give each series a unique prefix. This is useful for blending queries which use the same measure multiple times.

// For the queries
{
  measures: ['Stories.count'],
  timeDimensions: [
    {
      dimension: 'Stories.time',
      dateRange: ['2015-01-01', '2015-12-31'],
      granularity: 'month',
    },
  ],
},
{
  measures: ['Stories.count'],
  timeDimensions: [
    {
      dimension: 'Stories.time',
      dateRange: ['2015-01-01', '2015-12-31'],
      granularity: 'month',
    },
  ],
  filters: [
    {
      member: 'Stores.read',
      operator: 'equals',
      value: ['true'],
    },
  ],
},
 
// ResultSet.chartPivot({ aliasSeries: ['one', 'two'] }) will return
[
  {
    x: '2015-01-01T00:00:00',
    'one,Stories.count': 27120,
    'two,Stories.count': 8933,
    xValues: ['2015-01-01T00:00:00'],
  },
  {
    x: '2015-02-01T00:00:00',
    'one,Stories.count': 25861,
    'two,Stories.count': 8344,
    xValues: ['2015-02-01T00:00:00'],
  },
  {
    x: '2015-03-01T00:00:00',
    'one,Stories.count': 29661,
    'two,Stories.count': 9023,
    xValues: ['2015-03-01T00:00:00'],
  },
  //...
];

`decompose`

decompose(): Object

Can be used when you need access to the methods that can't be used with some query types (eg compareDateRangeQuery or blendingQuery)

resultSet.decompose().forEach((currentResultSet) => {
  console.log(currentResultSet.rawData());
});

`drillDown`

drillDown(drillDownLocator: DrillDownLocator, pivotConfig?: PivotConfig): Query | null

Returns a measure drill down query.

Provided you have a measure with the defined drillMemebers on the Orders cube

measures: {
  count: {
    type: `count`,
    drillMembers: [Orders.status, Users.city, count],
  },
  // ...
}

Then you can use the drillDown method to see the rows that contribute to that metric

resultSet.drillDown(
  {
    xValues,
    yValues,
  },
  // you should pass the `pivotConfig` if you have used it for axes manipulation
  pivotConfig
)

the result will be a query with the required filters applied and the dimensions/measures filled out

{
  measures: ['Orders.count'],
  dimensions: ['Orders.status', 'Users.city'],
  filters: [
    // dimension and measure filters
  ],
  timeDimensions: [
    //...
  ]
}

In case when you want to add order or limit to the query, you can simply spread it

// An example for React
const drillDownResponse = useCubeQuery(
   {
     ...drillDownQuery,
     limit: 30,
     order: {
       'Orders.ts': 'desc'
     }
   },
   {
     skip: !drillDownQuery
   }
 );

`pivot`

pivot(pivotConfig?: PivotConfig): PivotRow[]

Base method for pivoting ResultSet data. Most of the times shouldn't be used directly and chartPivot or tablePivot should be used instead.

You can find the examples of using the pivotConfig here

// For query
{
  measures: ['Stories.count'],
  timeDimensions: [{
    dimension: 'Stories.time',
    dateRange: ['2015-01-01', '2015-03-31'],
    granularity: 'month'
  }]
}
 
// ResultSet.pivot({ x: ['Stories.time'], y: ['measures'] }) will return
[
  {
    xValues: ["2015-01-01T00:00:00"],
    yValuesArray: [
      [['Stories.count'], 27120]
    ]
  },
  {
    xValues: ["2015-02-01T00:00:00"],
    yValuesArray: [
      [['Stories.count'], 25861]
    ]
  },
  {
    xValues: ["2015-03-01T00:00:00"],
    yValuesArray: [
      [['Stories.count'], 29661]
    ]
  }
]

`query`

query(): Query

`rawData`

rawData(): T[]

`serialize`

serialize(): Object

Can be used to stash the ResultSet in a storage and restored later with deserialize

`series`

series‹SeriesItem›(pivotConfig?: PivotConfig): Series‹SeriesItem›[]

Returns an array of series with key, title and series data.

// For the query
{
  measures: ['Stories.count'],
  timeDimensions: [{
    dimension: 'Stories.time',
    dateRange: ['2015-01-01', '2015-12-31'],
    granularity: 'month'
  }]
}
 
// ResultSet.series() will return
[
  {
    key: 'Stories.count',
    title: 'Stories Count',
    series: [
      { x: '2015-01-01T00:00:00', value: 27120 },
      { x: '2015-02-01T00:00:00', value: 25861 },
      { x: '2015-03-01T00:00:00', value: 29661 },
      //...
    ],
  },
]

Type parameters:

SeriesItem

`seriesNames`

seriesNames(pivotConfig?: PivotConfig): SeriesNamesColumn[]

Returns an array of series objects, containing key and title parameters.

// For query
{
  measures: ['Stories.count'],
  timeDimensions: [{
    dimension: 'Stories.time',
    dateRange: ['2015-01-01', '2015-12-31'],
    granularity: 'month'
  }]
}
 
// ResultSet.seriesNames() will return
[
  {
    key: 'Stories.count',
    title: 'Stories Count',
    yValues: ['Stories.count'],
  },
]

`tableColumns`

tableColumns(pivotConfig?: PivotConfig): TableColumn[]

Returns an array of column definitions for tablePivot.

For example:

// For the query
{
  measures: ['Stories.count'],
  timeDimensions: [{
    dimension: 'Stories.time',
    dateRange: ['2015-01-01', '2015-12-31'],
    granularity: 'month'
  }]
}
 
// ResultSet.tableColumns() will return
[
  {
    key: 'Stories.time',
    dataIndex: 'Stories.time',
    title: 'Stories Time',
    shortTitle: 'Time',
    type: 'time',
    format: undefined,
  },
  {
    key: 'Stories.count',
    dataIndex: 'Stories.count',
    title: 'Stories Count',
    shortTitle: 'Count',
    type: 'count',
    format: undefined,
  },
  //...
]

In case we want to pivot the table axes

// Let's take this query as an example
{
  measures: ['Orders.count'],
  dimensions: ['Users.country', 'Users.gender']
}
 
// and put the dimensions on `y` axis
resultSet.tableColumns({
  x: [],
  y: ['Users.country', 'Users.gender', 'measures']
})

then tableColumns will group the table head and return

{
  key: 'Germany',
  type: 'string',
  title: 'Users Country Germany',
  shortTitle: 'Germany',
  meta: undefined,
  format: undefined,
  children: [
    {
      key: 'male',
      type: 'string',
      title: 'Users Gender male',
      shortTitle: 'male',
      meta: undefined,
      format: undefined,
      children: [
        {
          // ...
          dataIndex: 'Germany.male.Orders.count',
          shortTitle: 'Count',
        },
      ],
    },
    {
      // ...
      shortTitle: 'female',
      children: [
        {
          // ...
          dataIndex: 'Germany.female.Orders.count',
          shortTitle: 'Count',
        },
      ],
    },
  ],
},
// ...

`tablePivot`

tablePivot(pivotConfig?: PivotConfig): Array‹object›

Returns normalized query result data prepared for visualization in the table format.

You can find the examples of using the pivotConfig here

For example:

// For the query
{
  measures: ['Stories.count'],
  timeDimensions: [{
    dimension: 'Stories.time',
    dateRange: ['2015-01-01', '2015-12-31'],
    granularity: 'month'
  }]
}
 
// ResultSet.tablePivot() will return
[
  { "Stories.time": "2015-01-01T00:00:00", "Stories.count": 27120 },
  { "Stories.time": "2015-02-01T00:00:00", "Stories.count": 25861 },
  { "Stories.time": "2015-03-01T00:00:00", "Stories.count": 29661 },
  //...
]

`deserialize`

static deserialize‹TData›(data: Object, options?: Object): ResultSet‹TData›

import { ResultSet } from '@cubejs-client/core';
 
const resultSet = await cubeApi.load(query);
// You can store the result somewhere
const tmp = resultSet.serialize();
 
// and restore it later
const resultSet = ResultSet.deserialize(tmp);

Type parameters:

TData

Parameters:

Name	Type	Description
data	Object	the result of serialize
options?	Object	-

`getNormalizedPivotConfig`

static getNormalizedPivotConfig(query: PivotQuery, pivotConfig?: Partial‹PivotConfig›): PivotConfig

`SqlQuery`

`rawQuery`

rawQuery(): SqlData

`sql`

sql(): string

`ITransport`

`request`

request(method: string, params: any): () => Promise‹void›

Types

`Annotation`

Name	Type
format?	"currency" \| "percent" \| "number"
shortTitle	string
title	string
type	string

`BinaryFilter`

Name	Type
and?	BinaryFilter[]
dimension?	string
member?	string
operator	BinaryOperator
or?	BinaryFilter[]
values	string[]

`BinaryOperator`

BinaryOperator: "equals" | "notEquals" | "contains" | "notContains" | "gt" | "gte" | "lt" | "lte" | "inDateRange" | "notInDateRange" | "beforeDate" | "afterDate"

`ChartPivotRow`

Name	Type
x	string
xValues	string[]

`Column`

Name	Type
key	string
series	[]
title	string

`CubeApiOptions`

Name	Type	Description
apiUrl	string	URL of your Cube API. By default, in the development environment it is `http://localhost:4000/cubejs-api/v1`
credentials?	"omit" \| "same-origin" \| "include"	-
headers?	Record‹string, string›	-
parseDateMeasures?	boolean	-
pollInterval?	number	-
transport?	ITransport	Transport implementation to use. HttpTransport will be used by default.

`DateRange`

DateRange: string | [string, string]

`DrillDownLocator`

Name	Type
xValues	string[]
yValues?	string[]

`Filter`

Filter: BinaryFilter | UnaryFilter

`LoadMethodCallback`

LoadMethodCallback: function

`LoadMethodOptions`

Name	Type	Optional?	Description
`castNumerics`	`boolean`	✅ Yes	Pass `true` if you'd like all members with the `number` type to be automatically converted to JavaScript `Number` type. Note that this is a potentially unsafe operation since numbers more than `Number.MAX_SAFE_INTEGER` (opens in a new tab) or less than `Number.MIN_SAFE_INTEGER` can't be represented as JavaScript `Number`
`mutexKey`	`string`	✅ Yes	Key to store the current request's MUTEX inside the `mutexObj`. MUTEX object is used to reject orphaned queries results when new queries are sent. For example: if two queries are sent with the same `mutexKey` only the last one will return results.
`mutexObj`	`Object`	✅ Yes	Object to store MUTEX
`progressCallback`		✅ Yes
`subscribe`	`boolean`	✅ Yes	Pass `true` to use continuous fetch behavior.

`LoadResponse`

Name	Type
pivotQuery	PivotQuery
queryType	QueryType
results	LoadResponseResult‹T›[]

`LoadResponseResult`

Name	Type
annotation	QueryAnnotations
data	T[]
lastRefreshTime	string
query	Query

`MemberType`

MemberType: "measures" | "dimensions" | "segments"

`PivotConfig`

Configuration object that contains information about pivot axes and other options.

Let's apply pivotConfig and see how it affects the axes

// Example query
{
  measures: ['Orders.count'],
  dimensions: ['Users.country', 'Users.gender']
}

If we put the Users.gender dimension on y axis

resultSet.tablePivot({
  x: ['Users.country'],
  y: ['Users.gender', 'measures']
})

The resulting table will look the following way

Users Country	male, Orders.count	female, Orders.count
Australia	3	27
Germany	10	12
US	5	7

Now let's put the Users.country dimension on y axis instead

resultSet.tablePivot({
  x: ['Users.gender'],
  y: ['Users.country', 'measures'],
});

in this case the Users.country values will be laid out on y or columns axis

Users Gender	Australia, Orders.count	Germany, Orders.count	US, Orders.count
male	3	10	5
female	27	12	7

It's also possible to put the measures on x axis. But in either case it should always be the last item of the array.

resultSet.tablePivot({
  x: ['Users.gender', 'measures'],
  y: ['Users.country'],
});

Users Gender	measures	Australia	Germany	US
male	Orders.count	3	10	5
female	Orders.count	27	12	7

Name	Type	Description
aliasSeries?	string[]	Give each series a prefix alias. Should have one entry for each query:measure. See chartPivot
fillMissingDates?	boolean \| null	`true` by default. If set to `true`, missing dates on the time dimensions will be filled with `0` for all measures. Note: setting this option to `true` will override any `order` applied to the query.
x?	string[]	Dimensions to put on x or rows axis.
y?	string[]	Dimensions to put on y or columns axis.

`PivotQuery`

PivotQuery: Query & object

`PivotRow`

Name	Type
xValues	Array‹string \| number›
yValuesArray	Array‹[string[], number]›

`ProgressResponse`

Name	Type
stage	string
timeElapsed	number

`Query`

Name	Type
dimensions?	string[]
filters?	Filter[]
limit?	number
measures?	string[]
offset?	number
order?	TQueryOrderObject \| TQueryOrderArray
renewQuery?	boolean
segments?	string[]
timeDimensions?	TimeDimension[]
timezone?	string
ungrouped?	boolean

`QueryAnnotations`

Name	Type
dimensions	Record‹string, Annotation›
measures	Record‹string, Annotation›
timeDimensions	Record‹string, Annotation›

`QueryOrder`

QueryOrder: "asc" | "desc"

`QueryType`

QueryType: "regularQuery" | "compareDateRangeQuery" | "blendingQuery"

`Series`

Name	Type
key	string
series	T[]
title	string

`SeriesNamesColumn`

Name	Type
key	string
title	string
yValues	string[]

`SqlApiResponse`

Name	Type
sql	SqlData

`SqlData`

Name	Type
aliasNameToMember	Record‹string, string›
cacheKeyQueries	object
dataSource	boolean
external	boolean
sql	SqlQueryTuple

`SqlQueryTuple`

SqlQueryTuple: [string, boolean | string | number]

`TCubeDimension`

TCubeDimension: TCubeMember & object

`TCubeMeasure`

TCubeMeasure: TCubeMember & object

`TCubeMember`

Name	Type
name	string
shortTitle	string
title	string
isVisible?	boolean
meta?	any
type	TCubeMemberType

`TCubeMemberByType`

TCubeMemberByType: T extends "measures" ? TCubeMeasure : T extends "dimensions" ? TCubeDimension : T extends "segments" ? TCubeSegment : never

`TCubeMemberType`

TCubeMemberType: "time" | "number" | "string" | "boolean"

`TCubeSegment`

TCubeSegment: Pick‹TCubeMember, "name" | "shortTitle" | "title"›

`TDefaultHeuristicsOptions`

Name	Type
meta	Meta
sessionGranularity?	TimeDimensionGranularity

`TDryRunResponse`

Name	Type
normalizedQueries	Query[]
pivotQuery	PivotQuery
queryOrder	Array‹object›
queryType	QueryType

`TFlatFilter`

Name	Type
dimension?	string
member?	string
operator	BinaryOperator
values	string[]

`TQueryOrderArray`

TQueryOrderArray: Array‹[string, QueryOrder]›

`TQueryOrderObject`

`TableColumn`

Name	Type
children?	TableColumn[]
dataIndex	string
format?	any
key	string
meta	any
shortTitle	string
title	string
type	string \| number

`TimeDimension`

TimeDimension: TimeDimensionComparison | TimeDimensionRanged

`TimeDimensionBase`

Name	Type
dimension	string
granularity?	TimeDimensionGranularity

`TimeDimensionComparison`

TimeDimensionComparison: TimeDimensionBase & object

`TimeDimensionGranularity`

TimeDimensionGranularity: "second" | "minute" | "hour" | "day" | "week" | "month" | "year"

`TimeDimensionRanged`

TimeDimensionRanged: TimeDimensionBase & object

`TransportOptions`

Name	Type	Description
apiUrl	string	path to `/cubejs-api/v1`
authorization	string	jwt auth token
credentials?	"omit" \| "same-origin" \| "include"	-
headers?	Record‹string, string›	custom headers

`UnaryFilter`

Name	Type
and?	UnaryFilter[]
dimension?	string
member?	string
operator	UnaryOperator
or?	UnaryFilter[]
values?	never

`UnaryOperator`

UnaryOperator: "set" | "notSet"

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