Generate fake Qlik Engine data in the shape of hypercubes and listobjects.
npm install qix-faker> Powered by faker.js
Generate fake Qlik Engine data in the shape of hypercubes and listobjects.
Great for prototyping with picasso.js, nebula.js and any other time when a real Qlik Engine is not available.
``sh`
npm install qix-faker
`js
const { hypercube, listobject } = require('qix-faker');
const hc = hypercube({
dimensions: [f => f.commerce.product()],
measures: [f => f.commerce.price(100, 5000, 2, '$')],
numRows: 3,
});
const lo = listobject({
dimension: [f => f.commerce.product()],
numRows: 5,
});
`
- options
`js`
qixFaker.hypercube({
numRows: 10,
seed: 5,
dimensions: [
{
value: (faker, rowIdx) => faker.date.month(),
},
],
});
- options
`js`
qixFaker.listobject({
numRows: 10,
seed: 5678,
dimension: f => d.name.firstName(),
});
#### field
A field configuration can take on two different shapes:
#####
A function which is provided the faker instance as first parameter, and the rowIndex as the second. See the faker api for details on params of each method.
`js`
hypercube({
dimensions: [(faker, idx) => faker.commerce.product()],
measures: [(faker, idx) => faker.finance.amount(100, 3000, 2, '$')],
});
#####
Using an object provides more control:
- value: - Same function as above.
- maxCardinalRatio - A value between 0 - 1 to limit the uniqueness of dimension values.
- attrDims - Attribute dimensions.
- attrExps - Attribute expression.
- override
`js`
hypercube({
numRows: 100,
dimensions: [
{
value: f => f.address.city(),
maxCardinalRatio: 0.4,
attrDim: [f => f.commerce.color()],
attrExps: [f => f.random.number()]
override: {
qFallbackTitle: 'City',
qLocked: true,
},
},
],
});
In the above example, 100 rows of data is generated using the city dataset provided in faker, there is however no guarantee that all 100 rows will be unique.
In some cases though, it might be desirable to limit the uniqueness of the generated values; maxCardinalRatio provides a way to do just that.
Assume we want to generate some data containing _cities_ grouped by _country_, by setting maxCardinalRatio to a low number we can create such a dataset.
`js``
hypercube({
numRows: 80,
dimensions: [
{
value: f => f.address.country(),
maxCardinalRatio: 0.1,
},
{
value: f => f.address.city(),
},
],
});
In the above example, the first 8 rows (numRows \* ratio) of _country_ will be the same, while _city_ will be randomized, thus creating groups.