Managing data cubes

This section explains how to create and configure your data cubes.

Creating a data cube

Create a new data cube from the home view by clicking the + Data cube button. The Create new data cube modal allows you to define the source data for a data cube.

Add cube modal

Checking the Auto fill dimensions and measures checkbox will automatically detect your schema and create dimensions and measures accordingly.

Another way to create a new data cube is to duplicate an existing data cube and edit its properties.

After creating the data cube you will be taken to the edit screen to adjust any details.

Editing a data cube

Click the pencil icon in the data cube header to edit a data cube.

Cube edit view

Within the edit view you can change the title, description, and default timezone of the data cube. You can also edit and create dimensions and measures from their respective tabs.

Schema detection

Pivot will automatically detect the columns in your data source and recommend some possible dimensions and measures.

Schema detection can be helpful when you are creating a new data cube in which case it will allow you to initialize the data cube with some dimensions and measures.

The other use for schema detection is when you evolve your schema and add columns over time. If you have just added a column and you want a corresponding dimension to be created for it, you can use schema detection to automatically construct that column for you. This functionality is accessible from the Suggestions tab of the New dimension and New measure modals.

Cube edit new dimension modal

How schema detection works

Pivot looks at the dataset metadata and uses the returned list of columns, their types, and their aggregation (in case of rollup) to determine what dimensions and measures to suggest.

Pivot generates dimensions and measures by applying the following rules to the discovered underlying column types:

Limitations

While schema detection is an invaluable tool for quickly getting you up and running with a new data source, it can never do a perfect job. Once you create a data cube you should play with it and tailor it to be perfectly suited to your needs. Don't hesitate to change and delete the auto-generated dimensions and measures; they will always be there in the Suggestions tab.

In particular, schema detection cannot detect these common scenarios:

Defaults

The default settings control what options are selected in the data cube view when a Pivot user first navigates to the data cube. By default, the data cube shows data from the latest day and which represents the first measure in the list of available measures for that data cube.

You can modify these defaults, add filtering conditions, or set a specific dimension to be shown with the default settings.

To do so, in the data cube edit view, expand the More chevron and select Defaults.

Data cube defaults

For the default time filter duration, selected measures, pinned dimensions, refresh rate, or timezone, you can choose new values by clicking the field and selecting new values from the options shown. Be sure to click the Save button to apply your changes.

Default filter values allow you to supplement the default filter, Latest day, with additional filtering criteria. Specify filters as Plywood expressions. For example, you can show data generated in New York by adding the following filter:

$cityName.match("New York")

Any Plywood expression is available to you to use in the Default filter field.

By default, no dimensions are chosen in the data cube view.

To add a default dimension, type the name of the dimension, such as cityName, in the Default shown dimensions field. When you enter a dimension, the editor automatically substitutes the dimension name with a full JSON expression that matches the name and specifies how the results are to be sorted.

For example, the following configuration filters by city name and sets the city name to appear by default in the data cube:

Data cube defaults edited

The data cube view would appear as:

Data cube defaults edited

Advanced options

The following advanced options are available when you expand the More chevron.

Data cube advanced

Subset filter formula: Specify a plywood formula that applies a mandatory, hidden filter to all queries made through this data cube. See row level restriction.

Required filter token: Specify a token that corresponds to a filter token defined in a user role. See token based row level access control.

Primary time dimension: Specify the time dimension that corresponds to when the data point was actually added. This is used for checking the freshness of data and also potentially enforcing a time filter. Defaults to Time.

Enforce time filter: If set to true, ensures that every query is filtered on the primary time dimension. This should be used if time unbounded queries are likely to be slow due to the volume and tearing of data.

Instances: Configure multiple filtered instances within a single data cube via a configuration object. See example configuration.

Data cube instances

Refresh time: Configure how the data cube will calculate the data's latest time. Defaults to Query, which means it will query the data to learn about the latest data ingested. This can also be set to predefined in which case latest current time will always be assumed. An alternative 'fixed' time can also be defined as the 'fixedMaxTime' property in the options.

Query caching: Specify query caching behavior. Allowing caching can greatly speed up exploration but can also cause results to be a little out of date especially in realtime rolled up datasets.

Data cube options

Additional data cube configuration options as JSON with any of the following properties:

Time filter presets

Each section of presets in the time filter menu can be configured with custom presets. See an example of latest presets.

Querying options
Visual options

Examples

Instances
{
  "dimensionFormula": "$language",
  "values": [
    {
      "title": "English",
      "value": "en"
    },
    {
      "title": "Chinese",
      "value": "zh"
    },
    {
      "title": "Spanish",
      "value": "es"
    }
  ]
}

dimensionFormula is a plywood formula that operates on a dimension.

values is a list of objects with keys of title, value where value is the value that will be matched with the dimensionFormula expression and title is the label displayed in the UI.

piiMask
{
  "piiMask": {
    "aggregate": "$main.count()",
    "threshold": 10,
    "maskText": "HIDDEN [%i]",
    "otherText": "Other (%i)",
    "hideAggregates": false
  }
}

Note that %i will be resolved as the row index of the datum

latestPresets

latestPresets appear as time filter values in the data cube view. You can create presets for custom time values, as in the following example:

{
  "latestPresets": [
    { "name": "1M", "pillLabel": "1 minute", "selection": "$m.timeRange('PT1M', -1)" },
    { "name": "5M", "pillLabel": "5 minutes", "selection": "$m.timeRange('PT1M', -5)" },
    { "name": "30M", "pillLabel": "30 minutes", "selection": "$m.timeRange('PT1M', -30)" },
    { "name": "1H", "pillLabel": "1 hour", "selection": "$m.timeRange('PT1H', -1)" },
    { "name": "6H", "pillLabel": "6 hours", "selection": "$m.timeRange('PT1H', -6)" }
  ]
}

In the UI, the value of name appears as the UI label in the presets dropdown, while the optional pillLabel appears as the label when the value is selected. If pillLabel is not provided, name is used in both places. The selection value is the interval of time selected. The sample presets above would appear as follows:

Data cube latest time presets

In the selection value, $m is resolved as the data's maximum time and $n as the current time.

Since $m is a time value, if you would like to add a preset to view all data for the last seven days, with full data for each day, you would need to adjust $m as shown in the following example:

{
  "latestPresets": [
    { "name": "7D", "pillLabel": "7 days", "selection": "$m.timeFloor('P1D').timeRange('P1D', -7)" }
  ]
}
restrictedModeProperties

When set, this property will expose an additional access control list configuration in the data cube's "Access" section, enabling the ability to grant users and/or roles a subset of full edit functionality.

{
  "restrictedModeProperties": [
    "dimensions",
    "measures"
  ]
}

The configuration above would allow users and roles granted "Restricted edit access" on the "Access" screen the ability to edit the data cube's dimensions and measures, but nothing else.

The values that can be set for this array are:

Overview

Administer

Manage Data

Query Data

Visualize

Configure

Misc