Cubes 1.0 release notes

These release notes cover the new features and changes (some of them backward incompatible).


The biggest new feature in cubes is the “pluggable” model. You are no longer limited to one one model, one type of data store (database) and one set of cubes. The new Workspace is now framework-level controller object that manages models (model sources), cubes and datastores. To the future more features will be added to the workspace.


Analytical Workspace Overview

New Workspace related objects:

  • model provider – creates model objects from a model source (might be a foreign API/service or custom database)
  • store – provides access and connection to cube’s data

For more information see the Workspace documentation.

Other notable new features in Cubes 1.0 are:

Python Versions

Cubes 1.0 supports Python >= 2.7 for Python 2 series and Python >= 3.4.1 for Python 3 series.

Analytical Workspace

The old backend architecture was limiting. It allowed only one store to be used, the model had to be known before the server started, it was not possible to get the model from a remote source.

For more details about the new workspace see the Analytical Workspace documentation.


The slicer.ini configuration has changed to reflect new features.

The section [workspace] now contains global configuration of a cubes workspace session. The database connection has moved into [store] (or similar, if there are more).

The database connection is specified either in the [store] section or in a separate stores.ini file where one section is one store, section name is store name (as referenced from cube models).

If there is only one model, it can be specified either in the [workspace] section as model. Multiple models are specified in the [models] section.

To sum it up:

  • [server] backend is now [store] type for every store
  • [server] log and log_level has moved to [workspace]
  • [model] is now either model option of [workspace] or list of multiple models in the [models] section

The old configuration:

host: localhost
port: 5000
reload: yes
log_level: info

url: postgres://localhost/mydata"

path: grants_model.json

Is now:

log_level: info
model: grants_model.json

host: localhost
port: 5000
reload: yes

type: sql
url: postgres://localhost/mydata

Check your configuration files.

See also



Slicer server is now a Flask application and a reusable Blueprint. It is possible to include the Slicer in your application at an end-point of your choice.

For more information, see the recipe.

Other server changes:

  • do not expose internal exceptions, only user exceptions
  • added simple authentication methods: HTTP Basic (behind a proxy) and parameter-based identity. Both are permissive and serve just for passing an identity to the authorizer.


Server end-points have changed.

New end-points:

  • /version
  • /info
  • /cubes
  • /cube/<cube>/model
  • /cube/<cube>/aggregate
  • /cube/<cube>/facts
  • /cube/<cube>/fact
  • /cube/<cube>/members/<dimension>
  • /cube/<cube>/cell
  • /cube/<cube>/report

Removed end-points:

  • /model – without replacement doe to the new concepts of workspace. Alternative is to get list of basic cube info using /cubes.
  • /model/cubes – without replacement, use /cubes
  • /model/cube/<cube> – use /cube/<cube>/model instead
  • /model/dimension/* – without replacement due to the new concepts of workspace
  • all top-level browser actions such as /aggregate – now the cube name has to be explicit

Parameter changes:

  • /aggregate uses aggregates=, does not accept measure= any more
  • /aggregate now accepts format= to generate CSV output
  • new parameter headers= for CSV output: with headers as attribute names, headers as attribute labels (human readable) or no headers at all
  • it is now possible to specify multiple drilldowns, separated by | in one drilldown= parameter
  • cuts for date dimension accepts named relative time references such as cut=date:90daysago-today. See the server documentation for more information.
  • dimension path elements can contain special characters if they are escaped by a backslash \ such as cut=city:Nové\ Mesto

Many actions now accept format= parameter, which can be json, csv or json_lines (new-line separated JSON).

Response changes:

  • /cubes (replacement for /model) returns a list of basic cubes info: name, label, description and category. It does not return full cube description with dimensions.
  • /cube/<cube>/model has new keys: aggregates and features

See also

OLAP Server

Outer Joins

Support for thee types of joins was added to the SQL backend: match (inner), master (left outer) and detail (right outer).

The outer joins allows for example to use whole date dimension table and have “empty cells” for dates where there are no facts.

When an right outer join (detail method) is present, then aggregate values are coalesced to zero (based on the function either the values or the result is coalesced). For example: AVG coalesces values: AVG(COALESCE(c, 0)), SUM coalesces result: COALESCE(SUM(c), 0).


Module with statistical aggregate functions such as simple moving average or weighted moving average.

Provided functions:

  • wma – weighted moving average
  • sma – simple moving average
  • sms – simple moving sum
  • smstd – simple moving st. deviation
  • smrsd – simple moving relative st. deviation
  • smvar – simple moving variance

The function are applied on the already computed aggregation results. Backends migh handle the function internally if they can.

Window functions respect window_size property of aggregates.


  • cuts now have an invert flag (might not be supported by all backends)
  • aggregate() has new argument split which is a cell that defines artificial flag-like dimension with two values: 0 – aggergated cell is outside of the split cell, 1 – aggregated cell is within the split cell

Both invert and split features are still provisional, their interface might change.


  • added slicer model convert to convert between json and directory bundle


Model and modeling related changes are:

  • new concept of model providers (see details below)
  • measure aggregates (see details below)
  • cardinality of dimensions and dimension levels
  • dimension and level roles
  • attribute missing values
  • format property of a measure and aggregate
  • namespaces


cubes, dimensions, levels and hierarchies can no longer be dictionaries, they should be lists of dictionaries and the dictionaries should have a name property set. This was depreciated long ago.

Model Providers

The models of cubes are now being created by the model providers. Model provider is an object that creates Cubes and Dimension instances from it’s source. Built-in model provider is cubes.StaticModelProvider which creates cubes objects from JSON files and dictionaries.


Cubes from stores can be wrapped in a model namespace. By-default, the namespace is the same as the name of the store. The cubes are referenced as NAMESPACE.CUBE such as foreign.sales. For backward compatibility reasons and for simplicity there are two cube lookup methods: recursive and global.

Measures and Aggregates

Cubes now distinguishes between measures and aggregates. measure represents a numerical fact property, aggregate represents aggregated value (applied aggregate function on a property, or provided natively by the backend).

This new approach of aggregates makes development of backends and clients much easier. There is no need to construct and guess aggregate measures or splitting the names from the functions. Backends receive concrete objects with sufficient information to perform the aggregation (either by a function or fetch already computed value).

Functionality additions and changes:


Implicit aggregate record_count is no longer provided for every cube. It has to be explicitly defined as an aggregate:

"aggregates": [
        "name": "item_count",
        "label": "Total Items",
        "function": "count"

It can be named and labelled in any way.

If cube has no aggregates, then new default aggregate named fact_count is created.


  • Backends should now implement provide_aggregate() method instead of aggregate() – the later takes care of argument conversion and preparation. See Backends for more information.

SQL Backend

  • New module functions with new AggregationFunction objects
  • Added get_aggregate_function() and available_aggregate_functions()
  • Renamed star module to browser
  • Updated the code to use the new aggregates instead of old measures. Affected parts of the code are now cleaner and more understandable
  • Moved calculated_aggregations_for_measure to library-level statutils module as calculators_for_aggregates
  • function dictionary is no longer used

New Backends


There is a cubes visualizer included in the Cubes that can connect to any cubes slicer server over HTTP. It is purely HTML/JavaScript application.

Other Minor Changes

  • Cell.contains_level(dim, level, hierarhy) – returns True when the cell contains level level of dimension dim
  • renamed AggregationBrowser.values() to cubes.AggregationBrowser.members()
  • AggregationResult.measures changed to AggregationResult.aggregates (see AggregationResult)
  • browser’s __init__ signature has changed to include the store
  • changed the exception hierarchy. Now has two branches: UserError and InternalError – the UserError can be returned to the client, the InternalError should remain privade on the server side.
  • to_dict() of model objects returns an ordered dictionary for nicer JSON output
  • New class cubes.Facts that should be returned by cubes.AggregationBrowser.facts()
  • cubes.cuts_from_string() has two new arguments member_converters and role_member_converters
  • New class cubes.Drilldown to get more information about the drilldown

Migration to 1.0

Checklists for migrating a Cubes project from pre-1.0 to 1.0:

The slicer.ini

  1. Rename [workspace] to [store]
  2. Create new empty [workspace]
  3. Move [server] backend to [store] type
  4. Move [server] log, log_level to the new [workspace]
  5. Rename [model] path to [models] main and remove all non-model references (such as locales).

The minimal configuration looks like:

type: sql
url: sqlite:///data.sqlite

main: model.json

See configuration changes for an example and configuration documentation for more information.

The Model

There are not many model changes, mostly measures and aggregates related.

  1. Make sure that dimensions, cubes, levels and hierarchies are not dictionaries but lists of dictionaries with name property.
  2. Create the explicit record_count aggregate, if you are using it. Note that you can name and label the aggregate as you like.
"aggregates": [
        "name": "record_count",
        "label": "Total Items",
        "function": "count"
  1. In measures rename aggregations to aggregates or even better: create explicit, full aggregate definitions.

See Aggregates for more information.

Slicer Front-end

The biggest change in the front-ends is the removal of the /model end-point without equivalend replacement. Use /cubes to get list of provided cubes. The cube definition contains whole dimension descriptions.

  1. Change from /model to /cubes
  2. Change from /model/cube/<name> to /cube/<name>/model
  3. Cube has to be explicit in every request, therefore /aggregate does not work any more, use /cube/<name>/aggregate
  4. Change aggregate parameter measure to aggregates

Refer to the OLAP Server documentation for the new response structures. There were minor changes, mostly additions.

Additional and Optional Considerations for Migration

  • if your model is too big, split it into multiple models and add them to the [models] section. Note that the dimensions can be shared between models.
  • put all your models into a separate directory and use the [workspace] models_path property. The paths in [models] are relative to the models_path
  • if you have muliple stores, create a separate stores.ini file where the section names are store names. Set the [workspace] stores to the stores.ini path if it is different than default.
  • Add "role"="time" to a date dimension – you might benefit from new date-related additions and special dimension handling in the available front-ends
  • Review joins and set appropriate join method if desired, for example detail for a date dimension.
  • Add cardinality metadata to dimension levels if appropriate.
  • Look at the cube’s model features property to learn what the front-end can expect from the backend for that cube
  • Look at the /info response