Cubes 1.0 Released - Pluggable Data Warehouse

Finally it is here: Cubes 1.0. Many of you are already using it from Github or from PyPi, it just has not been officially released, so here we go.

Cubes now brings a light-weight way to create concept-oriented pluggable data warehouse from multipe sources.

Summary:

1. Analytical Workspace and Model Providers
2. Model Objects Redesign
3. HTTP API changes
4. New Backends
5. New SQL Backend Features
6. Authentication and Authorization

Detailed list of changes.

The changes are major, backward incompatible, but necessary for the future direction of the Cubes.

Analytical Workspace

The biggest change is the Workspace – pluggable data-warehouse. 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.

• Multiple models per workspace/server instead of only one
• Multiple backends per workspace/server instead of only one
• Multiple data stores per workspace/server instead of only one

Models can now be generated or converted on-the-fly from another service with the new concept of Model Providers.

See also: Workspace, Providers

Model Objects Redesign

Notable change is addition of new object: Measure Aggregate. 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).

Now you can name the "record_count" as you like or you might not have it at all, if you do not like it.

More info about model can be found in the model documentation.

Other model changes:

• cardinality - metadata that helps front-end to determine which kind of UI item to use or might restrict hich-cardinality queries
• dimension linking – cubes can specify how the dimensions are going to be linked: specify what hierarchies are relevant to the cube, how what is the cardinality of dimension in the context of the cube and more.
• roles dimensions and levels can have roles – metadata that might make dims/levels be handled in a special way. Currently only the time role is implemented.

HTTP API Changes

The server end-points have changed. Concept of global model was dropped, now there is just list of cubes. The front-end should approach the server in two steps:

1. Get list of cubes with /cubes – only basic information, no structure metadata
2. Get full cube model with /cube/NAME/model

Other changes:

• Many actions now accept format= parameter, which can be json, csv or json_lines (new-line separated JSON).
• Cuts for date dimension accepts named relative time references such as cut=date:90daysago-today
• Dimension path elements can contain special characters if they are escaped by a backslash such as cut=city:Nové\ Mesto

More info

Backends

New backends:

• MongoDB (thanks to Robin Thomas)
• full implementation of the Slicer backend
• Mixpanel
• Google Analytics

With model providers you can easily create a backend for any other service which serves cube-like data and plug it into your data warehouse.

SQL Features

Notable addition to the SQL backend are outer joins (finally!): three types of joins were added to the SQL backend: match (inner), master (left outer) and detail (right outer).

More info about the SQL features.

Non-additive

Provisional semi-additive time dimension support was added. An aggregate can specify that it is non-additive through the time dimension (such as account amount snapshots) and the generated query will handle the situation by choosing the latest entry used.

The initial metadata infrastructure is in place. More flexible implementation that will include other dimensions and element selection functions will be introduced in the future releases.

Credit goes to Robin Thomas for this feature.

Authentication and Authorization

Authentication at the server level and authorization at the workspace level. The interface is extensible, so you can implement any method you like. The built-in methods are pretty simple:

permissive authentication methods: pass-parameter – just pass api_key parameter in the URL or Basic HTTP proxy – using username, ignoring password (there is one for testing purposes called "adminadmin" ...)

authorization: JSON configuration for roles (inheritable) and rights.

The authorization has two parts: access to the cube and restriction cell for a cube.

More info about authorization

Creating an auth extension

Visualizer

Cubes comes with a built-in Visualizer – a web app for visualizing cubes data over time series. Main features: drill-down, filtering, many chart options, connects to any cubes server. The application was developed by Robin Thomas and Ryan Berlew.

Instructions

About the Release

This release is a milestone in Cubes interface: the model metadata structure and the slicer API. They are very unlikely to be changed, may be slighly adjusted with maintaining backward compatibility or at least some kind of conversion tools.

Things that might change, due to planned refactoring:

• Python interface – mostly Workspace and model compilation
• Localization – definition of model localization
• Extensions interface - which methods the extensions should implement and how
• Configuration – slight changes in the slicer.ini sections

The above changes will be stabilized around v1.1 or v1.2 release.

To sum it up: it is safe to build applications on top of the Slicer/HTTP interface and it is safe to generate models to be used by cubes.

Credits

Many thanks to Robin Thomas and Ryan Berlew for major code contributions and for the Visualizer. Credit also goes to Jose Juan Montes, Tomas Levine and Byron Yi.

Links

Read the detailed list of changes.

Important note: The cubes repository has moved to the Data Brewery github organization group (read more).

Most recent sources can be found on github.

Questions, comments, suggestions for discussion can be posted to the Cubes Google Group for discussion, problem solving and announcements.

Submit issues and suggestions on github

IRC channel #databrewery on irc.freenode.net

Welcome Robin and Thanks to Squarespace

Before the upcoming 1.0 release, I would like to introduce Cubes core developer Robin Thomas. Robin is experienced data warehouse engineer with profound knowledge of OLAP and multidimensional modeling. Robin and his team did a great job, contributed many new features and concepts.

We have quite a lot of new features and ideas thanks to Robin. Just to name a few:

• new, completely rewritten Mongo backend
• authorization and authentication
• non-additive time dimension
• statistical functions

and many more.

Thanks and credit goes also to: Brad Willard, Mathew Thomas, Ryan Berlew, Andrew Bartholomew and Emily Wagner.

In addition, I would like to thank Squarespace for their kindness and for contributing their additions back to the community as open-source.

Cubes Workspace Redesign – Pluggable Model

Here is a short presentation about the Cubes workspace changes:

Most recent Cubes sources can be found on github. Read the development documentation.

Introducing Expressions for Python

Expressions is a lightweight arithmetic expression parser for creating simple arithmetic expression compilers.

Goal is to provide minimal and understandable interface for handling arithmetic expressions of the same grammar but slightly different dialects (see below). The framework will stay lightweight and it is unlikely that it will provide any more complex gramatical constructs.

Parser is hand-written to avoid any dependencies. The only requirement is Python 3.

Source: github.com/Stiivi/expressions

Features

The expression is expected to be an infix expression that might contain:

• numbers and strings (literals)
• variables
• binary and unary operators
• function calls with variable number of arguments

The compiler is then used to build an object as a result of the compilation of each of the tokens.

Dialects

Grammar of the expression is fixed. Slight differences can be specified using a dialect structure which contains:

• list of operators, their precedence and associativeness
• case sensitivity (currently used only for keyword based operators)

Planned options of a dialect that will be included in the future releases:

• string quoting characters (currently single ' and double " quotes)
• identifier quoting characters (currently unsupported)
• identifier characters (currently _ and alpha-numeric characters)
• decimal separator (currently .)
• function argument list separator (currently comma ,)

Use

Intended use is embedding of customized expression evaluation into an application.

Example uses:

• Variable checking compiler with an access control to variables.
• Unified expression language where various other backends are possible.
• Compiler for custom object structures, such as for frameworks providing functional-programing like interface.

How-to

Write a custom compiler class and implement methods:

• compile_literal taking a number or a string object
• compile_variable taking a variable name
• compile_operator taking a binary operator and two operands
• compile_unary taking an unary operator and one operand
• compile_function taking a function name and list of arguments

Every method receives a compilation context which is a custom object passed to the compiler in compile(expression, context) call.

The following compiler re-compiles an expression back into it's original form with optional access restriction just to certain variables specified as the compilation context:

class AllowingCompiler(Compiler):
    def compile_literal(self, context, literal):
        return repr(literal)

    def compile_variable(self, context, variable):
        """Returns the variable if it is allowed in the `context`"""

        if context and variable not in context:
            raise ExpressionError("Variable %s is not allowed" % variable)

        return variable

    def compile_operator(self, context, operator, op1, op2):
        return "(%s %s %s)" % (op1, operator, op2)

    def compile_function(self, context, function, args):
        arglist = ", " % args
        return "%s(%s)" % (function, arglist)

Create a compiler instance and try to get the result:

compiler = AllowingCompiler()

result = compiler.compile("a + b", context=["a", "b"])
a = 1
b = 1
print(eval(result))

The output would be 2 as expected. The following will fail:

result = compiler.compile("a + c")

For more examples, such as building a SQLAlchemy structure from an expression, see the examples folder.

Summary

Source: github.com/Stiivi/expressions

If you have any questions, comments, requests, do not hesitate to ask.

Introducing Bubbles – virtual data objects framework

After a while of silence, here is first release of Bubbles – virtual data objects framework.

Motto: Focus on the process, not the data technology

Here is a short presentation:

Introduction

I have started collecting functionality from various private data frameworks into one, cleaning the APIs in the process.

Priorities of the framework are:

• understandability of the process
• auditability of the data being processed (frequent use of metadata)
• usability
• versatility

Working with data:

• keep data in their original form
• use native operations if possible
• performance provided by technology
• have options

Bubbles is performance agnostic at the low level of physical data implementation. Performance should be assured by the data technology and proper use of operations.

What bubbles is not?

• Numerical or statistical data tool. Use for example Pandas instead.
• Datamining tool. It might provide data mining functionality in some sense, but that will be provided by some other framework. For now, use
• All-purpose SQL abstraction framework. It provides operations on top of SQL, but is not covering all the possibilities. Use Scikit Learn SQLAlchemy for special constructs.

Data Objects and Representations

Data object might have one or multiple representations. A SQL table might act as python iterator or might be composed as SQL statement. The more abstract and more flexible representation, the better. If representations can be composed or modified at metadta level, then it is much better than streaming data all around the place.

Operations

Functionality of Bubbles is provided by operations. Operation takes one or more objects as operands and set of parameters that affect the operation. There are multiple versions of the operation – for various object representations. Which operation is used is decided during runtime. For example: if there is a SQL and iterator version and operand is SQL, then SQL statement composition will be used.

Implementing custom operations is easy through an @operation decorator.

I will be talking about them in detail in one of the upcoming blog posts.

Here is a list:

Bubbles (Brewery2) - Operations by Stefan Urbanek

Epilogue

Bubbles comes as Python 3.3 framework. There is no plan to have Python 2 back-port.

Bubbles will follow: 'provide mechanisms, not policies' rule as much as it will be possible. Even some policies are introduced at the early stages of the framework, such as operation dispatch or graph execution, they will be opened later for custom replacement.

Version 0.2 is already planned and will contain:

• processing graph – connected nodes, like in the old Brewery
• more basic backends, at least Mongo and some APIs
• bubbles command line tool

Links

Sources can be found on github. Read the documentation.

Join the Google Group for discussion, problem solving and announcements.

Submit issues and suggestions on github

IRC channel #databrewery on irc.freenode.net

If you have any questions, comments, requests, do not hesitate to ask.