2012-07-27 by Stefan Urbanek

Using Pandas as Brewery Backend

UPDATE: Added info about caching.

First time I looked at Pandas (python data analysis framework) I thought: that would be great backend/computation engine for data Brewery.

To recap core principle of Brewery: it is flow based data streaming framework with processing nodes connected by pipes. A typical node can have one or multiple inputs and has output. Source nodes have no inputs, target nodes have no outputs.

Current brewery implementation uses one thread per node (was written in times when Python was new to me and I did not know about GIL and stuff). Can be considered just as prototype...

Had this idea in mind for quite a some time, however coming from database world, the only potential implementation was through database tables with nodes performing SQL operations on them. I was not happy by requirement of some SQL DB server for data processing, not mentioning speed and function set (well, ok, pandas is missing the non-numeric stuff).

Here is the draft of the idea, how to implement data transfer between nodes in Brewery using tables. The requirements are

  • follow data modeller's workflow
  • do not rewrite data – I want to be able to see what was the result at each step
  • have some kind of provenance (where this field comes from?)

See larger image on imgur.

Table represents a pipe: each pipe field is mapped to a table column. If node performs only field operation, then table can be shared between nodes. If node affects rows, then new table should be considered. Every "pipe" can be cached and stream can be run from the cached point, if the computation takes longer time than desired during model development process.

Pandas offers structure called DataFrame, which holds data in a tabular form consisting of series of Series (fancier array objects). Each of the series represents a collection of field's values for analytical/computational step. Nodes that share same field structure and same records can share the series which can be grouped in a table/DataFrame.

Node can:

  • create completely new field structure (source node, aggregation, join, ...)
  • add a field (various derive/compute nodes)
  • remove a field (field filter, field replacement)

Just adding or removing a field does not affect the series, therefore nodes can just point to series they "need". Aggregation or join nodes generate not only new field structure, they affect number and representation of records as well, therefore the field series differ form their respective source series (compare: "year" in invoices and "year" in summary of invoices). For those kind of nodes new table/DataFrame should be created.

Sampling nodes or selection nodes can generate additional Series with boolean values based on selection. Each node can have hidden input column representing the selection.

There are couple of things I am missing so far: DataFrame that will be a "view" of another data frame – that is: DataFrame will not copy series, only reference them. Another feature is more custom metadata for a table column (DataFrame series), including "analytical datatype" (I will write about this later as it is not crucial in this case). They might be there, I just did not discovered them yet.

I am not an expert in Pandas, I have just started exploring the framework. Looks very promising for this kind of problem.

2012-06-12 by Stefan Urbanek

Cubes and Slicer are going to EuroPython 2012

Cubes is going to EuroPython 2012.

EDIT: Added "Need help?".

There are going to be two sessions. First there will be talk introducing to light-weight OLAP with Cubes and Slicer, on Friday at 9:45 in room Tagliatelle (add to calendar). Afterwards there will be longer, more in-depth and hands-on training about Slicing and Dicing with Cubes on Friday at 14:30 in room Pizza Napoli (add to calendar)

In the talk I will introduce the framework and explain reasons for it's existence. Then I will dig into architecture, features and briefly show examples how to use it for slicing and dicing. Newbies are welcome.

The training will go into more details and the participants will learn:

  • how to prepare data for aggregated browsing - star and snowflake schemas
  • how to create a logical model, define cubes, dimensions and hierarchies
  • how to browse aggregated data and how to slice and dice cubes from within Python
  • how to create a WSGI OLAP server ("in 15 minutes" style) for aggregated data browsing and how to use it in your web application for providing (browsable) data to end-user reports
  • how to provide localized reporting

If the time permits, we can look at the anatomy of the framework and see how to implement a backend for another kind of data store.

I will be focusing on the existing SQL (relational OLAP) backend.

Customized examples

You might use the training session (and not only the session) to solve your problem - just bring your own sample data, if you like.

Do you have any data that you would like to slice and dice? Have a database schema and do not know how to create a logical model? You can send me a data sample or a schema, so I can prepare examples based on problem you are solving.

Please, do not send any confidential data or schemas under NDA.


Need help?

If you have any questions or would like to help with your data: from data preparation, through data modeling to slicing and dicing. You can grab me during the whole event. If you can not find me, just tweet me: @Stiivi.


If anyone is interested in participating in the project, he is welcome. Here are some features that are either out of scope of my skills and I would like to cooperate with someone more professional, or I do not have available resources to do that:

I am also very open to new feature suggestions and feature change requests. Just little note: Cubes is meant to be small and simple. At least for now. There are plenty of complex and feature-rich solutions out there. If we can make new, more complex features as non-critical, optional plug-ins, that would be great.

Links and Calendar Events

You can add the talks to your calendar by following the links:

2012-06-01 by Stefan Urbanek

School of Data: The Pipeline

"We are missing data literacy" was mentioned by Tim Berners-Lee at Open Government Data Camp 2010 in London. Here we are in 2012 in Berlin, together with OKFN, P2PU and their friends preparing content of School of Data from the very beginning.

Based on our lively discussions lead by Rufus Pollock, reviews by Peter Murray-Rust and Friedrich Lindenberg, I've created the the pipeline based skill map that I will talk about here.

The Pipeline

Data have many forms, from ore-like solid (think of web and documents), crystalized solid (think of databases), through flowing liquid (think of those being processed) to vaporing gas (think of paying no attention). The best way of looking at the data is to look at them in all their stages as they go through a connected, but dismantle-able processing pipeline:

The flow is divided into the following parts:

  • discovery and acquisition – covers data source understanding, ways of getting data from the web and knowing when we have gathered enough
  • extraction – when data has to be scraped from unstructured documents into structured tables, loaded from a tabular file into a database
  • cleansing, transformation and integration – majority of skills for data processing, from understanding data formats, through knowing how to merge multiple sources to process optimization
  • analytical modeling – changing data to be viewed from analytical point of view, finding various patterns
  • presentation, analysis and publishing – mostly non-technical or just very slightly technical skills for story tellers, investigators, publishers and decision makers

There are two more side-pipes:

  • governance – making sure that everything goes well, that process is understandable and that content is according to expectations
  • tools and technologies – from SQL to Python (or other) frameworks

Here is the full map:

Download the PNG image or PDF.

Modules or skills

The pipeline skills, or rather modules, are based mostly on experience from projects in the open-data domain with inspiration of best practices from corporate environment.

We tried to cover most of the necessary knowledge and concepts so potential data users would be able to dive-in to their problem and get some reasonable (sometimes even partial) result at any stage of the pipe. Some of the corporate best practices are too mature at this moment to be included, some of them were tuned either with different semantics, different grouping. It was done intentionally.

Most of the modules will be based on hands-on problem-solving. They will provide source dataset (or bunch of unknown sources, for the purpose of discovery), sandbox or a playground environment, and few questions to be answered. Learner will try to solve the problem using guiding lecture notes. In ideal module, the dataset would be from existing open-data project, so the learner would be able to see the big picture as well.

Next Steps

Outline in a form of a pipeline is nice and fine ... as a guideline. Content has to follow and content will follow. If you would like be involved, visit the School of Data website. Follow @SchoolofData on Twitter.

Questions? Comments? Ideas?


2012-05-29 by Stefan Urbanek

Cubes 0.9.1: Ranges, denormalization and query cell

The new minor release of Cubes – light-weight Python OLAP framework – brings range cuts, denormalization with the slicer tool and cells in /report query, together with fixes and important changes.

See the second part of this post for the full list.

Range Cuts

Range cuts were implemented in the SQL Star Browser. They are used as follows:


cut = RangeCut("date", [2010], [2012,5,10])
cut_hi = RangeCut("date", None, [2012,5,10])
cut_low = RangeCut("date", [2010], None)

To specify a range in slicer server where keys are sortable:


Open ranges:


Denormalization with slicer Tool

Now it is possible to denormalize tour data with the slicer tool. You do not have to denormalize using python script. Data are denormalized in a way how denormalized browser would expect them to be. You can tune the process using command line switches, if you do not like the defaults.

Denormalize all cubes in the model:

$ slicer denormalize slicer.ini

Denormalize only one cube::

$ slicer denormalize -c contracts slicer.ini

Create materialized denormalized view with indexes::

$ slicer denormalize --materialize --index slicer.ini

Example slicer.ini:

denormalized_view_prefix = mft_
denormalized_view_schema = denorm_views

# This switch is used by the browser:
use_denormalization = yes

For more information see Cubes slicer tool documentation

Cells in Report

Use cell to specify all cuts (type can be range, point or set):

    "cell": [
            "dimension": "date",
            "type": "range",
            "from": [2010,9],
            "to": [2011,9]
    "queries": {
        "report": {
            "query": "aggregate",
            "drilldown": {"date":"year"}

For more information see the slicer server documentation.

New Features

  • cut_from_string(): added parsing of range and set cuts from string; introduced requirement for key format: Keys should now have format "alphanumeric character or underscore" if they are going to be converted to strings (for example when using slicer HTTP server)
  • cut_from_dict(): create a cut (of appropriate class) from a dictionary description
  • Dimension.attribute(name): get attribute instance from name
  • added exceptions: CubesError, ModelInconsistencyError, NoSuchDimensionError, NoSuchAttributeError, ArgumentError, MappingError, WorkspaceError and BrowserError


  • implemented RangeCut conditions

Slicer Server:

  • /report JSON now accepts cell with full cell description as dictionary, overrides URL parameters

Slicer tool:

  • denormalize option for (bulk) denormalization of cubes (see the the slicer documentation for more information)


  • important: all /report JSON requests should now have queries wrapped in the key queries. This was originally intended way of use, but was not correctly implemented. A descriptive error message is returned from the server if the key queries is not present. Despite being rather a bug-fix, it is listed here as it requires your attention for possible change of your code.
  • warn when no backend is specified during slicer context creation


  • Better handling of missing optional packages, also fixes #57 (now works without slqalchemy and without werkzeug as expected)
  • see change above about /report and queries
  • push more errors as JSON responses to the requestor, instead of just failing with an exception


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.

2012-05-14 by Stefan Urbanek

Cubes 0.9 Released

The new version of Cubes – light-weight Python OLAP framework – brings new StarBrowser, which we discussed in previous blog posts:

The new SQL backend is written from scratch, it is much cleaner, transparent, configurable and open for future extensions. Also allows direct browsing of star/snowflake schema without denormalization, therefore you can use Cubes on top of a read-only database. See DenormalizedMapper and SnowflakeMapper for more information.

Just to name a few new features: multiple aggregated computations (min, max,...), cell details, optional/configurable denormalization.

Important Changes

Summary of most important changes that might affect your code:

Slicer: Change all your slicer.ini configuration files to have [workspace] section instead of old [db] or [backend]. Depreciation warning is issued, will work if not changed.

Model: Change dimensions in model to be an array instead of a dictionary. Same with cubes. Old style: "dimensions" = { "date" = ... } new style: "dimensions" = [ { "name": "date", ... } ]. Will work if not changed, just be prepared.

Python: Use Dimension.hierarchy() instead of Dimension.default_hierarchy.

New Features

  • slicer_context() - new method that holds all relevant information from configuration. can be reused when creating tools that work in connected database environment
  • added Hierarchy.all_attributes() and .key_attributes()
  • Cell.rollup_dim() - rolls up single dimension to a specified level. this might later replace the Cell.rollup() method
  • Cell.drilldown() - drills down the cell
  • create_workspace(backend,model, **options) - new top-level method for creating a workspace by specifying backend name. Easier to create browsers (from possible browser pool) programmatically. The backend name might be full module name path or relative to the cubes.backends, for example sql.star for new or sql.browser for old SQL browser.
  • get_backend() - get backend by name

  • AggregationBrowser.cell_details(): New method returning values of attributes representing the cell. Preliminary implementation, return value might change.

  • AggregationBrowser.cut_details(): New method returning values of attributes representing a single cut. Preliminary implementation, return value might change.

  • Dimension.validate() now checks whether there are duplicate attributes

  • Cube.validate() now checks whether there are duplicate measures or details

SQL backend:

  • new StarBrowser implemented:
    • StarBrowser supports snowflakes or denormalization (optional)
    • for snowflake browsing no write permission is required (does not have to be denormalized)
  • new DenormalizedMapper for mapping logical model to denormalized view
  • new SnowflakeMapper for mapping logical model to a snowflake schema
  • ddl_for_model() - get schema DDL as string for model
  • join finder and attribute mapper are now just Mapper - class responsible for finding appropriate joins and doing logical-to-physical mappings
  • coalesce_attribute() - new method for coalescing multiple ways of describing a physical attribute (just attribute or table+schema+attribute)
  • dimension argument was removed from all methods working with attributes (the dimension is now required attribute property)
  • added create_denormalized_view() with options: materialize, create_index, keys_only

Slicer tool/server:

  • slicer ddl - generate schema DDL from model
  • slicer test - test configuration and model against database and report list of issues, if any
  • Backend options are now in [workspace], removed configurability of custom backend section. Warning are issued when old section names [db] and [backend] are used
  • server responds to /details which is a result of AggregationBrowser.cell_details()


  • added simple Flask based web example - dimension aggregation browser


  • in Model: dimension and cube dictionary specification during model initialization is depreciated, list should be used (with explicitly mentioned attribute "name") -- important
  • important: Now all attribute references in the model (dimension attributes, measures, ...) are required to be instances of Attribute() and the attribute knows it's dimension
  • removed hierarchy argument from Dimension.all_attributes() and .key_attributes()
  • renamed builder to denormalizer
  • Dimension.default_hierarchy is now depreciated in favor of Dimension.hierarchy() which now accepts no arguments or argument None - returning default hierarchy in those two cases
  • metadata are now reused for each browser within one workspace - speed improvement.


  • Slicer version should be same version as Cubes: Original intention was to have separate server, therefore it had its own versioning. Now there is no reason for separate version, moreover it can introduce confusion.
  • Proper use of database schema in the Mapper


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.

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