mondrian-olap

JRuby gem for performing multidimensional queries of relational database data using Mondrian OLAP Java library.

DESCRIPTION

SQL language is good for doing ad-hoc queries from relational databases but it becomes very complicated when doing more complex analytical queries to get summary results. Alternative approach is OLAP (On-Line Analytical Processing) databases and engines that provide easier multidimensional analysis of data at different summary levels.

One of the most popular open-source OLAP engines is Mondrian. Mondrian OLAP engine can be put in front of relational SQL database and it provides MDX multidimensional query language which is much more suited for analytical purposes.

mondrian-olap is JRuby gem which includes Mondrian OLAP engine and provides Ruby DSL for creating OLAP schemas on top of relational database schemas and provides MDX query language and query builder Ruby methods for making analytical queries.

mondrian-olap is used in eazyBI data analysis and reporting web application. Private eazyBI can be used to create easy-to-use web based reports and dashboards on top of mondrian-olap based backend database. There is also mondrian-olap demo Rails application for trying MDX queries. The mondrian-rest uses mondrian-olap to implement a REST API interface for a Mondrian schema.

USAGE

Schema definition

At first you need to define OLAP schema mapping to relational database schema tables and columns. OLAP schema consists of:

  • Cubes

    Multidimensional cube is a collection of measures that can be accessed by dimensions. In relational database cubes are stored in fact tables with measure columns and dimension foreign key columns.

  • Dimensions

    Dimension can be used in one cube (private) or in many cubes (shared). In relational database dimensions are stored in dimension tables.

  • Hierarchies and levels

    Dimension has at least one primary hierarchy and optional additional hierarchies and each hierarchy has one or more levels. In relational database all levels can be stored in the same dimension table as different columns or can be stored also in several tables.

  • Members

    Dimension hierarchy level values are called members.

  • Measures

    Measures are values which can be accessed at detailed level or aggregated (e.g. as sum or average) at higher dimension hierarchy levels. In relational database measures are stored as columns in cube table.

  • Calculated measures

    Calculated measures are not stored in database but calculated using specified formula from other measures.

Read more about about defining Mondrian OLAP schema.

Here is example how to define OLAP schema and its mapping to relational database tables and columns using mondrian-olap:

```ruby require “rubygems” require “mondrian-olap”

schema = Mondrian::OLAP::Schema.define do cube ‘Sales’ do table ‘sales’ dimension ‘Customers’, foreign_key: ‘customer_id’ do hierarchy has_all: true, all_member_name: ‘All Customers’, primary_key: ‘id’ do table ‘customers’ level ‘Country’, column: ‘country’, unique_members: true level ‘State Province’, column: ‘state_province’, unique_members: true level ‘City’, column: ‘city’, unique_members: false level ‘Name’, column: ‘fullname’, unique_members: true end end dimension ‘Products’, foreign_key: ‘product_id’ do hierarchy has_all: true, all_member_name: ‘All Products’, primary_key: ‘id’, primary_key_table: ‘products’ do join left_key: ‘product_class_id’, right_key: ‘id’ do table ‘products’ table ‘product_classes’ end level ‘Product Family’, table: ‘product_classes’, column: ‘product_family’, unique_members: true level ‘Brand Name’, table: ‘products’, column: ‘brand_name’, unique_members: false level ‘Product Name’, table: ‘products’, column: ‘product_name’, unique_members: true end end dimension ‘Time’, foreign_key: ‘time_id’, type: ‘TimeDimension’ do hierarchy has_all: false, primary_key: ‘id’ do table ‘time’ level ‘Year’, column: ‘the_year’, type: ‘Numeric’, unique_members: true, level_type: ‘TimeYears’ level ‘Quarter’, column: ‘quarter’, unique_members: false, level_type: ‘TimeQuarters’ level ‘Month’, column: ‘month_of_year’, type: ‘Numeric’, unique_members: false, level_type: ‘TimeMonths’ end hierarchy ‘Weekly’, has_all: false, primary_key: ‘id’ do table ‘time’ level ‘Year’, column: ‘the_year’, type: ‘Numeric’, unique_members: true, level_type: ‘TimeYears’ level ‘Week’, column: ‘week_of_year’, type: ‘Numeric’, unique_members: false, level_type: ‘TimeWeeks’ end end measure ‘Unit Sales’, column: ‘unit_sales’, aggregator: ‘sum’ measure ‘Store Sales’, column: ‘store_sales’, aggregator: ‘sum’ end end ```

Connection creation

When schema is defined it is necessary to establish OLAP connection to database. Here is example how to connect to MySQL database using the schema object that was defined previously:

```ruby require “jdbc/mysql”

olap = Mondrian::OLAP::Connection.create( driver: ‘mysql’, host: ‘localhost’, database: ‘mondrian_test’, username: ‘mondrian_user’, password: ‘secret’, schema: schema ) ```

MDX queries

Mondrian OLAP provides MDX query language. Read more about MDX. mondrian-olap allows executing of MDX queries, for example query for “Get sales amount and number of units (on columns) of all product families (on rows) sold in California during Q1 of 2010”:

ruby result = olap.execute <<-MDX SELECT {[Measures].[Unit Sales], [Measures].[Store Sales]} ON COLUMNS, {[Products].children} ON ROWS FROM [Sales] WHERE ([Time].[2010].[Q1], [Customers].[USA].[CA]) MDX

which would correspond to the following SQL query:

SELECT SUM(unit_sales) unit_sales_sum, SUM(store_sales) store_sales_sum
FROM sales
  LEFT JOIN products ON sales.product_id = products.id
  LEFT JOIN product_classes ON products.product_class_id = product_classes.id
  LEFT JOIN time ON sales.time_id = time.id
  LEFT JOIN customers ON sales.customer_id = customers.id
WHERE time.the_year = 2010 AND time.quarter = 'Q1'
  AND customers.country = 'USA' AND customers.state_province = 'CA'
GROUP BY product_classes.product_family
ORDER BY product_classes.product_family

and then get axis and cells of result object:

ruby result.axes_count # => 2 result.column_names # => ["Unit Sales", "Store Sales"] result.column_full_names # => ["[Measures].[Unit Sales]", "[Measures].[Store Sales]"] result.row_names # => e.g. ["Drink", "Food", "Non-Consumable"] result.row_full_names # => e.g. ["[Products].[Drink]", "[Products].[Food]", "[Products].[Non-Consumable]"] result.values # => [[..., ...], [..., ...], [..., ...]] # (three rows, each row containing value for "unit sales" and "store sales")

Query builder methods

MDX queries could be built and executed also using Ruby methods in a similar way as ActiveRecord/Arel queries are made. Previous MDX query can be executed as:

ruby olap.from('Sales'). columns('[Measures].[Unit Sales]', '[Measures].[Store Sales]'). rows('[Products].children'). where('[Time].[2010].[Q1]', '[Customers].[USA].[CA]'). execute

Here is example of more complex query “Get sales amount and profit % of top 50 products cross-joined with USA and Canada country sales during Q1 of 2010”:

ruby olap.from('Sales'). with_member('[Measures].[ProfitPct]'). as('Val((Measures.[Store Sales] - Measures.[Store Cost]) / Measures.[Store Sales])', format_string: 'Percent'). columns('[Measures].[Store Sales]', '[Measures].[ProfitPct]'). rows('[Products].children').crossjoin('[Customers].[Canada]', '[Customers].[USA]'). top_count(50, '[Measures].[Store Sales]'). where('[Time].[2010].[Q1]'). execute

See more examples of queries in spec/query_spec.rb.

Currently there are query builder methods just for most frequently used MDX functions, there will be new query builder methods in next releases of mondrian-olap gem.

Cube dimension and member queries

mondrian-olap provides also methods for querying dimensions and members:

ruby cube = olap.cube('Sales') cube.dimension_names # => ['Measures', 'Customers', 'Products', 'Time'] cube.dimensions # => array of dimension objects cube.dimension('Customers') # => customers dimension object cube.dimension('Time').hierarchy_names # => ['Time', 'Time.Weekly'] cube.dimension('Time').hierarchies # => array of hierarchy objects cube.dimension('Customers').hierarchy # => default customers dimension hierarchy cube.dimension('Customers').hierarchy.level_names # => ['(All)', 'Country', 'State Province', 'City', 'Name'] cube.dimension('Customers').hierarchy.levels # => array of hierarchy level objects cube.dimension('Customers').hierarchy.level('Country').members # => array of all level members cube.member('[Customers].[USA].[CA]') # => lookup member by full name cube.member('[Customers].[USA].[CA]').children # => get all children of member in deeper hierarchy level cube.member('[Customers].[USA]').descendants_at_level('City') # => get all descendants of member in specified hierarchy level

See more examples of dimension and member queries in spec/cube_spec.rb.

User defined MDX functions

You can define new MDX functions using JavaScript, CoffeeScript or Ruby language that you can later use either in calculated member formulas or in MDX queries. Here are examples of user defined functions in Ruby:

ruby schema = Mondrian::OLAP::Schema.define do # ... cube definitions ... user_defined_function 'Factorial' do ruby do parameters :numeric returns :numeric def call(n) n <= 1 ? 1 : n * call(n - 1) end end end user_defined_function 'UpperName' do ruby do parameters :member returns :string syntax :property def call(member) member.getName.upcase end end end end

See more examples of user defined functions in spec/schema_definition_spec.rb.

Data access roles

In schema you can define data access roles which can be selected for connection and which will limit access just to subset of measures and dimension members. Here is example of data access role definition:

ruby schema = Mondrian::OLAP::Schema.define do # ... cube definitions ... role 'California manager' do schema_grant access: 'none' do cube_grant cube: 'Sales', access: 'all' do dimension_grant dimension: '[Measures]', access: 'all' hierarchy_grant hierarchy: '[Customers]', access: 'custom', top_level: '[Customers].[State Province]', bottom_level: '[Customers].[City]' do member_grant member: '[Customers].[USA].[CA]', access: 'all' member_grant member: '[Customers].[USA].[CA].[Los Angeles]', access: 'none' end end end end end

See more examples of data access roles in spec/connection_role_spec.rb.

REQUIREMENTS

mondrian-olap gem is compatible with JRuby versions 1.7 and 9.0 and Java 7 or 8 VM. mondrian-olap works only with JRuby and not with other Ruby implementations as it includes Mondrian OLAP Java libraries.

mondrian-olap supports MySQL, PostgreSQL, Oracle, LucidDB and Microsoft SQL Server databases as well as other databases that are supported by Mondrian OLAP engine (using jdbc_driver and jdbc_url connection parameters). When using MySQL, PostgreSQL or LucidDB databases then install jdbc-mysql, jdbc-postgres or jdbc-luciddb gem and require “jdbc/mysql”, “jdbc/postgres” or “jdbc/luciddb” to load the corresponding JDBC database driver. When using Oracle then include Oracle JDBC driver (ojdbc7.jar for Java 7) in CLASSPATH or copy to JRUBY_HOME/lib or require it in application manually. When using SQL Server you can choose between the jTDS or Microsoft JDBC drivers. If you use jTDS require “jdbc/jtds”. If you use the Microsoft JDBC driver include sqljdbc.jar or sqljdbc4.jar in CLASSPATH or copy to JRUBY_HOME/lib or require it in application manually.

INSTALL

Install gem with:

gem install mondrian-olap

or include in your project’s Gemfile:

gem "mondrian-olap"
  • Source code: http://github.com/rsim/mondrian-olap
  • Bug reports / Feature requests: http://github.com/rsim/mondrian-olap/issues
  • General discussions and questions at: http://groups.google.com/group/mondrian-olap
  • mondrian-olap demo Rails application: https://github.com/rsim/mondrian_demo

LICENSE

mondrian-olap is released under the terms of MIT license; see LICENSE.txt.

Mondrian OLAP Engine is released under the terms of the Eclipse Public License v1.0 (EPL); see LICENSE-Mondrian.html.