Multidimensional Data Analysis with JRuby
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This document discusses multidimensional data analysis using JRuby and OLAP technologies. It provides examples of SQL queries for relational data that become complex for analytical queries. It then introduces multidimensional data modeling and cubes with dimensions, hierarchies and measures. Examples are given of MDX queries and equivalent Ruby queries against OLAP cubes for analytical reporting. Finally, it discusses defining an OLAP schema to map cubes to database tables.
![MDX query language
Get total units sold and sales amount
in USA, California in Q1, 2011
by main product groups
SELECT {[Measures].[Unit Sales], [Measures].[Store Sales]} ON COLUMNS,
[Product].children ON ROWS
FROM [Sales]
WHERE ( [Time].[2011].[Q1], [Customers].[USA].[CA] )](https://image.slidesharecdn.com/multidimensionaldataanalysiswithjruby-110518174641-phpapp02/85/Multidimensional-Data-Analysis-with-JRuby-9-320.jpg)
![Or in Ruby like this
Get total units sold and sales amount
in USA, California in Q1, 2011
by main product groups
olap.from('Sales').
columns('[Measures].[Unit Sales]',
'[Measures].[Store Sales]').
rows('[Product].children').
where('[Time].[2011].[Q1]', '[Customers].[USA].[CA]').
execute](https://image.slidesharecdn.com/multidimensionaldataanalysiswithjruby-110518174641-phpapp02/85/Multidimensional-Data-Analysis-with-JRuby-10-320.jpg)
![Also more complex
queries
Get sales amount and profit %
of top 50 products sold in USA and Canada
during Q1, 2011
olap.from('Sales').
with_member('[Measures].[ProfitPct]').
as('(Measures.[Store Sales] - Measures.[Store Cost]) / Measures.[Store Sales]',
:format_string => 'Percent').
columns('[Measures].[Store Sales]', '[Measures].[ProfitPct]').
rows('[Product].children').crossjoin('[Customers].[Canada]', '[Customers].[USA]').
top_count(50, '[Measures].[Store Sales]')
where('[Time].[2011].[Q1]').
execute](https://image.slidesharecdn.com/multidimensionaldataanalysiswithjruby-110518174641-phpapp02/85/Multidimensional-Data-Analysis-with-JRuby-11-320.jpg)
Multidimensional Data Analysis with JRuby
- 1. Multidimensional Data Analysis with JRuby Raimonds Simanovskis github.com/rsim @rsim
- 3. SQL is good for detailed data queries Get all sales transactions in USA, California SELECT customers.fullname, products.product_name, sales.sales_date, sales.unit_sales, sales.store_sales FROM sales LEFT JOIN products ON sales.product_id = products.id LEFT JOIN customers ON sales.customer_id = customers.id WHERE customers.country = 'USA' AND customers.state_province = 'CA'
- 4. SQL becomes complex for analytical queries Get total sales in USA, California in Q1, 2011 by main product groups SELECT product_class.product_family, SUM(sales.unit_sales) unit_sales_sum, SUM(sales.store_sales) store_sales_sum FROM sales LEFT JOIN product ON sales.product_id = product.product_id LEFT JOIN product_class ON product.product_class_id = product_class.product_class_id LEFT JOIN time_by_day ON sales.time_id = time_by_day.time_id LEFT JOIN customer ON sales.customer_id = customer.customer_id WHERE time_by_day.the_year = 2011 AND time_by_day.quarter = 'Q1' AND customer.country = 'USA' AND customer.state_province = 'CA' GROUP BY product_class.product_family
- 5. Maybe write distributed map reduce function?
- 6. Multidimensional Data Model Multidimensional cubes Dimensions Hierarchies and levels Measures
- 7. OLAP technologies On-Line Analytical Processing
- 9. MDX query language Get total units sold and sales amount in USA, California in Q1, 2011 by main product groups SELECT {[Measures].[Unit Sales], [Measures].[Store Sales]} ON COLUMNS, [Product].children ON ROWS FROM [Sales] WHERE ( [Time].[2011].[Q1], [Customers].[USA].[CA] )
- 10. Or in Ruby like this Get total units sold and sales amount in USA, California in Q1, 2011 by main product groups olap.from('Sales'). columns('[Measures].[Unit Sales]', '[Measures].[Store Sales]'). rows('[Product].children'). where('[Time].[2011].[Q1]', '[Customers].[USA].[CA]'). execute
- 11. Also more complex queries Get sales amount and profit % of top 50 products sold in USA and Canada during Q1, 2011 olap.from('Sales'). with_member('[Measures].[ProfitPct]'). as('(Measures.[Store Sales] - Measures.[Store Cost]) / Measures.[Store Sales]', :format_string => 'Percent'). columns('[Measures].[Store Sales]', '[Measures].[ProfitPct]'). rows('[Product].children').crossjoin('[Customers].[Canada]', '[Customers].[USA]'). top_count(50, '[Measures].[Store Sales]') where('[Time].[2011].[Q1]'). execute
- 12. OLAP schema (mapping cube to tables) schema = Mondrian::OLAP::Schema.define do cube 'Sales' do table 'sales' dimension 'Gender', :foreign_key => 'customer_id' do hierarchy :has_all => true, :primary_key => 'customer_id' do table 'customer' level 'Gender', :column => 'gender', :unique_members => true end end dimension 'Time', :foreign_key => 'time_id' do hierarchy :has_all => false, :primary_key => 'time_id' do table 'time_by_day' level 'Year', :column => 'the_year', :type => 'Numeric', :unique_members => true level 'Quarter', :column => 'quarter', :unique_members => false level 'Month',:column => 'month_of_year',:type => 'Numeric',:unique_members => false end end measure 'Unit Sales', :column => 'unit_sales', :aggregator => 'sum' measure 'Store Sales', :column => 'store_sales', :aggregator => 'sum' end end
- 13. mondrian-olap gem eazybi.com