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.

1. Multidimensional
Data Analysis
with JRuby
Raimonds Simanovskis
github.com/rsim
@rsim

2. Relational
data model

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

8. http://github.com/rsim/mondrian-olap

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