Presentation at Pentaho Community Meetup 2014 in Antwerp

1. mondrian-olap
JRuby library

2. Raimonds Simanovskis
@rsim
github.com/rsim

3. Mondrian!
is nice…

4. But I don’t like
Java and XML
so much…

5. And I like!
Ruby!

6. object-oriented
dynamic
programming language
simple from outside
powerful inside
Yukihiro
Matsumoto
or “Matz”

9. What is!
mondrian-olap!
JRuby gem?

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

11. Mondrian 3.x schema
schema = Mondrian::OLAP::Schema.define do definition
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: false
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
end
measure 'Unit Sales', column: 'unit_sales', aggregator: 'sum'
measure 'Store Sales', column: 'store_sales', aggregator: 'sum'
end
end

12. Mondrian connection
olap = Mondrian::OLAP::Connection.create(
driver: 'mysql',
host: 'localhost',
database: 'mondrian_test',
username: 'mondrian_user',
password: 'secret',
schema: schema
)

13. MDX queries
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
!
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")

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

15. Query builder
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

16. Cube and member
queries
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

17. User defined
MDX functions
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

18. UDF in JavaScript
schema = Mondrian::OLAP::Schema.define do
# ... cube definitions ...
user_defined_function 'Factorial' do
javascript <<-JS
function getParameterTypes() {
return new Array(
new mondrian.olap.type.NumericType());
}
function getReturnType(parameterTypes) {
return new mondrian.olap.type.NumericType();
}
function execute(evaluator, arguments) {
var n = arguments[0].evaluateScalar(evaluator);
return factorial(n);
}
function factorial(n) {
return n <= 1 ? 1 : n * factorial(n - 1);
}
JS
end
end

19. JavaScript is
OK … but
CoffeeScript
is better!

21. Sample CoffeeScript
# Assignment:
number = 42
opposite = true
# Conditions:
number = -42 if opposite
# Functions:
square = (x) -> x * x
# Arrays:
list = [1, 2, 3, 4, 5]
# Objects:
math =
root: Math.sqrt
square: square
cube: (x) -> x * square x
# Splats:
race = (winner, runners...) ->
print winner, runners
# Existence:
alert "I knew it!" if elvis?
# Array comprehensions:
cubes = (math.cube num for num in list)

22. UDF in CoffeeScript
schema = Mondrian::OLAP::Schema.define do
# ... cube definitions ...
user_defined_function 'Factorial' do
coffeescript <<-JS
parameters: ["Numeric"]
returns: "Numeric"
execute: (n) ->
if n <= 1 then 1 else n * @execute(n - 1)
JS
end
end

23. Cell, property, member
formatters in
Ruby and CoffeeScript
cell_formatter { ruby {|value| "%020d" % value} }
property_formatter do
ruby do |member, property_name, property_value|
property_value.upcase
end
end
member_formatter { ruby {|member| member.getName().upcase } }
!
cell_formatter do
coffeescript <<-JS
s = value.toString()
s = "0" + s while s.length < 20
s
JS
end
member_formatter { coffeescript "member.getName().toUpperCase()" }
property_formatter { coffeescript "propertyValue.toUpperCase()" }

24. Next idea:!
More
CoffeeScript

25. Next idea:
Mondrian schema
in CoffeeScript
@Schema ->
@Cube 'Sales', ->
@Table 'sales'
@Dimension 'Customers', foreignKey: 'customer_id', ->
@Hierarchy hasAll: true, allMemberName: 'All Customers', primaryKey: 'id', ->
@Table 'customers'
@Level 'Country', column: 'country', uniqueMembers: true
@Level 'State Province', column: 'state_province', uniqueMembers: true
@Level 'City', column: 'city', uniqueMembers: false
@Level 'Name', column: 'fullname', uniqueMembers: false
@Dimension 'Time', foreignKey: 'time_id', type: 'TimeDimension', ->
@Hierarchy hasAll: false, primaryKey: 'id', ->
@Table 'time'
@Level 'Year', column: 'the_year', type: 'Numeric', uniqueMembers: true,
levelType: 'TimeYears'
@Level 'Quarter', column: 'quarter', uniqueMembers: false,
levelType: 'TimeQuarters'
@Level 'Month', column: 'month_of_year', type: 'Numeric', uniqueMembers: false,
levelType: 'TimeMonths'
@Measure 'Unit Sales', column: 'unit_sales', aggregator: 'sum'
@Measure 'Store Sales', column: 'store_sales', aggregator: 'sum'

26. Questions?