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LET’S TALK ABOUT ruby

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Ian Bishop @ianbishop

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Ruby is

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Ruby is simple in appearance,

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Ruby is simple in appearance, but is very complex inside,

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Ruby is simple in appearance, but is very complex inside, just like our human body

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Ruby is simple in appearance, but is very complex inside, just like our human body - Yukihiro “matz” Matsumoto

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philosophy

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principle of least surprise

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principle of least surprise array.length string.length() collection.size()

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principle of least surprise array.length string.length() collection.size() array.length string.length collection.length

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features

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everything is an object 5.times do print “Everyone loves ruby!” end

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everything is an object 5.times

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everything is a message /abc/ === “abc” => true “abc” === /abc/ => false

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everything is a message /abc/ === “abc” => true

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everything is a message /abc/ /abc/

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everything is a message /abc/ /abc/.send

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everything is a message /abc/ === /abc/.send(:===

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everything is a message /abc/ === “abc” /abc/.send(:===, “abc”)

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everything is a message /abc/ === “abc” => true /abc/.send(:===, “abc”) => true

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everything is a message /abc/ === “abc” => true “abc” === /abc/ => false

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everything is a message case “HELLO” when /^[a-z]*$/ “lowercase” when /^[A-Z]*$/ “uppercase” end => “uppercase”

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everything is a message case “HELLO” when “hello” “lowercase” when “HELLO” “uppercase” end => “uppercase”

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everything is a message /abc/ === “abc” => true “abc” === /abc/ => false

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dynamic runtime a = [1,2,3] a.first => 1 a.second NoMethodError: Undefined method ‘second’ for [1, 2, 3]:Array

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dynamic runtime class Array def second if self.length > 1 return self[1] end nil end end

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dynamic runtime a = [1,2,3] a.first => 1 a.second

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dynamic runtime a = [1,2,3] a.first => 1 a.second => 2

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No content

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DEALING WITH data

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using collections a = [1,2,3]

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using collections a = [1,2,3,“meow”]

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using collections a.each do |x| puts x end 1 2 3 meow

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using collections a.each

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using collections a.each do |x| end

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using collections a.each do |x| puts x end

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using collections a.each do |x| puts x end 1 2 3 meow

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.each Iterate over elements in a collection

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.map Iterate over elements in a collection, returning a new collection of elements of the same size

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using map a = [1,2,3]

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using map a = [1,2,3] a.map

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using map a = [1,2,3] a.map do |x| end

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using map a = [1,2,3] a.map do |x| 2 * x end

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using map a = [1,2,3] a.map do |x| return 2 * x end

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using map a = [1,2,3] a.map do |x| 2 * x end

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using map a = [1,2,3] a.map do |x| 2 * x end => [2,4,6]

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.map Iterate over elements in a collection, returning a new collection of elements of the same size

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.select Iterate over elements in a collection, returning elements which match a specified criteria

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using select a = [1,2,3]

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using select a = [1,2,3] a.select

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using select a = [1,2,3] a.select do |x| end

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using select a = [1,2,3] a.select do |x| x.odd? end

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using select a = [1,2,3] a.select do |x| x.odd? end => [1,3]

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.select Iterate over elements in a collection, returning elements which match a specified criteria

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.reduce Combines all elements in a collection using a binary operation, returning an accumulator value.

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using reduce a = [1,2,3]

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using reduce a = (1..100)

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using reduce a = (1..100) a.reduce

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using reduce a = (1..100) a.reduce do |sum, x| end

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using reduce a = (1..100) a.reduce do |sum, x| sum + x end

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using reduce a = (1..100) a.reduce do |sum, x| sum + x end => 5050

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.reduce Combines all elements in a collection using a binary operation, returning an accumulator value.

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.reduce Combines all elements in a collection using a binary operation, returning an accumulator value.

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using reduce (again) a = (1..100) a.reduce

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using reduce (again) a = (1..100) a.reduce(:*)

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using reduce (again) a = (1..100) a.reduce(:*) =>9332621544394415268169923885626670049 071596826438162146859296389521759999322 991560894146397615651828625369792082722 37582511852109168640000000000000…

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SOLVING HARDER PROBLEMS

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generating poker hands

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building a deck of cards suits = %w(S C H D)

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building a deck of cards suits = %w(S C H D) => [“S”, “C”, “H”, “D”]

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building a deck of cards suits = %w(S C H D) => [“S”, “C”, “H”, “D”] “S C H D”.split /\s+/ => [“S”, “C”, “H”, “D”]

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building a deck of cards suits = %w(S C H D) faces =

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A)

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[1, 2, 3] [“a”, “b”, “c”]

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[1, 2, 3] x [“a”, “b”, “c”]

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[1, “a”], [1, “b”], [1, “c”], [2, “a”], [2, “b”], [2, “c”], [3, “a”], [3, “b”], [3, “c”] cross product

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces)

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces) => [[“S”, 2], [“S”, 3], …, [“S”, “A”], [“C”, 1], …, [“C”, “A”], …]

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces) => [“S2”, “S3”, …, “SA”, “C1”, …, “C2”, …]

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join(sep=$,) -> str Returns a string created by converting each element of the array to a string, seperated by sep. [ “a”, “b”, “c”].join => “abc” [ “a”, “b”, “c”].join(“-”) => “a-b-c”

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces) deck = deck.map do |pair| pair.join end

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces) deck.map! do |pair| pair.join end

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building a deck of cards .map!

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces) deck.map! do |pair| pair.join end

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces) deck.map! { |pair| pair.join }

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building a deck of cards suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces).map(&:join)

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generating poker hands suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces).map(&:join)

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sample(n) -> new_ary Choose n random elements from the array. The elements are chosen by using random and unique indices in order to ensure that an element doesn’t repeat itself unless the array already contained duplicate elements.

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generating poker hands suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces).map(&:join) deck.sample(5)

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generating poker hands suits = %w(S C H D) faces = (2..10).to_a + %w(J Q K A) deck = suits.product(faces).map(&:join) deck.sample(5) => [“C2”, “D5”, “S7”, “D8”, “C8”]

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LET’S TALK ABOUT MAP REDUCE

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LET’S TALK ABOUT MAP REDUCE ©

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Count of URL Access Frequency The map function processes logs of web page requests and outputs . The reduce function adds together all values for the same URL and emits a pair. from Introduction to Parallel Programming and MapReduce (Google)

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counting url access frequency log

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counting url access frequency log => [“example.com”, “google.com”, “userevents.com”, “unb.ca”, “frederictonug.net”, ..]

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Count of URL Access Frequency The map function processes logs of web page requests and outputs . The reduce function adds together all values for the same URL and emits a pair. from Introduction to Parallel Programming and MapReduce (Google)

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generate count pairs count_pairs = log.map do |url| [url, 1] end

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Count of URL Access Frequency The map function processes logs of web page requests and outputs . The reduce function adds together all values for the same URL and emits a pair. from Introduction to Parallel Programming and MapReduce (Google)

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BUT FIRST

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Collection of key-value pairs. Similar to an array, except indexing is done via unique keys.

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brief introduction to hashes my_hash = { :abc => 5, “def” => 9 }

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brief introduction to hashes my_hash = { :abc => 5, “def” => 9 } my_hash[:abc]

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brief introduction to hashes my_hash = { :abc => 5, “def” => 9 } my_hash[:abc] => 5

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brief introduction to hashes my_hash = { :abc => 5, “def” => 9 } my_hash[:abc] => 5 my_hash[“def”] = 14

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brief introduction to hashes my_hash = { :abc => 5, “def” => 9 } my_hash[:abc] => 5 my_hash[“def”] = 14 => { :abc => 5, “def” => 14 }

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Count of URL Access Frequency The map function processes logs of web page requests and outputs . The reduce function adds together all values for the same URL and emits a pair. from Introduction to Parallel Programming and MapReduce (Google)

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combining count pairs count_pairs.reduce

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combining count pairs count_pairs.reduce({})

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combining count pairs count_pairs.reduce({}) do |hash, pair| end

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combining count pairs count_pairs.reduce({}) do |hash, pair| url, count = pair end

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combining count pairs count_pairs.reduce({}) do |hash, pair| url, count = pair if hash.has_key? url hash[url] += count end

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combining count pairs count_pairs.reduce({}) do |hash, pair| url, count = pair if hash.has_key? url hash[url] += count else hash[url] = count end end

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combining count pairs count_pairs.reduce({}) do |hash, pair| url, count = pair if hash.has_key? url hash[url] += count else hash[url] = count end hash end

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counting url access frequency log = [“example.com”, “google.com”, “example.com”, “unb.ca”]

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counting url access frequency log = [“example.com”, “google.com”, “example.com”, “unb.ca”] => { “example.com” => 2, “google.com” => 1, “unb.ca” => 1 }