Data-centric Metaprogramming - Scala Days 2016

Transcript

1. Data-centric Metaprogramming Vlad Ureche

2. Vlad Ureche @ @VladUreche @VladUreche vlad.ureche@gmail.com

3. Vlad Ureche Software Engineer at Cyberhaven.io scala-miniboxing.org Ex-Scala Team at

   EPFL

4. STOP Please ask if things are not clear!

5. Motivation Transformation Applications Challenges Conclusion Functions

6. Object Composition

7. Object Composition class Vector[T] { … }

8. Object Composition class Vector[T] { … } The Vector collection

   in the Scala library

9. Object Composition class Employee(...) ID NAME SALARY class Vector[T] {

   … } The Vector collection in the Scala library

10. Object Composition class Employee(...) ID NAME SALARY class Vector[T] {

    … } The Vector collection in the Scala library Corresponds to a table row

11. Object Composition class Employee(...) ID NAME SALARY class Vector[T] {

    … }

12. Object Composition class Employee(...) ID NAME SALARY class Vector[T] {

    … }

13. Object Composition class Employee(...) ID NAME SALARY Vector[Employee] ID NAME

    SALARY ID NAME SALARY class Vector[T] { … }

14. Object Composition class Employee(...) ID NAME SALARY Vector[Employee] ID NAME

    SALARY ID NAME SALARY class Vector[T] { … } Traversal requires dereferencing a pointer for each employee.

15. A Better Representation Vector[Employee] ID NAME SALARY ID NAME SALARY

16. A Better Representation NAME ... NAME EmployeeVector ID ID ...

    ... SALARY SALARY Vector[Employee] ID NAME SALARY ID NAME SALARY

17. A Better Representation • more efficient heap usage • faster

    iteration NAME ... NAME EmployeeVector ID ID ... ... SALARY SALARY Vector[Employee] ID NAME SALARY ID NAME SALARY

18. The Problem • Vector[T] is unaware of Employee

19. The Problem • Vector[T] is unaware of Employee – Which

    makes Vector[Employee] suboptimal

20. The Problem • Vector[T] is unaware of Employee – Which

    makes Vector[Employee] suboptimal • Not limited to Vector, other constructs also affected

21. The Problem • Vector[T] is unaware of Employee – Which

    makes Vector[Employee] suboptimal • Not limited to Vector, other constructs also affected – Generics (including all collections)

22. The Problem • Vector[T] is unaware of Employee – Which

    makes Vector[Employee] suboptimal • Not limited to Vector, other constructs also affected – Generics (including all collections) and Functions

23. The Problem • Vector[T] is unaware of Employee – Which

    makes Vector[Employee] suboptimal • Not limited to Vector, other constructs also affected – Generics (including all collections) and Functions • We know better representations

24. The Problem • Vector[T] is unaware of Employee – Which

    makes Vector[Employee] suboptimal • Not limited to Vector, other constructs also affected – Generics (including all collections) and Functions • We know better representations – Manual changes don't scale

25. The Problem • Vector[T] is unaware of Employee – Which

    makes Vector[Employee] suboptimal • Not limited to Vector, other constructs also affected – Generics (including all collections) and Functions • We know better representations – Manual changes don't scale – The compiler should do that

26. Current Optimizers

27. Current Optimizers What about the Scala.js optimizer?

28. Current Optimizers What about the Scala.js optimizer? What about the

    Dotty Linker?

29. Current Optimizers What about the Scala.js optimizer? What about the

    Dotty Linker? Scala Native?

30. Current Optimizers • They do a great job What about

    the Scala.js optimizer? What about the Dotty Linker? Scala Native?

31. Current Optimizers • They do a great job – But

    have to respect semantics What about the Scala.js optimizer? What about the Dotty Linker? Scala Native?

32. Current Optimizers • They do a great job – But

    have to respect semantics – Support every corner case What about the Scala.js optimizer? What about the Dotty Linker? Scala Native?

33. Current Optimizers • They do a great job – But

    have to respect semantics – Support every corner case – Have to be conservative :( What about the Scala.js optimizer? What about the Dotty Linker? Scala Native?

34. Current Optimizers • They do a great job – But

    have to respect semantics – Support every corner case – Have to be conservative :( • Programmers have control What about the Scala.js optimizer? What about the Dotty Linker? Scala Native?

35. Current Optimizers • They do a great job – But

    have to respect semantics – Support every corner case – Have to be conservative :( • Programmers have control – What/When/How is accessed What about the Scala.js optimizer? What about the Dotty Linker? Scala Native?

36. Current Optimizers • They do a great job – But

    have to respect semantics – Support every corner case – Have to be conservative :( • Programmers have control – What/When/How is accessed – Can break semantics (speculate) What about the Scala.js optimizer? What about the Dotty Linker? Scala Native?

37. Current Optimizers • They do a great job – But

    have to respect semantics – Support every corner case – Have to be conservative :( • Programmers have control – What/When/How is accessed – Can break semantics (speculate) What about the Scala.js optimizer? What about the Dotty Linker? Scala Native? Challenge: No means of telling the compiler what/when to speculate

38. Choice: Safe or Fast

39. Choice: Safe or Fast This is where my work comes

    in...

40. Data-Centric Metaprogramming • compiler plug-in that allows • Tuning data

    representation • Website: scala-ildl.org

41. Motivation Transformation Applications Challenges Conclusion Functions

42. Transformation Definition Application

43. Transformation Definition Application • can't be automated • based on

    experience • based on speculation • one-time effort

44. Transformation programmer Definition Application • can't be automated • based

    on experience • based on speculation • one-time effort

45. Transformation programmer Definition Application • can't be automated • based

    on experience • based on speculation • one-time effort • repetitive and complex • affects code readability • is verbose • is error-prone

46. Transformation programmer Definition Application • can't be automated • based

    on experience • based on speculation • one-time effort • repetitive and complex • affects code readability • is verbose • is error-prone compiler (automated)

47. Transformation programmer Definition Application • can't be automated • based

    on experience • based on speculation • one-time effort • repetitive and complex • affects code readability • is verbose • is error-prone compiler (automated)

48. Data-Centric Metaprogramming object VectorOfEmployeeOpt extends Transformation { type Target =

    Vector[Employee] type Result = EmployeeVector def toResult(t: Target): Result = ... def toTarget(t: Result): Target = ... def bypass_length: Int = ... def bypass_apply(i: Int): Employee = ... def bypass_update(i: Int, v: Employee) = ... def bypass_toString: String = ... ... }

49. object VectorOfEmployeeOpt extends Transformation { type Target = Vector[Employee] type

    Result = EmployeeVector def toResult(t: Target): Result = ... def toTarget(t: Result): Target = ... def bypass_length: Int = ... def bypass_apply(i: Int): Employee = ... def bypass_update(i: Int, v: Employee) = ... def bypass_toString: String = ... ... } Data-Centric Metaprogramming What to transform? What to transform to?

50. object VectorOfEmployeeOpt extends Transformation { type Target = Vector[Employee] type

    Result = EmployeeVector def toResult(t: Target): Result = ... def toTarget(t: Result): Target = ... def bypass_length: Int = ... def bypass_apply(i: Int): Employee = ... def bypass_update(i: Int, v: Employee) = ... def bypass_toString: String = ... ... } Data-Centric Metaprogramming How to transform?

51. Data-Centric Metaprogramming object VectorOfEmployeeOpt extends Transformation { type Target =

    Vector[Employee] type Result = EmployeeVector def toResult(t: Target): Result = ... def toTarget(t: Result): Target = ... def bypass_length: Int = ... def bypass_apply(i: Int): Employee = ... def bypass_update(i: Int, v: Employee) = ... def bypass_toString: String = ... ... } How to run methods on the updated representation?

52. Transformation programmer Definition Application • can't be automated • based

    on experience • based on speculation • one-time effort • repetitive and complex • affects code readability • is verbose • is error-prone compiler (automated)

53. Transformation programmer Definition Application • can't be automated • based

    on experience • based on speculation • one-time effort • repetitive and complex • affects code readability • is verbose • is error-prone compiler (automated)

54. http://infoscience.epfl.ch/record/207050?ln=en

55. Motivation Transformation Applications Challenges Conclusion Functions

56. Motivation Transformation Applications Challenges Conclusion Functions Open World Best Representation?

    Composition

57. Scenario class Employee(...) ID NAME SALARY class Vector[T] { …

    }

58. Scenario class Employee(...) ID NAME SALARY Vector[Employee] ID NAME SALARY

    ID NAME SALARY class Vector[T] { … }

59. Scenario class Employee(...) ID NAME SALARY Vector[Employee] ID NAME SALARY

    ID NAME SALARY class Vector[T] { … } NAME ... NAME EmployeeVector ID ID ... ... SALARY SALARY

60. Scenario class Employee(...) ID NAME SALARY Vector[Employee] ID NAME SALARY

    ID NAME SALARY class Vector[T] { … } NAME ... NAME EmployeeVector ID ID ... ... SALARY SALARY class NewEmployee(...) extends Employee(...) ID NAME SALARY DEPT

61. Scenario class Employee(...) ID NAME SALARY Vector[Employee] ID NAME SALARY

    ID NAME SALARY class Vector[T] { … } NAME ... NAME EmployeeVector ID ID ... ... SALARY SALARY class NewEmployee(...) extends Employee(...) ID NAME SALARY DEPT

62. Scenario class Employee(...) ID NAME SALARY Vector[Employee] ID NAME SALARY

    ID NAME SALARY class Vector[T] { … } NAME ... NAME EmployeeVector ID ID ... ... SALARY SALARY class NewEmployee(...) extends Employee(...) ID NAME SALARY DEPT Oooops...

63. Open World Assumption • Globally anything can happen

64. Open World Assumption • Globally anything can happen • Locally

    you have full control: – Make class Employee final or – Limit the transformation to code that uses Employee

65. Open World Assumption • Globally anything can happen • Locally

    you have full control: – Make class Employee final or – Limit the transformation to code that uses Employee How?

66. Open World Assumption • Globally anything can happen • Locally

    you have full control: – Make class Employee final or – Limit the transformation to code that uses Employee How? Using Scopes!

67. Scopes transform(VectorOfEmployeeOpt) { def indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] =

    for (employee ← employees) yield employee.copy( salary = (1 + by) * employee.salary )

68. Scopes transform(VectorOfEmployeeOpt) { def indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] =

    for (employee ← employees) yield employee.copy( salary = (1 + by) * employee.salary ) }

69. Scopes transform(VectorOfEmployeeOpt) { def indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] =

    for (employee ← employees) yield employee.copy( salary = (1 + by) * employee.salary ) } Now the method operates on the EmployeeVector representation.

70. Scopes • Can wrap statements, methods, even entire classes –

    Inlined immediately after the parser – Definitions are visible outside the "scope"

71. Scopes • Can wrap statements, methods, even entire classes –

    Inlined immediately after the parser – Definitions are visible outside the "scope" No, it's not a macro. It's a marker for the compiler plugin. (You can't do this with macros)

72. Scopes • Can wrap statements, methods, even entire classes –

    Inlined immediately after the parser – Definitions are visible outside the "scope"

73. Scopes • Can wrap statements, methods, even entire classes –

    Inlined immediately after the parser – Definitions are visible outside the "scope" • Mark locally closed parts of the code – Incoming/outgoing values go through conversions – You can reject unexpected values

74. Motivation Transformation Applications Challenges Conclusion Functions Open World Best Representation?

    Composition

75. Best Representation? Vector[Employee] ID NAME SALARY ID NAME SALARY

76. Best Representation? It depends. Vector[Employee] ID NAME SALARY ID NAME

    SALARY

77. Best ...? NAME ... NAME EmployeeVector ID ID ... ...

    SALARY SALARY It depends. Vector[Employee] ID NAME SALARY ID NAME SALARY

78. Best ...? Compact binary repr. <compact binary blob> NAME ...

    NAME EmployeeVector ID ID ... ... SALARY SALARY It depends. Vector[Employee] ID NAME SALARY ID NAME SALARY

79. Best ...? EmployeeJSON { id: 123, name: “John Doe” salary:

    100 } Compact binary repr. <compact binary blob> NAME ... NAME EmployeeVector ID ID ... ... SALARY SALARY It depends. Vector[Employee] ID NAME SALARY ID NAME SALARY

80. Scopes allow mixing data representations transform(VectorOfEmployeeOpt) { def indexSalary(employees: Vector[Employee],

    by: Float): Vector[Employee] = for (employee ← employees) yield employee.copy( salary = (1 + by) * employee.salary ) }

81. Scopes transform(VectorOfEmployeeOpt) { def indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] =

    for (employee ← employees) yield employee.copy( salary = (1 + by) * employee.salary ) } Operating on the EmployeeVector representation.

82. Scopes transform(VectorOfEmployeeCompact) { def indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] =

    for (employee ← employees) yield employee.copy( salary = (1 + by) * employee.salary ) } Operating on the compact binary representation.

83. Scopes transform(VectorOfEmployeeJSON) { def indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] =

    for (employee ← employees) yield employee.copy( salary = (1 + by) * employee.salary ) } Operating on the JSON-based representation.

84. Motivation Transformation Applications Challenges Conclusion Functions Open World Best Representation?

    Composition

85. Composition def index1Percent(employees: Vector[Employee]) = indexSalary(employees, 0.01)

86. Composition def index1Percent(employees: Vector[Employee]) = indexSalary(employees, 0.01) transform(VectorOfEmployeeJSON) { def

    indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] = ... }

87. Composition def index1Percent(employees: Vector[Employee]) = indexSalary(employees, 0.01) transform(VectorOfEmployeeJSON) { def

    indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] = ... }

88. Composition def index1Percent(employees: Vector[Employee]) = indexSalary(employees, 0.01) transform(VectorOfEmployeeJSON) { def

    indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] = ... } • Original code (using the default representation)

89. Composition def index1Percent(employees: Vector[Employee]) = indexSalary(employees, 0.01) transform(VectorOfEmployeeJSON) { def

    indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] = ... } • Original code (using the default representation) • Transformed code (using a different representation)

90. Composition def index1Percent(employees: Vector[Employee]) = indexSalary(employees, 0.01) transform(VectorOfEmployeeJSON) { def

    indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] = ... } • Original code (using the default representation) • Transformed code (using a different representation) • Calls between them

91. Composition def index1Percent(employees: Vector[Employee]) = indexSalary(employees, 0.01) transform(VectorOfEmployeeJSON) { def

    indexSalary(employees: Vector[Employee], by: Float): Vector[Employee] = ... } • Original code (using the default representation) • Transformed code (using a different representation) • Calls between them ???

92. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation

93. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation

94. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation Easy one. Do nothing

95. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation

96. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation

97. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation

98. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation Automatically introduce conversions between values in the two representations e.g. EmployeeVector Vector[Employee] or back →

99. Composition calling • Original code • Transformed code • Original

    code • Transformed code • Same transformation • Different transformation

100. Composition calling • Original code • Transformed code • Original

     code • Transformed code • Same transformation • Different transformation

101. Composition calling • Original code • Transformed code • Original

     code • Transformed code • Same transformation • Different transformation

102. Composition calling • Original code • Transformed code • Original

     code • Transformed code • Same transformation • Different transformation Hard one. Do not introduce any conversions. Even across separate compilation

103. Composition calling • Original code • Transformed code • Original

     code • Transformed code • Same transformation • Different transformation

104. Composition calling • Original code • Transformed code • Original

     code • Transformed code • Same transformation • Different transformation Hard one. Automatically introduce double conversions (and warn the programmer) e.g. EmployeeVector Vector[Employee] CompactEmpVector → →

105. Composition calling • Original code • Transformed code • Original

     code • Transformed code • Same transformation • Different transformation

106. Composition calling overriding • Original code • Transformed code •

     Original code • Transformed code • Same transformation • Different transformation

107. Scopes trait Printer[T] { def print(elements: Vector[T]): Unit }

108. Scopes trait Printer[T] { def print(elements: Vector[T]): Unit } class

     EmployeePrinter extends Printer[Employee] { def print(elements: Vector[Employee]) = ... }

109. trait Printer[T] { def print(elements: Vector[T]): Unit } class EmployeePrinter

     extends Printer[Employee] { def print(elements: Vector[Employee]) = ... } Scopes Method print in the class implements method print in the trait

110. Scopes trait Printer[T] { def print(elements: Vector[T]): Unit } class

     EmployeePrinter extends Printer[Employee] { def print(elements: Vector[Employee]) = ... }

111. Scopes trait Printer[T] { def print(elements: Vector[T]): Unit } transform(VectorOfEmployeeOpt)

     { class EmployeePrinter extends Printer[Employee] { def print(elements: Vector[Employee]) = ... } }

112. Scopes trait Printer[T] { def print(elements: Vector[T]): Unit } transform(VectorOfEmployeeOpt)

     { class EmployeePrinter extends Printer[Employee] { def print(elements: Vector[Employee]) = ... } } The signature of method print changes according to the transformation → it no longer implements the trait

113. Scopes trait Printer[T] { def print(elements: Vector[T]): Unit } transform(VectorOfEmployeeOpt)

     { class EmployeePrinter extends Printer[Employee] { def print(elements: Vector[Employee]) = ... } } The signature of method print changes according to the transformation → it no longer implements the trait Taken care by the compiler for you!

114. Motivation Transformation Applications Challenges Conclusion Functions Open World Best Representation?

     Composition

115. Column-oriented Storage NAME ... NAME EmployeeVector ID ID ... ...

     SALARY SALARY Vector[Employee] ID NAME SALARY ID NAME SALARY

116. Column-oriented Storage NAME ... NAME EmployeeVector ID ID ... ...

     SALARY SALARY Vector[Employee] ID NAME SALARY ID NAME SALARY iteration is 5x faster

117. Retrofitting value class status (3,5) 3 5 Header reference

118. Retrofitting value class status Tuples in Scala are specialized but

     are still objects (not value classes) = not as optimized as they could be (3,5) 3 5 Header reference

119. Retrofitting value class status 0l + 3 << 32 +

     5 (3,5) Tuples in Scala are specialized but are still objects (not value classes) = not as optimized as they could be (3,5) 3 5 Header reference

120. Retrofitting value class status 0l + 3 << 32 +

     5 (3,5) Tuples in Scala are specialized but are still objects (not value classes) = not as optimized as they could be (3,5) 3 5 Header reference 14x faster, lower heap requirements

121. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum

122. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4)

123. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8)

124. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8) 18

125. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8) 18

126. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8) 18 transform(ListDeforestation)

     { List(1,2,3).map(_ + 1).map(_ * 2).sum }

127. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8) 18 transform(ListDeforestation)

     { List(1,2,3).map(_ + 1).map(_ * 2).sum } accumulate function

128. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8) 18 transform(ListDeforestation)

     { List(1,2,3).map(_ + 1).map(_ * 2).sum } accumulate function accumulate function

129. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8) 18 transform(ListDeforestation)

     { List(1,2,3).map(_ + 1).map(_ * 2).sum } accumulate function accumulate function compute: 18

130. Deforestation List(1,2,3).map(_ + 1).map(_ * 2).sum List(2,3,4) List(4,6,8) 18 transform(ListDeforestation)

     { List(1,2,3).map(_ + 1).map(_ * 2).sum } accumulate function accumulate function compute: 18 6x faster

131. Motivation Transformation Applications Challenges Conclusion Functions Open World Best Representation?

     Composition

132. Research ahead* ! * This may not make it into

     a product. But you can play with it nevertheless.

133. Spark

134. Spark RDD (Reliable Distributed Dataset)

135. Spark RDD (Reliable Distributed Dataset) Key abstraction in Spark

136. Spark RDD (Reliable Distributed Dataset)

137. Spark RDD (Reliable Distributed Dataset)

138. Spark RDD (Reliable Distributed Dataset) Primary Data (e.g. CSV file)

139. Spark RDD (Reliable Distributed Dataset) Primary Data (e.g. CSV file)

     Primary Data (e.g. CSV file) Derived Data (e.g. primary.map(f)) Primary Data (e.g. CSV file)

140. Spark RDD (Reliable Distributed Dataset) Primary Data (e.g. CSV file)

     Primary Data (e.g. CSV file) Derived Data (e.g. primary.map(f)) Primary Data (e.g. CSV file) How does mapping work?

141. Mapping an RDD X Y user function f

142. Mapping an RDD serialized data encoded data X Y user

     function f decode

143. Mapping an RDD serialized data encoded data X Y encoded

     data user function f decode encode

144. Mapping an RDD serialized data encoded data X Y encoded

     data user function f decode encode Allocate object Allocate object

145. Mapping an RDD serialized data encoded data X Y encoded

     data user function f decode encode Allocate object Allocate object

146. Mapping an RDD serialized data encoded data X Y encoded

     data user function f decode encode

147. Mapping an RDD serialized data encoded data X Y encoded

     data user function f decode encode Modified user function (automatically derived by the compiler)

148. Mapping an RDD serialized data encoded data encoded data Modified

     user function (automatically derived by the compiler)

149. Mapping an RDD serialized data encoded data encoded data Modified

     user function (automatically derived by the compiler) Nowhere near as simple as it looks

150. Challenge: Transformation not possible • Example: Calling outside (untransformed) method

151. Challenge: Transformation not possible • Example: Calling outside (untransformed) method

     • Solution: Issue compiler warnings

152. Challenge: Transformation not possible • Example: Calling outside (untransformed) method

     • Solution: Issue compiler warnings – Explain why it's not possible: due to the method call

153. Challenge: Transformation not possible • Example: Calling outside (untransformed) method

     • Solution: Issue compiler warnings – Explain why it's not possible: due to the method call – Suggest how to fix it: enclose the method in a scope

154. Challenge: Transformation not possible • Example: Calling outside (untransformed) method

     • Solution: Issue compiler warnings – Explain why it's not possible: due to the method call – Suggest how to fix it: enclose the method in a scope • Reuse the machinery in miniboxing scala-miniboxing.org

155. Challenge: Internal API

156. Challenge: Internal API • Spark internals rely on Iterator[T] –

     Requires materializing values – Needs to be replaced throughout the code base – By rather complex buffers

157. Challenge: Internal API • Spark internals rely on Iterator[T] –

     Requires materializing values – Needs to be replaced throughout the code base – By rather complex buffers • Solution: Extensive refactoring/rewrite

158. Prototype

159. Prototype Hack

160. Prototype Hack • Modified version of Spark core – RDD

     data representation is configurable

161. Prototype Hack • Modified version of Spark core – RDD

     data representation is configurable • It's very limited: – Custom data repr. only in map, filter and flatMap – Otherwise we revert to costly objects – Large parts of the automation still need to be done

162. Prototype Hack sc.parallelize(/* 1 million */ records). map(x => ...).

     filter(x => ...). collect()

163. sc.parallelize(/* 1 million */ records). map(x => ...). filter(x =>

     ...). collect() Prototype Hack More details in my talk at Spark Summit EU 2015

164. Motivation Transformation Applications Challenges Conclusion Functions Open World Best Representation?

     Composition

165. Conclusion • Object-oriented composition → inefficient representation

166. Conclusion • Object-oriented composition → inefficient representation • Solution: data-centric

     metaprogramming

167. Conclusion • Object-oriented composition → inefficient representation • Solution: data-centric

     metaprogramming – Use the best representation for your data!

168. Conclusion • Object-oriented composition → inefficient representation • Solution: data-centric

     metaprogramming – Use the best representation for your data! – Is it possible? Yes.

169. Conclusion • Object-oriented composition → inefficient representation • Solution: data-centric

     metaprogramming – Use the best representation for your data! – Is it possible? Yes. – Is it easy? Not really.

170. Conclusion • Object-oriented composition → inefficient representation • Solution: data-centric

     metaprogramming – Use the best representation for your data! – Is it possible? Yes. – Is it easy? Not really. – Is it worth it? You tell me!

171. Thank you! Check out scala-ildl.org.

172. Thank you! Check out scala-ildl.org.

173. None

174. Deforestation and Language Semantics • Notice that we changed language

     semantics: – Before: collections were eager – After: collections are lazy – This can lead to effects reordering

175. Deforestation and Language Semantics • Such transformations are only acceptable

     with programmer consent – JIT compilers/staged DSLs can't change semantics – metaprogramming (macros) can, but it should be documented/opt-in

176. Code Generation • Also known as – Deep Embedding –

     Multi-Stage Programming • Awesome speedups, but restricted to small DSLs • SparkSQL uses code gen to improve performance – By 2-4x over Spark

177. Low-level Optimizers • Java JIT Compiler – Access to the

     low-level code – Can assume a (local) closed world – Can speculate based on profiles

178. Low-level Optimizers • Java JIT Compiler – Access to the

     low-level code – Can assume a (local) closed world – Can speculate based on profiles • Best optimizations break semantics – You can't do this in the JIT compiler! – Only the programmer can decide to break semantics

179. Scala Macros • Many optimizations can be done with macros

     – :) Lots of power – :( Lots of responsibility • Scala compiler invariants • Object-oriented model • Modularity

180. Scala Macros • Many optimizations can be done with macros

     – :) Lots of power – :( Lots of responsibility • Scala compiler invariants • Object-oriented model • Modularity • Can we restrict macros so they're safer? – Data-centric metaprogramming