Data-centric Metaprogramming - Scala Days 2016

Presentation at Scala Days 2016

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.
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