Data Centric Metaprocessing by Vlad Ulreche

1. DATA-CENTRIC
METAPROGRAMMING
Vlad Ureche

2. Vlad Ureche
PhD in the Scala Team @ EPFL. Soon to graduate ;)
● Working on program transformations focusing on data representation
● Author of miniboxing, which improves generics performance by up to 20x
● Contributed to the Scala compiler and to the scaladoc tool.
@
@VladUreche
@VladUreche
vlad.ureche@gmail.com
scala-miniboxing.org

3. Research ahead*
!
* This may not make it into a product.
But you can play with it nevertheless.

4. STOP
Please ask if things
are not clear!

5. Motivation
Transformation
Applications
Challenges
Conclusion
Spark

6. Motivation
Comparison graph from http://fr.slideshare.net/databricks/spark-summit-eu-2015-spark-dataframes-simple-and-fast-analysis-of-
structured-data and used with permission.

7. Motivation
Comparison graph from http://fr.slideshare.net/databricks/spark-summit-eu-2015-spark-dataframes-simple-and-fast-analysis-of-
structured-data and used with permission.
Performance gap between
RDDs and DataFrames

8. Motivation
RDD DataFrame

9. Motivation
RDD
●
strongly typed
●
slower
DataFrame

10. Motivation
RDD
●
strongly typed
●
slower
DataFrame
●
dynamically typed
●
faster

11. Motivation
RDD
●
strongly typed
●
slower
DataFrame
●
dynamically typed
●
faster

12. Motivation
RDD
●
strongly typed
●
slower
DataFrame
●
dynamically typed
●
faster
?
●
strongly typed
●
faster

13. Motivation
RDD
●
strongly typed
●
slower
DataFrame
●
dynamically typed
●
faster
Dataset
●
strongly typed
●
faster

14. Motivation
RDD
●
strongly typed
●
slower
DataFrame
●
dynamically typed
●
faster
Dataset
●
strongly typed
●
faster mid-way

15. Motivation
RDD
●
strongly typed
●
slower
DataFrame
●
dynamically typed
●
faster
Dataset
●
strongly typed
●
faster mid-way
Why just mid-way?
What can we do to speed them up?

16. Object Composition

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

18. Object Composition
class Vector[T] { … }
The Vector collection
in the Scala library

19. Object Composition
class Employee(...)
ID NAME SALARY
class Vector[T] { … }
The Vector collection
in the Scala library

20. Object Composition
class Employee(...)
ID NAME SALARY
class Vector[T] { … }
The Vector collection
in the Scala library
Corresponds to
a table row

21. Object Composition
class Employee(...)
ID NAME SALARY
class Vector[T] { … }

22. Object Composition
class Employee(...)
ID NAME SALARY
class Vector[T] { … }

23. Object Composition
class Employee(...)
ID NAME SALARY
Vector[Employee]
ID NAME SALARY
ID NAME SALARY
class Vector[T] { … }

24. 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.

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

26. A Better Representation
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY
Vector[Employee]
ID NAME SALARY
ID NAME SALARY

27. A Better Representation
●
more efficient heap usage
●
faster iteration
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY
Vector[Employee]
ID NAME SALARY
ID NAME SALARY

28. The Problem
●
Vector[T] is unaware of Employee

29. The Problem
●
Vector[T] is unaware of Employee
– Which makes Vector[Employee] suboptimal

30. The Problem
●
Vector[T] is unaware of Employee
– Which makes Vector[Employee] suboptimal
●
Not limited to Vector, other classes also affected

31. The Problem
●
Vector[T] is unaware of Employee
– Which makes Vector[Employee] suboptimal
●
Not limited to Vector, other classes also affected
– Spark pain point: Functions/closures

32. The Problem
●
Vector[T] is unaware of Employee
– Which makes Vector[Employee] suboptimal
●
Not limited to Vector, other classes also affected
– Spark pain point: Functions/closures
– We'd like a "structured" representation throughout

33. The Problem
●
Vector[T] is unaware of Employee
– Which makes Vector[Employee] suboptimal
●
Not limited to Vector, other classes also affected
– Spark pain point: Functions/closures
– We'd like a "structured" representation throughout
Challenge: No means of
communicating this
to the compiler

34. Choice: Safe or Fast

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

36. Data-Centric Metaprogramming
●
compiler plug-in that allows
●
Tuning data representation
●
Website: scala-ildl.org

37. Motivation
Transformation
Applications
Challenges
Conclusion
Spark

38. Transformation
Definition Application

39. Transformation
Definition Application
●
can't be automated
●
based on experience
●
based on speculation
●
one-time effort

40. Transformation
programmer
Definition Application
●
can't be automated
●
based on experience
●
based on speculation
●
one-time effort

41. 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

42. 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)

43. 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)

44. 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 = ...
...
}

45. 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 = ...
...
}
What to transform?
What to transform to?

46. 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
transform?

47. 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?

48. 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)

49. 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)

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

51. Motivation
Transformation
Applications
Challenges
Conclusion
Spark

52. Motivation
Transformation
Applications
Challenges
Conclusion
Spark
Open World
Best Representation?
Composition

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

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

55. Scenario
class Employee(...)
ID NAME SALARY
Vector[Employee]
ID NAME SALARY
ID NAME SALARY
class Vector[T] { … }
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY

56. 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

57. 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

58. 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...

59. Open World Assumption
●
Globally anything can happen

60. 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

61. 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?

62. 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!

63. Scopes
transform(VectorOfEmployeeOpt) {
def indexSalary(employees: Vector[Employee],
by: Float): Vector[Employee] =
for (employee ← employees)
yield employee.copy(
salary = (1 + by) * employee.salary
)
}

64. Scopes
transform(VectorOfEmployeeOpt) {
def indexSalary(employees: Vector[Employee],
by: Float): Vector[Employee] =
for (employee ← employees)
yield employee.copy(
salary = (1 + by) * employee.salary
)
}

65. 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.

66. Scopes
●
Can wrap statements, methods, even entire classes
– Inlined immediately after the parser
– Definitions are visible outside the "scope"

67. 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

68. Motivation
Transformation
Applications
Challenges
Conclusion
Spark
Open World
Best Representation?
Composition

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

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

71. Best ...?
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY
It depends.
Vector[Employee]
ID NAME SALARY
ID NAME SALARY

72. Best ...?
Tungsten repr.
<compressed binary blob>
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY
It depends.
Vector[Employee]
ID NAME SALARY
ID NAME SALARY

73. Best ...?
EmployeeJSON
{
id: 123,
name: “John Doe”
salary: 100
}
Tungsten repr.
<compressed binary blob>
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY
It depends.
Vector[Employee]
ID NAME SALARY
ID NAME SALARY

74. 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
)
}

75. 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.

76. 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.

77. 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.

78. Motivation
Transformation
Applications
Challenges
Conclusion
Spark
Open World
Best Representation?
Composition

79. Composition
●
Code can be
– Left untransformed (using the original representation)
– Transformed using different representations

80. Composition
●
Code can be
– Left untransformed (using the original representation)
– Transformed using different representations
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

81. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

82. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

83. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation
Easy one. Do nothing

84. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

85. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

86. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

87. 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→

88. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

89. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

90. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

91. 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

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
Hard one. Automatically introduce double
conversions (and warn the programmer)
e.g. EmployeeVector Vector[Employee] CompactEmpVector→ →

94. Composition
calling
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

95. Composition
calling
overriding
●
Original code
●
Transformed code
●
Original code
●
Transformed code
●
Same transformation
●
Different transformation

96. Scopes
trait Printer[T] {
def print(elements: Vector[T]): Unit
}
class EmployeePrinter extends Printer[Employee] {
def print(employee: Vector[Employee]) = ...
}

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

98. Scopes
trait Printer[T] {
def print(elements: Vector[T]): Unit
}
class EmployeePrinter extends Printer[Employee] {
def print(employee: Vector[Employee]) = ...
}

99. Scopes
trait Printer[T] {
def print(elements: Vector[T]): Unit
}
transform(VectorOfEmployeeOpt) {
class EmployeePrinter extends Printer[Employee] {
def print(employee: Vector[Employee]) = ...
}
}

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

101. Scopes
trait Printer[T] {
def print(elements: Vector[T]): Unit
}
transform(VectorOfEmployeeOpt) {
class EmployeePrinter extends Printer[Employee] {
def print(employee: 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!

102. Motivation
Transformation
Applications
Challenges
Conclusion
Spark
Open World
Best Representation?
Composition

103. Column-oriented Storage
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY
Vector[Employee]
ID NAME SALARY
ID NAME SALARY

104. Column-oriented Storage
NAME ...NAME
EmployeeVector
ID ID ...
...SALARY SALARY
Vector[Employee]
ID NAME SALARY
ID NAME SALARY
iteration is 5x faster

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

106. 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 5Header
reference

107. 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 5Header
reference

108. 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 5Header
reference
14x faster, lower
heap requirements

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

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

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

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

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

114. 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
}

115. 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

116. 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

117. 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

118. 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

119. Motivation
Transformation
Applications
Challenges
Conclusion
Spark
Open World
Best Representation?
Composition

120. Research ahead*
!
* This may not make it into a product.
But you can play with it nevertheless.

121. Spark
●
Optimizations
– DataFrames do deforestation
– DataFrames do predicate push-down
– DataFrames do code generation
●
Code is specialized for the data representation
●
Functions are specialized for the data representation

122. Spark
●
Optimizations
– RDDs do deforestation
– RDDs do predicate push-down
– RDDs do code generation
●
Code is specialized for the data representation
●
Functions are specialized for the data representation

123. Spark
●
Optimizations
– RDDs do deforestation
– RDDs do predicate push-down
– RDDs do code generation
●
Code is specialized for the data representation
●
Functions are specialized for the data representation
This is what
makes them slower

124. Spark
●
Optimizations
– Datasets do deforestation
– Datasets do predicate push-down
– Datasets do code generation
●
Code is specialized for the data representation
●
Functions are specialized for the data representation

125. User Functions
X Y
user
function
f

126. User Functions
serialized
data
encoded
data
X Y
user
function
f
decode

127. User Functions
serialized
data
encoded
data
X Y
encoded
data
user
function
f
decode encode

128. User Functions
serialized
data
encoded
data
X Y
encoded
data
user
function
f
decode encode
Allocate object Allocate object

129. User Functions
serialized
data
encoded
data
X Y
encoded
data
user
function
f
decode encode
Allocate object Allocate object

130. User Functions
serialized
data
encoded
data
X Y
encoded
data
user
function
f
decode encode

131. User Functions
serialized
data
encoded
data
X Y
encoded
data
user
function
f
decode encode
Modified user function
(automatically derived
by the compiler)

132. User Functions
serialized
data
encoded
data
encoded
data
Modified user function
(automatically derived
by the compiler)

133. User Functions
serialized
data
encoded
data
encoded
data
Modified user function
(automatically derived
by the compiler) Nowhere near as
simple as it looks

134. Challenge: Transformation not possible
●
Example: Calling outside (untransformed) method

135. Challenge: Transformation not possible
●
Example: Calling outside (untransformed) method
●
Solution: Issue compiler warnings

136. Challenge: Transformation not possible
●
Example: Calling outside (untransformed) method
●
Solution: Issue compiler warnings
– Explain why it's not possible: due to the method call

137. 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

138. 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

139. Challenge: Internal API changes

140. Challenge: Internal API changes
●
Spark internals rely on Iterator[T]
– Requires materializing values
– Needs to be replaced throughout the code base
– By rather complex buffers

141. Challenge: Internal API changes
●
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

142. Challenge: Automation

143. Challenge: Automation
●
Existing code should run out of the box

144. Challenge: Automation
●
Existing code should run out of the box
●
Solution:
– Adapt data-centric metaprogramming to Spark
– Trade generality for simplicity
– Do the right thing for most of the cases

145. Challenge: Automation
●
Existing code should run out of the box
●
Solution:
– Adapt data-centric metaprogramming to Spark
– Trade generality for simplicity
– Do the right thing for most of the cases
Where are we now?

146. Prototype

147. Prototype Hack

148. Prototype Hack
●
Modified version of Spark core
– RDD data representation is configurable

149. 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

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

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

152. Prototype Hack
sc.parallelize(/* 1 million */ records).
map(x => ...).
filter(x => ...).
collect() Not yet 2x faster,
but 1.45x faster

153. Motivation
Transformation
Applications
Challenges
Conclusion
Spark
Open World
Best Representation?
Composition

154. Conclusion
●
Object-oriented composition → inefficient representation

155. Conclusion
●
Object-oriented composition → inefficient representation
●
Solution: data-centric metaprogramming

156. Conclusion
●
Object-oriented composition → inefficient representation
●
Solution: data-centric metaprogramming
– Opaque data → Structured data

157. Conclusion
●
Object-oriented composition → inefficient representation
●
Solution: data-centric metaprogramming
– Opaque data → Structured data
– Is it possible? Yes.

158. Conclusion
●
Object-oriented composition → inefficient representation
●
Solution: data-centric metaprogramming
– Opaque data → Structured data
– Is it possible? Yes.
– Is it easy? Not really.

159. Conclusion
●
Object-oriented composition → inefficient representation
●
Solution: data-centric metaprogramming
– Opaque data → Structured data
– Is it possible? Yes.
– Is it easy? Not really.
– Is it worth it? You tell me!

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

161. Deforestation and Language Semantics
●
Notice that we changed language semantics:
– Before: collections were eager
– After: collections are lazy
– This can lead to effects reordering

162. 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

163. 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

164. Low-level Optimizers
●
Java JIT Compiler
– Access to the low-level code
– Can assume a (local) closed world
– Can speculate based on profiles

165. 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

166. Scala Macros
●
Many optimizations can be done with macros
– :) Lots of power
– :( Lots of responsibility
●
Scala compiler invariants
●
Object-oriented model
●
Modularity

167. 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