Distributed, Eventually Consistent Computations

Transcript

1. Distributed Eventually Consistent Computations Christopher Meiklejohn // @cmeik Strange Loop

   2015, September 24 - 26th, 2015 1

2. RA RB

3. RA RB 1 set(1)

4. RA RB 1 set(1) 3 2 set(2) set(3)

5. RA RB 1 set(1) 3 2 set(2) set(3) ? ?

6. Synchronization • To enforce an order
 Makes programming easier 6

7. Synchronization • To enforce an order
 Makes programming easier •

   Eliminate accidental nondeterminism
 Prevent race conditions 6

8. Synchronization • To enforce an order
 Makes programming easier •

   Eliminate accidental nondeterminism
 Prevent race conditions • Techniques
 Locks, mutexes, semaphores, monitors, etc. 6

9. Difficult Cases • “Internet of Things”
 Low power, limited memory

   and connectivity 7

10. Difficult Cases • “Internet of Things”
 Low power, limited memory

    and connectivity • Mobile Gaming
 Offline operation with replicated, shared state 7

11. Zero Synchronization • We want to have our cake and

    eat it too. 8

12. Zero Synchronization • We want to have our cake and

    eat it too. • “Zero synchronization” as a starting point
 Add synchronization only where it is necessary 8

13. Zero Synchronization • We want to have our cake and

    eat it too. • “Zero synchronization” as a starting point
 Add synchronization only where it is necessary • Explore the limits
 Can we design a model of computation where it’s easy to write coordination-free computations that are free from concurrency anomalies? 8

14. 9 Strong Weak Strong (Data Center) Weak (Open Internet) Sharing

    (Application Property) Coupling (Infrastructure Property)

15. 9 Strong Weak Strong (Data Center) Weak (Open Internet) Sharing

    (Application Property) Coupling (Infrastructure Property) MapReduce SETI@Home

16. 9 Strong Weak Strong (Data Center) Weak (Open Internet) Sharing

    (Application Property) Coupling (Infrastructure Property) Twitter MapReduce SETI@Home

17. 9 Strong Weak Strong (Data Center) Weak (Open Internet) Sharing

    (Application Property) Coupling (Infrastructure Property) Twitter MapReduce SETI@Home Synchronization-Free Computations

18. The Fundamentals of Distributed Computation 10

19. Distributed Computation • Distributed to concurrent programming
 Consistency and now

    partial failure 11

20. Distributed Computation • Distributed to concurrent programming
 Consistency and now

    partial failure • Enforcing the “single system” illusion 11

21. Distributed Computation • Distributed to concurrent programming
 Consistency and now

    partial failure • Enforcing the “single system” illusion • Consistency model as a contract 11

22. Distributed Computation • Distributed to concurrent programming
 Consistency and now

    partial failure • Enforcing the “single system” illusion • Consistency model as a contract • Enforced through synchronization 11

23. Distributed Computation • Distributed to concurrent programming
 Consistency and now

    partial failure • Enforcing the “single system” illusion • Consistency model as a contract • Enforced through synchronization • Consistency model
 We can think of a consistency model as analogous to a programming paradigm 11

24. Why Is Synchronization Undesirable? 12

25. The Avatars of Time • The problem of time
 Handling

    physical time in applications is difficult 13

26. The Avatars of Time • The problem of time
 Handling

    physical time in applications is difficult • Time has three major avatars in computing 13

27. The Avatars of Time • The problem of time
 Handling

    physical time in applications is difficult • Time has three major avatars in computing • Mutable state in sequential systems 13

28. The Avatars of Time • The problem of time
 Handling

    physical time in applications is difficult • Time has three major avatars in computing • Mutable state in sequential systems • Nondeterminism in concurrent systems 13

29. The Avatars of Time • The problem of time
 Handling

    physical time in applications is difficult • Time has three major avatars in computing • Mutable state in sequential systems • Nondeterminism in concurrent systems ★ Network latency in distributed systems 13

30. The Avatars of Time • The problem of time
 Handling

    physical time in applications is difficult • Time has three major avatars in computing • Mutable state in sequential systems • Nondeterminism in concurrent systems ★ Network latency in distributed systems • Unavoidable
 Time is how users interact with programs 13

31. Parable of the Car • “Car driving on a highway”

    14

32. Parable of the Car • “Car driving on a highway”

    • Friction needed for the car to grip the road 14

33. Parable of the Car • “Car driving on a highway”

    • Friction needed for the car to grip the road • Motors want as little friction as possible 14

34. Parable of the Car • “Car driving on a highway”

    • Friction needed for the car to grip the road • Motors want as little friction as possible • Time is like friction
 We can not completely eliminate friction, but aim to reduce it as much as possible 14

35. Physical Time (real world) Parable of the Car 15 •

    Time only at the interface
 The interface is a small part of the system, and this is where we introduction friction

36. Physical Time (real world) Parable of the Car 15 •

    Time only at the interface
 The interface is a small part of the system, and this is where we introduction friction • Physical time-free execution
 Internally, avoid synchronization as much as possible and virtualize notion of time

37. No Time In The Box? 16

38. No Time In The Box? 16 You program the box.

39. Weak Synchronization • Can we achieve anything without synchronization?
 Not

    really. 17

40. Weak Synchronization • Can we achieve anything without synchronization?
 Not

    really. • Strong Eventual Consistency (SEC)
 “Replicas that deliver the same updates have equivalent state” 17

41. Weak Synchronization • Can we achieve anything without synchronization?
 Not

    really. • Strong Eventual Consistency (SEC)
 “Replicas that deliver the same updates have equivalent state” • Primary requirement
 Eventual replica-to-replica communication 17

42. Weak Synchronization • Can we achieve anything without synchronization?
 Not

    really. • Strong Eventual Consistency (SEC)
 “Replicas that deliver the same updates have equivalent state” • Primary requirement
 Eventual replica-to-replica communication • Order insensitive! (Commutativity) 17

43. Weak Synchronization • Can we achieve anything without synchronization?
 Not

    really. • Strong Eventual Consistency (SEC)
 “Replicas that deliver the same updates have equivalent state” • Primary requirement
 Eventual replica-to-replica communication • Order insensitive! (Commutativity) • Duplicate insensitive! (Idempotent) 17

44. RA RB

45. RA RB 1 set(1)

46. RA RB 1 set(1) 3 2 set(2) set(3)

47. RA RB 1 3 2 3 3 set(1) set(2) set(3)

    max(2,3) max(2,3)

48. How can we succeed with Strong Eventual Consistency? 22

49. Programming SEC 1. Eliminate accidental nondeterminism
 (ex. deterministic, modeling non-monotonic

    operations monotonically) 23

50. Programming SEC 1. Eliminate accidental nondeterminism
 (ex. deterministic, modeling non-monotonic

    operations monotonically) 2. Retain the properties of functional programming
 (ex. confluence, referential transparency over composition) 23

51. Programming SEC 1. Eliminate accidental nondeterminism
 (ex. deterministic, modeling non-monotonic

    operations monotonically) 2. Retain the properties of functional programming
 (ex. confluence, referential transparency over composition) 3. Distributed, and fault-tolerant runtime
 (ex. replication) 23

52. Programming SEC 1. Eliminate accidental nondeterminism
 (ex. deterministic, modeling non-monotonic

    operations monotonically) 2. Retain the properties of functional programming
 (ex. confluence, referential transparency over composition) 3. Distributed, and fault-tolerant runtime
 (ex. replication) 24

53. Convergent Objects
 Conflict-Free 
 Replicated Data Types 25 SSS 2011

54. Conflict-Free 
 Replicated Data Types • Many types exist with

    different properties
 Sets, counters, registers, flags, maps, graphs 26

55. Conflict-Free 
 Replicated Data Types • Many types exist with

    different properties
 Sets, counters, registers, flags, maps, graphs • Strong Eventual Consistency
 Instances satisfy SEC property per-object 26

56. RA RB RC

57. RA RB RC {1} (1, {a}, {}) add(1)

58. RA RB RC {1} (1, {a}, {}) add(1) {1} (1,

    {b}, {}) add(1)

59. RA RB RC {1} (1, {a}, {}) add(1) {1} (1,

    {b}, {}) add(1) {} (1, {b}, {b}) remove(1)

60. RA RB RC {1} (1, {a}, {}) add(1) {1} (1,

    {b}, {}) add(1) {} (1, {b}, {b}) remove(1) {1} {1} {1} (1, {a, b}, {b}) (1, {a, b}, {b}) (1, {a, b}, {b})

61. Programming SEC 1. Eliminate accidental nondeterminism
 (ex. deterministic, modeling non-monotonic

    operations monotonically) 2. Retain the properties of functional programming
 (ex. confluence, referential transparency over composition) 3. Distributed, and fault-tolerant runtime
 (ex. replication) 32

62. Convergent Programs Lattice Processing 33 PPDP 2015

63. Lattice Processing (Lasp) • Distributed, deterministic dataflow
 Distributed, deterministic dataflow

    programming model for “eventually consistent” computations 34

64. Lattice Processing (Lasp) • Distributed, deterministic dataflow
 Distributed, deterministic dataflow

    programming model for “eventually consistent” computations • Convergent data structures
 Primary data abstraction is the CRDT 34

65. Lattice Processing (Lasp) • Distributed, deterministic dataflow
 Distributed, deterministic dataflow

    programming model for “eventually consistent” computations • Convergent data structures
 Primary data abstraction is the CRDT • Enables composition
 Provides functional composition of CRDTs that preserves the SEC property 34

66. 35 %% Create initial set. S1 = declare(set), %% Add

    elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

67. 36 %% Create initial set. S1 = declare(set), %% Add

    elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

68. 37 %% Create initial set. S1 = declare(set), %% Add

    elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

69. 38 %% Create initial set. S1 = declare(set), %% Add

    elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

70. 39 %% Create initial set. S1 = declare(set), %% Add

    elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

71. Lattice Processing (Lasp) • Functional and set-theoretic operations on sets


    Product, intersection, union, filter, map, fold 40

72. Lattice Processing (Lasp) • Functional and set-theoretic operations on sets


    Product, intersection, union, filter, map, fold • Metadata computation
 Performs transformation on the internal metadata of CRDTs allowing creation of “composed” CRDTs 40

73. Lasp Processes • Replicas as monotonic streams
 Each replica of

    a CRDT produces a monotonic stream of states 41

74. Lasp Processes • Replicas as monotonic streams
 Each replica of

    a CRDT produces a monotonic stream of states • Monotonic processes
 Read from one or more input replica streams and produce a single output replica stream 41

75. Lasp Processes • Replicas as monotonic streams
 Each replica of

    a CRDT produces a monotonic stream of states • Monotonic processes
 Read from one or more input replica streams and produce a single output replica stream • Inflationary reads
 Read operation ensures that we only read inflationary updates to replicas 41

76. Lattice Processing Monotonic Streams 42

77. RA {} C1 {} C2 {} 43

78. RA {} (1, {a}, {}) C1 (1, {a}, {}) {}

    C2 {} 44

79. RA {} (1, {a}, {}) (1, {a, b}, {}) C1

    (1, {a}, {}) {} C2 (1, {b}, {}) {} 45

80. RA {} (1, {a}, {}) (1, {a, b}, {}) (1,

    {a, b}, {a}) C1 (1, {a}, {}) {} (1, {a}, {a}) C2 (1, {b}, {}) {} 46

81. RA {} (1, {a}, {}) (1, {a, b}, {}) (1,

    {a, b}, {a}) C1 (1, {a}, {}) {} (1, {a}, {a}) C2 (1, {b}, {}) {} (1, {a, b}, {a}) (1, {a, b}, {a}) (1, {a, b}, {a}) 47

82. RA {} (1, {a}, {}) (1, {a, b}, {}) (1,

    {a, b}, {a}) C1 (1, {a}, {}) {} (1, {a}, {a}) C2 (1, {b}, {}) {} (1, {a, b}, {a}) (1, {a, b}, {a}) (1, {a, b}, {a}) 48

83. Lattice Processing Monotonic Processes 49

84. RA {} P1 F(RA) {} 50

85. RA {} P1 F(RA) {} strict_read({}) 51

86. RA {} P1 F(RA) {} strict_read({}) (1, {a}, {}) 52

87. RA {} P1 F(RA) {} strict_read({}) (1, {a}, {}) F((1,

    {a}, {})) 53

88. RA {} P1 F(RA) {} strict_read({}) (1, {a}, {}) F((1,

    {a}, {})) (2, {a}, {}) 54

89. RA {} P1 F(RA) {} strict_read({}) (1, {a}, {}) F((1,

    {a}, {})) (2, {a}, {}) strict_read((1, {a}, {}) (1, {a}, {}) 55

90. RA {} P1 F(RA) {} strict_read({}) (1, {a}, {}) F((1,

    {a}, {})) (2, {a}, {}) strict_read((1, {a}, {}) (1, {a}, {}) (1, {a}, {a}) F((1, {a}, {a})) (2, {a}, {a}) strict_read((1, {a}, {a}) (1, {a}, {a}) 56

91. RA {} P1 F(RA) {} strict_read({}) (1, {a}, {}) (1,

    {a}, {}) (1, {a}, {a}) F((1, {a}, {a})) (2, {a}, {a}) strict_read((1, {a}, {a}) (1, {a}, {a}) 57

92. Programming SEC 1. Eliminate accidental nondeterminism
 (ex. deterministic, modeling non-monotonic

    operations monotonically) 2. Retain the properties of functional programming
 (ex. confluence, referential transparency over composition) 3. Distributed, and fault-tolerant runtime
 (ex. replication) 58

93. Distributed Runtime Selective Hearing 59 W-PSDS 2015

94. Selective Hearing • Epidemic broadcast based runtime system
 Provide a

    runtime system that can scale to large numbers of nodes, that is resilient to failures and provides efficient execution 60

95. Selective Hearing • Epidemic broadcast based runtime system
 Provide a

    runtime system that can scale to large numbers of nodes, that is resilient to failures and provides efficient execution • Well-matched to Lattice Processing (Lasp) 60

96. Selective Hearing • Epidemic broadcast based runtime system
 Provide a

    runtime system that can scale to large numbers of nodes, that is resilient to failures and provides efficient execution • Well-matched to Lattice Processing (Lasp) • Epidemic broadcast mechanisms provide weak ordering but are resilient and efficient 60

97. Selective Hearing • Epidemic broadcast based runtime system
 Provide a

    runtime system that can scale to large numbers of nodes, that is resilient to failures and provides efficient execution • Well-matched to Lattice Processing (Lasp) • Epidemic broadcast mechanisms provide weak ordering but are resilient and efficient • Lasp’s programming model is tolerant to message re-ordering, disconnections, and node failures 60

98. Selective Hearing • Epidemic broadcast based runtime system
 Provide a

    runtime system that can scale to large numbers of nodes, that is resilient to failures and provides efficient execution • Well-matched to Lattice Processing (Lasp) • Epidemic broadcast mechanisms provide weak ordering but are resilient and efficient • Lasp’s programming model is tolerant to message re-ordering, disconnections, and node failures • “Selective Receive”
 Nodes selectively receive and process messages based on interest. 60

99. Programming SEC 1. Eliminate accidental nondeterminism
 (ex. deterministic, modeling non-monotonic

    operations monotonically) 2. Retain the properties of functional programming
 (ex. confluence, referential transparency over composition) 3. Distributed, and fault-tolerant runtime
 (ex. replication) 61

100. What can we build? Advertisement Counter 62

101. Advertisement Counter • Mobile game platform selling advertisement space
 Advertisements

     are paid according to a minimum number of impressions 63

102. Advertisement Counter • Mobile game platform selling advertisement space
 Advertisements

     are paid according to a minimum number of impressions • Clients will go offline
 Clients have limited connectivity and the system still needs to make progress while clients are offline 63

103. Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot

     Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 64

104. Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot

     Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Riot Ads Rovio Ads Product Read 50,000 Remove Increment Union 65 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

105. Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot

     Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 1 Client 66 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

106. Ads ovio Ad ounter 1 ovio Ad ounter 2 iot

     Ad ounter 1 iot Ad ounter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Ro C Rovio Ad Counter 1 Ro C Rovio Ad Counter 1 Ro C Rovio Ad Counter 1 Ro C Client Side, Sing 67 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

107. Ads Contracts Ads Contracts Ads With Contracts Riot Ads Rovio

     Ads Filter Product move Read Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 68 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

108. Ads Contracts Ads Contracts Ads With Contracts Filter Product Read

     Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 69 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

109. Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot

     Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 70 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

110. Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot

     Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Riot Ads Rovio Ads Fil Product Read 50,000 Remove Increment Union 71 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

111. Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot

     Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 72 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

112. Advertisement Counter • Completely monotonic
 Disabling advertisements and contracts are

     all modeled through monotonic state growth 73

113. Advertisement Counter • Completely monotonic
 Disabling advertisements and contracts are

     all modeled through monotonic state growth • Arbitrary distribution
 Use of convergent data structures allows computational graph to be arbitrarily distributed 73

114. Advertisement Counter • Completely monotonic
 Disabling advertisements and contracts are

     all modeled through monotonic state growth • Arbitrary distribution
 Use of convergent data structures allows computational graph to be arbitrarily distributed • Divergence
 Divergence is a factor of synchronization period 73

115. We’ve build up from zero synchronization 74

116. We’ve build up from zero synchronization 74 Instead of working

     to remove synchronization

117. What’s next? 75

118. What’s Next? • Reduce metadata
 Metadata can grow very large

     76

119. What’s Next? • Reduce metadata
 Metadata can grow very large

     • Provide a more general ‘fold’ operation
 Currently very restrictive, how general can it be? 76

120. What’s Next? • Reduce metadata
 Metadata can grow very large

     • Provide a more general ‘fold’ operation
 Currently very restrictive, how general can it be? • Auto-placement
 What can the dataflow graph tell us about placement? 76

121. What have we learned? 77

122. Programming SEC 1. Eliminate accidental nondeterminism
 Convergent Objects: Conflict-Free Replicated

     Data Types 78

123. Programming SEC 1. Eliminate accidental nondeterminism
 Convergent Objects: Conflict-Free Replicated

     Data Types 2. Retain the properties of functional programming
 Convergent Programs: Lattice Processing 78

124. Programming SEC 1. Eliminate accidental nondeterminism
 Convergent Objects: Conflict-Free Replicated

     Data Types 2. Retain the properties of functional programming
 Convergent Programs: Lattice Processing 3. Distributed, and fault-tolerant runtime
 Distributed Runtime: Selective Hearing 78

125. Key Points • Synchronization is expensive
 Locking and other synchronization

     mechanisms limit scalability to the critical section 79

126. Key Points • Synchronization is expensive
 Locking and other synchronization

     mechanisms limit scalability to the critical section • Synchronization is sometimes not possible
 Mobile and “Internet of Things” applications make synchronization for replicated state impractical 79

127. Key Points • Synchronization is expensive
 Locking and other synchronization

     mechanisms limit scalability to the critical section • Synchronization is sometimes not possible
 Mobile and “Internet of Things” applications make synchronization for replicated state impractical • Apply synchronization only where required
 Global invariants, atomic visibility, etc. 79

128. How do I learn more? 80

129. Publications • “Lasp: A Language for Distributed, Coordination-Free Programming” 


     ACM SIGPLAN PPDP 2015 81

130. Publications • “Lasp: A Language for Distributed, Coordination-Free Programming” 


     ACM SIGPLAN PPDP 2015 • “Selective Hearing: An Approach to Distributed, Eventually Consistent Edge Computation”
 IEEE W-PSDS 2015 81

131. Publications • “Lasp: A Language for Distributed, Coordination-Free Programming” 


     ACM SIGPLAN PPDP 2015 • “Selective Hearing: An Approach to Distributed, Eventually Consistent Edge Computation”
 IEEE W-PSDS 2015 • “The Implementation and Use of a Generic Dataflow Behaviour in Erlang”
 ACM SIGPLAN Erlang Workshop ’15 81

132. Publications • “Lasp: A Language for Distributed, Coordination-Free Programming” 


     ACM SIGPLAN PPDP 2015 • “Selective Hearing: An Approach to Distributed, Eventually Consistent Edge Computation”
 IEEE W-PSDS 2015 • “The Implementation and Use of a Generic Dataflow Behaviour in Erlang”
 ACM SIGPLAN Erlang Workshop ’15 • “Lasp: A Language for Distributed, Eventually Consistent Computations with CRDTs"
 PaPoC 2015 81

133. 82 Lasp Release 0.0.1 http://lasp-lang.org

134. SyncFree is a European research project taking place for 3

     years, staring October 2013, and is funded by the European Union, grant agreement n° 609551. 83

135. Thanks! 84 Christopher Meiklejohn @cmeik