Distributed, Eventually Consistent Computations

Strange Loop 2015

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