The Potential Dangers of Causal Consistency and an Explicit Solution

Transcript

1. the potential dangers of causal consistency and an explicit solution

   Peter Bailis, Alan Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica SOCC 2012

2. A Story

3. A Story

4. “Sally’s in a coma!” A Story

5. “Sally’s in a coma!” “Sally’s in a coma!” A Story

   “Sally’s okay!”

6. “Sally’s in a coma!” “Sally’s in a coma!” A Story

   “Sally’s okay!”

7. “Sally’s in a coma!” “Sally’s in a coma!” A Story

   “Great news!” “Sally’s okay!”

8. “Sally’s in a coma!” “Sally’s in a coma!” A Story

   “Great news!” “Sally’s okay!”

9. “Sally’s in a coma!” “Sally’s in a coma!” “Great news!”

   “Sally’s okay!” The Wrong Story

10. “Sally’s in a coma!” “Great news!” The Wrong Story

11. “Sally’s in a coma!” “Great news!” The Wrong Story

12. “Sally’s in a coma!” “Great news!” The Wrong Story

13. “Sally’s in a coma!” “Great news!” The Wrong Story ???

14. The Right Order “Sally’s in a coma!” “Great news!” “Sally’s

    okay!”

15. The Right Order “Sally’s in a coma!” “Great news!” “Sally’s

    okay!”

16. The Right Order “Sally’s in a coma!” “Great news!” “Sally’s

    okay!”

17. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT

18. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT

19. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT

20. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT

21. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT

22. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT
23. None

24. 1978 “Time, Clocks, and the Ordering of Events in a

    Distributed System” by Leslie Lamport

25. 1978 “Time, Clocks, and the Ordering of Events in a

    Distributed System” by Leslie Lamport 1994 “Causal Memory” by Ahamad et al.

26. 1978 “Time, Clocks, and the Ordering of Events in a

    Distributed System” by Leslie Lamport 1994 “Causal Memory” by Ahamad et al. 2011-12

27. 2011-12

28. 2011-12

29. 2011-12 IEEE CAP There are many hard, trade-offs,

30. 2011-12 IEEE CAP SOSP: COPS There are many hard, trade-offs,

    but causal consistency can work well

31. 2011-12 IEEE CAP SOSP: COPS Texas: CAC There are many

    hard, trade-offs, but causal consistency can work well and it’s the best you can do*!
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33. “Sally’s okay!”

34. “Sally’s okay!”

35. “Sally’s okay!”

36. “Sally’s okay!”

37. “Sally’s okay!”

38. “Sally’s okay!”

39. “Great news!” “Sally’s okay!”

40. “Great news!” “Sally’s okay!”

41. “Great news!” “Sally’s okay!”

42. “Great news!” “Sally’s okay!”

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46. Deliver writes in the “right” order

47. Deliver writes in the “right” order “Great news!” “Sally’s okay!”

    “Sally’s in a coma!”

48. Causal Consistency reads obey partial order called “happens-before”

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58. Write value

59. Write value Happens-before metadata

60. Write value Happens-before metadata

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64. Have we seen all dependencies?

65. Have we seen all dependencies?

66. Have we seen all dependencies? Y

67. Have we seen all dependencies?

68. Have we seen all dependencies? Y

69. Have we seen all dependencies? Yes: apply write

70. Have we seen all dependencies? Yes: apply write

71. Have we seen all dependencies? No: wait Yes: apply write

72. Sounds great, right?

73. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT

74. Causal Consistency reads obey partial order called “happens-before”

75. Causal Consistency traditional happens-before: potential causality reads obey partial order

    called “happens-before”

76. If you wish to make an apple pie from scratch,

    you must first invent the universe. -Carl Sagan
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78. “Great news!”

79. “Great news!” “Sally’s okay!”

80. “Great news!” “Sally’s okay!” “Great picture!” “Rad party!” “Lol!” “Can’t

    wait for SOCC!” “Want to go skiing?”

81. “Great news!” “Sally’s okay!” “Great picture!” “Rad party!” “Lol!” “Can’t

    wait for SOCC!” “Want to go skiing?” “I hope my paper gets in.” “You who?” “I love Tahoe!” “Coming tonight?

82. “Great news!” “Sally’s okay!” “Great picture!” “Rad party!” “Lol!” “Can’t

    wait for SOCC!” “Want to go skiing?” “Snow rocks!” “I hear the PC is great!” “You.” “Hello, “I hope my paper gets in.” “You who?” “I love Tahoe!” “Coming tonight?

83. “Great news!” “Sally’s okay!” “Great picture!” “Rad party!” “Lol!” “Can’t

    wait for SOCC!” “Want to go skiing?” “Snow rocks!” “I hear the PC is great!” “You.” “Hello, “Who’s there?” “Are you submitting?” “Have you met Larry?” “Great food here” “I hope my paper gets in.” “You who?” “I love Tahoe!” “Coming tonight?

84. “Great news!” “Sally’s okay!” “Great picture!” “Rad party!” “Lol!” “Can’t

    wait for SOCC!” “Want to go skiing?” “Snow rocks!” “I hear the PC is great!” “You.” “Hello, “Who’s there?” “Are you submitting?” “Have you met Larry?” “Great food here” “Knock, knock.” “I hope my paper gets in.” “You who?” “I love Tahoe!” “Coming tonight?

85. “Great news!” “Sally’s okay!” “Great picture!” “Rad party!” “Lol!” “Can’t

    wait for SOCC!” “Want to go skiing?” “Snow rocks!” “I hear the PC is great!” “You.” “Hello, “Who’s there?” “Are you submitting?” “Have you met Larry?” “Great food here” “Knock, knock.” “I hope my paper gets in.” “You who?” “I love Tahoe!” “Coming tonight?

86. Twitter.com 20 Tweets at login 20 more auto-scroll 600+ per

    min

87. Twitter.com quickly approaches asymptotic limit

88. r e c a p

89. potential danger Potential causality graphs are huge, limiting local apply

    rates and throughput r e c a p

90. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

    OUT BETTER LIVING VIA APP CONTEXT
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96. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER!

97. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! Unstable queue!

98. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! Unstable queue!

    Sustained throughput limited to slowest DC

99. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! Unstable queue!

    Sustained throughput limited to slowest DC

100. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! Sustained throughput

     limited to slowest DC

101. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! Adding DCs

     doesn’t help slowest site Sustained throughput limited to slowest DC

102. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! During partitions/failures,

     sustainable throughput is zero writes/s zero Sustained throughput limited to slowest DC

103. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! During partitions/failures,

     sustainable throughput is zero writes/s zero

104. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! During partitions/failures,

     sustainable throughput is zero writes/s zero

105. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! During partitions/failures,

     sustainable throughput is zero writes/s zero Metadata garbage collection stalls stalls

106. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! During partitions/failures,

     sustainable throughput is zero writes/s zero Metadata garbage collection stalls stalls

107. DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! DANGER! During partitions/failures,

     sustainable throughput is zero writes/s zero Metadata garbage collection stalls stalls

108. r e c a p

109. r e c a p potential danger Write throughput limited

     to slowest DC Violation 㱺 arbitrarily high visibility latency Adding DCs does not increase throughput

110. Potential dangers spell trouble for causality bad news

111. Potential dangers spell trouble for causality to it? Are live

     without we destined bad news

112. Potential dangers spell trouble for causality to it? Are live

     without we destined bad news

113. SOME DEPENDENCIES METADATA AND MODERN SERVICES (NOT) SCALING UP AND

     OUT BETTER LIVING VIA APP CONTEXT

114. Observation Why track dependencies that don’t matter?

115. Observation Why track dependencies that don’t matter?

116. Observation Why track dependencies that don’t matter? Need to know

     application semantics

117. Explicit Causality app-defined “happens-before” transitivity enforced subset of potential causality

118. Explicit Causality app-defined “happens-before” transitivity enforced subset of potential causality

     not a new idea (e.g., Cheriton and Skeen SOSP 1993, Ladin et al. PODC 1990) but...

119. Explicit Matters

120. Explicit Matters Twitter 28% of Tweets in conversations 69% of

     convos are depth two average depth is 10.7 [Ye and Wu SocInfo 2010, Ritter et al. HLT 2010]

121. Explicit Matters Twitter 28% of Tweets in conversations 69% of

     convos are depth two average depth is 10.7 [Ye and Wu SocInfo 2010, Ritter et al. HLT 2010] reply-to degree and depth are limited

122. Explicit Matters Twitter 28% of Tweets in conversations 69% of

     convos are depth two average depth is 10.7 [Ye and Wu SocInfo 2010, Ritter et al. HLT 2010] reply-to degree and depth are limited 109 smaller graph for a year of Tweets

123. Explicit API put(key, value)

124. put_after(key, value, deps) Explicit API

125. put_after(key, value, deps) Explicit API

126. put_after(key, value, deps) Explicit API (possibly empty) set of references

     to other writes

127. put_after(key, value, deps) Explicit API track what matters (possibly empty)

     set of references to other writes

128. put_after(key, value, deps) Explicit API track what matters frequently in

     data model already (possibly empty) set of references to other writes

129. put_after(key, value, deps) Explicit API track what matters frequently in

     data model already can simulate fencing (possibly empty) set of references to other writes

130. put_after(key, value, deps) Explicit API track what matters frequently in

     data model already can simulate fencing (possibly empty) set of references to other writes won’t track non-explicit references

131. NATURAL EXTENSION NOT ROCKET SCIENCE

132. NATURAL EXTENSION NOT ROCKET SCIENCE http://twitter.com/rbranson/status/256795094387142657

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134. potential causality

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136. A WORLD OF PHYSICAL OPERATIONS MESSAGES REGISTERS READS WRITES MODEST

     SCALE
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139. A WORLD OF HUGE SCALE MASSIVE INTERACTIVITY PLANET-WIDE NETWORKS REAL-WORLD

     INTERFACES UBICOMP BIG DATA
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141. potential dangers

142. potential dangers huge causality graphs

143. potential dangers huge causality graphs throughput scalability limited

144. potential dangers huge causality graphs explicit causality semantic context to

     the rescue consider modern apps helps with #1, indirectly with #2 throughput scalability limited