Scalable Atomic Visibility with RAMP Transactions

Slides for "Scalable Atomic Visibility with RAMP Transactions" by Bailis et al., appearing in SIGMOD 2014

This deck also contains a proposal for implementation in Cassandra. If you're interested in implementing RAMP in your own system, don't hesitate to get in touch: pbailis at cs.berkeley.edu or @pbailis

Paper: http://www.bailis.org/papers/ramp-sigmod2014.pdf
Blog post intro: http://www.bailis.org/blog/scalable-atomic-visibility-with-ramp-transactions/
Source code from paper and "executable pseudocode" in Python: https://github.com/pbailis/ramp-sigmod2014-code

Transcript

1. SCALABLE ATOMIC VISIBILITY WITH RAMP TRANSACTIONS Peter Bailis, Alan Fekete, Ali Ghodsi, Joseph M. Hellerstein, Ion Stoica UC Berkeley and University of Sydney Overview deck with Cassandra discussion @pbailis
2. NOSQL
3. NO SQL NOSQL
4. NO SQL DIDN’T WANT SQL NOSQL
5. NO SQL DIDN’T WANT SERIALIZABILITY NOSQL
6. POOR PERFORMANCE NO SQL DIDN’T WANT SERIALIZABILITY NOSQL
7. POOR PERFORMANCE NO SQL DIDN’T WANT SERIALIZABILITY NOSQL
8. POOR PERFORMANCE DELAY NO SQL DIDN’T WANT SERIALIZABILITY NOSQL
9. POOR PERFORMANCE DELAY PEAK THROUGHPUT: 1/DELAY FOR CONTENDED OPERATIONS NO SQL DIDN’T WANT SERIALIZABILITY NOSQL
10. POOR PERFORMANCE DELAY PEAK THROUGHPUT: 1/DELAY FOR CONTENDED OPERATIONS at .5MS, 2K TXN/s at 50MS, 20 TXN/s NO SQL DIDN’T WANT SERIALIZABILITY NOSQL
11. NO SQL DIDN’T WANT SERIALIZABILITY NOSQL POOR PERFORMANCE HIGH LATENCY
12. NO SQL DIDN’T WANT SERIALIZABILITY NOSQL POOR PERFORMANCE LIMITED AVAILABILITY HIGH LATENCY
13. STILL DON’T WANT SERIALIZABILITY “NOT ONLY SQL”
14. STILL DON’T WANT SERIALIZABILITY “NOT ONLY SQL” (DON’T WANT THE COSTS)
15. STILL DON’T WANT SERIALIZABILITY “NOT ONLY SQL” BUT WANT MORE FEATURES (DON’T WANT THE COSTS)
16. STILL DON’T WANT SERIALIZABILITY “NOT ONLY SQL” BUT WANT MORE FEATURES This paper! (DON’T WANT THE COSTS)
17. “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
18. “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
19. “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
20. “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
21. “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 FRIENDS FRIENDS
22. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 FRIENDS FRIENDS
23. as s “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
24. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List
25. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads…
26. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates
27. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates s
28. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates s
29. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates s
30. as “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates Not cleanly partitionable s
31. FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
32. FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 “On Brewing Fresh Espresso: LinkedIn’s Distributed Data Serving Platform” SIGMOD 2013
33. FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 “On Brewing Fresh Espresso: LinkedIn’s Distributed Data Serving Platform” SIGMOD 2013 “PNUTS: Yahoo!’s Hosted Data Serving Platform” VLDB 2008
35. ID: 532 AGE: 42 ID: 123 AGE: 22 ID: 2345 AGE: 1 ID: 412 AGE: 72 ID: 892 AGE: 13
36. ID: 532 AGE: 42 ID: 123 AGE: 22 ID: 2345 AGE: 1 ID: 412 AGE: 72 ID: 892 AGE: 13
37. ID: 532 AGE: 42 ID: 123 AGE: 22 ID: 2345 AGE: 1 ID: 412 AGE: 72 ID: 892 AGE: 13
38. ID: 532 AGE: 42 ID: 123 AGE: 22 ID: 2345 AGE: 1 ID: 412 AGE: 72 ID: 892 AGE: 13
39. ID: 532 AGE: 42 ID: 123 AGE: 22 ID: 2345 AGE: 1 ID: 412 AGE: 72 ID: 892 AGE: 13
40. ID: 532 AGE: 42 ID: 123 AGE: 22 ID: 2345 AGE: 1 ID: 412 AGE: 72 ID: 892 AGE: 13 Partition by primary key (ID)
41. ID: 532 AGE: 42 ID: 123 AGE: 22 ID: 2345 AGE: 1 ID: 412 AGE: 72 ID: 892 AGE: 13 Partition by primary key (ID) How should we look up by age?
42. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age?
43. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing
44. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data
45. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data WRITE ONE SERVER, READ ALL
46. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data poor scalability WRITE ONE SERVER, READ ALL
47. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute poor scalability WRITE ONE SERVER, READ ALL
48. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute WRITE 2+ SERVERS, READ ONE poor scalability WRITE ONE SERVER, READ ALL
49. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute WRITE 2+ SERVERS, READ ONE scalable lookups poor scalability WRITE ONE SERVER, READ ALL
50. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute WRITE 2+ SERVERS, READ ONE scalable lookups poor scalability WRITE ONE SERVER, READ ALL
51. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute scalable lookups poor scalability WRITE 2+ SERVERS, READ ONE WRITE ONE SERVER, READ ALL
52. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute scalable lookups poor scalability WRITE 2+ SERVERS, READ ONE WRITE ONE SERVER, READ ALL OVERVIEW
53. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute scalable lookups poor scalability WRITE 2+ SERVERS, READ ONE WRITE ONE SERVER, READ ALL OVERVIEW INCONSISTENT GLOBAL 2i
54. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute scalable lookups poor scalability WRITE 2+ SERVERS, READ ONE WRITE ONE SERVER, READ ALL OVERVIEW INCONSISTENT GLOBAL 2i INCONSISTENT GLOBAL 2i
55. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute scalable lookups poor scalability WRITE 2+ SERVERS, READ ONE WRITE ONE SERVER, READ ALL OVERVIEW INCONSISTENT GLOBAL 2i INCONSISTENT GLOBAL 2i (PROPOSED) INCONSISTENT GLOBAL 2i
56. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute scalable lookups poor scalability WRITE 2+ SERVERS, READ ONE WRITE ONE SERVER, READ ALL OVERVIEW INCONSISTENT GLOBAL 2i INCONSISTENT GLOBAL 2i (PROPOSED) INCONSISTENT GLOBAL 2i INCONSISTENT GLOBAL 2i
57. SECONDARY INDEXING Partition by primary key (ID) How should we look up by age? Option I: Local Secondary Indexing Build indexes co-located with primary data Option II: Global Secondary Indexing Partition indexes by secondary key Partition by secondary attribute scalable lookups poor scalability WRITE 2+ SERVERS, READ ONE WRITE ONE SERVER, READ ALL OVERVIEW INCONSISTENT GLOBAL 2i INCONSISTENT GLOBAL 2i (PROPOSED) INCONSISTENT GLOBAL 2i INCONSISTENT GLOBAL 2i INCONSISTENT GLOBAL 2i
58. TABLE: ALL USERS
59. TABLE: ALL USERS
60. TABLE: ALL USERS TABLE: USERS OVER 25
61. TABLE: ALL USERS TABLE: USERS OVER 25
62. TABLE: ALL USERS TABLE: USERS OVER 25
63. TABLE: ALL USERS TABLE: USERS OVER 25
64. MATERIALIZED VIEWS TABLE: ALL USERS TABLE: USERS OVER 25
65. MATERIALIZED VIEWS TABLE: ALL USERS TABLE: USERS OVER 25 RELEVANT RECENT EXAMPLES IN GOOGLE PERCOLATOR TWITTER RAINBIRD LINKEDIN ESPRESSO PAPERS
66. FOREIGN KEY DEPENDENCIES SECONDARY INDEXES MATERIALIZED VIEWS HOW SHOULD WE CORRECTLY MAINTAIN
67. SERIALIZABILITY
68. SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY SERIALIZABILITY
69. SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY SERIALIZABILITY
70. REPEATABLE READ (PL-2.99) SERIALIZABILITY SNAPSHOT ISOLATION CURSOR STABILITY READ UNCOMMITTED READ COMMITTED LINEARIZABILITY CAUSAL PRAM RYW EVENTUAL CONSISTENCY
71. REPEATABLE READ (PL-2.99) SERIALIZABILITY SNAPSHOT ISOLATION CURSOR STABILITY READ UNCOMMITTED READ COMMITTED LINEARIZABILITY MANY SUFFICIENT CAUSAL PRAM RYW EVENTUAL CONSISTENCY
72. REPEATABLE READ (PL-2.99) SERIALIZABILITY SNAPSHOT ISOLATION CURSOR STABILITY READ UNCOMMITTED READ COMMITTED LINEARIZABILITY REQUIRE SYNCHRONOUS COORDINATION MANY SUFFICIENT CAUSAL PRAM RYW EVENTUAL CONSISTENCY
73. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY REQUIRE SYNCHRONOUS COORDINATION MANY SUFFICIENT COORDINATION-FREE
74. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY REQUIRE SYNCHRONOUS COORDINATION INSUFFICIENT MANY SUFFICIENT COORDINATION-FREE
75. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY LINEARIZABILITY REQUIRE SYNCHRONOUS COORDINATION INSUFFICIENT MANY SUFFICIENT COORDINATION-FREE Facebook TAO LinkedIn Espresso Yahoo! PNUTS Google Megastore Google App Engine Twitter Rainbird Amazon DynamoDB CONSCIOUS CHOICES!
76. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY COORDINATION-FREE INSUFFICIENT REQUIRE SYNCHRONOUS COORDINATION SUFFICIENT
77. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY COORDINATION-FREE RAMP (THIS PAPER) INSUFFICIENT REQUIRE SYNCHRONOUS COORDINATION SUFFICIENT
78. TRANSACTIONS R A M P TOMIC EAD ULTI- ARTITION
79. TRANSACTIONS R A M P TOMIC EAD ULTI- ARTITION
80. TRANSACTIONS RAMP EFFICIENTLY MAINTAIN
81. TRANSACTIONS RAMP FOREIGN KEY DEPENDENCIES EFFICIENTLY MAINTAIN
82. TRANSACTIONS RAMP FOREIGN KEY DEPENDENCIES SECONDARY INDEXES EFFICIENTLY MAINTAIN
83. TRANSACTIONS RAMP FOREIGN KEY DEPENDENCIES SECONDARY INDEXES MATERIALIZED VIEWS EFFICIENTLY MAINTAIN
84. TRANSACTIONS RAMP FOREIGN KEY DEPENDENCIES SECONDARY INDEXES MATERIALIZED VIEWS BY PROVIDING ATOMIC VISIBILITY EFFICIENTLY MAINTAIN
85. ATOMIC VISIBILITY
86. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY
87. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY
88. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY WRITE X = 1 WRITE Y = 1
89. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY WRITE X = 1 WRITE Y = 1 READ X = 1 READ Y = 1
90. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY WRITE X = 1 WRITE Y = 1 READ X = 1 READ Y = 1 OR
91. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY WRITE X = 1 WRITE Y = 1 READ X = 1 READ Y = 1 READ X = ∅ READ Y = ∅ OR
92. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY READ X = 1 READ Y = 1 READ X = ∅ READ Y = ∅ OR
93. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY READ X = 1 READ Y = 1 READ X = ∅ READ Y = ∅ OR BUT NOT READ Y = ∅ READ X = 1
94. Informally: Either all of each transaction’s updates are visible, or none are ATOMIC VISIBILITY READ X = 1 READ Y = 1 READ X = ∅ READ Y = ∅ OR BUT NOT READ X = ∅ READ Y = ∅ READ X = 1 OR READ Y = 1
95. ATOMIC VISIBILITY We also provide per-item PRAM guarantees with per-transaction regular semantics (see paper Appendix) Formally: A transaction Tj exhibits fractured reads if transaction Ti writes versions xm and yn (in any order, with x possibly but not necessarily equal to y), Tj reads version xm and version yk , and k <n. ! A system provides Read Atomic isolation (RA) if it prevents fractured reads anomalies and also prevents transactions from reading uncommitted, aborted, or intermediate data. FORMALIZED AS READ ATOMIC ISOLATION
96. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY
97. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY WHILE ENSURING
98. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY WHILE ENSURING PARTITION INDEPENDENCE
99. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY WHILE ENSURING PARTITION INDEPENDENCE clients only access servers responsible for data in transactions
100. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY WHILE ENSURING PARTITION INDEPENDENCE clients only access servers responsible for data in transactions W(X=1) W(Y=1) X Y Z
101. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY WHILE ENSURING PARTITION INDEPENDENCE clients only access servers responsible for data in transactions W(X=1) W(Y=1) X Y Z
102. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY WHILE ENSURING PARTITION INDEPENDENCE AND SYNCHRONIZATION INDEPENDENCE clients only access servers responsible for data in transactions transactions always commit* and no client can cause another client to block
103. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY ARE NOT SERIALIZABLE DO NOT PREVENT LOST UPDATE DO NOT PREVENT WRITE SKEW ALLOW CONCURRENT UPDATES
104. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY ARE NOT SERIALIZABLE DO NOT PREVENT LOST UPDATE DO NOT PREVENT WRITE SKEW ALLOW CONCURRENT UPDATES ARE GUIDED BY REAL WORLD USE CASES FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS
105. TRANSACTIONS RAMP GUARANTEE ATOMIC VISIBILITY ARE NOT SERIALIZABLE DO NOT PREVENT LOST UPDATE DO NOT PREVENT WRITE SKEW ALLOW CONCURRENT UPDATES ARE GUIDED BY REAL WORLD USE CASES FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS Facebook TAO LinkedIn Espresso Yahoo! PNUTS Google Megastore Google App Engine Twitter Rainbird Amazon DynamoDB
106. STRAWMAN: LOCKING X=0 Y=0
107. STRAWMAN: LOCKING X=0 Y=0 W(X=1) W(Y=1)
108. STRAWMAN: LOCKING X=0 Y=0 W(X=1) W(Y=1)
109. STRAWMAN: LOCKING X=0 Y=0 W(X=1) W(Y=1)
110. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1)
111. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1)
112. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1)
113. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1)
114. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) R(X=1)
115. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) R(X=1) R(Y=1)
116. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1)
117. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1)
118. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) R(X=?)
119. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) R(X=?) R(Y=?)
120. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION
121. STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION
122. STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION
123. STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION RTT
124. STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION RTT unavailability!
125. X=1 W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION at .5 MS < 2K TPS! unavailable during failures SIMILAR ISSUES IN MVCC, PRE-SCHEDULING SERIALIZABLE OCC, (global timestamp assignment/application) (multi-partition validation, liveness) (scheduling, multi-partition execution) STRAWMAN: LOCKING
126. X=1 W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION at .5 MS < 2K TPS! unavailable during failures SIMILAR ISSUES IN MVCC, PRE-SCHEDULING SERIALIZABLE OCC, (global timestamp assignment/application) (multi-partition validation, liveness) (scheduling, multi-partition execution) FUNDAMENTAL TO “STRONG” SEMANTICS STRAWMAN: LOCKING
127. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) X=1
128. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) X=1
129. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1
130. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 METADATA
131. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 + LIMITED MULTI-VERSIONING METADATA
132. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 + LIMITED MULTI-VERSIONING METADATA FOR NOW:
 READ-ONLY, WRITE-ONLY TXNS
133. last committed stamp for x: 0 RAMP-Fast last committed stamp for y: 0
134. last committed stamp for x: 0 RAMP-Fast last committed stamp for y: 0
135. last committed stamp for x: 0 RAMP-Fast last committed stamp for y: 0
136. last committed stamp for x: 0 RAMP-Fast known versions of x last committed stamp for y: 0 known versions of y
137. last committed stamp for x: 0 RAMP-Fast known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {}
138. last committed stamp for x: 0 RAMP-Fast known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {}
139. last committed stamp for x: 0 RAMP-Fast known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {}
140. last committed stamp for x: 0 RAMP-Fast known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {}
141. last committed stamp for x: 0 RAMP-Fast known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {}
142. last committed stamp for x: 0 RAMP-Fast known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {}
143. last committed stamp for x: 0 RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {}
144. last committed stamp for x: 0 RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. timestamp: 242 e.g., time concat client ID concat sequence number
145. last committed stamp for x: 0 RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. timestamp: 242
146. last committed stamp for x: 0 RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. timestamp: 242
147. last committed stamp for x: 0 RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 242 1 timestamp: 242
148. last committed stamp for x: 0 RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 242 1 242 1 timestamp: 242
149. last committed stamp for x: 0 RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242
150. RAMP-Fast W(X=1) W(Y=1) known versions of x last committed stamp for y: 0 known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 last committed stamp for x: 242 timestamp: 242
151. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 242 timestamp: 242
152. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 R(X=?) R(Y=?) last committed stamp for x: 242 last committed stamp for y: 242
153. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 R(X=1) R(Y=1) last committed stamp for x: 242 last committed stamp for y: 242
154. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 R(X=1) R(Y=1) last committed stamp for x: 242 last committed stamp for y: 242
155. R(X=?) R(Y=?) RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 last committed stamp for x: 242 last committed stamp for y: 0
156. R(X=?) R(Y=?) RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 last committed stamp for x: 242 last committed stamp for y: 0
157. R(X=?) R(Y=?) RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 RACE!!! last committed stamp for x: 242 last committed stamp for y: 0
158. R(X=?) R(Y=?) RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 RACE!!! R(X=1) R(Y=0) last committed stamp for x: 242 last committed stamp for y: 0
159. R(X=?) R(Y=?) RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 RACE!!! R(X=1) R(Y=0) last committed stamp for x: 242 last committed stamp for y: 0
160. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 RACE!!! R(X=1) R(Y=0) last committed stamp for x: 242 last committed stamp for y: 0
161. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 RACE!!! R(X=1) R(Y=0) last committed stamp for x: 242 last committed stamp for y: 0 RECORD THE ITEMS WRITTEN IN THE TRANSACTION
162. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 RACE!!! R(X=1) R(Y=0) last committed stamp for x: 242 last committed stamp for y: 0 {y} RECORD THE ITEMS WRITTEN IN THE TRANSACTION
163. RAMP-Fast W(X=1) W(Y=1) known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 1.) Assign unique (logical) transaction timestamp. 2.) Add write to known versions on partition. 3.) Commit and update last committed stamp. 242 1 242 1 timestamp: 242 RACE!!! R(X=1) R(Y=0) last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} RECORD THE ITEMS WRITTEN IN THE TRANSACTION
164. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?)
165. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?)
166. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?)
167. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed:
168. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: X=1 @ 242, {Y}
169. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: X=1 @ 242, {Y} Y=NULL @ 0, {}
170. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: 2.) Calculate missing versions: X=1 @ 242, {Y} Y=NULL @ 0, {}
171. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: 2.) Calculate missing versions: X=1 @ 242, {Y} Y=NULL @ 0, {} ITEM HIGHEST TS X 242 Y 242
172. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: 2.) Calculate missing versions: X=1 @ 242, {Y} Y=NULL @ 0, {} ITEM HIGHEST TS X 242 Y 242
173. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: 2.) Calculate missing versions: 3.) Fetch missing versions. X=1 @ 242, {Y} Y=NULL @ 0, {} ITEM HIGHEST TS X 242 Y 242
174. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: 2.) Calculate missing versions: 3.) Fetch missing versions. X=1 @ 242, {Y} Y=NULL @ 0, {} ITEM HIGHEST TS X 242 Y 242 Y=1 @ 242, {X} (Send required timestamp in request)
175. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: 2.) Calculate missing versions: 3.) Fetch missing versions. X=1 @ 242, {Y} Y=NULL @ 0, {} ITEM HIGHEST TS X 242 Y 242 Y=1 @ 242, {X} (Send required timestamp in request) 2PC ENSURES NO WAIT AT SERVER
176. RAMP-Fast known versions of x known versions of y TIMESTAMP VALUE METADATA 0 NULL {} TIMESTAMP VALUE METADATA 0 NULL {} 242 1 242 1 last committed stamp for x: 242 last committed stamp for y: 0 {y} {x} R(X=?) R(Y=?) 1.) Read last committed: 2.) Calculate missing versions: 3.) Fetch missing versions. X=1 @ 242, {Y} Y=NULL @ 0, {} ITEM HIGHEST TS X 242 Y 242 Y=1 @ 242, {X} (Send required timestamp in request) 4.) Return resulting set. R(X=1) R(Y=1) 2PC ENSURES NO WAIT AT SERVER
177. RAMP-Fast
178. RAMP-Fast 2 RTT writes:
179. RAMP-Fast 2 RTT writes: 2PC, without blocking synchronization
180. RAMP-Fast 2 RTT writes: 2PC, without blocking synchronization ENSURES READERS NEVER WAIT!
181. RAMP-Fast 2 RTT writes: 2PC, without blocking synchronization metadata size linear in transaction size ENSURES READERS NEVER WAIT!
182. RAMP-Fast 2 RTT writes: 2PC, without blocking synchronization metadata size linear in transaction size 1 RTT reads: in race-free case ENSURES READERS NEVER WAIT!
183. RAMP-Fast 2 RTT writes: 2PC, without blocking synchronization metadata size linear in transaction size 1 RTT reads: in race-free case 2 RTT reads: otherwise ENSURES READERS NEVER WAIT!
184. RAMP-Fast 2 RTT writes: 2PC, without blocking synchronization metadata size linear in transaction size 1 RTT reads: in race-free case 2 RTT reads: otherwise no fast-path synchronization ENSURES READERS NEVER WAIT!
185. RAMP-Fast 2 RTT writes: 2PC, without blocking synchronization metadata size linear in transaction size 1 RTT reads: in race-free case 2 RTT reads: otherwise no fast-path synchronization ENSURES READERS NEVER WAIT! CAN WE USE LESS METADATA?
186. RAMP-Small
187. RAMP-Small 2 RTT writes:
188. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata
189. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads
190. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads always
191. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. always
192. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads INTUITION: 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. always
193. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads INTUITION: 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. X time 523 always
194. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads INTUITION: 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. X time 523 Y time 247 always
195. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads INTUITION: 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. X time 523 Y time 247 Z time 842 always
196. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads INTUITION: 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. X time 523 Y time 247 Z time 842 {247, 523, 842} always
197. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads partial commits will be in this set INTUITION: 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. X time 523 Y time 247 Z time 842 {247, 523, 842} always
198. RAMP-Small 2 RTT writes: same basic protocol as RAMP-Fast but drop all RAMP-Fast metadata 2 RTT reads partial commits will be in this set INTUITION: 1.) For each item, fetch the highest committed timestamp. 2.) Request highest matching write with timestamp in step 1. X time 523 Y time 247 Z time 842 {247, 523, 842} send it to all participating servers always
199. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(1) Bloom filter
200. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(1) Bloom filter
201. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(1) Bloom filter
202. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(1) Bloom filter
203. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(1) Bloom filter
204. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 log(2)2 O([txn len]*log(1/ε))
205. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 log(2)2 O([txn len]*log(1/ε))
206. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 BLOOM FILTER SUMMARIZES WRITE SET FALSE POSITIVES: EXTRA RTTs log(2)2 O([txn len]*log(1/ε))
207. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 BLOOM FILTER SUMMARIZES WRITE SET FALSE POSITIVES: EXTRA RTTs log(2)2 O([txn len]*log(1/ε))
208. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter
209. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter • AVOID IN-PLACE UPDATES
210. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter • AVOID IN-PLACE UPDATES • EMBRACE RACES TO IMPROVE CONCURRENCY
211. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter • AVOID IN-PLACE UPDATES • EMBRACE RACES TO IMPROVE CONCURRENCY • ALLOW READERS TO REPAIR PARTIAL WRITES
212. RAMP Summary Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter • AVOID IN-PLACE UPDATES • EMBRACE RACES TO IMPROVE CONCURRENCY • ALLOW READERS TO REPAIR PARTIAL WRITES • USE 2PC TO AVOID READER STALLS
213. Additional Details
214. Additional Details Garbage collection: limit read transaction duration to K seconds GC overwritten versions after K seconds
215. Additional Details Garbage collection: limit read transaction duration to K seconds GC overwritten versions after K seconds Replication paper assumes linearizable masters extendable to “AP” systems see HAT by Bailis et al., VLDB 2014
216. Additional Details Garbage collection: limit read transaction duration to K seconds GC overwritten versions after K seconds Failure handling: blocked 2PC rounds do not block clients stalled commits? versions are not GC’d if desirable, use CTP termination protocol Replication paper assumes linearizable masters extendable to “AP” systems see HAT by Bailis et al., VLDB 2014
217. RAMP PERFORMANCE Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter
218. RAMP PERFORMANCE Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter EVALUATED ON EC2 cr1.8xlarge instances (cluster size: 1-100 servers; default: 5) ! open sourced on GitHub; see link at end of talk
219. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s)
220. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control
221. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) Doesn’t provide atomic visibility RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control
222. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control
223. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL
224. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only
225. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) Also doesn’t provide atomic visibility RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only
226. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) Representative of coordinated approaches RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only
227. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only
228. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast
229. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) Within ~5% of baseline ! Latency in paper (comparable) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast
230. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast
231. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small
232. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) Always needs 2 RTT reads RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small
233. YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small
234. RAMP-F RAMP-S RAMP-H NWNR RAMP-Hybrid YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small
235. RAMP-F RAMP-S RAMP-H NWNR RAMP-Hybrid YCSB: WorkloadA, 95% reads, 1M items, 4 items/txn 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) 0 2000 4000 6000 8000 10000 Concurrent Clients 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small
236. YCSB: WorkloadA, 1M items, 4 items/txn, 5K clients 0 25 50 75 100 Percentage Reads 0 30K 60K 90K 120K 150K 180K Throughput (txn/s)
237. YCSB: WorkloadA, 1M items, 4 items/txn, 5K clients 0 25 50 75 100 Percentage Reads 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control
238. YCSB: WorkloadA, 1M items, 4 items/txn, 5K clients 0 25 50 75 100 Percentage Reads 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only
239. RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small YCSB: WorkloadA, 1M items, 4 items/txn, 5K clients 0 25 50 75 100 Percentage Reads 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-F RAMP-S RAMP-H NWNR RAMP-Hybrid
240. RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small YCSB: WorkloadA, 1M items, 4 items/txn, 5K clients 0 25 50 75 100 Percentage Reads 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-F RAMP-S RAMP-H NWNR RAMP-Hybrid Linear scaling; due to 2RTT writes, races
241. RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control LWSR LWLR E-PCI Serializable 2PL NWNR LWNR LWSR LWLR E-PCI Write Locks Only RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small YCSB: WorkloadA, 1M items, 4 items/txn, 5K clients 0 25 50 75 100 Percentage Reads 0 30K 60K 90K 120K 150K 180K Throughput (txn/s) RAMP-F RAMP-S RAMP-H NWNR RAMP-Hybrid
242. YCSB: uniform access, 1M items, 4 items/txn, 95% reads 0 25 50 75 100 Number of Servers 0 2M 4M 6M 8M Throughput (ops/s)
243. RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control YCSB: uniform access, 1M items, 4 items/txn, 95% reads 0 25 50 75 100 Number of Servers 0 2M 4M 6M 8M Throughput (ops/s)
244. RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small RAMP-F RAMP-S RAMP-H NWNR RAMP-Hybrid YCSB: uniform access, 1M items, 4 items/txn, 95% reads 0 25 50 75 100 Number of Servers 0 2M 4M 6M 8M Throughput (ops/s)
245. RAMP-H NWNR LWNR LWSR LWLR E-PCI No Concurrency Control RAMP-F RAMP-S RAMP-Fast RAMP-F RAMP-S RAMP-H RAMP-Small RAMP-F RAMP-S RAMP-H NWNR RAMP-Hybrid YCSB: uniform access, 1M items, 4 items/txn, 95% reads 0 25 50 75 100 Number of Servers 0 40K 80K 120K 160K 200K operations/s/server
246. RAMP PERFORMANCE Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter
247. RAMP PERFORMANCE Algorithm Write RTT READ RTT (best case) READ RTT (worst case) METADATA RAMP-Fast 2 1 2 O(txn len) write set summary RAMP-Small 2 2 2 O(1) timestamp RAMP-Hybrid 2 1+ε 2 O(B) Bloom filter More results in paper: Transaction length, contention, value size, latency, failures
248. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES:
249. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES: as s
250. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES: as s
251. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES: as s
252. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES: as s MULTI-PUT (DELETES VIA TOMBSTONES)
253. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES:
254. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES:
255. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES:
256. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES: Maintain list of matching record IDs and versions e.g., HAS_BEARD={52@512, 412@52, 123@512} merge lists on commit/read (LWW by timestamp for conflicts)
257. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES: Maintain list of matching record IDs and versions e.g., HAS_BEARD={52@512, 412@52, 123@512} merge lists on commit/read (LWW by timestamp for conflicts) LOOKUPs: READ INDEX, THEN FETCH DATA
258. FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS HOW RAMP HANDLES: Maintain list of matching record IDs and versions e.g., HAS_BEARD={52@512, 412@52, 123@512} merge lists on commit/read (LWW by timestamp for conflicts) LOOKUPs: READ INDEX, THEN FETCH DATA SIMILAR FOR SELECT/PROJECT
259. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY REQUIRE SYNCHRONOUS COORDINATION INSUFFICIENT SUFFICIENT COORDINATION-FREE
260. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY REQUIRE SYNCHRONOUS COORDINATION INSUFFICIENT SUFFICIENT COORDINATION-FREE
261. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY COORDINATION-FREE INSUFFICIENT SUFFICIENT REQUIRE SYNCHRONOUS COORDINATION
262. SERIALIZABILITY SNAPSHOT ISOLATION REPEATABLE READ (PL-2.99) CURSOR STABILITY READ UNCOMMITTED READ COMMITTED CAUSAL PRAM RYW LINEARIZABILITY EVENTUAL CONSISTENCY COORDINATION-FREE ATOMIC VISIBILITY VIA RAMP INSUFFICIENT SUFFICIENT REQUIRE SYNCHRONOUS COORDINATION
263. RAMP IN CASSANDRA
264. RAMP IN CASSANDRA USES
265. RAMP IN CASSANDRA USES REQUIREMENTS
266. RAMP IN CASSANDRA USES REQUIREMENTS IMPLEMENTATION
267. RAMP IN CASSANDRA STRAIGHTFORWARD USES: •Add atomic visibility to atomic batch operations •Expose as CQL isolation level • USING CONSISTENCY READ_ATOMIC •Encourage use in multi-put, multi-get •Treat as basis for global secondary indexing •CREATE GLOBAL INDEX on users (age )
268. RAMP IN CASSANDRA REQUIREMENTS: •Unique timestamp generation for transactions •Use node ID from ring •Other form of UUID •Hash transaction contents* •Limited multi-versioning for prepared and old values •RAMP doesn’t actually require true MVCC •One proposal: keep a look aside cache
269. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: Lookaside cache for prepared and old values ! Standard C* Table stores last committed write 1 52 335 1240 1402 2201
270. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: Lookaside cache for prepared and old values ! Standard C* Table stores last committed write Shadow table stores prepared-but-not-committed and overwritten versions 1 52 335 1240 1402 2201 64 335 2201
271. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: Lookaside cache for prepared and old values ! Standard C* Table stores last committed write Shadow table stores prepared-but-not-committed and overwritten versions 1 52 335 1240 1402 2201 64 335 2201 Transparent to end-users
272. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: Lookaside cache for prepared and old values ! Standard C* Table stores last committed write Shadow table stores prepared-but-not-committed and overwritten versions 1 52 335 1240 1402 2201 64 335 2201 Overwritten versions have TTL set to max read transaction time, do not need durability Transparent to end-users
273. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION:
274. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: OPERATION CONSISTENCY LEVEL Write Prepare CL.QUORUM Write Commit CL.ANY or higher First-round Read CL.ANY/CL.ONE Second-round Read CL.QUORUM
275. RAMP IN CASSANDRA To avoid stalling, second-round reads must be able to access prepared writes POSSIBLE IMPLEMENTATION: OPERATION CONSISTENCY LEVEL Write Prepare CL.QUORUM Write Commit CL.ANY or higher First-round Read CL.ANY/CL.ONE Second-round Read CL.QUORUM
276. RAMP IN CASSANDRA To avoid stalling, second-round reads must be able to access prepared writes POSSIBLE IMPLEMENTATION: OPERATION CONSISTENCY LEVEL Write Prepare CL.QUORUM Write Commit CL.ANY or higher First-round Read CL.ANY/CL.ONE Second-round Read CL.QUORUM
277. RAMP IN CASSANDRA To avoid stalling, second-round reads must be able to access prepared writes POSSIBLE IMPLEMENTATION: OPERATION CONSISTENCY LEVEL Write Prepare CL.QUORUM Write Commit CL.ANY or higher First-round Read CL.ANY/CL.ONE Second-round Read CL.QUORUM
278. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION:
279. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: DC1
280. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: DC1 DC2
281. RAMP IN CASSANDRA POSSIBLE IMPLEMENTATION: DC1 DC2 Run algorithms on a per-DC basis, with use of CL.LOCAL_QUORUM instead of full CL.QUORUM
283. RAMP TRANSACTIONS:
284. RAMP TRANSACTIONS: • Provide atomic visibility, as required for maintaining FKs, scalable indexing, mat views
285. RAMP TRANSACTIONS: • Provide atomic visibility, as required for maintaining FKs, scalable indexing, mat views • Avoid in-place updates, mutual exclusion, any synchronous/blocking coordination
286. RAMP TRANSACTIONS: • Provide atomic visibility, as required for maintaining FKs, scalable indexing, mat views • Avoid in-place updates, mutual exclusion, any synchronous/blocking coordination • Use metadata with limited multi versioning, reads repair partial writes
287. RAMP TRANSACTIONS: • Provide atomic visibility, as required for maintaining FKs, scalable indexing, mat views • Avoid in-place updates, mutual exclusion, any synchronous/blocking coordination • Use metadata with limited multi versioning, reads repair partial writes • 1-2RTT overhead, pay only during contention
288. RAMP TRANSACTIONS: • Provide atomic visibility, as required for maintaining FKs, scalable indexing, mat views • Avoid in-place updates, mutual exclusion, any synchronous/blocking coordination • Use metadata with limited multi versioning, reads repair partial writes • 1-2RTT overhead, pay only during contention Thanks! http://tiny.cc/ramp-code @pbailis http://tiny.cc/ramp-intro
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