Coordination and the Art of Scaling

CloudantCON 2014
17 June 2014
http://www.cloudantcon.com/#schedule

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http://www.bailis.org/blog/
http://www.bailis.org/pubs.html
@pbailis

Transcript

1. COORDINATION AND THE ART OF SCALING Peter Bailis • UC Berkeley • @pbailis CloudantCON 2014
2. A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable. —Leslie Lamport 2013 Turing Award Winner
6. THE NETWORK INCURS LATENCY
7. THE NETWORK INCURS LATENCY THE NETWORK IS UNRELIABLE
8. THE NETWORK INCURS LATENCY THE NETWORK IS UNRELIABLE SO HOW CAN WE BUILD ROBUST AND SCALABLE DISTRIBUTED SYSTEMS?
9. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE
10. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE (SERIALIZABILITY/LINEARIZABILITY)
11. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE Impose a total order on events in the system
12. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE TIME Impose a total order on events in the system
13. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE TIME Impose a total order on events in the system Ask Am anda: “how ’s the w eather on the farm ?” Am anda replies: “Let m e check w ith the tractor.” Am anda replies: “It’s a beautiful day!” Tractor replies: current tem perature is 75°F
14. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE Impose a total order on events in the system TIME Illusion created by a partially ordered protocol
15. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE TIME Impose a total order on events in the system Illusion created by a partially ordered protocol Remarkably powerful abstraction core to ACID transactions
16. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE TIME Impose a total order on events in the system Illusion created by a partially ordered protocol Remarkably powerful abstraction This is the way you’d want to program distributed systems, but… core to ACID transactions
17. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE TIME Impose a total order on events in the system Illusion created by a partially ordered protocol COST:
18. THE SIMPLE ANSWER: SINGLE-SYSTEM IMAGE TIME Impose a total order on events in the system Illusion created by a partially ordered protocol COST: BLOCKING COMMUNICATION COORDINATION
19. COORDINATION (BLOCKING COMMUNICATION) Can I make progress without waiting?
20. COORDINATION (BLOCKING COMMUNICATION) Can I make progress without waiting? UNDER SINGLE SYSTEM IMAGE, MUST WAIT!
22. COORDINATION REQUIRED? Throughput: 1/delay
23. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources
24. SERIALIZABLE TRANSACTIONS ON EC2 IN-MEMORY LOCKING “Coordination-Avoiding Database Systems” arXiv:1402.2237
25. 1 2 3 4 5 6 7 Number of Items per Transaction Throughput (txns/s) SERIALIZABLE TRANSACTIONS ON EC2 IN-MEMORY LOCKING LOG SCALE! “Coordination-Avoiding Database Systems” arXiv:1402.2237
26. 1 2 3 4 5 6 7 Number of Items per Transaction Throughput (txns/s) SERIALIZABLE TRANSACTIONS ON EC2 IN-MEMORY LOCKING COORDINATED “Coordination-Avoiding Database Systems” arXiv:1402.2237
27. SERIALIZABLE TRANSACTIONS ON EC2 IN-MEMORY LOCKING 1 2 3 4 5 6 7 Number of Items per Transaction Throughput (txns/s) COORDINATED COORDINATION-FREE “Coordination-Avoiding Database Systems” arXiv:1402.2237
28. SERIALIZABLE TRANSACTIONS ON EC2 IN-MEMORY LOCKING SINGLE SERVER: 10x faster (multi-core parallelism) MULTI-SERVER: ~1000x faster 1 2 3 4 5 6 7 Number of Items per Transaction Throughput (txns/s) COORDINATED COORDINATION-FREE “Coordination-Avoiding Database Systems” arXiv:1402.2237
29. do not support! SSI/serializability HANA
30. do not support! SSI/serializability HANA Actian Ingres YES Aerospike NO! N Persistit NO! N Clustrix NO! N Greenplum YES IBM DB2 YES IBM Informix YES MySQL YES MemSQL NO! N MS SQL Server YES NuoDB NO! N Oracle 11G NO! N Oracle BDB YES Oracle BDB JE YES Postgres 9.2.2 YES SAP HANA NO! N ScaleDB NO! N VoltDB YES 8/18 databases! surveyed did not “Highly Available Transactions: Virtues and Limitations” VLDB 2014
31. do not support! SSI/serializability HANA Actian Ingres YES Aerospike NO! N Persistit NO! N Clustrix NO! N Greenplum YES IBM DB2 YES IBM Informix YES MySQL YES MemSQL NO! N MS SQL Server YES NuoDB NO! N Oracle 11G NO! N Oracle BDB YES Oracle BDB JE YES Postgres 9.2.2 YES SAP HANA NO! N ScaleDB NO! N VoltDB YES 8/18 databases! surveyed did not 15/18 used! weaker models! by default “Highly Available Transactions: Virtues and Limitations” VLDB 2014
32. do not support! SSI/serializability HANA Actian Ingres YES Aerospike NO! N Persistit NO! N Clustrix NO! N Greenplum YES IBM DB2 YES IBM Informix YES MySQL YES MemSQL NO! N MS SQL Server YES NuoDB NO! N Oracle 11G NO! N Oracle BDB YES Oracle BDB JE YES Postgres 9.2.2 YES SAP HANA NO! N ScaleDB NO! N VoltDB YES 8/18 databases! surveyed did not 15/18 used! weaker models! by default “Highly Available Transactions: Virtues and Limitations” VLDB 2014
33. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources
34. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately
35. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately SINGLE DC: .5 ms on public cloud 5 µs on Infiniband
36. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately SINGLE DC: .5 ms on public cloud 5 µs on Infiniband MULTI-DC?
39. 133.7+ ms RTT
40. 133.7+ ms RTT
41. 133.7+ ms RTT
42. 133.7+ ms RTT 85.1+ ms RTT
43. THOSE LIGHT CONES_
44. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately Unavailable during failures Progress despite failures
45. COORDINATION-FREE EXECUTION IS KEY TO INDEFINITE SCALABILITY
46. COORDINATION IS THE BANE OF SCALABLE SYSTEMS
47. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately Unavailable during failures Progress despite failures WHEN DO WE HAVE TO COORDINATE?
48. THAT SIMULTANEITY_
49. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately Unavailable during failures Progress despite failures WHEN DO WE HAVE TO COORDINATE?
50. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately Unavailable during failures Progress despite failures CAP Theorem (for recency guarantees) FLP result (for consensus; e.g., Paxos) WHEN DO WE HAVE TO COORDINATE? Davidson result (for SSI)
51. COORDINATION REQUIRED? COORDINATION FREE? Throughput: 1/delay Limited by physical resources Latency: 1+ RTT Can return immediately Unavailable during failures Progress despite failures CAP Theorem (for recency guarantees) FLP result (for consensus; e.g., Paxos) BUT DO APPS ALWAYS HAVE TO COORDINATE? WHEN DO WE HAVE TO COORDINATE? Davidson result (for SSI)
53. TICKET 241 TICKET 242 TICKET 243 TICKET 244
54. TICKET 241 TICKET 242 TICKET 243 TICKET 244
56. INVARIANT: TICKET IDs SHOULD BE SEQUENTIAL
57. INVARIANT: TICKET IDs SHOULD BE SEQUENTIAL TICKET 241 TICKET 242 TICKET 243
58. INVARIANT: TICKET IDs SHOULD BE SEQUENTIAL TICKET 241 TICKET 241 COORDINATION REQUIRED!
59. INVARIANT: TICKET IDs SHOULD BE UNIQUE TICKET 241 TICKET 242 PRE-PARTITION ID SPACE (1,4,…) (2,5,…) (3,6,…)
60. INVARIANT: TICKET IDs SHOULD BE NON-NEGATIVE TICKET 241 TICKET 242 COORDINATION-FREE!
61. INVARIANT: TICKET IDs SHOULD BE NON-NEGATIVE COORDINATION-FREE! INVARIANT: TICKET IDs SHOULD BE UNIQUE PRE-PARTITION ID SPACE INVARIANT: TICKET IDs SHOULD BE SEQUENTIAL COORDINATION REQUIRED!
62. INVARIANT: TICKET IDs SHOULD BE NON-NEGATIVE COORDINATION-FREE! INVARIANT: TICKET IDs SHOULD BE UNIQUE PRE-PARTITION ID SPACE INVARIANT: TICKET IDs SHOULD BE SEQUENTIAL COORDINATION REQUIRED! WHEN DO WE HAVE TO COORDINATE? DEPENDS ON APPLICATION SAFE ANSWER: ALWAYS COORDINATE
63. WHEN DO WE HAVE TO COORDINATE? SAFE ANSWER: ALWAYS COORDINATE
64. WHEN DO WE HAVE TO COORDINATE? SAFE ANSWER: ALWAYS COORDINATE BETTER ANSWER: (YOUR TAX DOLLARS AT WORK)
65. WHEN DO WE HAVE TO COORDINATE? SAFE ANSWER: ALWAYS COORDINATE BETTER ANSWER: COORDINATION AVOIDANCE COORDINATE ONLY WHEN STRICTLY NECESSARY MOVE COMMUNICATION TO BACKGROUND “Coordination-Avoiding Database Systems” arXiv:1402.2237
67. SAFETY correctness always guaranteed LIVENESS database states agree (converge)
68. Invariant Confluence is necessary and sufficient for ensuring safety, convergence, availability, and coordination-free execution. Invariant Confluence holds?! A safe, c-free execution strategy exists. Invariant Confluence fails?! No safe, c-free mechanism exists. “Coordination-Avoiding Database Systems” arXiv:1402.2237
69. Invariant Operation C.F. Equality, Inequality Any ??? Generate unique ID Any ??? Specify unique ID Insert ??? >! Increment ??? >! Decrement ??? < Decrement ??? < Increment ??? Foreign Key Insert ??? Foreign Key Delete ??? Secondary Indexing Any ??? Materialized Views Any ??? AUTO_INCREMENT Insert ??? Typical DB! operations and ! invariants! (SQL) “Coordination-Avoiding Database Systems” arXiv:1402.2237
70. Invariant Operation C.F. Equality, Inequality Any Y Generate unique ID Any Y Specify unique ID Insert N >! Increment Y >! Decrement N < Decrement Y < Increment N Foreign Key Insert Y Foreign Key Delete Y* Secondary Indexing Any Y Materialized Views Any Y! AUTO_INCREMENT Insert N Typical DB! operations and ! invariants! (SQL) “Coordination-Avoiding Database Systems” arXiv:1402.2237
71. Test fails? Cannot avoid coordination Invariant Operation C.F. Equality, Inequality Any Y Generate unique ID Any Y Specify unique ID Insert N >! Increment Y >! Decrement N < Decrement Y < Increment N Foreign Key Insert Y Foreign Key Delete Y* Secondary Indexing Any Y Materialized Views Any Y! AUTO_INCREMENT Insert N Typical DB! operations and ! invariants! (SQL) “Coordination-Avoiding Database Systems” arXiv:1402.2237
72. Test fails? Cannot avoid coordination Invariant Operation C.F. Equality, Inequality Any Y Generate unique ID Any Y Specify unique ID Insert N >! Increment Y >! Decrement N < Decrement Y < Increment N Foreign Key Insert Y Foreign Key Delete Y* Secondary Indexing Any Y Materialized Views Any Y! AUTO_INCREMENT Insert N MANY TRADITIONAL DB APPS OK Typical DB! operations and ! invariants! (SQL) “Coordination-Avoiding Database Systems” arXiv:1402.2237
73. Test fails? Cannot avoid coordination Invariant Operation C.F. Equality, Inequality Any Y Generate unique ID Any Y Specify unique ID Insert N >! Increment Y >! Decrement N < Decrement Y < Increment N Foreign Key Insert Y Foreign Key Delete Y* Secondary Indexing Any Y Materialized Views Any Y! AUTO_INCREMENT Insert N MANY TRADITIONAL DB APPS OK Typical DB! operations and ! invariants! (SQL) “Coordination-Avoiding Database Systems” arXiv:1402.2237
74. FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
75. FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 FRIENDS FRIENDS
76. as FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 FRIENDS FRIENDS
77. as s FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013
78. as FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates
79. as FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates s
80. as FOREIGN KEY DEPENDENCIES “TAO: Facebook’s Distributed Data Store for the Social Graph” USENIX ATC 2013 s Denormalized Friend List Fast reads… …multi-entity updates s
81. as FOREIGN KEY DEPENDENCIES “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
82. NEED ATOMIC VISIBILITY FOREIGN KEY DEPENDENCIES “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
83. NEED ATOMIC VISIBILITY SEE ALL OF A TXN’S UPDATES, OR NONE OF THEM FOREIGN KEY DEPENDENCIES “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
84. NEED ATOMIC VISIBILITY SEE ALL OF A TXN’S UPDATES, OR NONE OF THEM FOREIGN KEY DEPENDENCIES SECONDARY INDEXING MATERIALIZED VIEWS “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
85. X=0 Y=0 HOW TO ACHIEVE ATOMIC VISIBILITY “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
86. STRAWMAN: LOCKING X=0 Y=0 “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
87. STRAWMAN: LOCKING X=0 Y=0 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
88. STRAWMAN: LOCKING X=0 Y=0 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
89. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
90. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
91. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
92. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
93. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) R(X=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
94. STRAWMAN: LOCKING X=1 Y=1 W(X=1) W(Y=1) R(X=1) R(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
95. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
96. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) R(X=?) R(Y=?) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
97. Y=0 STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
98. STRAWMAN: LOCKING X=1 W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) ATOMIC VISIBILITY COUPLED WITH MUTUAL EXCLUSION SLOW unavailable “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
99. TRANSACTIONS R A M P TOMIC EAD ULTI- ARTITION “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
100. TRANSACTIONS R A M P TOMIC EAD ULTI- ARTITION “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
101. TRANSACTIONS RAMP DECOUPLE ATOMIC VISIBILITY MUTUAL EXCLUSION “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
102. TRANSACTIONS RAMP DECOUPLE ATOMIC VISIBILITY MUTUAL EXCLUSION from “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
103. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) X=1 “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
104. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) X=1 “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
105. BASIC IDEA W(X=1) W(Y=1) Y=0 R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
106. 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 “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
107. BASIC IDEA LET CLIENTS RACE, but HAVE READERS “CLEAN UP” LIMITED MULTI-VERSIONING + METADATA X=0 Y=0 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
108. BASIC IDEA LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 LIMITED MULTI-VERSIONING + METADATA X=0 Y=0 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
109. BASIC IDEA LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 LIMITED MULTI-VERSIONING + METADATA X=0 Y=1 Y=0 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
110. BASIC IDEA LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 LIMITED MULTI-VERSIONING + METADATA X=0 Y=1 Y=0 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
111. BASIC IDEA LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 LIMITED MULTI-VERSIONING + METADATA X=0 Y=1 Y=0 W(X=1) W(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
112. BASIC IDEA W(X=1) W(Y=1) R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 [t=124, {Y}] LIMITED MULTI-VERSIONING + METADATA X=0 [t=0, {}] Y=1 [t=124, {X}] Y=0 [t=0, {}] R(X=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
113. BASIC IDEA W(X=1) W(Y=1) R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 [t=124, {Y}] LIMITED MULTI-VERSIONING + METADATA X=0 [t=0, {}] Y=1 [t=124, {X}] Y=0 [t=0, {}] R(Y=0) R(X=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
114. BASIC IDEA W(X=1) W(Y=1) R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 [t=124, {Y}] LIMITED MULTI-VERSIONING + METADATA X=0 [t=0, {}] Y=1 [t=124, {X}] Y=0 [t=0, {}] R(Y=0) R(X=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
115. BASIC IDEA W(X=1) W(Y=1) R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 [t=124, {Y}] LIMITED MULTI-VERSIONING + METADATA X=0 [t=0, {}] Y=1 [t=124, {X}] Y=0 [t=0, {}] R(Y=0) ITEM HIGHEST TS X 124 Y 124 R(X=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
116. BASIC IDEA W(X=1) W(Y=1) R(X=?) R(Y=?) LET CLIENTS RACE, but HAVE READERS “CLEAN UP” X=1 [t=124, {Y}] LIMITED MULTI-VERSIONING + METADATA X=0 [t=0, {}] Y=1 [t=124, {X}] Y=0 [t=0, {}] R(Y=0) ITEM HIGHEST TS X 124 Y 124 R(X=1) R(Y=1) “Scalable Atomic Visibility with RAMP Transactions” SIGMOD 2014
117. TPCC Combine fkeys with sequence number insert on commit... 500K txns/s
118. 47,852 Serializable locking bottlenecks on coordination over network “Coordination-Avoiding Database Systems” arXiv:1402.2237 New-Order Transactions/s
119. 47,852 Serializable locking bottlenecks on coordination over network 632,589 Coordination-avoiding implementation (RAMP with fast ID assignment) bottlenecks on CPU EC2 cr1.8xlarge here, 8 servers “Coordination-Avoiding Database Systems” arXiv:1402.2237 New-Order Transactions/s
120. 0 50 100 150 200 Number of Servers 2M 4M 6M 8M 10M 12M 14M Total Throughput (txn/s)
121. 0 50 100 150 200 Number of Servers 2M 4M 6M 8M 10M 12M 14M Total Throughput (txn/s) INDUSTRY-STANDARD TRANSACTIONAL WORKLOADS CAN SCALE JUST FINE*
122. INDUSTRY-STANDARD TRANSACTIONAL WORKLOADS CAN SCALE JUST FINE* GIVEN THE RIGHT MANY
123. INDUSTRY-STANDARD TRANSACTIONAL WORKLOADS CAN SCALE JUST FINE* GIVEN THE RIGHT SYSTEM DESIGN CONCURRENCY PRIMITIVES ATTENTION TO SCALE MANY
124. INDUSTRY-STANDARD TRANSACTIONAL WORKLOADS CAN SCALE JUST FINE* GIVEN THE RIGHT SYSTEM DESIGN CONCURRENCY PRIMITIVES ATTENTION TO SCALE LEVEL OF COORDINATION MANY
125. THE NETWORK INCURS LATENCY THE NETWORK IS UNRELIABLE SO HOW CAN WE BUILD ROBUST AND SCALABLE DISTRIBUTED SYSTEMS?
126. THE NETWORK INCURS LATENCY THE NETWORK IS UNRELIABLE SO HOW CAN WE BUILD ROBUST AND SCALABLE DISTRIBUTED SYSTEMS? UNDERSTAND COORDINATION
127. COORDINATION AVOIDANCE UNDERSTAND IF/WHEN COORDINATION IS REQUIRED
128. COORDINATION AVOIDANCE UNDERSTAND IF/WHEN COORDINATION IS REQUIRED INVARIANT CONFLUENCE (arXiv 2014) necessary and sufficient condition for c-free operation HIGHLY AVAILABLE TRANSACTIONS (CACM, VLDB 2014) what database isolation levels are coordination-free? RAMP ATOMIC VISIBILITY (SIGMOD 2014) fast and intuitive multi-put, multi-get, indexing BLOOM and BLAZES (ICDE 2014) language-level automated coordination analysis CRDTS and BLOOM^L (SoCC 2013, USENIX ATC 2014) correct-by-design distributed data types PBS INCONSISTENCY (VLDBJ 2014) how stale is data if we don’t coordinate?
129. Traditional distributed systems designs! suffer from coordination bottlenecks By understanding application requirements,! we can avoid coordination We can build systems that actually scale! while providing correct behavior Thanks!! ! pbailis@cs.berkeley.edu! @pbailis! http://bailis.org/ http://amplab.cs.berkeley.edu/!
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