Consistency in Distributed Systems
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This document provides an overview of key concepts in distributed systems including: 1) The CAP theorem which states that a distributed system cannot simultaneously provide consistency, availability, and partition tolerance. 2) Consistency models such as strong, weak, and eventual consistency. Eventual consistency guarantees that if no new writes are made, reads will return the last updated value. 3) Consensus protocols like Paxos which allow distributed systems to agree on a value despite potential failures. Vector clocks are used to order events in a distributed system.
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Consistency in Distributed Systems
- 1. Consistency in Distributed Systems Shane K Johnson CITYECH, Inc. March 31, 2010
- 3. CAP Theorem BASE Consistency Eventual Consistency Consistent Hashing
- 7. Partitioned
- 8. Both
- 9. CAP Theorem Brewer's Conjecture Eric Brewer
- 10. ACM Symposium on the Principles of Distributed Computing (PODC)
- 11. July 19th 2000 Brewer's Theorom Formally Proved by Seth Gilbert/Nancy Lynch
- 12. MIT
- 13. 2002
- 14. CAP Theorem Consistent "...the C in ACID...a preset contraint of distributed systems that multiple values for the same piece of data are not allowed..." Available
- 16. BASE Alternative to ACID Brewer
- 17. SOSP Article
- 18. 1997
- 19. BASE Basically
- 20. Available
- 21. Soft State
- 23. Consistency Strong “After the update completes, any subsequent access will return the updated value.” Weak “The system does not guarantee that subsequent accesses will return the updated value.” Inconsistency Windows Eventual “The storage system guarantees that if no new updates are made to the object, eventually all accesses will return the last updated value.”
- 24. Eventually Consistent Werner Vogels – CTO, Amazon
- 26. Read Your Writes
- 27. Session Session Based Read Your Writes Monotonic Read Once a process has seen a particular value, it will never see an older one. Monotonic Write Writes by the same process are serialized.
- 28. Eventually Consistent N = # Nodes
- 29. W = # Replicas to Acknowledge Update
- 30. R = # Replicas Contacted for Read
- 31. Strong (W + R > N) Synchronous Master/Master N=2, W=2, R=1 Weak/Eventual (W + R <= N) Asynchronous Master/Master N=2, W=1, R=1 Optimized Read R=1, N=W
- 32. Optimized Write W=1, R=N
- 33. Consistent Hashing Before Object o
- 34. # Nodes n hash(o) mod n Problem Rebalancing (Add/Remove Nodes)
- 35. Consistent Hashing Hash Nodes
- 36. Treat Hash Table as Hash Ring
- 37. Hash Object
- 38. Traverse Until Node Found
- 40. Paxos Leslie Lamport The Part-Time Parliament, 1998 Roles Client
- 41. Acceptor
- 42. Proposer Notified Quorum of Acceptors on Behalf of Client Learner Execute Request, Send Response to Client
- 43. Vector Clocks Lamport Timestamps -> Logical Clocks