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Client-centric Consistency Models

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A brief presentation about client-centric consistency models and how they can be modelled on Hazelcast

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Client-centric Consistency Models

  1. 1. Client-centric consistency models Ensar Basri Kahveci
  2. 2. Data-centric Consistency Models - Maintains a globally-accessible and globally-consistent data store. - Linearizability, sequential consistency, …
  3. 3. Client-centric Consistency Models - Instead of a globally-consistent view, maintains consistent views for individual clients. - Easier to deal with inconsistencies. - Eventual consistency, monotonic reads, monotonic writes, read your writes, writes following reads… [1], [5]
  4. 4. Monotonic reads (MR) - If a client reads a data entry X, any successive read of X by the same client returns the same value or a more recent value.
  5. 5. Monotonic WRITES (MW) - If a client first performs Write1 then Write2, Write1 is ordered and executed before Write2 on all copies of the data.
  6. 6. Writes Following Reads (WFR) - The client makes a write operation: Write1. Then, it eventually observes result of Write1 with a ReadX, which is not necessarily the successive operation of Write1. After ReadX, the client makes a new write: Write2. - On all copies of the entry, Write1 is ordered and executed before Write2. - Happens-before relationship …
  7. 7. Read Your Writes (RYW) - The effect of a write operation by a client on a data entry X will always be seen by a successive read operation on X by the same client.
  8. 8. Session Guarantees for Weakly ConsistentReplicated Data [1] - Defines MR, MW, WFR and RYW guarantees using sessions. - A client connects to the system and operates within the boundaries of a session. - It can talk to a single server or a set of servers, and it can behave like a server. - Consistency guarantees are either ensured by the session manager in the client during the session, or the client learns that the guarantees cannot be continued, which means that the session has ended.
  9. 9. Read Your Writes (RYW)
  10. 10. Highly Available Transactions: Virtues andLimitations [2] - In the presence of arbitrary, indefinitely-long network partitions … - High availability: If a client can contact to a non-failing server, it eventually receives a response. - Sticky availability: If a client’s transactions is executed against a copy of the database that reflects all of its prior operations, the client eventually receives a response. - Any guarantee achievable in a highly-available system is also achievable in a sticky-available system, but not vice-versa.
  11. 11. Aphyr’s consistency models tree [2], [3]
  12. 12. What does it mean? - You can get linearizability and sequential consistency in a CP system. - You can get MR, MW, WFR in an AP system. - If you have sticky sessions, you can get RYW in an AP system. Otherwise, you need a CP system for it. - MR + MW + RYW = PRAM consistency - PRAM + WFR = Causal consistency
  13. 13. How does it work? - RYW against MR, MW and WFR - Delaying visibility of writes in MR, MW and WFR [2], [4]
  14. 14. How TO map these models to hazelcast? - In a Hazelcast cluster: - Each partition has a single owner and possibly multiple backups. - All operations are executed on the partition owner. - Partition operations are linearizable in a stable Hazelcast cluster.
  15. 15. DETECTING INCONSISTENCY CASES IN HAzELCAST - Can we detect if there is a possibility of data inconsistency on failures? - A hypothetical ConsistencyLostListener implementation - For the following examples, a single partition with 2 replicas is considered. Additionally, lets assume partition operations return partition version in responses and our proxy classes can track them.
  16. 16. EXAMPLE #1: MR is lost
  17. 17. EXAMPLE #2: RYW is lost
  18. 18. EXAMPLE #3: RYW - Vector clocks NEEDED
  19. 19. EXAMPLE #4: RYW with Vector clocks
  20. 20. EXAMPLE #5: MW is lost. We need a Dirty flag.Same for WFR
  21. 21. RECAP - Linearizability and sequential consistency is achievable in CP systems. - Hazelcast already provides linearizability on partition level in a stable cluster (e.g., no network partitioning or partition owner crash). - In AP systems, causal consistency is achievable with sticky sessions. - Hazelcast proxies already are already as they talk to partition owners. - Hazelcast proxies can detect the possibility of inconsistency situations. - On partition owner failure, they can check if they continue the current session or not.
  22. 22. REFERENCES [1] Terry, Douglas B., et al. "Session guarantees for weakly consistent replicated data." Parallel and Distributed Information Systems, 1994., Proceedings of the Third International Conference on. IEEE, 1994. [2] Bailis, Peter, et al. "Highly available transactions: Virtues and limitations." Proceedings of the VLDB Endowment 7.3 (2013): 181-192. [3] https://aphyr.com/posts/313-strong-consistency-models [4] http://www.bailis.org/blog/stickiness-and-client-server-session-guarantees/ [5] http://www.allthingsdistributed.com/2008/12/eventually_consistent.html
  23. 23. THANKS!Any questions?

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