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Replicated Databases

Replicated Databases. Reading. Textbook: Ch.13. Review. Centralized DBMS Distributed DBMS Data fragmentation and allocation Top-down design Bottom-up design Transaction processing Serializability theorem Locking protocols Reliability. Replicated Databases.

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Replicated Databases

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  1. Replicated Databases

  2. Reading • Textbook: Ch.13 CSCE 824 - Spring 2011

  3. Review • Centralized DBMS • Distributed DBMS • Data fragmentation and allocation • Top-down design • Bottom-up design • Transaction processing • Serializability theorem • Locking protocols • Reliability CSCE 824 - Spring 2011

  4. Replicated Databases • Multiple copies of the same data items (databases) • Consistency: • Local consistency • Mutual consistency CSCE 824 - Spring 2011

  5. Why Replication? • System availability • Performance • Scalability • Application requirements CSCE 824 - Spring 2011

  6. Risk of Replication • Worse performance: updates must be applied to all replicas and synchronized • Worse availability: some algorithms require multiple replicas to be operational for any of them to be used CSCE 824 - Spring 2011

  7. Transaction Correctness • 2-Phase Locking – serializability • 2-Phase Commit – reliability • Replica control – mutual consistency • Database design: local vs. global transactions • Database consistency: strong consistency vs. weak consistency • Location of updates: master vs. distributed • Update propagation: eager vs. lazy • Degree of transparency: limited vs. full CSCE 824 - Spring 2011

  8. Mutual Consistency vs. Transaction Consistency • Transaction consistency: global serializability • Mutual consistency: replicas having the same values • Strong: all replicas have the same value at the end of the execution of an update transaction • Quorum: a quorum of replicas have the same value • Weak: eventually the values of all replicas become identical CSCE 824 - Spring 2011

  9. Replica Control • Hides replication from transaction • Knows location of all replicas • Translates transaction’s request to access an item into request to access particular replica(s) • Maintains some form of mutual consistency CSCE 824 - Spring 2011 9

  10. One-Copy Serializability (1SR) • Extension of the serializability theory • Effects of transactions on replicated data items should be the same as if they had been performed one at-a-time on a single set of date items CSCE 824 - Spring 2011

  11. x1 Transaction x2 x3 Example Replication • Issues • May reduce performance (complex operations) • Too expensive • Can’t control when replicas are updated CSCE 824 - Spring 2011 11 7/22/99

  12. Replica Control • Pessimistic replica control: at most one group can make an update – mutual consistency at all times • Optimistic replica control: system must be available at all times. Correct if there is any violation of mutual consistency CSCE 824 - Spring 2011

  13. Read One / Write All Replica Control • Pessimistic approach • Read the nearest replica • Write all replicas • Synchronous : before transaction commits • Asynchronous case: eventually • Advantage: • Mutual consistency • Performance benefits: reads transactions • Disadvantage: availability is not always guaranteed • E.g., Primary site approach CSCE 824 - Spring 2011 13

  14. Primary Site – static • Primary site: most recent copy • What happens if the network is partitioned? 2 DB0 1 Primary DB3 DB1 DB2 DB6 DB5 DB4 CSCE 824 - Spring 2011

  15. Majority Approach • The group that contains the majority of the sites can process an update DB0 1 DB3 DB1 DB2 DB6 DB5 DB4 CSCE 824 - Spring 2011

  16. Majority Approach • The group that contains the majority of the sites can process an update 2 DB0 (N+1)/2 1 DB3 DB1 DB2 DB6 DB5 DB4 Farkas CSCE 824 - Spring 2011 CSCE 824 - Spring 2011 16

  17. Majority Approach • Advantages: more flexible than primary site • Disadvantages: zero availability may still happen • Who has the most recent copy? • Version number: • Each site assigns a version number to the copy (initially VN=0) • After an update, the VN is incremented by 1 CSCE 824 - Spring 2011

  18. Quorum Consensus • Each sites are not equal • Special case of majority approach W=5 DB0 W=3 W=2 DB3 DB1 W=1 W=1 DB2 DB6 DB5 DB4 W=1 W=15 CSCE 824 - Spring 2011

  19. Other Approaches • Dynamic Linear: order sites linearly to calculate majority • Token-based primary site (moving token): change the location of the primary site CSCE 824 - Spring 2011

  20. Pessimistic Replica Control • Advantages: • Mutual consistency at all times • Know the latest version ( between two consecutive updates, there is a site in common) • Disadvantage: • May result in zero availability CSCE 824 - Spring 2011

  21. Optimistic Replica Control • Goal: availability at all time • Issues: consistency may not be guaranteed • Need an algorithm to detect whether an inconsistency occurred • Take actions to fix any inconsistencies CSCE 824 - Spring 2011

  22. Example Optimistic Alg. • Two partitions P1, P2 • Assumption: separately, P1 and P2 produces serializable histories • Need: after P1 and P2 joins again: Detect which transactions violate global serializability CSCE 824 - Spring 2011

  23. Example cont. • Items read by transaction T: read(T) • Items written by transaction T: write(T) • Assume: write(T)  read(T) • Transactions in P1: T1i , in P2: T2i CSCE 824 - Spring 2011

  24. Example cont. • Precedence graph: G • Nodes: {T11, …,T1n, T21, …, T2m} • Edges: • Dependency edge (ripple effect): there is an edge TijTikif j<k and there is a data item d, s.t., d  write (Tij)  read(Tik) and there is no l s.t., j<l<k and d is in the write set in Til (to consider dirty read within the same partition) CSCE 824 - Spring 2011

  25. Example cont. • Precendence edges: there is an edge TijTikif j<k and there is a data item d, s.t., d  read(Tij)  write(Tik) and there is no l s.t., j<l<k and d is in the write set in Til (to consider the first transaction to write a data item after a read within the same partition) CSCE 824 - Spring 2011

  26. Example cont. • Interference edges: there is an edge T1i T2j if j<k and there is a data item d, s.t., d  read(T1i)  write(T2j) or vice verse (to consider when T1i reads something written by T2j) CSCE 824 - Spring 2011

  27. Example cont. • Theorem: The combined histories are correct iff the precendense graph is acyclic • Correct inconsistencies: remove (undo) transactions that make the graph cyclic CSCE 824 - Spring 2011

  28. Summary • Correctness: If the transactions are ACID, local execution in serializable, distributed transactions are reliable, and update replication is synchronous then distributed transactions are globally atomic & serializable • Performance: • Applications: transactions are not always serializable (e.g., WS-transactions) • Replication: update propagation is not always asynchronous • Compensating transactions CSCE 824 - Spring 2011

  29. Next Class Review distributed databases Design Concurrency control Reliability Replication CSCE 824 - Spring 2011

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