1. Database Replication in WAN Yi Lin Supervised by: Prof. Kemme April 8, 2005

2. Contents 1.Introduction 2.Centralized Snapshot Isolation Replication (SIR) protocol 3.Decentralized SIR protocol for WAN 4.Experiments 5.Further optimizations 6.Related work 7.Conclusions and milestones

3. 1.Introduction: What,Why,How? …… Montreal TorontoOttawa Toronto Montreal Ottawa Without ReplicationWith Replication Benefits: Performance, Fault Tolerance Replica control WAN

4. x 1.Introduction, challenge x w(x) x x General Correctness Criteria: 1-copy-serializability

5. 1. Introduction, 1-copy-serializability 1-copy-serializability –The replicated system behaves as one database providing serializability Serializability –Highest txn isolation level to what extend txns interfere with each other –The result is the same as executing them serially. –Conflict: read/write and write/write time T0 T1 T2 w(x) r(x) w(x) w(y) r(z), w(z) T3 w(x) r(x)w(y) r(x),w(x) r(z)w(z) time

6. 1. Introduction, 1-copy-SI Snapshot Isolation (SI): –Conflict: only write/write –Read from a snapshot of the committed data as of the time txn starts. –2 concurrent write txns. If one commits, the other aborts –Very popular (Oracle, PostgreSQL) 1-copy-SI –The replicated system behaves as one database providing SI time T0 T1 T2 w(x) r(x) w(x) w(y) commit abort

7. Challenge: –How to detect concurrent conflicting txns?  Validation r(x) 2. Centralized Snapshot Isolation Replication (SIR) Protocol xx x commitw(x) validation w(x) apply ws, commit Extract writeset x validation commit succeedfail

8. How to detect two txns are conflicting? –Writeset contains modified tuples and their corresponding primary keys. –If two writesets share some primary keys, they conflict. –Note: Snapshot Isolation only cares about write/write conflicts. Key=1T1T2 2. Centralized SIR Protocol

9. How to detect two txns are concurrent? T1 T2 start=1 A counter for each database, increased upon committing a txn Record start time and end time of txns T0.end  T1.start || T1.end  T0.start  T0 and T1 not concurrent. 2. Centralized SIR Protocol T0 start=0end=1 end=2 counter start=1

10. Centralized approach not good for WANs 3. Decentralized SIR Protocol for WANs Middleware replica DB WAN Centralized Architecture Middleware replica Middleware replica DB WAN Decentralized Architecture LAN

11. Group Comm, Total order 3. Decentralized SIR Protocol for WANs x x r(x) commit w(x) Extract writeset T1 T2 T1 T2 validation Challenge: 1.Validation  same as centralized approach 2.Total order  all middleware components make the same decision fail succeed apply ws, commit x x abort

12. 4. Experiments Fig. TPC-W benchmark, 5 sites, 50% update txns,

13. Group Comm, Total order 5. Some optimizations With GCS –Disadvantage: Total order expensive Large response time –Advantage: Uniform reliable for failover r(x) w(x) commit T1 T2 Without GCS, but with a sequencer –Advantage: Less communication overhead –Disadvantage: Complicated in Failover sequencer r(x) w(x) commit Extract writeset validation succeed fail commit abort

14. Kernel-based replica control Middleware-based replica control –Advantages Heterogeneous DB Easy to implement –Disadvantages No access to concurrency control in the kernel 6. Related work oralcePostgreSQL

15. 6. Related work Many have a centralized component. [Ganymed, Conflict Aware] –Does not work well in WANs Some are primary/secondary approaches.[Ganymed] –Updates must always be performed on primary copy –Need to mark read-only txn in advance Some n eed to know all operations in advance [ Conflict Aware] Some are table-based locking [Middle-R, Conflict Aware] Nearly all only look at 1-copy-serializability [Conflict Aware, GlobData, Middle-R, State Machine]

16. 7. Conclusions Work well in WANs –Only 1 multicast msg No restrictions such as –Marking read-only txn in advance –Knowing all operations in advance Tuple based locking 1-copy-SI

17. 7. Milestones Currently –1-copy-SI –Centralized and decentralized protocol formulized, implemented Sep, 2005: –Failover (coordinated with a Master project) Dec, 2005: –Further optimizations proposed in report May, 2006: –Recovery GCS Total order

18. References [SIR] Y. Lin, B. Kemme, R. Jimenez-Peris, and M. Patiòno-Martnez. Middleware based data replication providing snapshot isolation. In SIGMOD, June 2005. [Ganymed] C. Plattner and G. Alonso. Ganymed: Scalable replication for transactional web applications. In Middleware, 2004. [GlobData] L. Rodrigues, H. Miranda, R. Almeida, J. Martins, and P. Vicente. Strong Replication in the GlobData Middleware. In Workshop on Dependable Middleware-Based Systems, 2002. [Middle-R] R. Jimenez-Peris, M. Patiòno-Martnez, B. Kemme, and G. Alonso. Improving Scalability of Fault Tolerant Database Clusters. In ICDCS'02. [Conflict-Aware] C. Amza, A. L. Cox, and W. Zwaenepoel. Conict-Aware Scheduling for Dynamic Content Applications. In USENIX Symp. on Internet Tech. and Sys., 2003. [Postgres-R] S. Wu and B. Kemme. Postges-R(SI): Combining replica control with concurrency control based on snapshot isolation. In ICDE, Tokoyo, Japan, 2005. [State Machine] F. Pedone, R. Guerraoui, and A. Schiper. The Database State Machine Approach. Distributed and Parallel Databases, 14:71-98, 2003.