Replication and Query Processing in the APPA Data Management System

1. Replication and Query Processing in the APPA Data Management System Reza AKBARINIA Vidal MARTINS Esther PACITTI Patrick VALDURIEZ

2. Motivation • Advanced applications • They must deal with semantically rich data • They use a high-level SQL-like query language • Applications • Epidemiological study • Astronomic data sharing • Little work on managing data replication in the presence of updates • Gnutella and Kaaza: static files (no updates) • Freenet: update propagation downward to close connect peers • ActiveXML: on demand (web services) • P-Grid: rumor spreading (probabilistic guarantees for consistency)

3. Motivation • Replication in distributed systems • Synchronous replication (ROWA) • Asynchronous replication • Preventive replication • Optimistic replication • Rumor spreading • We propose a new P2P system to address • Data replication in the context of advanced applications • Query processing in the presence of advanced replication capabilities

4. Outline • Motivation • APPA Architecture • Data Replication • Query Processing • Validation • Conclusion • Motivation • APPA Architecture • Data Replication • Query Processing • Validation • Conclusion

5. Basic Services … P2P Data Management Consensus Advanced Services Replication Query Processing Caching ... P2P Network Key-based Storage and Retrieval Peer Linking Peer ID Assignment Peer Communication APPA Architecture APPA Internet

6. Outline • Motivation • APPA Architecture • Data Replication • Query Processing • Validation • Conclusion

7. Data ReplicationReplication Model • Assumptions • Frequent and unpredictable network changes • Small world • Based on lazy multi-master scheme • Log-based reconciliation to solve replica divergence • Schema management • r-lsd: local schema description of relation r • r-csd: common schema description of relation r • Each peer defines mapping functions between r-lsd and r-csd • Data storage • Each peer stores tuples using r-lsd and r-csd schemas • Updates in one schema are mapped to the other • Multi-master groups

8. Data ReplicationReconciliation Properties • Eventual consistency: when all clients stop submission of update actions, all replicas eventually achieve the same values • Mergeability: it is possible to schedule any arbitrary collection of log operations respecting constraints • Eventual decision: a decision is taken for each submitted action • Eventual propagation: actions and constraints known at peer “p” at time “t” are eventually known by an arbitrary peer of the group • Safe decisions: peers may not make conflicting decisions

9. Data ReplicationReconciliation Solutions • IceCube (Microsoft Research) • Centralized conflict detection and resolution • Resolution based on application semantics • Non-deterministic resolution • APPA • Distributed conflict detection and resolution • Resolution based on application semantics • Deterministic resolution – enables parallelism • Considers dynamique connections and disconnections

10. Data ReplicationAPPA Distribued Reconciliation • Foundation • Use a common action log (P2P data) • All tentative actions are stored in the action log • Action log actions are grouped by time interval (log unit) • The resolution (deterministic) is made on demand, comprises a log unit and produces a schedule • The schedules are available (P2P data) to all peers • Parallelism and distribution - scalability

11. Data ReplicationDistributed Reconciliation

12. Data ReplicationDistributed Reconciliation • Log units assure unique vision over unordered actions • Log unit life cycle must be managed • Decision factor eliminates non-determinism • Several peers can reconcile the same log unit concurrently • A peer can reuse the reconciliation made by another one • A peer can finish the reconciliation started by another one • Reconciliation properties are assured • Multi-master replication in P2P environment is reached

13. Data ReplicationService Architecture

14. Outline • Motivation • APPA Architecture • Data Replication • Query Processing • Validation • Conclusion

15. Query ProcessingProblem definition • Consider that • Each peer has a local schema to describe their data • Peers agree on a Common Schema Description (CSD) • Each peer maps its local schema to the CSD • Given a user query on a peer schema, the problem is • To find the minimum set of peers that should answer the query • To execute the query in these peers and return a list of (ranked) answers to the user • Assumption • A query answer includes data from several multi-master groups (all of them which store relevant data

16. Query ProcessingProposed Solution

17. Query ProcessingProposed Solution • Query reformulation • p:r(A,B,D)  csd:r1(A,B,C), csd:r2(C,D,E) • select A,D from r where B=b • select A,D from r1,r2 where B=b and r1.C=r2.C • Query matching • P: set of peers in the P2P system • ps(p,r): peer schema of peer “p” involves relation “r” • Problem: to find P’  P where each p in P’ has relevant data • Result: P’= { p | p P r R ps(p,r) }

18. r1 r1,s1 r2 r2,s2 s2 s1 r1 r1,s1 s1 r2 s2 r2,s2 t1,u1 t1,u1 r3,s3 r3,s3 t2,u2 t2,u2 v t1,u1 r3,s3 r3,s3 t2,u2 t2,u2 Query ProcessingProposed Solution P P’ Query matching Q = join (r,s,v) 1 – European data 2 – American data 3 – African data

19. Query ProcessingProposed Solution • Query optimization • Consider P’ a set of relevant peers • Goal: obtain P’’  P’ such that • For any two peers in P’’, their relevant data are not replicated • The relevant data of peers in P’’ are equal to that in P’ • The cost of query execution by peers in P’’ is minimum • Cost function • A function of communication, computing power, etc. • Phases of optimization • Determining relevant replicas for Q’s relations and their peers • Determining best peer per replica

20. r1 r1,s1 r2 r2,s2 s2 s1 r1 r1,s1 s1 r2 s2 r2,s2 t1,u1 t1,u1 r3,s3 r3,s3 t2,u2 t2,u2 v t1,u1 r3,s3 r3,s3 t2,u2 t2,u2 Query ProcessingProposed Solution P’’ P’ Query optimization Q = join (r,s,v) r1,s1 r2 1 – European data 2 – American data 3 – African data s2 v r3,s3

21. r s p1 p2 p3 Query ProcessingProposed Solution • Algorithms • Cost parameters • tcom(r,p): time to send the results of Q concerning to replica r from a peer p to the query originator • tresp(r,p): time which p needs to execute the part of Q concerning to replica r and start to send the results to the query originator • tdjoin(S): time to join the set of replicas S in a distributed way • Example tdjoin({r,s}) = 6 tcom(s,p2) + tresp(s,p2) = 7 tcom(r,p1) + tresp(r,p1) = 4 tcom(s,p3) + tresp(s,p3) = 5 tcom(r,p2) + tresp(r,p2) = 6 Total Cost = 6 + 7 = 13 Total Cost = 4 + 5 + 6 = 15 4 6 7 5

22. Query ProcessingProposed Solution • A none-linear programming model • Minimize • Complexity

23. Query ProcessingProposed Solution • Algorithms • Branch and bound • Optimal selection of peers • Complexity (worst case): O( ) • A heuristic solution • While there is an edge in the graph • Select the edge with minimum label • Set the peer p as selected peer for the replica r • Update the label edges of other peers that hold the replica r • Remove the replica r and its edges from the graph • Complexity: O((ma)2)

24. Outline • Motivation • APPA Architecture • Data Replication • Query processing • Validation • Conclusion

25. JXTA Applications JXTA Community Applications Sun JXTA Applications JXTA Shell Peer Commands APPA JXTA Services JXTA Community Services Sun JXTA Services Indexing GISP Advanced Services Replication Query Processing Caching ... Discover Basic Services Search P2P Data Management Consensus JXTA Core Peer Groups Peer Pipes Peer Monitoring Membership Peer Advertisements Peer IDs Security Any Connected Device P2P Network Key-based Storage and Retrieval Peer Linking Peer ID Assignment Peer Communication Implementation

26. JXTA Applications JXTA Community Applications Sun JXTA Applications JXTA Shell Peer Commands APPA JXTA Services JXTA Community Services APPA Simulation Sun JXTA Services P2P Simulation Chord Simulator Indexing GISP Advanced Services Internet Simulation GT/ITM Replication Query Processing Caching ... Discover Basic Services Search P2P Data Management Consensus JXTA Core Peer Groups Peer Pipes Peer Monitoring Membership Peer Advertisements Peer IDs Security Any Connected Device P2P Network P2P Network Key-based Storage and Retrieval Key-based Storage and Retrieval Peer Linking Peer Linking Peer ID Assignment Peer ID Assignment Peer Communication Peer Communication Simulation

27. Conclusion • Summary • Advanced cooperative applications (multi-master replication) • A new P2P network-independent data management system • A distributed optimistic multi-master replication solution • Eventual consistency guarantee • A query processing solution based on replication • Validation • Future work • Consider secondary copies • Consider replica quality in query optimization • Data caching • Implementation over other P2P architectures (e.g., flooding)