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Parallel Processing with Autonomous Databases in a Cluster System

Parallel Processing with Autonomous Databases in a Cluster System. Stéphane Gançarski 1 , Hubert Naacke 1 , Esther Pacitti 2 , Patrick Valduriez 3 1 LIP6, University Paris 6, Paris, France FirstName.LastName@lip6.fr 2 IRIN, Nantes, France Esther.Pacitti@irin.univ-nantes.fr

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Parallel Processing with Autonomous Databases in a Cluster System

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  1. Parallel Processing with Autonomous Databasesin a Cluster System Stéphane Gançarski1, Hubert Naacke1, Esther Pacitti2, Patrick Valduriez3 1LIP6, University Paris 6, Paris, France FirstName.LastName@lip6.fr 2IRIN, Nantes, France Esther.Pacitti@irin.univ-nantes.fr 3IRIN, Nantes, France Patrick.Valduriez@inria.fr

  2. TCP/IP • • • • • Cluster of PC Application Service Provider ASP context app1 app2 DBMS DB User site

  3. Potential benefits • For the users • No system administration • High availability • Security • For the provider • Centralized management of apps and databases • Use of a cluster => reduced cost • Economy of scale • new services used by every app.

  4. Challenge for the ASP • Exploit the cluster architecture • To obtain good performance/cost through parallelism • Apps can be update-intensive (≠ search engine) • Without hurting app and database autonomy • Apps and databases should remain unchanged These are conflicting objectives

  5. Solutions readily available • Solution 1: TP monitor • Replicate databases at multiple nodes to increase parallelism • Needs to interface applications to TP monitor • Solution 2: Parallel DBMS (shared disk, shared cache or shared nothing) • Requires heavy migration • Hurts database autonomy

  6. Our (optimistic) approach • Trade consistency for performance • Capture apps profile and consistency requirements • Replicate apps and databases at multiple nodes • Without any change • Use consistency requirements to perform load balancing • Detect and repair inconsistencies • Using database logs

  7. Outline • Cluster architecture • Replication model • Transaction model • Execution model • Future work

  8. Directory Conceptual architecture Request (user, app) authentification authorization connection app 1 connection querying DBMS DB

  9. Internet Application load balancer app app app 1 2 n Transaction load balancer Directory Preventive replication manager DBMS DBMS DBMS DBMS DB DB DB DB Conflicts manager Cluster Architecture

  10. Outline • Cluster architectures • Replication model • Transaction model • Execution model • Future work

  11. Symmetric replication Client Client Read or update Read or update Replication Master Master EMP EMP • Increase performance and availability • But may introduce inconsistencies

  12. Update propagation to replicas • Synchronous: all replicas are updated within the same transaction (2PC) • Replicas always consistent • But does not scale up • Asynchronous: each replica is updated (refreshed) in a separated transaction. We support 2 variants: • Preventive (new solution) : transactions are shortly delayed and ordered based on their timestamp so there is no conflict • Optimistic : most efficient but can create • conflicts to resolve • divergence to control

  13. Outline • Cluster architectures • Replication model • Transaction model • Execution model • Future work

  14. ? ? Example T2 • Case 1 • T1 and T2 data independent or commutative • T1 changes sent to N2 • T2 changes sent to N1 T1

  15. Example Q1 T2 • Case 2 • T1 and T2 perform conflicting updates • Conflict prevention • Conflict detection and resolution • Priority-based • Resolution mode • Dirty read from Q1 • Abort or compensation T1 ? ?

  16. Execution rules • Request profile (for any query or transaction) • stored procedure + parameter values • user id, priority, access control rules • Transaction profile • conflict class : data it may read or write • compatibility with other trans. (disjoint or commutative) • Integrity constraints • Max-table-change : {(Rel, max-#tuple)} • Max-tuple-change : {(Rel, {(att, max-value)})} • Query requirements • precision level, tolerated divergence, …

  17. Execute Trans. Data placement Load Transaction processing Trans (from app) Generate run-time policy Execution rules Trans exec. plan preventive replic. optimistic replic.

  18. Node 1 Stock[1,30,10] Node 2 Stock[1,30,10] Decr(1,15) Decr(1,10) 1 Stock[1,15,10] Stock[1,20,10] 2 Q = n - 1 Q = n -1 3 After synchronization [1,5,10] Q = n Example • Stock(item, quantity, threshold) • Decrease item id by q units • procedure Decr(id, q) • UPDATE Stock • SET quantity = quantity – q • WHERE item = id; • How many item to renew ? • query Q: • SELECT count(item) • FROM Stock • WHERE quantity < threshold Commutative updates parallel processing 1 2 Query tolerates imprecision Query with 100% precision 3

  19. Outline • Cluster architectures • Replication model • Transaction model • Execution model • Future work

  20. Execution model • Problem statement: given the cluster’s load and data placement, and transaction T’s execution plan, find the optimal node to run T • Cost function includes the cost of synchronizing replicas • Step 1: select data access method and replication mode (preventive or optimistic) • Step 2: select best node among those supporting the access method selected at step 1 and run T

  21. Load balancing with optimistic replication • Choice of the node is based on • data placement • node consistency • {Rel, Δ#tuplemax, {(att, Δvaluemax)}} • synchronization cost to meet consistency requir. • apply T’s such that node consistency after applying T’s  requirements • transaction execution cost • normalized estimated response time • node load: (load-avg, {(running T’s, elapsed-time)})

  22. Q2 Q1 T2 T1 ? ? Execution example N2 N1 consistent nodes

  23. T1 Execution example Q2 Q1 T2 T1 imprecision = 1 trans to sync = T1 N2 N1

  24. T2 Execution example Q2 Q1 T2 T1 imprecision = 1 trans to sync = T2 N2 N1

  25. Q1 Execution example Q2 Q1 T2 T1 N2 N1

  26. Q2 T1 Execution example Q2 Q1 T2 T1 N2 N1

  27. Experiments • Implementation • LIP6 cluster (Oracle8i/Linux) • benchmarking with TPC-C : 500MB - 2GB • Interconnection network : 1 GB/s • 5 nodes • Objectives • measure benefit on transaction response time • measure benefit on load balancing for transactions with low consistency requirements

  28. Validation for hot spot load • Incoming load with periodic hot spot : • 10 simultaneous tra nsaction requests • Each request lasts T/4, per period of T.

  29. Hot spot load : results • X: number of nodes : from 1 to 4 • Y: avg response time during hot spot • Benefit on response time • factor of 2 (with 4 nodes) • Even better • if sync starts earlier • improve low-load detection (hot spot end) • if sync faster than original trans • using log to get the update set of a trans.

  30. Future work • Validation by simulation up to 64 nodes • measure scale-up • measure directory access contention • Implement divergence control • capture user & transaction profile (semi-automatic) • generate execution rules (by inference or statistics) • improve node precision (n dimensions) • Implement conflicts resolution/detection

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