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Parallel and distributed databases

Parallel and distributed databases. R & G Chapter 22. What is a distributed database?. Why distribute a database. Scalability and performance Resilience to failures. Throughput. Data size. X. X. versus. Why distribute a database. Data is already distributed Or needs to be distributed

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Parallel and distributed databases

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  1. Parallel and distributed databases R & G Chapter 22

  2. What is a distributed database?

  3. Why distribute a database • Scalability and performance • Resilience to failures Throughput Data size X X versus

  4. Why distribute a database • Data is already distributed • Or needs to be distributed • Data is in multiple systems

  5. Why not distribute a database You must earn your complexity! • Communication needed • Must build a complex infrastructure • Unpredictable latencies must be masked • More types of failures • More components to fail • Network failures • Congestion, timeouts • More complex planning • Communication cost plus I/O cost • May have to deal with heterogeneity • Different types of systems • Different schemas, possibly incompatible • Different administrative domains

  6. Types of distributed databases

  7. The old days: mainframes Definitely not distributed!

  8. Client-server User interaction Network Data processing

  9. Parallel database

  10. Primary/secondary X

  11. Multidatabase

  12. How do they work? • What is shared? • How to distribute the data? • How to process the data? • How to update the data?

  13. What is shared? • Memory Most modern DBMSs CPUs RAM Disk

  14. What is shared? • Disk Oracle RAC RAM

  15. What is shared? • Nothing Search engines, Teradata RAM

  16. How to distribute the data? Couch $570 6/1/07 424252 Car $1123 6/1/07 256623 Bike $86 6/2/07 636353 Chair $10 6/5/07 662113 Lamp $19 6/7/07 121113 Bike $56 6/9/07 887734 Scooter $18 6/11/07 252111 Hammer $8000 6/11/07 116458 Server 1 Server 2 Server 3 Server 4

  17. How to distribute the data? Hash partitioning Range partitioning (key,value) (key,value) Hash() <= X > X

  18. How to distribute the data? Couch $570 6/1/07 424252 Car $1123 6/1/07 256623 Bike $86 6/2/07 636353 Chair $10 6/5/07 662113 Lamp $19 6/7/07 121113 Bike $56 6/9/07 887734 Scooter $18 6/11/07 252111 Hammer $8000 6/11/07 116458 Server 1 Server 2 Server 3 Server 4

  19. Query processing • Intra-operator parallelism • Inter-operator parallelism

  20. Parallel scanning Result filter filter filter filter filter filter

  21. Sorting

  22. Sorting

  23. Parallel hash join Hash()

  24. Join

  25. Semi-join

  26. Inter-operator parallelism

  27. Updating distributed data • Synchronous: read-any-write-all Reads are fast

  28. Updating distributed data • Synchronous: voting

  29. Updating distributed data • Synchronous: voting Writes tolerant to disconnection

  30. Consistency of distributed data • Should provide ACID

  31. Primary/secondary

  32. Two-phase commit PREPARE COMMIT PREPARED PREPARED

  33. Two-phase commit PREPARE ABORT PREPARED ABORT

  34. Two-phase commit PREPARE ABORT PREPARED

  35. Two-phase commit X PREPARE PREPARED PREPARED

  36. Conclusion • Parallelism and distribution very useful • Performance • Fault tolerance • Scale • But complex! • Rethink lots of aspects of the system • Must earn the complexity

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