This document discusses parallel and distributed databases. Distributed databases can improve scalability, performance and resilience to failures. However, distributing a database also introduces complexity, as communication is needed between distributed components and failures become more common. The document describes different types of distributed database architectures and how data can be distributed and queries processed in a parallel manner. Maintaining consistency across distributed nodes is challenging and usually requires techniques like two-phase commit for distributed transactions.

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
versus
X X

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
Data processing
Network

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
CPUs RAM Disk
Most modern DBMSs

14. What is shared?
 Disk
RAM
Oracle RAC

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

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

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

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

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

20. Parallel scanning
filter filter filter filter filter filter
Result

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
PREPARED PREPARED
COMMIT

33. Two-phase commit
PREPARE
PREPARED ABORT
ABORT

34. Two-phase commit
PREPARE
PREPARED
ABORT

35. Two-phase commit
PREPARE
PREPARED PREPARED
X

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