Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken...
-
Upload
alexis-thomason -
Category
Documents
-
view
215 -
download
0
Transcript of Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken...
![Page 1: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/1.jpg)
Parallel DatabasesParallel Databases
Michael French, Spencer Steele, Jill RochelleMichael French, Spencer Steele, Jill Rochelle
When Parallel Lines Meetby Ken Rudin (BYTE, May 98)
![Page 2: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/2.jpg)
What are Parallel/Scalable What are Parallel/Scalable Databases?Databases?
Parallel/Scalable Databases:Parallel/Scalable Databases: Hardware Architecture Hardware Architecture
Multiple ProcessorsMultiple Processors
Multiple Disk DrivesMultiple Disk Drives
Large Memory BanksLarge Memory Banks Software ArchitectureSoftware Architecture
Capable of processing parallel queriesCapable of processing parallel queries
Data shipping capabilitiesData shipping capabilities
![Page 3: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/3.jpg)
What makes Parallel Databases What makes Parallel Databases different from previous different from previous
technologies?technologies?
![Page 4: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/4.jpg)
Previous TechnologyPrevious Technology
HardwareHardwareSingle processorSingle processor
Small Disk CapacitySmall Disk Capacity
Less MemoryLess Memory SoftwareSoftware
Sequential QueriesSequential Queries
No partitioning of queriesNo partitioning of queries
![Page 5: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/5.jpg)
Parallel Query: Parallel Query:
A Query that partitions information A Query that partitions information to multiple processors and also has to multiple processors and also has the ability to pipeline informationthe ability to pipeline information
![Page 6: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/6.jpg)
Information PartitioningInformation Partitioning
Divide the information into smaller Divide the information into smaller taskstasks
Can have multiple meanings:Can have multiple meanings:– Distribution of info to multiple CPUsDistribution of info to multiple CPUs– Division of hard drive space to Division of hard drive space to
contain certain parts of the datacontain certain parts of the data
![Page 7: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/7.jpg)
Information Partitioning 2Information Partitioning 2
![Page 8: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/8.jpg)
Information PipeliningInformation Pipelining
Allows separate processors to work Allows separate processors to work on separate stages of a queryon separate stages of a query– ScanScan– JoinJoin– SortSort
Concept is akin to assembly line idea Concept is akin to assembly line idea Allows multiple queries to run at the Allows multiple queries to run at the
same timesame time
![Page 9: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/9.jpg)
Information Pipelining 2Information Pipelining 2
![Page 10: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/10.jpg)
Sequential Query ExampleSequential Query Example
Two Tables with 20 million rows Two Tables with 20 million rows each run on a uniprocessor machineeach run on a uniprocessor machine– To perform scan, join & sort, query To perform scan, join & sort, query
takes 12 mins.takes 12 mins. Add partitioningAdd partitioning
– Query takes 3 mins.Query takes 3 mins. Add PipeliningAdd Pipelining
– 12 queries can be run in 12 mins.12 queries can be run in 12 mins.
![Page 11: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/11.jpg)
Parallel KindsParallel Kinds
Share-EverythingShare-Everything– HardwareHardware– SoftwareSoftware
Share-DiskShare-Disk– HardwareHardware– SoftwareSoftware
Share-NothingShare-Nothing– HardwareHardware– SoftwareSoftware
![Page 12: Parallel Databases Michael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)](https://reader036.fdocuments.in/reader036/viewer/2022062421/56649ca65503460f94967ed8/html5/thumbnails/12.jpg)
ConclusionConclusion
ProsPros– Allows you to process more informationAllows you to process more information– Provides for faster processing of queriesProvides for faster processing of queries
ConsCons– Expensive hardware & softwareExpensive hardware & software– Much higher maintenance Much higher maintenance
Is a parallel database right for your Is a parallel database right for your organization?organization?