1 / 12

Parallel Databases Michael French, Spencer Steele, Jill Rochelle

Parallel Databases Michael French, Spencer Steele, Jill Rochelle. When Parallel Lines Meet by Ken Rudin (BYTE, May 98). What are Parallel/Scalable Databases?. Parallel/Scalable Databases: Hardware Architecture Multiple Processors Multiple Disk Drives Large Memory Banks

mason-kent
Download Presentation

Parallel Databases Michael French, Spencer Steele, Jill Rochelle

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Parallel DatabasesMichael French, Spencer Steele, Jill Rochelle When Parallel Lines Meet by Ken Rudin (BYTE, May 98)

  2. What are Parallel/Scalable Databases? • Parallel/Scalable Databases: • Hardware Architecture Multiple Processors Multiple Disk Drives Large Memory Banks • Software Architecture Capable of processing parallel queries Data shipping capabilities

  3. What makes Parallel Databases different from previous technologies?

  4. Previous Technology • Hardware Single processor Small Disk Capacity Less Memory • Software Sequential Queries No partitioning of queries

  5. Parallel Query: • A Query that partitions information to multiple processors and also has the ability to pipeline information

  6. Information Partitioning • Divide the information into smaller tasks • Can have multiple meanings: • Distribution of info to multiple CPUs • Division of hard drive space to contain certain parts of the data

  7. Information Partitioning 2

  8. Information Pipelining • Allows separate processors to work on separate stages of a query • Scan • Join • Sort • Concept is akin to assembly line idea • Allows multiple queries to run at the same time

  9. Information Pipelining 2

  10. Sequential Query Example • Two Tables with 20 million rows each run on a uniprocessor machine • To perform scan, join & sort, query takes 12 mins. • Add partitioning • Query takes 3 mins. • Add Pipelining • 12 queries can be run in 12 mins.

  11. Parallel Kinds • Share-Everything • Hardware • Software • Share-Disk • Hardware • Software • Share-Nothing • Hardware • Software

  12. Conclusion • Pros • Allows you to process more information • Provides for faster processing of queries • Cons • Expensive hardware & software • Much higher maintenance • Is a parallel database right for your organization?

More Related