Here are the slides on slideshare http://www.slideshare.net/huynhja/oql-querying-and-indexes
On Tue, Nov 3, 2015 at 11:24 AM, Gregory Chase <[email protected]> wrote: > Attachment didn't come through. Please post on Slideshare. > > Alternatively feel free to post editable form of the PPT to here: > https://github.com/Pivotal-Open-Source-Hub/POSH-Talks > > -Greg > > On Tue, Nov 3, 2015 at 11:21 AM, Jason Huynh <[email protected]> wrote: > > > Attached are the slides from todays talk. > > > > Please feel free to continue to post questions on the user or dev list > and > > we will answer them as best as we can. > > > > As a follow up to one of the questions about cluster sizing and indexes > > that came up during todays talk: > > Although each case will depend on the data itself, here are some things > to > > help keep in mind when calculating how much memory an index may take. > This > > does not include any temporary objects/garbage created when executing a > > query or created when initially creating the index. > > Each index will also have additional overhead for managing the indexes > per > > region but that size should be very small. > > > > Compact Functional Index: > > 1.) Reference for every region entry > > 2.) Size of extracted key > > 3.) Internal data structures (ConcurrentHashSet) used per index key > > > > Functional Index > > 1.) Size of copy of value and extracted key per entry to form the tuple > > 2.) Size of extracted key > > 3.) Internal data structures per index key, tuple structure > > > > Hash Index > > 1.) Reference per entry > > 2.) Internal array size > > > > Map Index > > 1.) Size of extracted key > > 2.) Size of internal map > > 3.) Sizes of Functional or Compact Index > > > > Primary Key > > 1.) Minor object creation > > > > > > Thanks, > > -Jason > > > > > > > > > > > -- > Greg Chase > > Director of Big Data Communities > http://www.pivotal.io/big-data > > Pivotal Software > http://www.pivotal.io/ > > 650-215-0477 > @GregChase > Blog: http://geekmarketing.biz/ >
