> Just out of curiosity, is there any underlying architectural reason why it's > not possible to order a row based on its counters values? or is it something > that might be in the roadmap in the future? it wouldn't work well with the consistency level. Also, sorting a list of values at the same time you want multiple clients to be modifying them would not work very well.
Cheers ----------------- Aaron Morton Freelance Developer @aaronmorton http://www.thelastpickle.com On 23/05/2012, at 12:25 AM, samal wrote: > Secondary index is not supported for counters plus you must know column name > to support secondary index on regular column. > > On 22-May-2012 5:34 PM, "Filippo Diotalevi" <fili...@ntoklo.com> wrote: > Thanks for all the answers, they definitely helped. > > Just out of curiosity, is there any underlying architectural reason why it's > not possible to order a row based on its counters values? or is it something > that might be in the roadmap in the future? > > -- > Filippo Diotalevi > > On Tuesday, 22 May 2012 at 08:48, Romain HARDOUIN wrote: > >> >> I mean iterate over each column -- more precisly: *bunches of columns* using >> slices -- and write new columns in the inversed index. >> Tamar's data model is made for real time analysis. It's maybe overdesigned >> for a daily ranking. >> I agree with Samal, you should split your data across the space of tokens. >> Only CF Ranking feeding would be affected, not the "top N" queries. >> >> Filippo Diotalevi <fili...@ntoklo.com> a écrit sur 21/05/2012 19:05:28 : >> >> > Hi Romain, >> > thanks for your suggestion. >> > >> > When you say " build every day a ranking in a dedicated CF by >> > iterating over events:" do you mean >> > - load all the columns for the specified row key >> > - iterate over each column, and write a new column in the inversed index >> > ? >> > >> > That's my current approach, but since I have many of these wide rows >> > (1 per day), the process is extremely slow as it involves moving an >> > entire row from Cassandra to client, inverting every column, and >> > sending the data back to create the inversed index. >
