You’re basically looking to query and aggregate the data arbitrarily - you may have better luck using spark or solr pointing to a single backing table in Cassandra
-- Jeff Jirsa > On Feb 18, 2018, at 3:38 AM, onmstester onmstester <onmstes...@zoho.com> > wrote: > > I have a single structured row as input with rate of 10K per seconds. Each > row has 20 columns. Some queries should be answered on these inputs. Because > most of queries needs different where, group by or orderby, The final data > model ended up like this: > primary key for table of query1 : ((column1,column2),column3,column4) > primary key for table of query2 : ((column3,column4),column2,column1) > and so on > > I am aware of the limit in number of tables in cassandra data model (200 is > warning and 500 would fail) Because for every input row i should do an insert > in every table, the final write per seconds became big * big data!: > > write per seconds = 10K (input) * number of tables (queries) * replication > factor > > The main question: am i in the right path? is this normal to have a table for > every query even when the input rate is already so high? Shouldn't i use > something like spark or hadoop upon instead of relying on bare datamodel Or > event Hbase instead of cassandra? > > > Sent using Zoho Mail > > > >
