That is not time-series modeling issue per se ... You can't come up with anything until you get the basic performance/load SLA numbers
1. How many updates per second in the system? 2. How many users? 3. Average # of followers per user with percentiles up to 99.9% Twitter architecture to support user-follower relationships is not based on a single data store and much more complex. Therefore, I think, in this case everything will depend on ## 1. 2. 3. Scale matters. -Vlad On Wed, Jul 1, 2015 at 2:17 PM, Stack <[email protected]> wrote: > To add to Amandeep's pointer, this one is good for concerns modeling > timeseries: > https://cloud.google.com/bigtable/pdf/CloudBigtableTimeSeries.pdf > > St.Ack > > On Wed, Jul 1, 2015 at 11:53 AM, Sleiman Jneidi <[email protected]> > wrote: > > > Hello everyone, I am working on a scheme design for a time series > database. > > Something very similar to Twitter where people can follow each other and > > see their posts. I've looked at opentsdb but I think my problem is more > > complicated because I don't have the leading "metricid" in the row key. > > I've made several attempts so far but I am not happy with the > performance. > > > > 1. Md5(user)+timestamp . The problem with is when I want to query the > feed, > > I have to do a scan with the highest user ( alphabetical order) and the > > lowest and then add column column filter. Getting the next batch is hard. > > > > 2. Md5(user)+day and then put the posts of the day in the columns with > > timestamp in the qualifier name. Not optimal, getting the next batch is > > hard. > > > > So... What do you guys think? Any ideas for making this efficient or > > possible? > > > > Thanks for your time in reading this. > > Sleiman > > >
