Just curious, how many indexes were on the structure you were inserting? Also, i am assuming 90ms per structure, if that is correct, then that is equal to 11 inserts per second which is unfortunately very slow..
You can't quite divide like that. In Rethink
r.table('users').insert({ 'name': 'Bob' })
r.table('users').insert({ 'name': 'Jim' })
r.table('users').insert([{ 'name': 'Bob' },
{ 'name': 'Jim' }])
There other nuances to this -- the complexity of measuring things properly is why we haven't published benchmarks to date. Check out this doc on insert performance for more details: http://rethinkdb.com/docs/troubleshooting/#my-insert-queries...
I am very aware of the increase in performance of batch inserts and batched disk writes and so on.. as an individual who has worked on the development of a major DBMS.
In effect if i wanted to do 10000 single writes per second typical of most interactive systems, i will need 200 nodes to pull that Off.
In Rethink you can turn on soft durability which will allow buffering writes in memory. That would drastically speed up write performance. Another option is to use concurrent clients. Rethink is optimized for throughput and concurrency, so if you use multiple concurrent clients the latency won't scale linearly.