[coffeemug]

> 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' })

is much slower than the equivalent batched insert

  r.table('users').insert([{ 'name': 'Bob' },
                           { 'name': 'Jim' }])

The latter isn't subject to network round trips and can batch disk writes, drastically increasing performance relative to multiple individual writes. You can see a similar effect in most other database systems.

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...

[mutex007]

Assuming a tcp connection can be established in 15ms worst case and another 10ms for data transmission, 65ms spent by the dbms for a single insert is incredibly slow.

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.

[dpweb]

No, 90ms including in transit, so 20ms in svr processing time.

[mutex007]

Assuming your 70ms in transit is accurate, although you have not provided details on how you measured that. 20ms svr processing time is only 50 inserts per second which is simply untenable in respect to a high performing rethink..

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.

[coffeemug]

By default Rethink requires an fsync from disk to guarantee data safety. So a typical default query goes like this: client request -> network trip -> wait on disk fsync -> network trip -> ack. That's going to be really slow in any system, and makes 10k writes/sec pretty much impossible on any rotational disk.

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.