Hi, Slava at RethinkDB here.
RethinkDB uses MVCC to do looking. Essentially, when we lock down a block for a write, we make a copy of the block. If another query comes along that wants to read, it reads from the copy. When the write completes, the old copies are destroyed.
There are lots of details I'm glossing over -- optimizations to avoid copying too much, copying entire subbranches of the btree to have a consistent view of the shard, etc. All this stuff isn't unique to RethinkDB -- it's pretty standard database internals stuff, and we haven't done anything new in that department. It's just an implementation of standard database architectures (as far as the caching/query engine/storage engine are concerned).
I actually really like where RethinkDB is headed, and within the year most of my issues should be resolved.
Another couple databases to consider, depending on your needs would be ElasticSearch and Cassandra... it reallly depends on your use case.
It's wild how many options there are that tailor themselves to all kinds of data out there.
We do not plan to implement secondary unique indexes. The philosophy behind RethinkDB is that if a feature cannot be efficiently scaled across multiple nodes we don't add it, and unfortunately unique secondary indexes are one such feature.
AFAIK most NoSQL databases don't implement it, and a few that do take two approaches -- forbid it on multiple shards, or just take a massive performance hit during sharding.
We chose to keep the feature out of the database. This way the application can be architected to account for it, so it remains fast as the database scales up.
And we are actually considering ElasticSearch to deal with this querying performance issue.
Based on that we also evaluated the approach of using something else like druid [1] [2] that is built for reading performance, but I am still studying possibilities and have no idea about the impact and problems a change like that would impose.
[1] http://druid.io/
[2] https://metamarkets.com/2014/building-a-data-pipeline-that-h...
I am using MongoDB and didn't have many issues when my databases had 120,000 documents either, the problem began when we hit the millions... The combination of write locks and our need for dynamic queries (meaning: we can't index) made the database the worst performance bottleneck in our system by far. Although I must be honest that we haven't yet tried MongoDB's new 3.0 version that promises a boost in performance [2] and also has 'document-level locking and compression' [3]
Is anybody aware of any benchmark that perform random writes (inserts/updates) and non-indexed reads for RethinkDB? (Is it even a common use scenario, anyways?)
[1] http://rethinkdb.com/docs/architecture/#how-does-rethinkdb-e...
[2] http://www.mongodb.com/mongodb-3.0#performance
[3] http://docs.mongodb.org/manual/release-notes/3.0/#wiredtiger...