If I had your problem, the first thing that I would do is try PostgreSQL to see if it does the joins fast enough. Second thing that I would try is to put the data in a SOLR db and translate the queries to a SOLR base query (q=) plus filter queries (fq=) on top.
Only if both of these fail to provide sufficient performance, would I look at a map reduce solution based on the Hadoop ecosystem. Actually, I wouldn't necessarily use the Hadoop ecosystem. It has a lot of parts/layers and the newer and generally better parts are not as well known so it is a bit more leading edge than lots of folks like. I'd also look at somethink like Riak http://docs.basho.com/riak/latest/dev/using/mapreduce/ because then you have your data storage and clustering issues solved in a bulletproof way (unlike Mongo) but you can do MapReduce as well.
Only if both of these fail to provide sufficient performance, would I look at a map reduce solution based on the Hadoop ecosystem. Actually, I wouldn't necessarily use the Hadoop ecosystem. It has a lot of parts/layers and the newer and generally better parts are not as well known so it is a bit more leading edge than lots of folks like. I'd also look at somethink like Riak http://docs.basho.com/riak/latest/dev/using/mapreduce/ because then you have your data storage and clustering issues solved in a bulletproof way (unlike Mongo) but you can do MapReduce as well.