Run analytics on huge MySQL database

1 TB is not that big. MySQL should be able to handle that. At least simple queries like that shouldn't take hours! Can't be very helpful without knowing the larger context, but I can suggest some questions that you might ask yourself, mostly related to how you use your data:

• Is there a way you can separate the reads and writes? How many read so you do per day and how many writes? Can you live with some lag, e.g write to a new table each day and merge it to the existing table at the end of the day?

• What are most of your queries like? Are they mostly aggregation queries? Can you do some partial aggregation beforehand? Can you pre-calculate number of new smokers every day?

• Can you use hadoop for the aggregation process above? Hadoop is kinda good at that stuff. Basically use hadoop just for daily or batch processing and store the results into the DB.

• On the DB side, are you using InnoDB or MyISAM? Are the indices on String columns? Can you make it ints etc.?

Hope that helps

Looking at this from the position of attempting to make MySQL work better rather than positing an entirely new architectural system:

Firstly, verify what's really happening. EXPLAIN the queries which are causing issues, rather than guessing what's going on.

Having said that, I'm going to guess as to what's going on since I don't have the query plans. I'm guessing that (a) your indexes aren't being used correctly and you're getting a bunch of avoidable table scans, (b) your DB servers are tuned for OLTP, not analytical queries, (c) writing data while reading is causing things to slow down greatly, (d) working with strings just sucks and (e) you've got some inefficient queries with horrible joins (everyone has some of these).

To improve things, I'd investigate the following (in roughly this order):

• Check the query plans, make sure the existing indexes are being used correctly - look at the table scans, make sure the queries actually make sense.

• Move the analytical queries off the OLTP system - the tunings required for fast inserts and short queries are very different to those for the sorts of queries which potentially read most of a large table. This might mean having another analytic-only slave, with a different config (and possibly table types - I'm not sure what the state of the art with MySQL is right now).

• Move the strings out of the fact table - rather than having the smoking status column with string values of (say) 'current smoker', 'recently quit', 'quit 1+ years', 'never smoked', push these values out to another table, and have the integer keys in the fact table (this will help the sizes of the indexes too).

• Stop the tables from being updated while the queries are running - if the indexes are moving while the query is running I can't see good things happening. It's (luckily) been a long time since I cared about MySQL replication, so I can't remember if you can batch up the writes to the analytical query slave without too much drama.

• If you get to this point without solving the performance issues, then it's time to think about moving off MySQL. I'd look at Infobright first - it's open source/$$ & based on MySQL, so it's probably the easiest to put into your existing system (make sure the data is going to the InfoBright DB, then point your analytical queries to the Infobright server, keep the rest of the system as it is, job done), or if Vertica ever releases its Community Edition. Hadoop+Hive has a lot of moving parts - its pretty cool (and great on the resume), but if it's only going to be used for the analytic portion of you system it may take more care & feeding than other options.

MySQL is have a serious limitation what prevent him to be able to perform good on such scenarious. The problem is a lack of parralel query capability - it can not utilize multiple CPUs in the single query.
Hadoop has an RDMBS like addition called Hive. It is application capable of translate your queries in Hive QL (sql like engine) into the MapReduce jobs. Since it is actually small adition on top of Hadoop it inherits its linear scalability
I would suggest to deploy hive alongside MySQL, replicate daily data to there and run heavy aggregations agains it. It will offload serious part of the load fro MySQL. You still need it for the short interactive queries, usually backed by indexes. You need them since Hive is iherently not-interactive - each query will take at least a few dozens of seconds.
Cassandra is built for the Key-Value type of access and does not have scalable GroupBy capability build-in. There is DataStax's Brisk which integrate Cassandra with Hive/MapReduce but it might be not trivial to map your schema into Cassandra and you still not get flexibility and indexing capabiilties of the RDBMS.

As a bottom line - Hive alongside MySQL should be good solution.