RDBMS for extremely large data sets - what are people using?

I have to perform some serious data mining on very large data sets stored in MySQL db. However, queries that require a bit more than a basic SELECT * FROM X WH开发者_C百科ERE ... tend to become rather inefficient since they return results on the order of 10e6 or more, especially when JOIN on one or more tables is introduced - think of joining 2 or more tables containing several tens of millions rows (after filtering data), which is something that pretty much happens on every query. More than often we'd like to run aggregate functions on these (sum, avg, count, etc), but this is impossible since MySQL simply chokes.

I should note that many efforts were put to optimize the current performance - all tables are indexed properly and queries are tuned, the hardware is top notch, the storage engine was configured and so on. However, still each query takes very long - to the point where "let's run it before we go home and hope for the best when we come to work tomorrow." Not good.

This has to be a solvable problem - many large companies perform very data and computational intensive mining, and handle it well (without writing their own storage engines, google). I'm willing to accept time penalty to get the job done, but on the order of hours, not days. My question is - what do people use to counter problems like this? I've heard of storage engines geared to this type of problem (greenplum, etc.), but I wanted to hear how this problem is typically approached. Our current data store is obviously relational and should probably remain such, but any thoughts or suggestions are welcome. Thanks.

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I suggest PostgreSQL, which I've been working with quite successfully on tables with ~0.5B rows that required some complex join operations. Oracle should be good for that too, but I don't have much experience with it.

It should be noted that switching an RDBMS isn't a magic solution, if you want to scale to those sizes there's a LOT of hard work to be done in optimizing your queries, optimizing the database structure and indexes, fine tuning the database configuration, using the right hardware for your usage, replication, using materialized views (which are extremely powerful when used correctly. see here and here - its postgres specific, but applies to other RDBMSs too)... and at some point, you just have to throw more money on the problem.

edited fixed some weird typos (useless android auto correct...) and added some resources about materialized views

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We have used MS SqlServer to run analytics on financial data with ten of millions of rows and more using complex JOIN and aggregation. Several things that we have done other than what you have mentioned are:

• We chunk the calculation into a lot of temporary tables instead of using sub-query. These tables then we apply proper keys, indexing and so on via the code. Query with sub-query just fails for us
• In the temporary tables, we often apply the clustered index that makes sense for us. Note that this temporary tables are filtered results so applying the index on the fly is not expensive compared to use the sub query in place of this temporary tables. Note I am speaking from our experience and might not apply to all cases
• As we have done a lot of aggregation function as well, we did a lot indexing on the group columns
• We do a lot of query run planning using SQL Query Analyzer that shows us the execution plan. Based on the plan, we revised the query, change the index
• We provide hints for the SQL Server that we think could help the execution such as the choice of JOIN Algorithm to take (Hash, Merged or Nested)