I've been reading up on MongoDB. I am particularly interested in the aggregation frameworks ability. I am looking at taking multiple dataset consisting of at least 10+ million rows per month and creating aggregations off of this data. This is time series data.

Example. Using Oracle OLAP, you can load data at the second/minute level and have this roll up to hours, days, weeks, months, quarters, years etc...simply define your dimensions and go from there. This works quite well.

So far I have read that MongoDB can handle the above using it's map reduce functionality. Map reduce functionality can be implemented so that it updates results incrementally. This makes sense since I would be loading new data say weekly or monthly and I would expect to only have to process new data that is being loaded.

I have also read that map reduce in MongoDB can be slow. To overcome this, the idea is to use a cheap commodity hardware and spread the load across multiple machines.

So here are my questions.

1. How good (or bad) does MongoDB handle map reduce in terms of performance? Do you really need a lot of machines to get acceptable performance?
2. In terms of workflow, is it relatively easy to store and merge the incremental results generated by map reduce?
3. How much of a performance improvement does the aggregation framework offer?
4. Does the aggregation framework offer the ability to store results incrementally in a similar manner that the map/reduce functionality that already exists does.

I appreciate your responses in advance!