问题
I am moving our messaging system to MongoDB and am curious what approach to take with respect to various stats, like number of messages per user etc. In MS SQL database I have a table where I have different counts per user and they get updated by trigger on corresponding tables, so I can for example know how many unread messages UserA has without calling an expensive SELECT Count(*) operation.
Is count function in MongoDB also expensive?
I started reading about map/reduce but my site is high load, so statistics has to update in real time, and my understanding is that map/reduce is time consuming operation.
What would be the best (performance-wise) approach on gathering various aggregate counts in MongoDB?
回答1:
If you've got a lot of data, then I'd stick with the same approach and increment an aggregate counter whenever a new message is added for a user, using a collection something like this:
counts
{
userid: 123,
messages: 10
}
Unfortunately (or fortunately?) there are no triggers in MongoDB, so you'd increment the counter from your application logic:
db.counts.update( { userid: 123 }, { $inc: { messages: 1 } } )
This'll give you the best performance, and you'd probably also put an index on the userid field for fast lookups:
db.counts.ensureIndex( { userid: 1 } )
回答2:
Mongodb good fit for the data denormaliztion. And if your site is high load then you need to precalculate almost everything, so use $inc for incrementing messages count, no doubt.
来源：https://stackoverflow.com/questions/6054464/mongodb-calling-count-vs-tracking-counts-in-a-collection