I know I shouldn't care but I am always weary of doing business with any company using MongoDB.
10gen put out claiming MongoDB was so much faster than SQL solutions but it seemed obvious to me than turning off fsync would make the SQL solutions run at about the same speed. Plus, why would I want to run my database in mode where it is easy to lose data? MongoDB may be a useful product but their marketing is deceptive, which will lead to companies using it in inappropriate situations.
Have you noticed that the problem isn't MongoDB? Every case I've seen has been the data traffic exceeding the capacity of the system. There is no database that works well under those circumstances. Another way of putting it is that these businesses have been sufficiently successful that they have outgrown their own planning and deployment.
As for your second paragraph, MongoDB has had journalling for quite a while, so you can make you writes durable and limited to the speed of your storage.
> Every case I've seen has been the data traffic exceeding the capacity of the system. There is no database that works well under those circumstances.
Is there a benchmark somewhere comparing the memory/disk consumption of MongoDB vs. other datastores?
If there's a significant overhead (and my early tests tend to show that there was - but I didn't make a strict benchmark though), then it would become very related to MongoDB then.
(honest and real question, I'm a MongoDB user btw, as well as Redis, MySQL, Postgresql etc).
The main overhead for MongoDB's storage is that the "column name" (keys) is stored in every record rather than just once as with traditional SQL database and some of the other NoSQL solutions. That is why you'll often see developers using very short key names, and one use for an "ORM" to translate between developer friendly names and short stored names.
Of course this can be solved fairly easily by the MongoDB developers by having a table mapping between short tokens/numbers and the long names. This is the ticket:
My question goes further though, as someone who has worked with, and implemented too, column-based stores: I'm curious to compare the respective space/ram consumption for the data part, too.
So yes it is periodic by default (in the millisecond range). However you can wait on any request until it has become durable.
As for replication, people seem to have some hate for that, but the reality is that a journalled system that has failed (any database/operating system) will take a large amount of time to come back up, replay/recover journals etc. Not that useful.
I would say that MongoDB durability used to be an issue, but now with journaling and replica sets it's not as much of a concern.
There were two reasons why the secondary was less capable than the primary. First, the data had become very fragmented due to our frequent purging. And second, we were in the middle of an upgrade to our servers and that one had not been tackled yet. The primary failure came at a bad time. I could have clarified that better in the post.
Regarding capped collections, yes they are faster. The problem is that they can't be sharded. With our dataset that would not allow us to scale.
10gen put out claiming MongoDB was so much faster than SQL solutions but it seemed obvious to me than turning off fsync would make the SQL solutions run at about the same speed. Plus, why would I want to run my database in mode where it is easy to lose data? MongoDB may be a useful product but their marketing is deceptive, which will lead to companies using it in inappropriate situations.