Mongodb – Merge 2 mongodb databases

Some thoughts....

Typically one does not want to store pieces of tightly interrelated information in different systems. The chances of things getting out of sync is significant and now instead of one problem on your hands you have two. One thing you can do with Mongo though is use it to pipeline your data in or data out. My preference is to keep everything in PostgreSQL to the extent this is possible. However, I would note that doing so really requires expert knowledge of PostgreSQL programming and is not for shops unwilling to dedicate to using advanced features. I see a somewhat different set of options than you do. Since my preference is not something I see listed I will give it to you.

You can probably separate your metadata into common data, data required for classes, and document data. In this regard you would have a general catalog table with the basic common information plus one table per class. In this table you would have an hstore, json, or xml field which would store the rest of the data along with columns where you are storing data that must be constrained significantly. This would reduce what you need to put in these tables per class, but would allow you to leverage constraints however you like. The three options have different issues and are worth considering separately:

hstore is relatively limited but also used by a lot of people. It isn't extremely new but it only is a key/value store, and is incapable of nested data structures, unlike json and xml.

json is quite new and doesn't really do a lot right now. This doesn't mean you can't do a lot with it, but you aren't going to do a lot out of the box. If you do you can expect to do a significant amount of programming, probably in plv8js or, if you want to stick with older environments, plperlu or plpython. json is better supported in 9.3 though at least in current development snapshots, so when that version is released things will get better.

xml is the best supported of the three, with the most features, and the longest support history. Then again, it is XML.....

However if you do decide to go with Mongo and PostgreSQL together, note that PostgreSQL supports 2 phase commit meaning you can run the write operations, then issue PREPARE TRANSACTION and if this succeeds do your atomic writes in Mongo. If that succeeds you can then COMMIT in PostgreSQL.

There are a couple of separate points here, but I don't think how MongoDB stores data in RAM is really relevant here - MongoDB just uses the mmap() call and then lets the kernel take care of the memory management (the Linux kernel will use Least Recently Used (LRU) by default to decide what to page out and what to keep - there are more specifics to that but it's not terribly relevant).

In terms of your issues, it sounds like you might have had a corrupt index, though the evidence is somewhat circumstantial. Now that you have done a repair (the validate() command would have confirmed/denied beforehand), there won't be any evidence in the current data but you may find more evidence in the logs, particularly when you were attempting to recreate the index, or using the index in queries.

As for the spikes in the page faults, btree stats, journal, lock percentage, and average flush time, that has all the hallmarks of a bulk delete that causes a lot of index updates, and causes a large amount of IO. The fact that mapped memory drops off later in the graphs would suggest that once you ran the repair the storage size was significantly reduced, which usually indicates significant fragmentation (bulk deletes, along with updates that grow documents are the leading causes of fragmentation).

Therefore, I would look for a large delete operation logged as slow in the logs - it will only be logged once complete, so look for it to appear after the end of the events in MMS. One of the quirks of not running in a replica set is that a bulk operation like this is relatively non-obvious - it shows up as a single delete operation in the MMS graphs (usually lost in the noise).

These bulk delete operations usually tend to be run on older data that has not been recently used and has hence been paged out of active memory by the kernel (LRU again). To delete it you must page it all the data back in, then flush the changes to disk, and of course deletes require the write lock, hence the spikes in faults, lock percentage etc.

To make room for the deleted data, your current working set is paged out, which will hit performance on your normal usage until the deletes complete and the memory pressure eases.

FYI - when you run a replica set, bulk ops are serialized in the oplog and hence replicated one at a time - as such you can track such operations by their footprints in the replicated ops stats of the secondaries. This is not possible with a standalone instance (without looking in the logs for the completed ops) and other secondary indications.

As for managing large deletes in the future, it is generally far more efficient to partition your data into separate databases (if possible) and then drop the old data when it is no longer needed by simply dropping the old databases. This requires some extra management on the application side but it negates the need for bulk deletes, is far quicker to complete, limits fragmentation, and dropped databases also remove the files on disk, preventing excessive storage use. Definitely recommended if possible with your use case.