At work we have something of a problem. We keep a ton of data, but most of that data is read only. In the spirit of avoiding optimizations, we’ve just kept all that data in one MongoDB process. This has been working pretty well, but recently we realized we’re hitting some performance limits that happen because our use cases for the data is different and the DB isn’t really equipped to handle each use case efficiently at the same time. The idea is to migrate the data out of that database to a read only archive database.
This feels like a pretty simple problem, but since it deals with our data, it is really important to get it right. A co-worker recommended Celery, which looks promising. I really want to make sure I have accurate logs on this project. It should be really easy to monitor and find interesting information from the constant stream of data. Obviously, some might be asking why not just use more hardware and that is a valid question. It is interesting because that was the initial plan, but when we starting thinking about the amount of hardware and space we’d need, it became rather clear that creating a MongoDB cluster wouldn’t be trivial or cheap. If we used EC2 it wouldn’t be a big deal, but, we also would have hit performance issues much sooner since the issues stem from reading old data off the disk.
The other reason for the design change is that the data usage really is different. The vast majority of the data is read-only. What happens then is that a read would be expensive and lock the DB. This automatically should raise red flags since MongoDB doesn’t lock on reads. The problem was actually that the query used enough resources to effectively lock the DB. The connections and queries would pile up and then finally stampede. This would interrupt our writing systems and generally just cause a lot errors, not to mention a poor user experience.
If the migration tool seems helpful, I’ll try to post it. It is interesting in that it reveals the power in customization and how generic solutions, while seemingly effective, usually end up falling down over the long haul.