The main internals I want to cover are, how Mongo actually manages it write concerns using locks and how journaling works. After that, I’ll probably ramble on for a bit and have a big emotional climax.
I’m not going to be covering how to create clusters as I already covered that in part 5 of the series – Sharding. I’m not even going to discuss provisioning solutions like Puppet, Chef, Ansible etc. What I am going to discuss however, is a collection of system choices that need to be thought, about when creating your cluster. So let’s get started.
Before getting into the nitty gritty of backups, it’s important to remember that there are multiple MongoDB setups you could be running. For example, you could be running a standalone MongoDB instance, a replica set, or using shards. Regardless of which setup you are using, the backup process is practically identical.
One of the most important tips when creating backups, is to always use secondary nodes where possible. This will prevent your application from being inaccessible. If you to were backup a primary node, your application will be unable to receive writes, which is less than ideal to put it lightly.
The concept of MapReduce is inspired by the map and reduce functions in functional programming. When MapReduce was defined, it was as a 3 step process (Map, Shuffle, Reduce), MongoDB have taken this one step further and added a finaliser step. So lets see what’s involved in each step.