You are correct, with MongoDB the way to engineer around a write contention issue is to shard.
Your environment sounds like it's fairly bursty, in that you're not continually ingesting data and instead ingesting it in fairly discrete chunks. With this in mind, you could go with a collector/distributor model such as this:
------>{ workers }<------
| |
[Shard01] [Shard02]
| |
---->[Persistant]<------
The workers would upsert/push their results into the sharded collection/database, and once the job was completed a batch-process then uses something like db.copyDatabases() to push the result set at the monolithic (and cheaper to run) single instance Mongo. As the copy database process can push all of its updates in one run, it should experience much fewer write-lock problems.
Since there is already and answer submitted, and a useful and valid one at that, I do not want to distract from its own usefulness but there are indeed points to raise that go way beyond just a short comment. So consider this "augmentation", which is hopefully valid but primarily in addition to what has already been said.
The truth is to really consider "how your application uses the data", and to also be aware of the factors in a "sharded environment" as well as your proposed "container environment" that affect this.
The Background Case
The general take on the practice recommendation for co-locating the mongos
process along with the application instance is to obviate any network overhead required in order for the application to communicate with that mongos
process. Of course it is also "recommended practice" to specify a number of mongos
instances in the application connection string in the case where that "nearest" node should not be available for some reason then another could be selected, albeit with the possible overhead of contacting a remote node.
The "docker" case you mentions seems somewhat arbitrary. While it is true that one of the primary goals of containers ( and before that, something like BSD jails or even chroot ) is generally to achieve some level of "process isolation", there is nothing really wrong with running multiple processes as long as you understand the implications.
In this particular case the mongos
is meant to be "lightweight" and run as an "additional function" to the application process in a way that it is pretty much a "paired" part of the application itself. So docker images themselves don't have an "initd" like process but there is not really anything wrong with with running a process controller like supervisord ( for example ) as the main process for the container which then gives you a point of process control over that container as well. This situation of "paired processes" is a reasonable case and also a common enough ask that there is official documentation for it.
If you chose that kind of "paired" operation for deployment, then it does indeed address the primary point of maintaining a mongos
instance on the same network connection and indeed "server instance" as the application server itself. It can also be viewed in some way as a case where the "whole container" were to fail then that node in itself would simply be invalid. Not that I would recommend it, and in fact you probably should still configure connections to look for other mongos
instances even if these are only accessible over a network connection that increases latency.
Version Specific / Usage Specific
Now that that point is made, the other consideration here comes back to that initial consideration of co-locating the mongos
process with the application for network latency purposes. In versions of MongoDB prior to 2.6 and specifically with regard to operations such as with the aggregation framework, then the case there was that there would be a lot more network traffic and subsequent after processing work performed by the mongos
process for dealing with data from different shards. That is not so much the case now as a good deal of the processing workload can now be performed on those shards themselves before "distilling" to the "router".
The other case is your application usage patterns itself with regard to the sharding. That means whether the primary workload is in "distributing the writes" across multiple shards, or indeed being a "scatter-gather" approach in consolidating read requests. In those scenarios
Test, Test and then Test Again
So the final point here is really self explanatory, and comes down to the basic consensus of any sane response to your question. This is not a new thing for MongoDB or any other storage solution, but your actual deployment environment needs to be tested on it's "usage patterns" as close to actual reality just as much as any "unit testing" of expected functionality from core components or overall results needs to be tested.
There really is not "definitive" statement to say "configure this way" or "use in this way" that actually makes sense apart from testing what "actually works best" for your application performance and reliability as is expected.
Of course the "best case" will always be to not "crowd" the mongos
instances with requests from "many" application server sources. But then to allow them some natural "parity" that can be distributed by the resource workloads available to having at "least" a "pool of resources" that can be selected, and indeed ideally in many cases but obviating the need to induce an additional "network transport overhead".
That is the goal, but ideally you can "lab test" the different perceived configurations in order to come to a "best fit" solution for your eventual deployment solution.
I would also strongly recommend the "free" ( as in beer ) courses available as already mentioned, and no matter what your level of knowledge. I find that various course material sources often offers "hidden gems" to give more insight into things that you may not have considered or otherwise overlooked. The M102 Class as mentioned is constructed and conducted by Adam Commerford for whom I can attest has a high level of knowledge on large scale deployments of MongoDB and other data architectures. Worth the time to at least consider a fresh perspective on what you may think you already know.
Best Answer
My answer is : It depends. If you are accessing files by _id field, which is already indexed then you don't need to add more memory soon.
The _id field which is type of ObjectID is 12 byte in size. That means it can hold up to 2^(12*8) files. 3 byte is for machine ID which is a hash value and has a fixed vale on the machine can be subtracted which gives you approx 2^72 files. For the reference, 2^20 is 1,048,576.
In terms of the memory, the index on the _id field needs 10,000,000 x 12 byte = 114 MiBytes. To be honest, I don't now how much overhead there will be for an index which holds 10 millions value but I don't think that it will need more than 1 Gigabyte.
Now, if your _id field is not a type of ObjectID than do the math.
In the gridfs, filename value of the files collection is also indexed. If you are not accessing files using filename, then you may leave it blank and drop the index for the filename.
On the other side, if you will add some metadata to the files you added and want to query the files according to those metadata, then you should have indexes for those metadata and do the math again.
I have a production environment which has over 3,000,000 pdf files (takes 180 Gig space on the disk). My server is a virtual server which has 4 vCPU and 4 Gig RAM, still no problem. The specs you provided is way too high for your needs. You can save billions of files with those servers. Especially if you have SSD. Because even if your indexes do not fit into the memory, swapping will be very fast, you won't even notice a slowdown.