As a cloud provider, though, you're trying to provide shared resources to a group of clients. A company rolling their own system doesn't have to share, and they can optimise specifically for their own requirements. You don't get these luxuries, and it's reasonable to expect a customised system to perform better than a general one.
If I may ask a few questions:
- Are they charged the same as external customers or do they get a 'wholesale' rate?
- As internal clients, do they run under the same conditions as external clients? Or is there a shared internal server pool that they use?
- Do they get any say in the hardware or low-level configuration of the systems they use? (ie. if someone needs ultra low latency or more storage, can they just ask Joe down the hall for a machine on a more lightly loaded network, or with bunch more RAM, for the week?)
- Do they have the same type of performance constraints as the ones encountered by gitlab?
I feel like most of the reason to use cloud services is when you have little idea what your actual requirements are, and need the ability to scale fast in both directions. Once you're established and have a fairly predictable workload, it makes more sense to move your hosting in-house.
Hopefully someone who actually knows what theyre talking about will be along shortly!
> Are they charged the same as external customers or do they get a 'wholesale' rate?
Id be quite surprised if internal customers are charged a markup. Presumably the whole point in operating an internal service is that you lower the cost as much as possible for your internal customers.
> As internal clients, do they run under the same conditions as external clients? Or is there a shared internal server pool that they use?
From the above book, it seems that the hardware is largely abstracted away so most services aren't really aware of servers. I assume there's some separation between internal and external customers, but at a guess that'd largely be because of the external facing services being forks of existing internal tools that have been untangled from other internal services.
> Do they get any say in the hardware or low-level configuration of the systems they use? (ie. if someone needs ultra low latency or more storage, can they just ask Joe down the hall for a machine on a more lightly loaded network, or with bunch more RAM, for the week?)
As above, the hardware is largely abstracted away. From memory, teams usually say "we think we need ~x hrs of cpu/day, y Gbps of network,..." then there's some very clever scheduling that goes on to fit all the services on to the available hardware. There's a really good chapter on this in the above book.
> Do they have the same type of performance constraints as the ones encountered by gitlab?
Presumably it depends entirely on the software being written.
Disk systems do get expensive at scale but the scale that they're usually sold at these days is pretty huge. You talk about going up to a petabyte but that's a fraction of a single rack's worth of disk these days. Not everyone wants to be a filesystem expert and distributed filesystems are jumping in at the deep end.
Clearly CephFS as weak spots, but for what I've seen those are sports that we can work out, rough edges here and there. The good thing is that we are much more aware of these edges.
We are already working on what the next step will be to soften out these weaknesses so we are not impacted again. And of course to ship this to all our customers, either they run on CephFS, NFS appliances, local disks or whatever makes sense for them.
We started using Ceph because we wanted to be able to grow our storage and compute independently. While it worked well for us we ended up having much larger latencies is as a result of this. So we developed FSCache support.
Even better yet, if you data is inherently shareable (or has some kind of locational affinity) you can end up always serving data with a Ceph backend from the local cache with the exception of a server going down or occasional request. I'm guessing it is (repo / account)
On your API machines serving out the git content of out the DFS you can setup a local SSD drive to read only caching. Depending on you workload you can end up significantly reducing the IOPs on the OSDs and also lowering network bandwidth.
With the network / IOPs savings we've decided to run our CephFS backed by Erasure Coded pool. Now we have lower cost of storage (1.7x vs 3x replication) and better reliability because now with our EC profile we can lose 5 chunks before data loss instead of 2 like before. That's because we more the 90% of requests are handled with local data and there's a long tail of old data that rarely accessed.
If you're going to give it a try, make sure you're using a recentish kernel such as a late 3.x series (or 4+). That has all the Cephfs FSCache / and upstream FSCache kinks work out.
If you're using relatively recent kernel such late 3.x series or 4+ (as in ubuntu 16.04).
We are running a recent kernel as in ubuntu 16.04.
The reason I'm framing the caching not so much at the CephFS level is because we are shipping a product, and I don't think that all our customers will be running CephFS on their infra. Therefore we will need to optimize for that use case also, and not only focus on what we do at GitLab.com.
Thanks for sharing! Will surely take a look at this.