| Actually, it doesn't work like that. Sun's ZFS7420 had a terabyte of RAM per controller, and these work in tandem, and after a certain pressure, the thing can't keep up even though it also uses specialized SSDs to reduce HDD array access during requests, and these were blazingly fast boxes for their time. When you drive a couple thousand physical nodes with a some-petabytes sized volumes, no amount of RAM can save you. This is why Lustre divides metadata servers and volumes from file ones. You can keep very small files in metadata area (a-la Apple's 0-sized, data-in-resource-fork implementation), but for bigger data, you need to have good filesystems. There are no workarounds from this. If you want to go faster, take a look at Weka and GPUDirect. Again, when you are pumping tons of data to your GPUs to keep them training/inferring, no amount of RAM can keep that data (or sustain the throughput) during that chaotic access for you. When we talked about performance, we used to say GB/sec. Now a single SSD provides that IOPS and throughput provided by storage clusters. Instead, we talk about TB/sec in some cases. You can casually connect terabit Ethernet (or Infiniband if you prefer that) to a server with a couple of cables. |
You aren't doing that with ZFS or btrfs, though. Datacenter-scale storage solutions (c.f. Lustre, which you mention) have long since abandoned traditional filesystem techniques like the one in the linked article. And they rely almost exclusively on RAM behavior for their performance characteristics, not the underlying storage (which usually ends up being something analogous to a pickled transaction log, it's not the format you're expected to manage per-operation)