You'd think they could build a ARM-powered, credit card sized controller for them with a disk breakout card and network IO. PC motherboard and full-sized cards seem like overkill.
Given the bandwidth of 60+ hard drives (150MB/s per hard drive x 60 == 9GB/s in/out), I'm pretty sure you need a decent CPU just to handle the PCIe traffic. At least PCIe 3.0 x16, just for the hard drives. And then another x16 for network connections (multiple PHY for Fiber in/out that can handle that 9GB/s to a variety of switches).
We're looking at PCIe 3.0 x32 just for HDDs and Networking. Throw down a NVMe-cache or other stuff and I'm not seeing any kind of small system working out here.
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Then the math comes in: matrix multiplications over every bit of data to verify checksums and reed-solomon error correction starts to get expensive. Maybe if you had an FPGA or some kind of specialist DSP (lol GPUs maybe, since they're good at matrix multiplication), you can handle the bandwidth. But it seems nontrivial to me.
Server CPU seems to be a cheap and simple answer: get the large number of PCIe I/O lanes plus a beefy CPU to handle the calculations. Maybe a cheap CPU with many I/O lanes going to a GPU / FPGA / ASIC for the error checking math, but... specialized chips cost money. I don't think a cheap low-power CPU would be powerful enough to perform real-time error correction calculations over 9GBps of data.
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We can leave Backblaze's workload and think about typical SAN or NAS workloads too. More I/O is needed if you add NVMe storage to cache hard drive reads/writes, tons of RAM is needed if you plan to dedup.
Which means 9GBps downstream (to be processed by the GPU) + 9GBps upstream (GPU is done with the data), or a total bandwidth of 18GBps aggregate to the GPU / FPGA / ASIC / whatever coprocessor you're using.
So that's what? Another 32x lanes of PCIe 3.0? Maybe a 16x PCIE 4.0 GPU can handle that kind of I/O... but you can see that moving all this data around is non-trivial, even if we assume the math is instantaneous.
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Practically speaking, it seems like any CPU with enough PCIe bandwidth to handle this traffic is a CPU beefy enough to seemingly run the math.
The issue is that Backblaze has a 2nd layer of error correction codes. This 2nd layer of error correction codes needs to be calculated somewhere. If enough errors come from a drive, the administrators take down the box and replace the hard-drives and resilver the data.
Backblaze physically distributes the data over 20 separate computers in 20 separate racks. Some computer needs to run the math to "Combine" the data (error correction + checksums and all) back into the original data on every single read. So singular hard drive can do this math because the data has been reliably dispersed to so many different computers.
Do those run on this machine? I imagine backblaze has redundancy at the cluster level rather than machine level. That allows them to lose a single machine without any data becoming unavailable. It also means we shouldn't assume the erasure code calculations happen on a machine with 60 drives attached. That's still possible but alternatively the client [1] could do those calculations and the drive machines could simply handle read/write raw chunks. This can mean less network bandwidth [2] and better load balancing (heavier calculations done further from the stateful component).
[1] Meaning a machine handling a user-facing request or re-replication after drive/machine loss.
[2] Assume data is divided into slices that are reconstructed from N/M chunks, such that each chunk is smaller than its slice. [3] On read, the client-side erasure code design means N chunk transfers from drive machine to client. If instead the client queries one of the relevant drive machines, that machine has to receive N-1 chunks from the others and send back a full slice. (Similar for writes.) More network traffic on the drive machine and across the network in total, less on the client.
[3] This assumption might not make sense if they care more about minimizing seeks on read than minimizing bytes stored. Then they might have at least one full copy that doesn't require accessing the others.
Given their scale and goal, it would be pretty wasteful to build it to max the writing speed of all hard drives. Considering you rarely write on the pod, you would be better of getting a fraction of that speed and writing on multiple pods at the same time to get the required peak performance.
In fact actually that makes much more sense to put that math on some ingests server and theses hard drive servers would simply write the resulting data. It makes it much easier and faster to divide it over 20 pods like they currently do.
Like most blade setups, like (random example): https://www.storagereview.com/review/supermicro-x11-microbla...
And no, you want to calculate checksums and fix bit errors right here in the RAM buffers you just read or received, because at such scales hardware is not error-free.
Isn't Backblaze's workload nearly exclusively writes? People back up far, far more often than they restore.
RS is normally used as erasure code: It's used when writing (to compute code blocks), and when reading _only when data is missing_. Checksums are used to detect corrupt data, which is then treated as missing and RS used to reconstruct it. Using RS to detect/correct corrupt data is very inefficient.
Checksums are also normally free (CRC + memcpy on most modern CPUs runs in the same time that memcpy does: it's entirely memory bound).
The generation of code blocks is also fairly cheap: Certainly no large matrix multiplications! This is because the erasure code generally only spans a small number of blocks (e.g. 10 data blocks), so every code byte is only dependent on 10 data bytes. The math for this is reasonably simple, and further simplified with some reasonable sized look-up tables.
That's not to say that there is no CPU needed, but it's really not all that much, certainly nothing that needs acceleration support.
There are a small number of high end ARM server boards that could do it, but you’re not saving much money at that point. Might be more expensive due to lack of scale.
Amortize a server-grade CPU and motherboard across 60+ high capacity drives and it’s not really worth pinching pennies at the risk of lower performance.
I don't think they actually sold any.
[1] https://www.hardkernel.com/shop/odroid-hc2-home-cloud-two/