The wondrous thing about modern CPU architectures (e.g. Zen3), though, is all the PCIe lanes you get with them. If you really need high random IOPS, you can now cram 24 four-lane NVMe disks into a commodity server (with PCIe M.2 splitter cards) and saturate the link bandwidth on all of them. Throw them all in a RAID0, and stick a filesystem on them with the appropriate stripe width, and you'll get something that's only about 3x higher-latency for cold(!) random reads, than a read from RAM.
(My company provides a data-analytics SaaS product; this is what our pool of [shared multitenant, high concurrency] DB read-replicas look like.)
In other words, when measured E2E in the context of a larger work-step (one large enough to be interrupted by a context-switch), the mean, amortized difference between the two types of fetch becomes <3x.
Top of my wishlist for future architectures is “more, lower-width memory channels” — i.e. increased intra-CPU NUMAification. Maybe something CXL.mem will roughly simulate — kind of a move from circuit-switched memory to packet-switched memory, as it were.
But it's not going to be easy - for a sense of scale I just tested a 7950x at stock speeds with stock JEDEC DDR5 timings. I inserted a bunch of numbers in an 8GB block of memory, and with a deterministic random seed randomly pick 4kb pages, computing their sum and eventually reporting that (to avoid overly clever dead-code analysis, and make sure the data is fully read).
With an SSD-friendly 4K page size that resulted in 2.8 million iops of QD1 random read. By comparison, a web search for intel's 5800x optane's QD1 results shows 0.11 million iops, and that's the fastest random read SSD there is at those queue depths, AFAIK.
If you add parallelism, then ddr5 reaches 11.6 million iops at QD16 (via 16 threads), fast SSDs reach around 1 million, the optane reaches 1.5 million. An Epyc Genoa server chip has 6 times as many DDR5 memory channels as this client system does; and I'm not sure how well that scales, but 60 million 4kb random read iops sounds reasonable, I assume. Intel's memory controllers are supposedly even better (at least for clients). Turning on XMP and PBO improves results by 15-20%; and even tighter secondary/tertiary timings are likely possible.
I don't think you're going to reach those numbers not even with 24 fast NVMe drives.
And then there's the fact that I picked the ssd-friendly 4kb size; 64-byte random reads reach 260 million iops - that's not quite as much bandwidth as @ 4kb, but the scaling is pretty decent. Good luck reaching those kind of numbers on SSDs, let alone the kind of numbers a 12-channel server might reach...
We're getting close enough that the loss in performance at highly parallel workloads is perhaps acceptable enough for some applications. But it's still going to be a serious engineering challenge to even get there, and you're only going to come close under ideal (for the NAND) circumstances - lower parallelism or smaller pages and it's pretty much hopeless to arrive at even the same order of magnitude.
If that scaled, it would be 9.6M IOPS from 24xNVMe.
Still, a mere factor 7 isn't a _huge_ difference. Plenty of use cases for that, especially since NAND has other advantages like cost/GB, capacity, and persistence.
But it's also not like this is going to replace dram very quickly. Iops is one thing, but latency is another, and there dram is still much faster; like close to 1000 times faster.
And you wouldn't see the speed improvement on RAID0 NVMe drives except extremely rare fully sequential operations lasting for at least tens of seconds.
You also can try it just by running a VM with iSCSI boot on your current desktop.
But I look at it this way. You need 40gbit networking for a single pci3 nvme ( and newer drives can saturate that, or close )
And because you're throttling throughput you'll see much more frequent, longer, queuing delays, on the back of a network stack that ( unless you're using rdma ) is already 5x-10x slower than nvme.
It'll be fast enough for lots of things, especially home/lab use, and it'll be amazing if you're upgrading from sata spinning disk.. but 10gbit is slow by modern storage standards.
Of course, that's not the only consideration. Shared storage and iscsi in particular can be extremely convenient! And sometimes offers storage functionality that clients don't have ( snapshots, compression, replication )
Don't have anything on the hands to look if the boot firmware even allows to set 9k, but I didn't touch iSCSI boot for a long time, so I would take your word for it.
> But I look at it this way. You need 40gbit networking ... is already 5x-10x slower than nvme.
This one.
> It'll be fast enough for lots of things, especially home/lab use
Yep, in OP's case I would consider just leaving the OS on the local [fast enough] drive and using iSCSI (if for some reason NFS/SMB doesn't fit) for any additional storage. It would be fast enough for almost everything, while completely eliminating any iSCSI boot shenanigans /me shudders in Broadcom flashbacks.
Another neat thing about iSCSI is what you can re/connect it to any device on the network in a couple of minutes (first time, even faster later), sometimes it comes really handy.
Ugh, ISCSI does have queueing so you can have many operations in flight, and one operation doesn't really translate to one packet in the first place, kernel will happily pack few smaller operations to TCP socket into one packet when there is load.
The single queue is the problem here but dumb admin trick is just to up more than one IP on the server and connect all of them via multipath
And here comes the latency! shining.jpg
It wouldn't be a problem for a desktop use of course[0], especially considering what 90% of operations are just read requests.
My example is crude and was more to highlight what iSCSI, by virtue of running over Ethernet, inherently has a limit of how many concurrent operations can go in one moment. It's not a problem for a HDD packed SAN (HDDs would impose an upper limit, because spinning rust is spinning) but for a NVMe (especially with a single target) it could diminish the benefits of such fast storage.
> The single queue is the problem here but dumb admin trick is just to up more than one IP on the server and connect all of them via multipath
Even on a single physical link? Could work if the load is queue bound...
[0] hell, even on 1Gb link you could run multiple VMs just fine, it's just when you start to move hundreds of GBs...
>And here comes the latency! shining.jpg
Not really, if you get data faster than you can send packets (link full) there wouldn't be that much extra latency from that (at most one packet length which at 10Gbit speeds is very short) and it would be more than offset by the savings
Then again I'd guess that's mostly academic as I'd imagine not very many ISCSI operations are small enough to matter. Most apps read more than a byte at a time after all, hell, you literally can't read less than a block from a block device which is at least 512 bytes.
>> The single queue is the problem here but dumb admin trick is just to up more than one IP on the server and connect all of them via multipath
> Even on a single physical link? Could work if the load is queue bound...
You can also use it to use multiple NICs without bonding/teaming, althought it is easier to have them in separate network, IIRC linux had some funny business when if you didn't configure it correctly for traffic in same network it would pick "first available" NIC to send it and it needed /proc setting to change
To elaborate, default setting for /proc/sys/net/ipv4/conf/interface/arp_ignore (and arp_announce) is 0 which means
> 0 - (default): reply for any local target IP address, configured on any interface
> 0 - (default) Use any local address, configured on any interface 1
IIRC to do what I said required
net.ipv4.conf.all.arp_ignore=1
net.ipv4.conf.all.arp_announce=2
It is, that mattered on 1Gbit links with multiple clients, ie any disk operations in VMs while there is vMotion running on the same links - you could see how everything started to crawl (and returned back after vMotion completed). For 10Gbit you need way, way more load for it to matter.
> You can also use it to use multiple NICs without bonding/teaming
You MUST (as in RFC) use multiple links without bonding and I learned to not to use LACP the hard way (yea, reading docs before is for pussies).
After second attempt I understood the implication (multiple NICs in the same IP network), but this is a self inflicted wound, usually. You don't even need a physically separate networks (VLANs), but using separate IP networks works fine, it's up to initiator to use RR/LB on them.
> it would pick "first available" NIC to send it
Yep, the usual magic of doing things to be easier for average folks. In the same vein - you need to disable Proxy ARP in any modern non-flat network or you will get shenanigans what would drive you mad.
iSCSI gotta eat some of your CPU (you're changing "send a request to disk controller and wait" to "do a bunch of work to create packet,send it over the network, and get it back) if you don't have card with offload, it also might kinda not be fast enough to get the most out of NVMe, especially more in RAID0
And, uh, just don't keep anything important there...
If you read 1,000,000 random bytes (~1 Mb) scattered across a huge file (let's say you're fetching from some humongous on-disk hash table), it will to a first order be about as slow as reading 4 Gb sequentially. This will incur the same number of page faults. There are ways of speeding this up, but only so much.
Although, I/O is like an onion of caching layers, so in practice this may or may not hold up depending on previous access patterns of the file, lunar cycles, whether venus is in retrograde.
I get ~30 GiB/s for threaded sequential memory reads, but ~4 GiB/s for SSD. However, I think the SSD number is single-threaded and not even with io_uring—so I need to regenerate those numbers. It's possible it could be 2-4x better.
But as I mentioned, there's caching upon caching, and also protocol level optimizations, and hardware-level considerations (physical block size may be quite large but is generally unknown).
It's nearly impossible to benchmark this stuff in a meaningful way. Or rather, it's nearly impossible to know what you are benchmarking, as there are a lot of nontrivially stateful parts all the way down that have real impact on your performance.
There are so many moving parts I think the only meaningful disk benchmarks consider whatever application you want to make go faster. Do the change. Is it faster? Great. Is it not? Well at least you learned.
Assuming that you run the experiments on NVMe SSD which is attached to PCIe 3.0, where theoretical maximum is around 1GB/s per each lane, I am not sure I understand how do you expect to go faster than 4 GiB/s? Isn't that already a theoretical maximum of what you can achieve?
And I'm pretty sure that parent comment doesn't own such a machine because otherwise I'd expect 7-8GB/s figure to be reported in the first place.
I’d guess that they’re a small minority of devices at the moment.
4.0 might not be common, but surprisingly it is now the previous generation!
I'm not sure how they calculated the theoretical limit of 42.4 GBPS, but they have multiple measurements higher than 30 GBPS.
Hilariously meanwhile, RAM has become significantly slower compared to CPU performance, i.e. you spend a disproportionate time to read and write to memory, so despite RAM is faster, CPU is way faster.
Which means I/O remains a bottleneck...
This applies to a single point query in a single tree.
The latency is reduced by overlap in obvious ways as soon as you have (1) a range query because it can read multiple subtrees in parallel, or (2) a query that reads multiple indexes in parallel, or (3) multiple queries from the application to the database in parallel.
This is why it's useful to design applications to make multiple queries in parallel. Web applications are a great example of this. Most applications where I/O performance matters at all have some natural way to parallelise queries.
Less obviously, the interior blocks of a B-tree are a relatively small part of a B-tree. I.e. most of the space is in used leaf blocks. If the database's cache strategy gives preference to interior nodes, and even more preference to nodes closer to the root of a tree, often several interior layers of the tree can fit entirely in RAM and the effect is is to reduce the latency of tree lookups further once the cache is warmed up.
Then even in large databases (a few TB), the latency of a single point query is reduced to a one or two read IOPS (because the leaf page to read which contains the query result is calculated from in-memory data). The application-visible query time is very similar to the I/O subsystem's timing characteristics, and a few MQPS are achievable (= "million queries per second"). Not many database engines achieve this, because they were designed in an area where I/O was much slower, but the I/O architecture does support it.
Source: Wrote a performance-optimised database engine for blockchain archive data, which is extremely random access (because of hashing), in the multiple terabytes range, and the application is bottlenecked on how many queries per second it can achieve. It's like the ideal case for working on random-access I/O performance :-)
For example a tree-index can be parallelized by walking down different branches. On top of that one can issue a prefetch for the next node (on each branch) while processing the current ones.
With spinning rust you have to wait for the sector you want to read to rotate underneath the read head. For a fast 10.000 RPM drive, a single rotation takes 6 milliseconds. This means that for random access the average latency is going to be 3 milliseconds - and even that's ignoring the need to move the read head between different tracks! Sequential data doesn't suffer from this, because it'll be passing underneath the read head in the exact order you want - you can even take the track switching time into account to make this even better.
SSDs have a different problem. Due to the way NAND is physically constructed it is only possible to read a single page at a time, and accessing a single page has a latency of a few nanoseconds. This immediately places a lower limit on the random read access time. However, SSDs allow you to send read commands which span many pages, allowing the SSD to reorder the reads in the most optimal way, and do multiple reads in parallel. This means that you only have to pay the random access penalty once - not to mention that you have to issue way fewer commands to the SSD.
SSDs try to make this somewhat better by having a very deep command queue: you can issue literally thousands of random reads at once, and the SSD will reorder them for faster execution. Unfortunately this doesn't gain you a lot if your random reads have dependencies, such as when traversing a tree structure, and you are still wasting a lot of effort reading entire pages when you only need a few bytes.
Curious to hear your thoughts on this thread if you have time to share: https://news.ycombinator.com/item?id=33752870
So, this mean Btrees suffer? Which could be the most optimal layout for a database storage where only SSD matters?
I'm working in one that is just WAL-only and scanning all in each operation (for now!) and wanna see what I can do for improve the situation.
What we would really benefit is storage which is efficient in small (cpu cache line) size IO