If the latter, I think I just read the specs at some point & traced the linux drivers.
There's prominent references to needing just one MMIO write for NVMe command submission in the NVMe spec:
"Does not require uncacheable / MMIO register reads in the command submission or completion path.
A maximum of one MMIO register write is necessary in the command submission path."
Page 7 of https://nvmexpress.org/wp-content/uploads/NVM-Express-1_4b-2020.09.21-Ratified.pdf https://nvmexpress.org/wp-content/uploads/NVM-Express-1_4b-2...
I didn't quickly re-find the relevant SATA/AHCI spec parts, but there's some references in other places, e.g. https://spdk.io/news/2019/05/06/nvme/ https://spdk.io/news/2019/05/06/nvme/ :
"The NVMe specification is designed to significantly reduce the number of required MMIO compared to older specifications like AHCI. AHCI requires as many as 7 total MMIO operations (many of which are reads) per I/O, whereas NVMe requires an MMIO write only to ring the doorbell both on the submission and completion side. MMIO writes are posted, meaning the CPU does not stall waiting for any acknowledgement that the write made it to the PCIe device. This means that MMIO writes are much faster than MMIO reads. That said, they still have a significant cost."
And even that one doorbell write (for each of submission and completion) can be batched.
I'm not sure why I remember three - that might just have been the average number in simple scenarios, rather than the max.
It's IOPS, not throughput, that NVMe gets you. (Or rather, if you're not being bottlenecked by IOPS, then NVMe won't get you anything over a JBOD of SATA SSDs.)
SATA SSDs will serve you just fine if you're Netflix or YouTube, streaming a bunch of video files to people. High throughput, low IOPS — no advantage to NVMe.
But you'll get a huge boost from NVMe if you're doing a lot of little concurrent random reads from a much-larger-than-memory dataset. For example, if you're Twitter, fetching updates to users' timelines.
If you've got higher concurrency, you'll want several NVMe SSDs behind a hardware RAID controller, in RAID0.
And if you've got really high concurrency, you'll want several of these NVMe SSD RAID controllers, on separate PCI-e lanes, under software RAID0.
That's the point when you're starting to reach those "basically identical to memory" figures that are the use-case for Intel's Optane.
Thanks for this -- this is what I'm missing. I was starting to essentially suspect that NVMe is really more of a benefit for "hyperscaler"/IaaS providers... This lays it out well for me, and the how too. I rent my hardware so I don't have access to the hardware RAID stuff but this really filled out holes for me.
> If you've got higher concurrency, you'll want several NVMe SSDs behind a hardware RAID controller, in RAID0.
Hmnn why would you want RAID0 here? faster speed but absolutely no redundancy? what about RAID1+0/0+1?
> That's the point when you're starting to reach those "basically identical to memory" figures that are the use-case for Intel's Optane.
I need to do more reading on this but thanks for pointing this out
> Hmnn why would you want RAID0 here? faster speed but absolutely no redundancy? what about RAID1+0/0+1?
I can't speak to others†, but for our use-case, we're not using NVMe as "persistent storage but faster and smaller" but rather as "main memory but larger and slower".
Our data isn't canonically on our nodes' NVMe pools, any more than it's canonically in the the nodes' local memory. The canonical representation of our (PGSQL data warehouse) data is its WAL log segments, shipped to/from object storage.
The NVMe pool can be thought of as holding a "working" representation of the data — sort of like how, if you have a business layer that fetches a JSON document from a document store, then the business-layer node's main memory then holds a deserialized working representation of the data.
You wouldn't back up/protect the data on your NVMe pool, any more than you'd back up the memory backing the deserialized JSON. If you wanted to modify the canonical data, you'd modify it in the canonical store. (For JSON, the document database; for our use-case, the WAL segments + base-backups in the object store.)
† I would note that "large ephemeral working-set backing store" is seemingly largely considered by IaaS providers to be the main use-case for having NVMe attached to compute. The IaaS providers that support NVMe (AWS, GCP) only support it as instanced or scratch (i.e. ephemeral) storage, that doesn't survive node shutdown. These are strictly "L4 memory", not stores-of-record.
> I can't speak to others†, but for our use-case, we're not using NVMe as "persistent storage but faster and smaller" but rather as "main memory but larger and slower".
> Our data isn't canonically on our nodes' NVMe pools, any more than it's canonically in the the nodes' local memory. The canonical representation of our (PGSQL data warehouse) data is its WAL log segments, shipped to/from object storage.
I appreciate this insight -- one of the features that drew me to KeyDB[0] was their FLASH feature, with drives being as fast as they are these days it feels like having tiered cache with DRAM + NVMe disk isn't so bad! Great to see others think this way as well.
Redis is one thing but treating pg this way is somewhat interesting -- Do you also have the usual hot standby setup with synchronous replication just in case of a machine going down before the latest WAL segment is shipped out?
> † I would note that "large ephemeral working-set backing store" is seemingly largely considered by IaaS providers to be the main use-case for having NVMe attached to compute. The IaaS providers that support NVMe (AWS, GCP) only support it as instanced or scratch (i.e. ephemeral) storage, that doesn't survive node shutdown. These are strictly "L4 memory", not stores-of-record.
Reasonable, thanks for explicitly noting this. I think the write characteristics (and technically the read as this article shows) of NVMe do make it attractive but don't preclude using it as the store of record medium but what you said definitely syncs up with the other configurations I've seen and the offerings at AWS (I haven't really looked at GCP node types).
[0]: https://github.com/EQ-Alpha/KeyDB/wiki/Legacy-FLASH-Storage https://github.com/EQ-Alpha/KeyDB/wiki/Legacy-FLASH-Storage
> Do you also have the usual hot standby setup with synchronous replication just in case of a machine going down before the latest WAL segment is shipped out?
We don't have to — the data in our data warehouse is a rearrangement/normalization/collation of origin data from public sources. If our DB cluster dies, we stand up a new one from a backup, let it catch up from WAL segments, and then our ETL pipeline will automatically re-bind to it, discover the newest data it has available inside it, and begin ETLing to it. From our ETL ingestion-agent's perspective, there's no difference between "loading to a DB freshly restored from backup because it lost 10 minutes of updates" and "loading to a DB that is/was up-to-date because we just received some new origin data."
(Our situation of our primary-source data being easily and reliably re-fetched is pretty unique to our vertical; but you can put pretty much any system into the same shape by first writing your primary-source data to an event store / durable unbounded-size MQ [e.g. Kafka] as you receive it; and then ETLing it into your DB from there. Forget DB disaster recovery; just keep the MQ data safe, and you'll always be able to replay the process that translates an MQ event-stream into a DB state.)
We do have hot standbys, but they're async (using wal-g — master writes WAL segments to object store; replicas immediately fetch WAL segments from object store, as quickly as they can manage.) They're for query load scaling, not disaster recovery.
> It's IOPS, not throughput, that NVMe gets you. (Or rather, if you're not being bottlenecked by IOPS, then NVMe won't get you anything over a JBOD of SATA SSDs
It's both. Throughput is a significant issue with SATA. You need 6 SATA drives to match the throughout of single PCIe 3x drive. The SATA JBOD will have a significant higher CPU overhead at combined line speed than NVMe.
> If you've got higher concurrency, you'll want several NVMe SSDs behind a hardware RAID controller, in RAID0.
The few examples of raid controllers with NVMe support I had access increased latency sufficiently to be quite painful.
I'm confused by how high their latencies are, something isn't right (/me goes and spends too much time that I instead should spend on paperwork).
The biggest contributor is that they tested random reads - that'll likely end up bottlenecked by the FTL, not the protocol & interface latency. Which, especially on consumer devices, can add a lot.
Testing sequential, non-buffered, 4k IO with fio, on my boot disk, a Samsung SSD 970 PRO 1TB, I see:
Average latency of 13.8us (99.9th percentile 33us), with fio running on the socket the NVMe is attached to, and 16.22us on the remote node (99.9th 37us). With polling that goes down to 8.19 / 8.79us.
If I instead test random reads, I see 69.68 / 70.63us (99.9th 147us/ 176us) local/remote node respectively, if I use a 128MB file. Unsurprisingly the local/remote difference nearly vanishes, as the transfer itself isn't the bottleneck anymore. Nor does polling help much. With larger files (I tested up to 128GB, didn't have enough free space for more), that increases a bit, but not by much.
Even taking the effect of randomness into account, my disk is several years old, and the latencies reported in the post (and linked PDF) are well above what I see, on a two socket system, with plenty other things going on at the same time. I see considerably lower latencies on two different fast server grade SSDs as well (not quite to the optane level though).
The PDF's benchmark description says:
> The main goal of this work is to evaluate the hardware and not the filesystem. On each device we create a file of size approximately 90% that of a device size. We fill it with specific data to be sure that the space is allocated and that the reading takes place. To assert the latter we verify that received bytes match the ones we expect.
I don't really understand why they're doing that. On most disks that will ensure that the FTL cannot operate from cache, even if that cache is generously sized. IME even random workloads tend to have more locality than a uniform distribution over 90% 4TB. But more importantly, this seems to counter the author's stated goals:
> In this work we present a detailed overview and evaluation of modern storage reading performance with regard to available Linux synchronous and asynchronous interfaces.
But the test setup chooses the scenario that is least likely to be influenced by the choice of interface?
There's a lot of odd stuff in the later sections. It's not at all clear what 7.1 really measures. The numbers are too high for QD1 sequential reads (even at 10us latency one would just be a bit below 1GB/s). But too low for high QD sequential reads. So is it buffered - but then I see higher throughput at 4kB? Then 7.2 talks about "AIO" in the graphs but I think just means doing one read per thread. But how would that be "asynchronous reading"?
Hi!
Thanks for taking interest in the paper. First of all I'm sorry the setting doesn't meet with your expectations. It's always the problem with benchmarking why one measure one thing and not the other. In my case I was interested in uniformy random distribution since the whole work has been motivated by a task of putting key-value storage backend to NVMe. Hot data is already present in main memory and NVMe is used for a long tail of infrequently-accessed but large amount of data. Alas said maybe some other distribution could match the real workload more closely, I just don't know any better estimation.
It's pretty interesting that you question the working set size. Given the complexity of FTL it won't be surprising if the latency depend on working set size. I dind't do such kind of experiments but I hope this discussion could motivate somebody to take such measurements. Anyway thanks for the provided timings, I should remember them and keep that in mind.
As for section 7.1 QD was something from 16 to 64 but I don't remember exactly. In section 8 more attention is given to to QD and I try to pick the best one.
Section 7.2 could be probably the most confusing due to the read pattern complexity. I mention there that I'm interested in requests where several disjoint blocks are asked at once. Obviously that is transformed into several AIO requests. You have every right to complain about asynchronousity here because the reader waits for all of them to complete before issuing the next request. It's just the AIO interface which makes such kind of compound requests more efficient than issuing them via pread which the section demonstrated.
> pgBackRest is probably strictly better than wal-g for our use-case, but I already knew wal-g was at least satisfactory in solving our problems (from previous usage in implementing something Heroku-Dataclips-alike before), so I threw it on and filed an issue for doing a compare-and-contrast down the line. So far it hasn't caused too many issues, and does indeed solve our main issue: allowing truly-async replication (e.g. replication to DCs in other countries, where the link has a huge RTT, high potential packet loss, and might even go down for hours at a time due to, say, politics — without that causing any kind of master-side buffering or slowdown.)
I always thought that dataclips was just them running on ZFS but honestly this would make more sense. I don't use Heroku much so I didn't see any limitations that jumped out to me as "oh they have that limitation because they're doing pgbackrest" but.
> Not yet, but we've certainly been considering it. Probably something we'll do when we have a bit more breathing room (i.e. when we get out from under all the scaling we're doing right now.)
Yup sounds reasonable, I certainly am not a customer (nor in a position to be one really, as I don't do any blockchain) so satisfying me brings you almost negative ROI. Scaling sounds fun in and of itself (when it isn't terrifying though) -- thanks for sharing so much about the setup!
> Nothing like that per se (though it'd very much be a data-layer match for how we're doing infra management using k8s "migrations" through GitOps. Imagine SQL schema-definition k8s CRDs.)
Now this is fascinating -- I'm also very much sold on k8s and SQL-schemas-as-CRDs sounds like it'd be relatively easy to implement and very high ROI. Migrations were always a sort of weird infrastructure-mixed-with-dev thing, and being able to tie the CRDs for migrations to the StatefulSet running the DB and the Deployment running the application seems like it could open up a whole new world of more intelligent deployment. cool stuff.
> But that's mostly because — to our constant surprise — our PG cluster's query plans improve the more we normalize our data. We hardly build matviews or other kinds of denormalized data representations at all (and when we do, we almost always eventually figure out a partial computed-expression GIN index or something of the sort we can apply instead.)
I don't know how people use open source DBs that are not postgres. I mean I do, but I can't wait until zheap removes the most common reason for people choosing MySQL to begin with.
> We are planning to offer our users something like dbt, and we may or may not use dbt under the covers to manage it. (Analogous to how GitHub created their own git library to use in their Rails backend.)
dbt does look pretty good, it seems like just enough structure. I do want to also bring up ddbt[0] if ya'll do some research/exploratory sprint on it.
> One thing we do want to enable is avoiding the recomputation of intermediate "steps" within long chained CTE queries (imagine: taking 10 seconds to compute A, and then using it in a join to quickly select some Bs; then, in another query, taking another 10 seconds to compute A again, because now you need to select Cs instead), without keeping around any sort of persistent explicitly-materialized intermediate, or writing ugly procedural logic. https://materialize.com/ https://materialize.com/ seems promising for that. (Just wish it was a library I could shove inside PG, rather than an external system that is apparently going to grow to having its own storage engine et al.)
Interesting -- would Incremental View Maintenance[2] work for this? Don't know if this is useful to you but the dbt conference I got sucked into watching most of had a talk with the materialize CEO and some other people. I don't remember it being particularly high signal (I mean let's be real it's basically a 3-way ad for the CEO involved) but it might be worth a watch[1].
> We've always mentioned to people that Tableau has first-class support for connecting to/querying Postgres. Given the type of data we deal with, that's usually all they need to know. :)
I really underestimate how much Tableau is the standard (because I'm not a data person probably) -- basically between that, looker and PowerBI I'm not sure people use much else, even if it's open source/free.
[0]: https://github.com/monzo/ddbt https://github.com/monzo/ddbt
[1]: https://www.youtube.com/watch?v=0DDNjB4O0As https://www.youtube.com/watch?v=0DDNjB4O0As
[2]: https://github.com/sraoss/pgsql-ivm https://github.com/sraoss/pgsql-ivm