A few factual inaccuracies in here that don't affect the general thrust. For example, the claim that S3 uses a 5:9 sharding scheme. In fact they use many different sharding schemes, and iirc 5:9 isn't one of them.
The main reason being that a ratio of 1.8 physical bytes to 1 logical byte is awful for HDD costs. You can get that down significantly, and you get wider parallelism and better availability guarantees to boot (consider: if a whole AZ goes down, how many shards can you lose before an object is unavailable for GET?).
Note that you can kind of infer that S3 is still using hard drives for their basic service by looking at pricing and calculating the IOPS rate that doubles the cost per GB per month.
S3 GET and PUT requests are sufficiently expensive that AWS can afford to let disk space sit idle to satisfy high-performance tenants, but not a lot more expensive than that.
> full-platter seek time: ~8ms; half-platter seek time (avg): ~4ms
Average distance between two points (first is current location, second is target location) when both are uniformly distributed in [ 0 .. +1 ] interval is not 0.5, it’s 1/3. If the full platter seek time is 8ms, average seek time should be 2.666ms.
Full seek on a modern drive is a lot closer to 25ms than 8ms. It's pretty easy to test yourself if you have a hard drive in your machine and root access - fire up fio with --readonly and feed it a handmade trace that alternates reading blocks at the beginning and end of disk. (--readonly does a hard disable of any code that could write to the drive)
There's acceleration of the read head to move it between the tracks. So it may well be 4ms because shorter distances are penalized by a lower peak speed of the read head as well as constant factors (settling at the end of motion)
I generally don't think about storage I/O speed at that scale (I mean really who does?). I once used a RAID0 to store data to HDDs faster, but that was a long time ago.
I would have naively guessed an interesting caching system, and to some degree tiers of storage for hot vs cold objects.
It was obvious after I read the article that parallelism was a great choice, but I definitely hadn't considered the detailed scheme of S3, or the error correction it used. Parallelism is the one word summary, but the details made the article worth reading. I bet minio also has a similar scaling story: parallelism.
AWS themselves have bragged that the biggest S3 buckets are striped across over 1 million hard drives. This doesn't mean they are using all of the space of all these drives, because one of the key concepts of S3 is to average IO of many customers over many drives.
Unless you have a large cluster with many tens of nodes/OSDs (and who does in a homelab?) then using Ceph is a bad idea (I've run large Ceph clusters at previous jobs).
I think the article's title question is a bit misleading because it focuses on peak throughput for S3 as a whole. The interesting question is "How can the throughput for a GET exceed the throughput of an HDD?"
If you just replicated, you could still get big throughput for S3 as a whole by doing many reads that target different HDDs. But you'd still be limited to max HDD throughput * number of GETs. S3 is not so limited, and that's interesting and non-obvious!
The storage itself is probably (mostly) on HDDs, but I'd imagine metadata, indices, etc are stored on much faster flash storage. At least, that's the common advice for small-ish Ceph cluster MDS servers. Obviously S3 is a few orders of magnitude bigger than that...
Repeating a comment I made above - for standard tier, requests are expensive enough that it's cost-effective to let space on the disks go unused if someone wants an IOPS/TB ratio that's higher than what disk drives can provide. But not much more expensive than that.
The latest generation of drives store about 30TB - I don't know how much AWS pays for them, but a wild-ass guess would be $300-$500. That's a lot cheaper than 30TB of SSD.
Also important - you can put those disks in high-density systems (e.g. 100 drives in 4U) that only add maybe 25% to the total cost, at least if you're AWS, a bit more for the rest of us. The per-slot cost of boxes that hold lots of SSDs seems to be a lot higher.
S3’s KeyMap Index uses SSDs. I also wouldn’t be surprised if at this point SSDs are somewhere along the read path for caching hot objects or in the new one zone product.
I've always felt it's probably a wrapper around the Amazon EFS due to the similar pricing and that S3 One Zone has "Directory" buckets, a very file system-y idea.
I think that's close to the truth. IIRC it's something like a massive cluster of machines that are effectively powered off 99% of the time with a careful sharding scheme where they're turned on and off in batches over a long period of time for periodic backup or restore of blobs.
There might be surprisingly little value in going tape due to all the specialization required. As the other comment suggest, many of the lower tiers likely represent basically IO bandwidth classes. a 16 TB disk with 100 IOPs can only offer 1 IOP/s over 1.6 TB for 100 customers, or 0.1 IOP/s over 160 GB for 1000, etc. Just scale up that thinking to a building full of disks, it still applies
See comments above about AWS per-request cost - if your customers want higher performance, they'll pay enough to let AWS waste some of that space and earn a profit on it.
Not even the higher tiers of Glacier were tape afaict (at least when it was first created), just the observation that hard drives are much bigger than you can reasonably access in useful time.
In the early days when there were articles speculating on what Glacier was backed by, it was actually on crusty old S3 gear (and at the very beginning, it was just on S3 itself as a wrapper and a hand wavy price discount, eating the costs to get people to buy in to the idea!). Later on (2018 or so) they began moving to a home grown tape-based solution (at least for some tiers).
I'm not aware of AWS ever confirming tape for glacier. My own speculation is they likely use hdd for glacier - especially so for the smaller regions - and eat the cost.
Someone recently came across some planning documents filed in London for a small "datacenter" which wasn't attached to their usual London compute DCs and built to house tape libraries (this was explicitly called out as there was concern about power - tape libraries don't use much). So I would be fairly confident they wait until the glacier volumes grow enough on hdd before building out tape infra.
Do you have any sources for that? I'm really curious about Glacier's infrastructure and AWS has been notoriously tight-lipped about it. I haven't found anything better than informed speculation.
My speculation: writes are to /dev/null, and the fact that reads are expensive and that you need to inventory your data before reading means Amazon is recreating your data from network transfer logs.
I'd be curious whether simulating a shitty restoration experience was part of the emulation when they first ran Glacier on plain S3 to test the market.
Author of the 2minutestreaming blog here. Good point! I'll add this as a reference at the end. I loved that piece. My goal was to be more concise and focus on the HDD aspect
If we assume enterprise HDDs in the double digit TB range then one can estimate that the total S3 storage volume of AWS is in the triple digit Exabyte range. That's propably the biggest storage system on planet earth.
This piece is interesting background, but worth noting that the actual numbers are highly speculative. The NSA has never disclosed hard data on capacity, and most of what's out there is inference from blueprints, water/power usage, or second-hand claims. No verifiable figures exist.
Is there an open source service designed with HDDs in mind that achieves similar performance? I know none of the big ones work that well with HDDs: MinIO, Swift, Ceph+RadosGW, SeaweedFS; they all suggest flash-only deployments.
Recently I've been looking into Garage and liking the idea of it, but it seems to have a very different design (no EC).
I would say that Ceph+RadosGW works well with HDDs, as long as 1) you use SDDs for the index pool, and 2) you are realistic about the number of IOPs you can get out of your pool of HDDs.
And remember that there's a multiplication of iops for any individual client iop, whether you're using triplicate storare or erasure coding. S3 also has iop multiplication, which they solve with tons of HDDs.
For big object storage that's mostly streaming 4MB chunks, this is no big deal. If you have tons of small random reads and writes across many keys or a single big key, that's when you need to make sure your backing store can keep up.
Apache Ozone has multiple 100+ petabyte clusters in production. The capacity is on HDDs and metadata is on SSDs. Updated docs (staging for new docs): https://kerneltime.github.io/ozone-site/
At a past job we had an object store that used SwiftStack. We just used SSDs for the metadata storage but all the objects were stored on regular HDDs. It worked well enough.
We've been running a production ceph cluster for 11 years now, with only one full scheduled downtime for a major upgrade in all those years, across three different hardware generations. I wouldn't call it easy, but I also wouldn't call it hard. I used to run it with SSDs for radosgw indexes as well as a fast pool for some VMs, and harddrives for bulk object storage. Since i was only running 5 nodes with 10 drives each, I was tired of occasional iop issues under heavy recovery so on the last upgrade I just migrated to 100% nvme drives. To mitigate the price I just bought used enterprise micron drives off ebay whenever I saw a good deal popup. Haven't had any performance issues since then no matter what we've tossed at it. I'd recommend it, though I don't have experience with the other options. On paper I think it's still the best option. Stay away from CephFS though, performance is truly atrocious and you'll footgun yourself for any use in production.
Thanks, but I forgot to specify that I'm interested in S3-compatible servers only.
Basically, I have a single big server with 80 high-capacity HDDs and 4 high-endurance NVMes, and it's the S3 endpoint that gets a lot of writes.
So yes, for now my best candidate is ZFS + Garage, this way I can get away with using replica=1 and rely on ZFS RAIDz for data safety, and the NVMEs can get sliced and diced to act as the fast metadata store for Garage, the "special" device/small records store for the ZFS, the ZIL/SLOG device and so on.
Currently it's a bit of a Frankenstein's monster: using XFS+OpenCAS as the backing storage for an old version of MinIO (containerized to run as 5 instances), I'm looking to replace it with a simpler design and hopefully get a better performance.
Getting Ceph erasure coding set up properly on a big hard disk pool is a pain - you can tell that EC was shoehorned into a system that was totally designed around triple replication.
It's great to see other people's working solutions, thanks. Can I ask if you have backup on something like this? In many systems it's possible to store some data on ingress or after processing, which serves as something that's rebuildable, even if it's not a true backup. I'm not familiar if your software layer has backup to off site as part of their system, for example, which would be a great feature.
If you can afford it, mirroring in some form is going to give you way better read perf than RAIDz. Using zfs mirrors is probably easiest but least flexible, zfs copies=2 with all devices as top level vdevs in a single zpool is not very unsafe, and something custom would be a lot of work but could get safety and flexibility if done right.
You're basically seek limited, and a read on a mirror is one seek, whereas a read on a RAIDz is one seek per device in the stripe. (Although if most of your objects are under the chunk size, you end up with more of mirroring than striping)
It is probably worth noting that most of the listed storage systems (including S3) are designed to scale not only in hard drives, but horizontally across many servers in a distributed system. They really are not optimized for a single storage node use case. There are also other things to consider that can limit performance, like what does the storage back plane look like for those 80 HDDs, and how much throughput can you effectively push through that. Then there is the network connectivity that will also be a limiting factor.
It's a very beefy server with 4 NVMe and 20 HDD bays + a 60-drive external enclosure, 2 enterprise grade HBA cards set to multipath round-robin mode, even with 80 drives it's nowhere near the data path saturation point.
The link is a 10G 9K MTU connection, the server is only accessed via that local link.
Essentially, the drives being HDD are the only real bottleneck (besides the obvious single-node scenario).
At the moment, all writes are buffered into the NVMes via OpenCAS write-through cache, so the writes are very snappy and are pretty much ingested at the rate I can throw data at it. But the read/delete operations require at least a metadata read, and due to the very high number of small (most even empty) objects they take a lot more time than I would like.
I'm willing to sacrifice the write-through cache benefits (the write performance is actually an overkill for my use case), in order to make it a little more balanced for better List/Read/DeleteObject operations performance.
On paper, most "real" writes will be sequential data, so writing that directly to the HDDs should be fine, while metadata write operations will be handled exclusively by the flash storage, thus also taking care of the empty/small objects problem.
> Essentially, the drives being HDD are the only real bottleneck
? on the low end a single HD can deliver 100MB/s, 80 can deliver 8,000MB/s, a single nvme can do 700MB/s and you have 4, 2,800MB/s - a 10Gb link can only do 1000MB/s, so isn't your bottle neck Network and then probably CPU?
For sure, there is zero expectations for any kind of hardware downtime tolerance, it's a secondary backup storage cobbled together from leftovers over many years :)
For software, at least with MinIO it's possible to do rolling updates/restarts since the 5 instances in docker-compose are enough for proper write quorum even with any single instance down.
ZFS/OpenZFS can do scrub and do block-level recovery. I'm not sure about Lustre, but since Petabyte sized storage is its natural habitat, there should be at least one way to handle that.
Doing some light googling aside from Ceph being listed, there's one called Gluster as well. Hypes itself as "using common off-the-shelf hardware you can create large, distributed storage solutions for media streaming, data analysis, and other data- and bandwidth-intensive tasks."
It's open source / free to boot. I have no direct experience with it myself however.
Gluster has been slowly declining for a while. It used to be sponsored by RedHat, but tha stopped a few years ago. Since then, development slowed significantly.
I used to keep a large cluster array with Gluster+ZFS (1.5PB), and I can’t say I was ever really that impressed with the performance. That said — I really didn’t have enough horizontal scaling to make it worthwhile from a performance aspect. For us, it was mainly used to make a union file system.
But, I can’t say I’d recommend it for anything new.
A decade ago where I worked we used gluster for ~200TB of HDD for a shared file system on a SLURM compute cluster, as a much better clustered version of NFS. And we used ceph for its S3 interface (RadowGW) for tens of petabytes of back storage after the high IO stages of compute were finished. The ceph was all HDD though later we added some SSDs for a caching pool.
For single client performance, ceph beat the performance I get from S3 today for large file copies. Gluster had difficult to characterize performance, but our setup with big fast RAID arrays seems to still outperform what I see of AWS's luster as a service today for our use case of long sequential reads and writes.
We would occasionally try cephFS, the POSIX shared network filesystem, but it couldn't match our gluster performance for our workload. But also, we built the ceph long term storage to maximize TB/$, so it was at a disadvantage compared to our gluster install. Still, I never heard of cephFS being used anywhere despite it being the original goal in the papers back at UCSC. Keep an eye on CERN for news about one of the bigger ceph installs with public info.
I love both of the systems, and see ceph used everywhere today, but am surprised and happy to see that gluster is still around.
I’ve used GlusterFS before because I was having tens of old PCs and it worked for me very well. It’s basically a PoC to see how it work than production though
I was an SDE on the S3 Index team 10 years ago, but I doubt much of the core stack has changed.
S3 is comprised primarily of layers of Java-based web services. The hot path (object get / put / list) are all served by synchronous API servers - no queues or workers. It is the best example of how many transactions per second a pretty standard Java web service stack can handle that I’ve seen in my career.
For a get call, you first hit a fleet of front-end HTTP API servers behind a set of load balancers. Partitioning is based on the key name prefixes, although I hear they’ve done work to decouple that recently. Your request is then sent to the Indexing fleet to find the mapping of your key name to an internal storage id. This is returned to the front end layer, which then calls the storage layer with the id to get the actual bits. It is a very straightforward multi-layer distributed system design for serving synchronous API responses at massive scale.
The only novel bit is all the backend communication uses a home-grown stripped-down HTTP variant, called STUMPY if I recall. It was a dumb idea to not just use HTTP but the service is ancient and originally built back when principal engineers were allowed to YOLO their own frameworks and protocols so now they are stuck with it. They might have done the massive lift to replace STUMPY with HTTP since my time.
Partitioning is based on the key name prefixes, although I hear they’ve done work to decouple that recently.
They may still use key names for partitioning. But they now randomly hash the user key name prefix on the back end to handle hotspots generated by similar keys.
Rest assured STUMPY was replaced with another home grown protocol! Though I think a stream oriented protocol is a better match for large scale services like S3 storage than a synchronous protocol like HTTP.
> The hot path (... list) are all served by synchronous API servers
Wait; how does that work, when a user is PUTting tons of objects concurrently into a bucket, and then LISTing the bucket during that? If the PUTs are all hitting different indexing-cluster nodes, then...?
(Or do you mean that there are queues/workers, but only outside the hot path; with hot-path requests emitting events that then get chewed through async to do things like cross-shard bucket metadata replication?)
LIST is dog slow, and everyone expects it to be. (my research group did a prototype of an ultra-high-speed S3-compatible system, and it really helps not needing to list things quickly)
I worked on lifecycle ~5 years ago and just the Standard -> Glacier transition path involved no fewer than 7 microservices.
Just determining which of the 400 trillion keys are eligible for a lifecycle action (comparing each object's metadata against the lifecycle policy on the bucket) is a massive big data job.
Always was a fun oncall when some bucket added a lifecycle rule that queued 1PB+ of data for transition or deletion on the same day. At the time our queuing had become good enough to handle these queues gracefully but our alarming hadn't figured out how to differentiate between the backlog for a single customer with a huge job and the whole system failing to process quickly enough. IIRC this was being fixed as I left.
Amazon biases towards Systems Oriented Architecture approach that is in the middle ground between monolith and microservices.
Biasing away from lots of small services in favour of larger ones that handle more of the work so that as much as possible you avoid the costs and latency of preparing, transmitting, receiving and processing requests.
I know S3 has changed since I was there nearly a decade ago, so this is outdated. Off the top of my head it used to be about a dozen main services at that time. A request to put an object would only touch a couple of services en route to disk, and similar on retrieval. There were a few services that handled fixity and data durability operations, the software on the storage servers themselves, and then stuff that maintained the mapping between object and storage.
A few factual inaccuracies in here that don't affect the general thrust. For example, the claim that S3 uses a 5:9 sharding scheme. In fact they use many different sharding schemes, and iirc 5:9 isn't one of them.
The main reason being that a ratio of 1.8 physical bytes to 1 logical byte is awful for HDD costs. You can get that down significantly, and you get wider parallelism and better availability guarantees to boot (consider: if a whole AZ goes down, how many shards can you lose before an object is unavailable for GET?).
VAST data uses 146+4
https://www.vastdata.com/whitepaper/#similarity-reduction-in...
page loads then quicky move up some video loads, and content is gone
Note that you can kind of infer that S3 is still using hard drives for their basic service by looking at pricing and calculating the IOPS rate that doubles the cost per GB per month.
S3 GET and PUT requests are sufficiently expensive that AWS can afford to let disk space sit idle to satisfy high-performance tenants, but not a lot more expensive than that.
> full-platter seek time: ~8ms; half-platter seek time (avg): ~4ms
Average distance between two points (first is current location, second is target location) when both are uniformly distributed in [ 0 .. +1 ] interval is not 0.5, it’s 1/3. If the full platter seek time is 8ms, average seek time should be 2.666ms.
Full seek on a modern drive is a lot closer to 25ms than 8ms. It's pretty easy to test yourself if you have a hard drive in your machine and root access - fire up fio with --readonly and feed it a handmade trace that alternates reading blocks at the beginning and end of disk. (--readonly does a hard disable of any code that could write to the drive)
Here's a good paper that explains why the 1/3 number isn't quite right on any drives manufactured in the last quarter century or so - https://www.msstconference.org/MSST-history/2024/Papers/msst...
I'd be happy to answer any other questions about disk drive mechanics and performance.
There's acceleration of the read head to move it between the tracks. So it may well be 4ms because shorter distances are penalized by a lower peak speed of the read head as well as constant factors (settling at the end of motion)
I enjoyed this article but I think the answer to the headline is obvious: parallelism
I generally don't think about storage I/O speed at that scale (I mean really who does?). I once used a RAID0 to store data to HDDs faster, but that was a long time ago.
I would have naively guessed an interesting caching system, and to some degree tiers of storage for hot vs cold objects.
It was obvious after I read the article that parallelism was a great choice, but I definitely hadn't considered the detailed scheme of S3, or the error correction it used. Parallelism is the one word summary, but the details made the article worth reading. I bet minio also has a similar scaling story: parallelism.
AWS themselves have bragged that the biggest S3 buckets are striped across over 1 million hard drives. This doesn't mean they are using all of the space of all these drives, because one of the key concepts of S3 is to average IO of many customers over many drives.
My homelab servers all have raidz out of 3 nvme drives for this reason: higher parallelism without loosing redundancy.
> I would have naively guessed an interesting caching system, and to some degree tiers of storage for hot vs cold objects.
Caching in this scenario usually done outside of S3 in something like Cloudfront
If you’re curious about this at home, try Ceph in Proxmox.
Unless you have a large cluster with many tens of nodes/OSDs (and who does in a homelab?) then using Ceph is a bad idea (I've run large Ceph clusters at previous jobs).
I think the article's title question is a bit misleading because it focuses on peak throughput for S3 as a whole. The interesting question is "How can the throughput for a GET exceed the throughput of an HDD?"
If you just replicated, you could still get big throughput for S3 as a whole by doing many reads that target different HDDs. But you'd still be limited to max HDD throughput * number of GETs. S3 is not so limited, and that's interesting and non-obvious!
Millions of hard drives cumulatively has enormous IO bandwidth.
That's like saying "how to get to the moon is obvious: traveling"
Thank you for setting me up for this...
It's not exactly rocket science.
I dunno, the article's tl;dr is just parallelism.
Data gets split into redundant copies, and is rebalanced in response to hot spots.
Everything in this article is the obvious answer you'd expect.
It's not really "redundant copies". It's erasure coding (ie, your data is the solution of an overdetermined system of equations).
So is any of S3 powered by SSD's?
I honestly figured that it must be powered by SSD for the standard tier and the slower tiers were the ones using HDD or slower systems.
The storage itself is probably (mostly) on HDDs, but I'd imagine metadata, indices, etc are stored on much faster flash storage. At least, that's the common advice for small-ish Ceph cluster MDS servers. Obviously S3 is a few orders of magnitude bigger than that...
Repeating a comment I made above - for standard tier, requests are expensive enough that it's cost-effective to let space on the disks go unused if someone wants an IOPS/TB ratio that's higher than what disk drives can provide. But not much more expensive than that.
The latest generation of drives store about 30TB - I don't know how much AWS pays for them, but a wild-ass guess would be $300-$500. That's a lot cheaper than 30TB of SSD.
Also important - you can put those disks in high-density systems (e.g. 100 drives in 4U) that only add maybe 25% to the total cost, at least if you're AWS, a bit more for the rest of us. The per-slot cost of boxes that hold lots of SSDs seems to be a lot higher.
> So is any of S3 powered by SSD's?
S3’s KeyMap Index uses SSDs. I also wouldn’t be surprised if at this point SSDs are somewhere along the read path for caching hot objects or in the new one zone product.
I expect they are storing metadata on SSDs. They might have SSD caches for really hot objects that get read a lot.
It's assumed that the new S3 Express One Zone is backed by SSDs but I believe Amazon doesn't say so explicitly.
I've always felt it's probably a wrapper around the Amazon EFS due to the similar pricing and that S3 One Zone has "Directory" buckets, a very file system-y idea.
I always assumed the really slow tiers were tape.
My own assumption was always that the cold tiers are managed by a tape robot, but managing offlined HDDs rather than actual tapes.
I think that's close to the truth. IIRC it's something like a massive cluster of machines that are effectively powered off 99% of the time with a careful sharding scheme where they're turned on and off in batches over a long period of time for periodic backup or restore of blobs.
There might be surprisingly little value in going tape due to all the specialization required. As the other comment suggest, many of the lower tiers likely represent basically IO bandwidth classes. a 16 TB disk with 100 IOPs can only offer 1 IOP/s over 1.6 TB for 100 customers, or 0.1 IOP/s over 160 GB for 1000, etc. Just scale up that thinking to a building full of disks, it still applies
See comments above about AWS per-request cost - if your customers want higher performance, they'll pay enough to let AWS waste some of that space and earn a profit on it.
Not even the higher tiers of Glacier were tape afaict (at least when it was first created), just the observation that hard drives are much bigger than you can reasonably access in useful time.
In the early days when there were articles speculating on what Glacier was backed by, it was actually on crusty old S3 gear (and at the very beginning, it was just on S3 itself as a wrapper and a hand wavy price discount, eating the costs to get people to buy in to the idea!). Later on (2018 or so) they began moving to a home grown tape-based solution (at least for some tiers).
I'm not aware of AWS ever confirming tape for glacier. My own speculation is they likely use hdd for glacier - especially so for the smaller regions - and eat the cost.
Someone recently came across some planning documents filed in London for a small "datacenter" which wasn't attached to their usual London compute DCs and built to house tape libraries (this was explicitly called out as there was concern about power - tape libraries don't use much). So I would be fairly confident they wait until the glacier volumes grow enough on hdd before building out tape infra.
Do you have any sources for that? I'm really curious about Glacier's infrastructure and AWS has been notoriously tight-lipped about it. I haven't found anything better than informed speculation.
My speculation: writes are to /dev/null, and the fact that reads are expensive and that you need to inventory your data before reading means Amazon is recreating your data from network transfer logs.
Maybe they ask the NSA for a copy.
That's surprising given how badly restoration worked (much more like tape than drives).
I'd be curious whether simulating a shitty restoration experience was part of the emulation when they first ran Glacier on plain S3 to test the market.
I think a more interesting article on S3 is "Building and operating a pretty big storage system called S3"
https://www.allthingsdistributed.com/2023/07/building-and-op...
Discussed at the time:
Building and operating a pretty big storage system called S3 - https://news.ycombinator.com/item?id=36894932 - July 2023 (160 comments)
Really nice read, thank you for that.
Author of the 2minutestreaming blog here. Good point! I'll add this as a reference at the end. I loved that piece. My goal was to be more concise and focus on the HDD aspect
> tens of millions of disks
If we assume enterprise HDDs in the double digit TB range then one can estimate that the total S3 storage volume of AWS is in the triple digit Exabyte range. That's propably the biggest storage system on planet earth.
A certain data center in Utah might top that, assuming that they have upgraded their hardware since 2013.
https://www.forbes.com/sites/kashmirhill/2013/07/24/blueprin...
This piece is interesting background, but worth noting that the actual numbers are highly speculative. The NSA has never disclosed hard data on capacity, and most of what's out there is inference from blueprints, water/power usage, or second-hand claims. No verifiable figures exist.
Production scale enterprise HDDs are in the 30TB range, 50TB on the horizon...
Google for Seagate Mozaic
Is there an open source service designed with HDDs in mind that achieves similar performance? I know none of the big ones work that well with HDDs: MinIO, Swift, Ceph+RadosGW, SeaweedFS; they all suggest flash-only deployments.
Recently I've been looking into Garage and liking the idea of it, but it seems to have a very different design (no EC).
I would say that Ceph+RadosGW works well with HDDs, as long as 1) you use SDDs for the index pool, and 2) you are realistic about the number of IOPs you can get out of your pool of HDDs.
And remember that there's a multiplication of iops for any individual client iop, whether you're using triplicate storare or erasure coding. S3 also has iop multiplication, which they solve with tons of HDDs.
For big object storage that's mostly streaming 4MB chunks, this is no big deal. If you have tons of small random reads and writes across many keys or a single big key, that's when you need to make sure your backing store can keep up.
Apache Ozone has multiple 100+ petabyte clusters in production. The capacity is on HDDs and metadata is on SSDs. Updated docs (staging for new docs): https://kerneltime.github.io/ozone-site/
At a past job we had an object store that used SwiftStack. We just used SSDs for the metadata storage but all the objects were stored on regular HDDs. It worked well enough.
We've been running a production ceph cluster for 11 years now, with only one full scheduled downtime for a major upgrade in all those years, across three different hardware generations. I wouldn't call it easy, but I also wouldn't call it hard. I used to run it with SSDs for radosgw indexes as well as a fast pool for some VMs, and harddrives for bulk object storage. Since i was only running 5 nodes with 10 drives each, I was tired of occasional iop issues under heavy recovery so on the last upgrade I just migrated to 100% nvme drives. To mitigate the price I just bought used enterprise micron drives off ebay whenever I saw a good deal popup. Haven't had any performance issues since then no matter what we've tossed at it. I'd recommend it, though I don't have experience with the other options. On paper I think it's still the best option. Stay away from CephFS though, performance is truly atrocious and you'll footgun yourself for any use in production.
Any of them will work just as well, but only with many datacenters worth of drives, which very few deployments can target.
It's the classic horizontal/vertical scaling trade off, that's why flash tends to be more space/cost efficient for speedy access.
SeaweedFS has evolved a lot the last few years, with RDMA support and EC.
Lustre and ZFS can do similar speeds.
However, if you need high IOPS, you need flash on MDS for Lustre and some Log SSDs (esp. dedicated write and read ones) for ZFS.
Thanks, but I forgot to specify that I'm interested in S3-compatible servers only.
Basically, I have a single big server with 80 high-capacity HDDs and 4 high-endurance NVMes, and it's the S3 endpoint that gets a lot of writes.
So yes, for now my best candidate is ZFS + Garage, this way I can get away with using replica=1 and rely on ZFS RAIDz for data safety, and the NVMEs can get sliced and diced to act as the fast metadata store for Garage, the "special" device/small records store for the ZFS, the ZIL/SLOG device and so on.
Currently it's a bit of a Frankenstein's monster: using XFS+OpenCAS as the backing storage for an old version of MinIO (containerized to run as 5 instances), I'm looking to replace it with a simpler design and hopefully get a better performance.
Ceph's S3 protocol implementation is really good.
Getting Ceph erasure coding set up properly on a big hard disk pool is a pain - you can tell that EC was shoehorned into a system that was totally designed around triple replication.
It's great to see other people's working solutions, thanks. Can I ask if you have backup on something like this? In many systems it's possible to store some data on ingress or after processing, which serves as something that's rebuildable, even if it's not a true backup. I'm not familiar if your software layer has backup to off site as part of their system, for example, which would be a great feature.
If you can afford it, mirroring in some form is going to give you way better read perf than RAIDz. Using zfs mirrors is probably easiest but least flexible, zfs copies=2 with all devices as top level vdevs in a single zpool is not very unsafe, and something custom would be a lot of work but could get safety and flexibility if done right.
You're basically seek limited, and a read on a mirror is one seek, whereas a read on a RAIDz is one seek per device in the stripe. (Although if most of your objects are under the chunk size, you end up with more of mirroring than striping)
You lose on capacity though.
It is probably worth noting that most of the listed storage systems (including S3) are designed to scale not only in hard drives, but horizontally across many servers in a distributed system. They really are not optimized for a single storage node use case. There are also other things to consider that can limit performance, like what does the storage back plane look like for those 80 HDDs, and how much throughput can you effectively push through that. Then there is the network connectivity that will also be a limiting factor.
It's a very beefy server with 4 NVMe and 20 HDD bays + a 60-drive external enclosure, 2 enterprise grade HBA cards set to multipath round-robin mode, even with 80 drives it's nowhere near the data path saturation point.
The link is a 10G 9K MTU connection, the server is only accessed via that local link.
Essentially, the drives being HDD are the only real bottleneck (besides the obvious single-node scenario).
At the moment, all writes are buffered into the NVMes via OpenCAS write-through cache, so the writes are very snappy and are pretty much ingested at the rate I can throw data at it. But the read/delete operations require at least a metadata read, and due to the very high number of small (most even empty) objects they take a lot more time than I would like.
I'm willing to sacrifice the write-through cache benefits (the write performance is actually an overkill for my use case), in order to make it a little more balanced for better List/Read/DeleteObject operations performance.
On paper, most "real" writes will be sequential data, so writing that directly to the HDDs should be fine, while metadata write operations will be handled exclusively by the flash storage, thus also taking care of the empty/small objects problem.
> Essentially, the drives being HDD are the only real bottleneck
? on the low end a single HD can deliver 100MB/s, 80 can deliver 8,000MB/s, a single nvme can do 700MB/s and you have 4, 2,800MB/s - a 10Gb link can only do 1000MB/s, so isn't your bottle neck Network and then probably CPU?
Hope you don't have expectations (over the long run) for high availability. At some point that server will come down (planned or unplanned).
For sure, there is zero expectations for any kind of hardware downtime tolerance, it's a secondary backup storage cobbled together from leftovers over many years :)
For software, at least with MinIO it's possible to do rolling updates/restarts since the 5 instances in docker-compose are enough for proper write quorum even with any single instance down.
It might not be the most ideal solution, but did you consider installing TrueNAS on that thing?
TrueNAS can handle the OpenZFS (zRAID, Caches and Logs) part and you can deploy Garage or any other S3 gateway on top of it.
It can be an interesting experiment, and 80 disk server is not too big for a TrueNAS installation.
I'm working on something that might be suited for this use-case at https://github.com/uroni/hs5 (not ready for production yet).
It would still need a resilience/cache layer like ZFS, though.
Do you know if some of these systems have components to periodically checksum the data at rest?
ZFS/OpenZFS can do scrub and do block-level recovery. I'm not sure about Lustre, but since Petabyte sized storage is its natural habitat, there should be at least one way to handle that.
Doing some light googling aside from Ceph being listed, there's one called Gluster as well. Hypes itself as "using common off-the-shelf hardware you can create large, distributed storage solutions for media streaming, data analysis, and other data- and bandwidth-intensive tasks."
It's open source / free to boot. I have no direct experience with it myself however.
https://www.gluster.org/
Gluster has been slowly declining for a while. It used to be sponsored by RedHat, but tha stopped a few years ago. Since then, development slowed significantly.
I used to keep a large cluster array with Gluster+ZFS (1.5PB), and I can’t say I was ever really that impressed with the performance. That said — I really didn’t have enough horizontal scaling to make it worthwhile from a performance aspect. For us, it was mainly used to make a union file system.
But, I can’t say I’d recommend it for anything new.
A decade ago where I worked we used gluster for ~200TB of HDD for a shared file system on a SLURM compute cluster, as a much better clustered version of NFS. And we used ceph for its S3 interface (RadowGW) for tens of petabytes of back storage after the high IO stages of compute were finished. The ceph was all HDD though later we added some SSDs for a caching pool.
For single client performance, ceph beat the performance I get from S3 today for large file copies. Gluster had difficult to characterize performance, but our setup with big fast RAID arrays seems to still outperform what I see of AWS's luster as a service today for our use case of long sequential reads and writes.
We would occasionally try cephFS, the POSIX shared network filesystem, but it couldn't match our gluster performance for our workload. But also, we built the ceph long term storage to maximize TB/$, so it was at a disadvantage compared to our gluster install. Still, I never heard of cephFS being used anywhere despite it being the original goal in the papers back at UCSC. Keep an eye on CERN for news about one of the bigger ceph installs with public info.
I love both of the systems, and see ceph used everywhere today, but am surprised and happy to see that gluster is still around.
I’ve used GlusterFS before because I was having tens of old PCs and it worked for me very well. It’s basically a PoC to see how it work than production though
Does anyone know what is the technology stack of S3? Monolith or multiple services?
I assume would have lots of queues, caches and long running workers.
I was an SDE on the S3 Index team 10 years ago, but I doubt much of the core stack has changed.
S3 is comprised primarily of layers of Java-based web services. The hot path (object get / put / list) are all served by synchronous API servers - no queues or workers. It is the best example of how many transactions per second a pretty standard Java web service stack can handle that I’ve seen in my career.
For a get call, you first hit a fleet of front-end HTTP API servers behind a set of load balancers. Partitioning is based on the key name prefixes, although I hear they’ve done work to decouple that recently. Your request is then sent to the Indexing fleet to find the mapping of your key name to an internal storage id. This is returned to the front end layer, which then calls the storage layer with the id to get the actual bits. It is a very straightforward multi-layer distributed system design for serving synchronous API responses at massive scale.
The only novel bit is all the backend communication uses a home-grown stripped-down HTTP variant, called STUMPY if I recall. It was a dumb idea to not just use HTTP but the service is ancient and originally built back when principal engineers were allowed to YOLO their own frameworks and protocols so now they are stuck with it. They might have done the massive lift to replace STUMPY with HTTP since my time.
Partitioning is based on the key name prefixes, although I hear they’ve done work to decouple that recently.
They may still use key names for partitioning. But they now randomly hash the user key name prefix on the back end to handle hotspots generated by similar keys.
Rest assured STUMPY was replaced with another home grown protocol! Though I think a stream oriented protocol is a better match for large scale services like S3 storage than a synchronous protocol like HTTP.
> The hot path (... list) are all served by synchronous API servers
Wait; how does that work, when a user is PUTting tons of objects concurrently into a bucket, and then LISTing the bucket during that? If the PUTs are all hitting different indexing-cluster nodes, then...?
(Or do you mean that there are queues/workers, but only outside the hot path; with hot-path requests emitting events that then get chewed through async to do things like cross-shard bucket metadata replication?)
LIST is dog slow, and everyone expects it to be. (my research group did a prototype of an ultra-high-speed S3-compatible system, and it really helps not needing to list things quickly)
It's not all java anymore. There's some rust now, too. ShardStore, at least (which the article mentions).
"It is the best example of how many transactions per second a pretty standard Java web service stack can handle that I’ve seen in my career."
can you give some numbers? or at least ballpark?
Tens of thousands of TPS per node.
Microservices for days.
I worked on lifecycle ~5 years ago and just the Standard -> Glacier transition path involved no fewer than 7 microservices.
Just determining which of the 400 trillion keys are eligible for a lifecycle action (comparing each object's metadata against the lifecycle policy on the bucket) is a massive big data job.
Always was a fun oncall when some bucket added a lifecycle rule that queued 1PB+ of data for transition or deletion on the same day. At the time our queuing had become good enough to handle these queues gracefully but our alarming hadn't figured out how to differentiate between the backlog for a single customer with a huge job and the whole system failing to process quickly enough. IIRC this was being fixed as I left.
I used to work on the backing service for S3's Index and the daily humps in our graphs from lifecycle running were immense!
The only scholarly paper they've written about it is this one: https://www.amazon.science/publications/using-lightweight-fo...
(well, I think they may have submitted one or two others, but this is the only one that got published)
Amazon biases towards Systems Oriented Architecture approach that is in the middle ground between monolith and microservices.
Biasing away from lots of small services in favour of larger ones that handle more of the work so that as much as possible you avoid the costs and latency of preparing, transmitting, receiving and processing requests.
I know S3 has changed since I was there nearly a decade ago, so this is outdated. Off the top of my head it used to be about a dozen main services at that time. A request to put an object would only touch a couple of services en route to disk, and similar on retrieval. There were a few services that handled fixity and data durability operations, the software on the storage servers themselves, and then stuff that maintained the mapping between object and storage.
Amusingly, I suspect that the "dozen main services" is still quite a few more than most smaller companies would consider on their stacks.
Probably. Conway's law comes into effect, naturally.
There's a pretty good talk on S3 under the hood from last year's re:Invent: https://www.youtube.com/watch?v=NXehLy7IiPM
> conway’s law and how it shapes S3’s architecture (consisting of 300+ microservices)
can we replicate something similar to this for homelab ?
garage