How AWS S3 Serves 1 Petabyte Per Second on Top of Slow Hdds
Posted3 months agoActive3 months ago
bigdata.2minutestreaming.comTechstoryHigh profile
calmmixed
Debate
60/100
AWS S3Storage SystemsDistributed StorageHDD Performance
Key topics
AWS S3
Storage Systems
Distributed Storage
HDD Performance
The article discusses how AWS S3 achieves high performance on top of slow HDDs, sparking a discussion on the technology stack, performance optimization, and potential alternatives.
Snapshot generated from the HN discussion
Discussion Activity
Very active discussionFirst comment
2h
Peak period
85
0-12h
Avg / period
20
Comment distribution160 data points
Loading chart...
Based on 160 loaded comments
Key moments
- 01Story posted
Sep 24, 2025 at 6:05 AM EDT
3 months ago
Step 01 - 02First comment
Sep 24, 2025 at 7:54 AM EDT
2h after posting
Step 02 - 03Peak activity
85 comments in 0-12h
Hottest window of the conversation
Step 03 - 04Latest activity
Sep 30, 2025 at 8:59 PM EDT
3 months ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
ID: 45358280Type: storyLast synced: 11/20/2025, 7:40:50 PM
Want the full context?
Jump to the original sources
Read the primary article or dive into the live Hacker News thread when you're ready.
https://www.allthingsdistributed.com/2023/07/building-and-op...
Check out the Olmez et al paper from MSST 2024 - I linked it above, but here it is again: https://www.msstconference.org/MSST-history/2024/Papers/msst...
And for the 1/2 vs 1/3rd - I'm just dumb. Thanks again. Super cool paper too
Building and operating a pretty big storage system called S3 - https://news.ycombinator.com/item?id=36894932 - July 2023 (160 comments)
Recently I've been looking into Garage and liking the idea of it, but it seems to have a very different design (no EC).
It's open source / free to boot. I have no direct experience with it myself however.
https://www.gluster.org/
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.
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.
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.
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.
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.
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.
? 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?
Same applies to your NVMe throughput since now you have the risk to congest the PCIe lanes if you're increasing line count with PCIe switches.
If there are gateway services or other software bound processes like zRAID, your processor will saturate way before your NIC, adding more jitter and inconsistency to your performance.
NIC is an independent republic on the motherboard. They can accelerate almost anything related to stack, esp. server grade cards. If you can pump the data to the NIC, you can be sure that it can be pushed at line speed.
However, running a NIC at line speed with data read from elsewhere on the system is not always that easy.
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.
I think I'm going to go with 8x RAIDz2 VDEVs 10x HDDs each, so that the 20 drives in the internal drive enclosure could be 2 separate VDEVs and not mix with the 60 in the external enclosure.
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.
Now the first OSD splits it into k data chunks plus d parity chunks, so the disk write size isn’t 4MB, it’s 4MB/k, while the efficient write size has gone up 2x? 4x? since the original 4MB decision as drive transfer rates increase.
You can change this, but still the tuning is based on the size of the block to be coded, not the size of the chunks to be written to disk. (and you might have multiple pools with much different values of k)
There is also striping [2], which is the equivalent of RAID-10 functionality to split a large file into independent segments that can be written in parallel. Perhaps you are referring to RGW's default stripe size of 4 MB [3]?
If yes, I can understand your point about one 4 MB RADOS object being erasure-coded to e.g. 6 = 4+2 "parity chunks", making it < 1 MB writes that are not efficient on HDDs.
But would you not simply raise `rgw_obj_stripe_size` to address that, according to the k you choose? E.g. 24 MB? You mention it can be changed, but I don't understand the "but still the tuning is based on the size of the block to be coded" part, (why) is that a problem?
Also, how else would you do it when designing EC writes?
Thanks!
[1]: https://docs.ceph.com/en/squid/rados/configuration/bluestore...
[2]: https://docs.ceph.com/en/squid/architecture/#data-striping
[3]: https://docs.ceph.com/en/squid/radosgw/config-ref/#confval-r...
It would still need a resilience/cache layer like ZFS, though.
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.
What performance issues and footguns do you have in mind?
I also like that CephFS has a performance benefits that doesn't seem to exist anywhere else: Automatic transparent Linux buffer caching, so that writes are extremely fast and local until you fsync() or other clients want to read, and repeat-reads or read-after-write are served from local RAM.
What you mean by no EC?
Seems like some kind of compression?
Is that how the error correction on DVD works? I
And is that how GridFS is can keep file store slow low compare to regular file system?
It's the classic horizontal/vertical scaling trade off, that's why flash tends to be more space/cost efficient for speedy access.
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.
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.
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.
It's actually a lot less user-facing per disk IO capacity that you will be able to "sell" in a large distributed storage system. There's constant maintenance churn to keep data available: - local hardware failure - planned larger scale maintenance - transient, unplanned larger scale failures (etc)
In general, you can fall back to using reconstruction from the erasure codes for serving during degradation. But that's a) enormously expensive in IO and CPU and b) you carry higher availability and/or durability risk because you lost redundancy.
Additionally, it may make sense to rebalance where data lives for optimal read throughput (and other performance reasons).
So in practice, there's constant rebalancing going on in a sophisticated distributed storage system that takes a good chunk of your HDD IOPS.
This + garbage collection also makes tape really unattractive for all but very static archives.
Also see this thread: https://news.ycombinator.com/item?id=13011396
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 assume would have lots of queues, caches and long running workers.
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.
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.
can you give some numbers? or at least ballpark?
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?)
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.
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 was just looking for this video so I can send it to my coworkers as one of the best introductory videos into the basics of cloud computing concepts.
(well, I think they may have submitted one or two others, but this is the only one that got published)
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 just saves AWS money is all, by achieving greater redundancy with less disk usage.
They shard the data as you might expect of a RAID, 5, 6, etc array and the distributed parity solves the problem of failure tolerance as you would expect and also improves bandwidth via parallelism as you describe.
The interesting part is their best strategy for sharding the data: plain-old-simple random. The decision of which disks and at which sectors to shard the data is done at random, and this creates the best change that at least one of the two copies of data can be accessed with much lower latency (~1ms instead of ~8ms).
The most crude, simple approach turns out to give them the best mileage. There's something vaguely poetic about it, an aesthetic beauty reminiscent of Euler's Identity or the solution to the Basel Problem; a very simple statement with powerful implications.
It's not exactly rocket science.
I still feel like you're underselling the article however.
Is obviously ultimately parallelism, but parallelism is hard at scale - because things often don't scale - and incorrect parallelism can even make things slower. And it's not always obvious why something gets slower by parallelism.
As a dumb example, if you have a fictional HDD with one disk and one head, you have two straightforward options to optimize performance:
Make sure only one file is read at the same time (otherwise the disk will keep seeking back and forth)
Make sure the file is persisted in a way that you're only accessing one sector, never entering the situation in which it would seek back and forth.
Ofc, that can be dumped down to "parallelism", because this is inherently a question about how to parallelize... But it's also ignoring that that's what is being elaborated on: ways s3 used to enable parallelism
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.
How does it work with no single front end[1] and no centralised lookup table of data placement (because that could be a bottleneck)? All the storage nodes use the same deterministic algorithm for data placement known as CRUSH, guided by placement rules which the admin has written into the CRUSH map, things like:
- these storage servers are grouped together by some label (e.g. same rack, same power feed, same data centre, same site).
- I want N copies of data blocks, separated over different redundancy / failure boundaries like different racks or different sites.
There's a monitoring daemon which shares the CRUSH map out to each node. They get some data coming in over the network, work through the CRUSH algorithm, and then send the data internally to the target node. The algorithm is probabalistic and not perfectly balanced so some nodes end up with more data than others, and because there's no central data placement table this design is "as full as the fullest disk" - one full disk anywhere in the cluster will put the entire cluster into read-only mode until you fix it. Ceph doesn't easily run well with random cheap different size disks for that reason, the smallest disk or host will be a crunch point. It runs best with raw storage below 2/3rds full. It also doesn't have a single head which can have a fast RAM cache like a RAID controller can have.[2] Nothing about this is designed for the small business or home use case, it's all designed to spread out over a lot of nodes[3].
It’s got a design where the units of storage are OSDs (Object Storage Devices) which correspond roughly to disks/partitions/LVM volumes, each one has a daemon controlling it. Those are pulled together as RADOS (Reliable Autonomic Distributed Object Store) where Ceph internally keeps data, and on top of that the admin can layer user-visible storage such as the CephFS filesystem, Amazon S3 compatible object storage, or a layer that presents as a block device which can be formatted with XFS/etc.
It makes a distributed system that can ingest a lot of data in parallel streams using every node’s network bandwidth, but quite a lot of internal shuffling of data around between nodes and layers adding latency, and there are monitor daemons and management daemons overseeing the whole cluster to keep track of failed storage units and make the CRUSH map available to all nodes, and those ought to be duplicated and redundant as well. It's a bit of a "build it yourself storage cluster kit" which is pretty nicely designed and flexible but complex and layered and non-trivial.
There are some talks on YouTube by people who managed and upgraded it at CERN as targets of particle accelerators data which are quite interesting. I can only recommend searching for "Ceph at Cern" and there are many hours of talks, I can't remember which ones I've seen. Titles like: "Ceph at CERN: A Year in the Life of a Petabyte-Scale Block Storage Service", "Ceph and the CERN HPC Infrastructure", "Ceph Days NYC 2023: Ceph at CERN: A Ten-Year Retrospective", "Ceph Operations at CERN: Where Do We Go From Here?".
[1] If you are not writing your own software that speaks to Ceph's internal object storage APIs, then you are fronting its with something like a Linux machine running an XFS filesystem or the S3-compatible gateway, and that machine becomes your single point of failure and bottleneck. Then you front one Ceph cluster with many separate Linux machines as targets, and have your users point their software to different front ends, and in that case why use Ceph at all? You may as well have had many Linux machines with their own separate internal storage and rsync, and no Ceph. Or two SANs with data replication between them. Do you need (or want) what Ceph does, specifically?
[2] I have only worked on HDD based clusters, with some SSDs for storing metadata to speed up performance. These clusters were not well specced and the metadata overflowed onto the HDDs which didn't help anything.
[3] There are ways to adjust the balance of data on each node to work with different size or nearly full disks, but if you get to read-only mode you end up waiting for it to internally rebalance while everything is down. This isn't so different to other storage like SANs, it's just that if you are going for Ceph you probably have a big cluster with a lot of things using it so a lot of things offline. You still have to consider running multiple Ceph clusters to limit blast radius of failures, if you are thinking "I don't want to bother with multiple storage targets I want one Ceph" you still need to plan that maybe you don't just want one Ceph.
With upmap and balancer it is very easy to run a Ceph cluster where every single node/disk is within 1-1.5% of the average raw utilization of the cluster. Yes, you need room for failures, but on a large cluster it doesn't require much.
80% is definitely achievable, 85% should be as well on larger clusters.
Also re scale, depending on how small we're talking of course, but I'd rather have a small Ceph cluster with 5-10 tiny nodes than a single Linux server with LVM if I care about uptime. It makes scheduled maintenances much easier, also a disk failure on a regular server means RAID group (or ZFS/btrfs?) rebuild. With Ceph, even at fairly modest scale you can have very fast recovery times.
Source, I've been running production workloads on Ceph at fortune-50 companies for more than a decade, and yes I'm biased towards Ceph.
That whole situation is probably easier the bigger the cluster gets; any system with three "units" that has to tolerate one failing can only have 66% usable. With a hundred "units" then 99% are usable. Too much free space is only wasting money, too full is a service down disaster, for that reason I would prefer to err towards the side of too much free rather than too little.
Other than Ceph I've only worked on systems where one disk failure needs one hotspare disk to rebuild, anything else is handled by a separate backup and DR plan. With Ceph, depending on the design it might need free space to handle a host or rack failure, and that's pretty new to me and also leads me to prefer more free space rather than less. With a hundred "units" of storage grouped into 5 failure domains then only 80% is usable, again probably better with scale and experienced design.
If I had 10,000 nodes I'd rather 10,100 nodes and better sleep than playing "how close to full can I get this thing" and constantly on edge waiting for a problem which takes down a 10,000 node cluster and all the things that needed such a big cluster. I'm probably taking some advice from Reddit threads talking about 3-node Ceph/Proxmox setups which say 66% and YouTube videos talking about Ceph at CERN - in those I think their use case is a bursty massive dump of particle accelerator data to ingest, followed by a quieter period of read-heavy analysis and reporting, so they need to keep enough free space for large swings. My company's use case was more backup data churn, lower peaks, less tidal, quite predictable, and we did run much fuller than 66%. We're now down below 50% used as we migrate away, and they're much more stable.
[1] it didn't help that we had nobody familiar with Ceph once the builder had left, and these had been running a long time and partially upgraded through different versions, and had one-of-everything; some S3 storage, some CephFS, some RBDs with XFS to use block cloning, some N+1 pools, some Erasure Coding pools, some physical hardware and some virtual machines, some Docker containerised services but not all, multiple frontends hooked together by password based SSH, and no management will to invest or pay for support/consultants, some parts running over IPv6 and some over IPv4, none with DNS names, some front-ends with redundant multiple back end links, others with only one. A well-designed, well-planned, management-supported cluster with skilled admins can likely run with finer tolerances.
It’s just worked. I’ve lost two of the machines due to memory failures at two different points in time and the k8s clusters sitting on top didn’t fail, even the Postgres databases running with cnpg remained ready and available during both hardware failures.
But then to offset the above, it also depends on how important your environment is; homelabs don't usually require five nines.
I am a big Proxmox fan but I dislike how easy it makes Ceph to run (or rather, how it appears to be easy). Ceph can fail in so many ways (I've seen a lot of them) and most people who set a Ceph cluster up through the UI are going to have a hard time recovering their data when things go south.
> 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 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!
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.
https://www.forbes.com/sites/kashmirhill/2013/07/24/blueprin...
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?).
https://www.vastdata.com/whitepaper/#similarity-reduction-in...
https://www.vastdata.com/blog/introducing-rack-scale-resilie...
To me though the idea that to read like a single 16MB chunk you need to actually read like 4MB of data from 5 different hard drives and that this is faster is baffling.
If we assume 3 AZs, then you lose 1/3 of shards when an AZ goes down. You could do at most 6:9, which is a 1.5 byte ratio. But that's unacceptable, because you know you will temporarily lose shards to HDD failure, and this scheme doesn't permit that in the AZ down scenario. So 1.5 is our limit.
To lower the ratio from 1.8, it's necessary to increase the denominator (the number of shards necessary to reconstruct the object). This is not possible while preserving availability guarantees with just 9 shards.
Note that Cloudflare's R2 makes no such guarantees, and so does achieve a more favorable cost with their erasure coding scheme.
Note also that if you increase the number of shards, it becomes possible to change the ratio without sacrificing availability. Example: if we have 18 shards, we can chose 11:18, which gives us 1.61 physical bytes per logical byte. And it still takes 1 AZ + 2 shards to make an object unavailable.
You can extrapolate from there to develop other sharding schemes that would improve the ratio and improve availability!
Another key hidden assumption is that you don't worry about correlated shard loss except in the AZ down case. HDDs fail, but these are independent events. So you can bound the probability of simultaneous shard loss using the mean time to failure and the mean time to repair that your repair system achieves.
The way it’s worded makes me understand that’s what scheme they’re using. Curious to hear what you know
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.
That’s a bogus number from an ancient slide deck for a class in 2001 or so, that’s misled generations of folks googling for the answer.
Note also that the outer tracks are longer (google ZCAV) and hold more data, so seeks across uniformly distributed block numbers do not generate uniformly distributed track numbers.
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.
Is "full seek" a synonymous for worst case time to reach a position occurring less than 1% of working time?
From the article: max seek time is 15.2 ms + additionally 0 to 8.3 ms of rotational latency.
Reordering of sector accesses by NCQ should reduce worst case scenario occurrences.
When I was reviewing it for publication I ran a couple of tests and found more like 18 on the devices I tested, but I’m sure there are some that do 15. 25 is probably on the slow end. (although I’ve never tested a HAMR drive - their head assemblies are probably heavier and more delicate)
Old SCSI 10K drives could hand a huge queue and reach 500 random read IOPS, sounding like a buzzsaw while they did it. Modern capacity drives treat their internals much more gently, and don’t get as much queuing gain. Note also that for larger objects the chunk size is probably 1+ rotations to amortize the seek overhead.
You can do this test yourself with fio —readonly and root access to a hard drive block device, even if it’s mounted. (good luck reading any files while the test is running, but no damage done) Pick a variety of very high and low blocks, and the min delay will be when rotational delay is close to zero.
But you can simplify this problem down and ask: with no loss of generality, if your starting point is always 0 degrees, how many degrees clockwise is a random point on average, if the target is uniformly distributed?
Since 0-180 has the same arc length as 180-360 then the average distance is 180 degrees. So average half-platter seek is half of the full-platter seek.
Flash is near the end of the “S-curve” of those technologies being rolled out.
During that time HDD technology was pretty stagnant, with a mere 2x increase due to higher platter count with the use of helium.
New HDD technologies (HAMR) are just starting their rollout, promising major improvements in $/GB over the next few years as they roll out.
You can’t just look at a price curve on a graph and predict where it’s going to go. The actual technologies responsible for that curve matter.
That "and" is doing a lot of work.
In 2012 most flash was MLC.
In 2025 most flash is TLC.
> During that time HDD technology was pretty stagnant, with a mere 2x increase due to higher platter count with the use of helium.
They've advanced slower than SSDs but it wasn't that slow. Between 2012 and 2025, excluding HAMR, sizes have improved from 4TB to 24TB and prices at the low end have improved from $50/TB to $12/TB.
If my first line was unclear: We might say the denser bits give us a 65% density improvement. And quick math shows that a 80-100x improvement is actually nine 65% improvements in a row. So the denser bits per cell aren't doing much, it's pretty much all process improvement.
3D flash is over 300 layers now. The size of a single 300-bit stack on the surface of the chip is bigger than an old planar cell, but that 300x does a lot more than make up for it.
3D NAND isn’t a “process improvement” - it’s a fundamental new architecture. It’s radically cheaper because it’s a set of really cheap steps to make all 300+ layers, not using any of the really expensive lithography systems in the fab, then a single (really complicated) set of steps to drill holes through the layers for the bit stacks and coat the insides of the holes. Chip cost basically = the depreciation of the fab investment during the time a chip spends in the fab, so 3D NAND is a huge win. (just stacking layers by running the chip through the process N times wouldn’t save any money, and would probably just decrease yields)
A total guess - 2x more expensive for extra steps, bit stacks take 4x more area than planar cells, 300 layer would have 300/8 = 37.5x cheaper bits. (That 4x is pulling a lot of weight - for all I know it might be more like 8x, but the point stands)
Anyway the point stands that bits per cell is barely doing anything compared to making the cells cheaper.
Also because “process improvement” usually refers to things where you get incremental improvements basically for free as each new generation of fab rolls out. Unless you can invent a 4D flash, this is a single (huge) improvement that’s mostly played out.
Same process node.
Node is part of process, but all the layering and etching techniques they figured out to make 3D cells are also process. At least that's how I see it.
Oh well, I don't want to argue definitions, I just want to clarify what I meant.
(Well, peak data transfer rate keeps going up as bits get packed tighter, but capacity goes up linearly with areal bit density, while the speed the bits go under the head goes up with the square root.)
(Well, sort of. For a while a lot of the progress came from making the bits skinnier but not much shorter, so transfer rates didn’t go up that much)
EDIT: clarity on monthly pricing.
Because as a shelf-hosted, I had to block a ton of AWS ipv4s because of those.
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.
7 more comments available on Hacker News