Note there is no intrinsic reason running multiple streams should be faster than one [EDIT: "at this scale"]. It almost always indicates some bottleneck in the application or TCP tuning. (Though, very fast links can overwhelm slow hardware, and ISPs might do some traffic shaping too, but this doesn't apply to local links).
SSH was never really meant to be a high performance data transfer tool, and it shows. For example, it has a hardcoded maximum receive buffer of 2MiB (separate from the TCP one), which drastically limits transfer speed over high BDP links (even a fast local link, like the 10gbps one the author has). The encryption can also be a bottleneck. hpn-ssh [1] aims to solve this issue but I'm not so sure about running an ssh fork on important systems.
The 2MiB are per SSH "channel" -- the SSH protocol multiplexes multiple independent transmission channels over TCP [1], and each one has its own window size.
rsync and `cat | ssh | cat` only use a single channel, so if their counterparty is an OpenSSH sshd server, their throughput is limited by the 2MiB window limit.
rclone seems to be able to use multiple ssh channels over a single connection; I believe this is what the `--sftp-concurrency` setting controls.
Some more discussion about the 2MiB limit and links to work for upstreaming a removal of these limits can be found in my post [3].
Looking into it just now, I found that the SSH protocol itself already supports dynamically growing per-channel window sizes with `CHANNEL_WINDOW_ADJUST`, and OpenSSH seems to generally implement that. I don't fully grasp why it doesn't just use that to extend as needed.
I also found that there's an official `no-flow-control` extension with the description
> channel behaves as if all window sizes are infinite.
>
> This extension is intended for, but not limited to, use by file transfer applications that are only going to use one channel and for which the flow control provided by SSH is an impediment, rather than a feature.
So this looks exactly as designed for rsync. But no software implements this extension!
I wrote those things down in [4].
It is frustrating to me that we're only a ~200 line patch away from "unlimited" instead of shitty SSH transfer speeds -- for >20 years!
I think a lot of file transfer issues that occur outside of the corporate intranet world involve hardware that you don't fully control on (at least) one hand. In science, for example, transferring huge amounts of data over long distances is pretty common, and I've had to do this on boxes that had poor TCP buffer configurations. Being able to multiplex your streams in situations like this is invaluable and I'd love to see more open source software that does this effectively, especially if it can punch through a firewall.
In general TCP just isn't great for high performance. In the film industry we used to use a commercial product Aspera (now owned by IBM) which emulated ftp or scp but used UDP with forward error correction (instead of TCP retransmission). You could configure it to use a specific amount of bandwidth and it would just push everything else off the network to achieve it.
I get 40 Gbit/s over a single localhost TCP stream on my 10 years old laptop with iperf3.
So the TCP does not seem to be a bottleneck if 40 Gbit/s is "high" enough, which it probably is currently for most people.
I have also seen plenty situations in which TCP is faster than UDP in datacenters.
For example, on Hetzner Cloud VMs, iperf3 gets me 7 Gbit/s over TCP but only 1.5 Gbit/s over UDP. On Hetzner dedicated servers with 10 Gbit links, I get 10 Gbit/s over TCP but only 4.5 Gbit/s over UDP. But this could also be due to my use of iperf3 or its implementation.
I also suspect that TCP being a protocol whose state is inspectable by the network equipment between endpoints allows implementing higher performance, but I have not validated if that is done.
Aspera was/is designed for high latency links. Ie sending multi terabytes from london to new Zealand, or LA
For that use case, Aspera was the best tool for the job. It's designed to be fast over links that single TCP streams couldn't
You could, if you were so bold, stack up multiple TCP links and send data down those. You got the same speed, but possible not the same efficiency. It was a fucktonne cheaper to do though.
> I get 40 Gbit/s over a single localhost TCP stream on my 10 years old laptop with iperf3.
Do you mean literally just streaming data from one process to another on the same machine, without that data ever actually transiting a real network link? There's so many caveats to that test that it's basically worthless for evaluating what could happen on a real network.
To measure other overhead of what's claimed (TCP the protocol being slow), one should exclude other things that necessarily affect alternative protocols as well (e.g. latency) as much as possible, which is what this does.
It sounds like you're reasoning starting from an assumption that any claimed slowness of TCP would be something like a fixed per-packet overhead or delay that could be isolated and added back in to the result of your local testing to get a useful prediction. And it sounds like you think alternative protocols must be equally affected by latency.
But it's much more complicated than that; TCP interacts with latency and congestion and packet loss as both cause and effect. If you're testing TCP without sending traffic over real networks that have their own buffering and congestion control and packet reordering and loss, you're going to miss all of the most important dynamics affecting real-world performance. For example, you're not going to measure how multiplexing multiple data streams onto one TCP connection allows head of line blocking to drastically inflate the impact of a lost or reordered packet, because none of that happens when all you're testing is the speed at which your kernel can context-switch packets between local processes.
And all of that is without even beginning to touch on what happens to wireless networks.
Somebody made a claim that TCP isn't high performance without specifying what that means, I gave a counterexample of just how high performance TCP is picking some arbitrary notion of "high performance".
Almost like it makes the point that arguing about "high performance" is useless without saying what that means.
That said:
> you're not going to measure how multiplexing multiple data streams onto one TCP connection
Of course not: When I want to argue against "TCP is not a high performance protocol", why would I want to measure some other protocol that multiplexes connections over TCP? That is not measuring the performance of TCP.
I could conjure any protocol that requires acknowledgement from the other side for each emitted packet before sending the next, and then claim "UDP is not high performance" when running that over UDP - that doesn't make sense.
UDP by itself cannot be used to transfer files or any other kind of data with a size bigger than an IP packet.
So it is impossible to compare the performance of TCP and UDP.
UDP is used to implement various other protocols, whose performance can be compared with TCP. Any protocol implemented over UDP must have a performance better than TCP, at least in some specific scenarios, otherwise there would be no reason for its existence.
I do not know how UDP is used by iperf3, but perhaps it uses some protocol akin to TFTP, i.e. it sends a new UDP packet when the other side acknowledges the previous UDP packet. In that case the speed of iperf3 over UDP will always be inferior to that of TCP.
Sending UDP packets without acknowledgment will always be faster than for any usable transfer protocol, but the speed for this case does not provide any information about the network, but only about the speed of executing a loop in the sending computer and network-interface card.
You can transfer data without using any transfer protocol, by just sending UDP packets at maximum rate, if you accept that a fraction of the data will be lost. The fraction that is lost can be minimized, but not eliminated, by using an error-correcting code.
> perhaps it [..] sends a new UDP packet when the other side acknowledges the previous UDP packet. In that case the speed of iperf3 over UDP will always be inferior to that of TCP
It does not, otherwise it would be impossible by a factor ~100x to measure 4.5 Gbit/s as per the bandwidth-delay calculation (the ping is around the usual 0.2 ms).
With iperf3, as with many other UDP measurement tools, you set a sending rate and the other side reports how many bytes arrived.
It is a long time since I have last used iperf3, but now that you have mentioned it I have also remembered this.
So the previous poster has misinterpreted the iperf3 results, by believing that UDP was slower, as iperf3 cannot demonstrate a speed difference between TCP and UDP, since for the former the speed is determined by the network, while for the latter the speed is determined by the "--bandwidth" iperf3 command-line option, so the poster has probably just seen some default UDP speed.
High performance means transferring files from NZ to a director's yacht in the Mediterranean with a 40Mbps satellite link and getting 40Mbps, to the point that the link is unusable for anyone else.
Can we throw a bunch of AI agents at it? This sounds like a pretty tightly defined problem, much better than wasting tokens on re-inventing web browsers.
If you strip out the swarm logic (ie. downloading from multiple peers), you're just left with a protocol that transfers big files via chunks, so there's no reason that'd be faster than any other sort of download manager that supports multi-thread downloads.
Aspera did the chunking and encryption for you, and it looked and acted like SFTP.
The cost of leaking data was/is catastrophic (as in company ending) So paying a bit of money to guarantee that your data was being sent to the right place (point to point) and couldn't leak was a worthwhile tradeoff.
For Point to point transfer torrenting is a lot higher overhead than you want. plus most clients have an anti-leaching setting, so you'd need not only a custom client, but a custom protocol as well.
The idea is sound though, have an index file with and then a list of chunks to pull over multiple TCP connections.
I'm in a tiny part of the film industry. Bigger clients lend us licenses to Aspera and FileCatalyst when receiving files from them, but for our own trans-oceanic transfers I dug up an ancient program called Tsunami UDP and fixed it up just enough.
Aspera's FASP [0] is very neat. One drawback to it is that the TCP stuff not being done the traditional way must be done on CPU. Say if one packet is missing or if packets are sent out of order, the Aspera client fixes those instead of all that being done as TCP.
As I understand it, this is also the approach of WEKA.io [1]. Another approach is RDMA [2] used by storage systems like Vast which pushes those order and resend tasks to NICs that support RDMA so that applications can read and write directly to the network instead of to system buffers.
FASP uses forward error correction instead of retransmission. So instead of waiting for something not to show up on the other end and sending it again, it calculates parity and transmits slightly more data up front, with enough redundancy that the receiving end is capable of reconstructing any missing bits.
This is basically how all storage systems work, not just Weka. You calculate enough parity bits to be able to reconstruct the missing data when a drive fails. The more disks you have, the smaller the parity overhead is. Object storage like S3 does this on a massive scale. With a network transfer you typically only need a few percent, unless it's really lossy like Wifi, in which case standards like 802.11n are doing FEC for you to reduce retransmissions at the TCP layer.
> Note there is no intrinsic reason running multiple streams should be faster than one.
The issue is the serialization of operations. There is overhead for each operation which translates into dead time between transfers.
However there are issues that can cause singular streams to underperform multiple streams in the real world once you reach a certain scale or face problems like packet loss.
The simple model for scp and rsync (it's likely more complex in rsync):
for loop over all files. for each file, determine its metadata with fstat, then fopen and copy bytes in chunks until done. Proceed to next iteration.
I don't know what rsync does on top of that (pipelining could mean many different things), but my empirical experience is that copying 1 1 TB file is far faster than copying 1 billion 1k files (both sum to ~1 TB), and that load balancing/partitioning/parallelizing the tool when copying large numbers of small files leads to significant speedups, likely because the per-file overhead is hidden by the parallelism (in addition to dealing with individual copies stalling due to TCP or whatever else).
I guess the question is whether rsync is using multiple threads or otherwise accessing the filesystem in parallel, which I do not think it does, while tools like rclone, kopia, and aws sync all take advantage of parallelism (multiple ongoing file lookups and copies).
> I don't know what rsync does on top of that (pipelining could mean many different things), but my empirical experience is that copying 1 1 TB file is far faster than copying 1 billion 1k files (both sum to ~1 TB), and that load balancing/partitioning/parallelizing the tool when copying large numbers of small files leads to significant speedups, likely because the per-file overhead is hidden by the parallelism (in addition to dealing with individual copies stalling due to TCP or whatever else).
That's because of fast paths:
- For a large file, assuming the disk isn't fragmented to hell and beyond, there isn't much to do for rsync / the kernel: the source reads data and copies it to the network socket, the receiver copies data from the incoming network socket to the disk, the kernel just dumps it in sequence directly to the disk, that's it.
- The slightly less performant path is on a fragmented disk. Source and network still doesn't have much to do, but the kernel has a bit more work every now and then to find a contiguous block on the disk to write the data to. For spinning rust HDDs, the disk also has to do some seeking.
- Many small files? Now that's more nasty. First, the source side has to do a lot of stat(2) calls to get basic attributes of the file. For HDDs, that seeking can incur a sometimes significant latency penalty as well. Then, this information needs to be transferred to the destination, the destination has to do the same stat call again, and then the source needs to transfer the data, involving more seeking, and the destination has to write it.
- The utter worst case is when the files are plenty and small, but large enough to not fit into an inode as inline data [1]. That means two writes and thus seeks per small file. Utterly disastrous for performance.
And that's before stepping into stuff such as systems disabling write caches, soft-RAID (or the impact of RAID in general), journaling filesystems, filesystems with additional metadata...
> I guess the question is whether rsync is using multiple threads or otherwise accessing the filesystem in parallel
No, that is not the question. Even Wikipedia explains that rsync is single-threaded. And even if it was multithreaded "or otherwise" used concurent file IO:
The question is whether rsync _transmission_ is pipelined or not, meaning: Does it wait for 1 file to be transferred and acknowledged before sending the data of the next?
Somebody has to go check that.
If yes: Then parallel filesystem access won't matter, because a network roundtrip has brutally higher latency than reading data sequentially of an SSD.
Note that rsync on many small files is slow even within the same machine (across two physical devices), suggesting that the network roundtrip latency is not the major contributor.
The filesystem access and general threading is the question because transmission is pipelined and not a thing "somebody has to go check". You just quoted the documentation for it.
The dead time isn't waiting for network trips between files, it's parts of the program that sometimes can't keep up with the network.
I quoted the documentation that claims _something_ is pipelined.
That is extremely vague on what that is and I also didn't check that it's true.
Both the original claim "the issue is the serialization of operations" and the counter-claim all sound like extreme guesswork or me. If you know for certain, please link the relevant code.
Otherwise somebody needs to go check what it actually does; everything else is just speculating "oh surely it's the files" and then people remember stuff that might just be plain wrong.
I’m not sure why, but just like with scp, I’ve achieved significant speeds ups by tarring the directory first (optionally compressing it), transferring and then decompressing. Maybe because it makes the tar and submit, and the receive, untar/uncompress, happen on different threads?
I've never verified this, but it feels like scp starts a new TCP connection per file. If that's the case, then scp-ing a tarred directory would be faster because you only hit the slow start once. https://www.rfc-editor.org/rfc/rfc5681#section-3.1
It's typically a disk-latency thing, as just stat-ing the many files in a directory can have significant latency implications (especially on spinning HDDs) vs opening a single file (the tar) and read-()ing that one file in memory before writing to the network.
If copying a folder with many files is slower than tarring that folder and the moving the tar (but not counting the untar) then disk latency is your bottleneck.
Not useful very often, but fast and kind of cool: You can also just netcat the whole block device if you wanted a full filesystem copy anyway. Optionally zero all empty space before using a tool like zerofree and use on-the-fly compression / decompression with lz4 or lzo. Of course, none of the block devices should be mounted, though you could probably get away with a source that's mounted read-only.
dd is not a magic tool that can deal with block devices while others can't. You can just cp myLinuxInstallDisk.iso to /dev/myUsbDrive, too.
Okay. In this case the whole operation is faster end to end. That includes the time it takes to tar and untar. Maybe those programs do something more efficient in disk access than scp and rsync?
Great point, and even beyond that I think (based on the paths) it was just a command line invocation, with something like NFS handling all the networking.
Uhh.. I work with this stuff daily and there are a LOT of intrinsic reasons a single stream would be slower than running multiple: MPLS ECMP hashing you over a single path, a single loss event with a high BDP causing congestion control to kick in for a single flow, CPU IRQ affinity, probably many more I’m not thinking like the inner workings of NIC offloading queues.
Source: Been in big tech for roughly ten years now trying to get servers to move packets faster
Ha, it sounds like the best way to learn something is to make a confident and incorrect claim :)
> MPLS ECMP hashing you over a single path
This is kinda like the traffic shaping I was talking about though, but fair enough. It's not an inherent limitation of a single stream, just a consequence of how your network is designed.
> a single loss event with a high BDP
I thought BBR mitigates this. Even if it doesn't, I'd still count that as a TCP stack issue.
At a large enough scale I'd say you are correct that multiple streams is inherently easier to optimize throughput for. But probably not a single 1-10gb link though.
> This is kinda like the traffic shaping I was talking about though, but fair enough. It's not an inherent limitation of a single stream, just a consequence of how your network is designed.
It is. one stream gets you traffic of one path to the infrastructure. Multiple streams get you multiple and possibly also hit different servers to accelerate it even more. Just the limitation isn't hardware but "our networking device have 4 10Gbit ports instead of single 40Gbit port"
Especially if link is saturated, you'd be essentially taking n-times your "fair share" of bandwidth on link.
> SSH was never really meant to be a high performance data transfer tool, and it shows.
A practical example can be `ssh -X` vs X11 over Wireguard. The lag is obvious with the former, but X11 windows from remote clients can be indistinguishable performance-wise from those of local ones with the latter.
> It almost always indicates some bottleneck in the application or TCP tuning.
Yeah, this has been my experience with low-overhead streams as well.
Interestingly, I see a ubiquity of this "open more streams to send more data" pattern all over the place for file transfer tooling.
Recent ones that come to mind have been BackBlaze's CLI (B2) and taking a peek at Amazon's SDK for S3 uploads with Wireshark. (What do they know that we don't seem to think we know?)
It seems like they're all doing this? Which is maybe odd, because when I analyse what Plex or Netflix is doing, it's not the same? They do what you're suggesting, tune the application + TCP/UDP stack. Though that could be due to their 1-to-1 streaming use case.
There is overhead somewhere and they're trying to get past it via semi-brute-force methods (in my opinion).
I wonder if there is a serialization or loss handling problem that we could be glossing over here?
that is a different problem. For S3-esque transfers you might very well be limited by ability for target to receive X MB/s and not more and so starting parallel streams will make it faster.
I used B2 as third leg for our backups and pretty much had to give rclone more connections at once because defaults were nowhere close to saturating bandwidth
Memory and CPU are cheap (up to a point) so why not just copy/paste TCP streams. It neatly fits into multi-processing/threading as well.
When we were doing 100TB backups of storage servers we had a wrapper that run multiple rsyncs over the file system, that got throughput up to about 20gigbits a second over lan
Tuning on Linux requires root and is systemwide. I don't think BBR is even available on other systems. And you need to tune the buffer sizes of both ends too. Using multiple streams is just less of a hassle for client users. It can also fool some traffic shaping tools. Internal use is a different story.
Note there is no intrinsic reason running multiple streams should be faster than one
If the server side scales (as cloud services do) it might end up using different end points for the parallel connections and saturate the bandwidth better. One server instance might be serving other clients as well and can't fill one particular client's pipe entirely.
If the application handles them serially, then yeah. But one can imagine the application opening files in threads, buffering them, and then finally sending it at full speed, so in that sense it is an application issue. If you truly have millions of small files, you're more likely to be bottlenecked by disk IO performance rather than application or network, though. My primary use case for ssh streams is zfs send, which is mostly bottlenecked by ssh itself.
Single file overheads (opening millions of tiny files whose metadata is not in the OS cache and reading them) appears to be an intrinsic reason (intrinsic to the OS, at least).
I mean isn't a single TCP connections throughput limited by the latency? Which is why in high(er) latency WAN links you generally want to open multiple connections for large file transfers.
Only simpler transfer protocols, like TFTP, are limited by latency.
The whole reason for the existence of TCP is to overcome the throughput limit determined by latency. On a network with negligible latency there is no need for TCP (you could just send each packet only after the previous is acknowledged, but the higher is the throughput of your network interface, the less likely is that the latency can be negligible).
However, for latency to not matter, the TCP windows must be large enough (i.e. the amount of data that is sent before an acknowledge is received, which happens after a delay caused by latency).
I use Windows very rarely today, so I do not know its current status, but until the Windows XP days it was very frequent for Windows computers to have very small default TCP window sizes, which caused low throughput on high-latency networks, so on such networks they had to be reconfigured.
On high-latency networks, opening multiple connections is just a workaround for not having appropriate network settings. However, even when your own computer is configured optimally, opening multiple connections can be a workaround against various kinds of throttling implemented either by some intermediate ISP or by the destination server, though nowadays most rate limits are applied globally, to all connections from the same IP address, in order to make this workaround ineffective.
> Note there is no intrinsic reason running multiple streams should be faster than one
Inherent reasons or no, it's been my experience across multiple protocols, applications, network connections and environments, and machines on both ends, that, _in fact_, splitting data up and operating using multiple streams is significantly faster.
So, ok, it might not be because of an "inherent reason", but we still have to deal with it in real life.
Rclone is a fantastic tool, but my favorite part of it is actually the underyling FS library. I've started baking Rclone FS into internal Go tooling and now everything transparently supports reading/writing to either local or remote storage. Really great for being able to test data analysis code locally and then running as batch jobs elsewhere.
What kind of data analysis do you run with Go and do you use an open source library for it? My experience with stats libraries in Go has been lukewarm so far.
RClone has been so useful over the years I built a fully managed service on top of it specifically for moving data between cloud storage providers: https://dataraven.io/
My goal is to smooth out some of the operational rough edges I've seen companies deal with when using the tool:
- Team workspaces with role-based access control
- Event notifications & webhooks – Alerts on transfer failure or resource changes via Slack, Teams, Discord, etc.
- Centralized log storage
- Vault integrations – Connect 1Password, Doppler, or Infisical for zero-knowledge credential handling (no more plain text files with credentials)
- 10 Gbps connected infrastructure (Pro tier) – High-throughput Linux systems for large transfers
I hope that you sponsor the rclone project given that it’s the core of your business! I couldn’t find any indication online that you do give back to the project. I hope I’m wrong.
Gifts do not confer obligation. If you give me a screwdriver and I use it to run my electrical installation service business, I don’t owe you a payment.
This idea that one must “give back” after receiving a gift freely given is simply silly.
If your neighbor kept baking and giving you cookies, to the point where you were wrapping and reselling them at the market, don't you think you should do something for them in return?
Not if they gave me a legal document explicitly stating I didn’t need to give them anything…and I could get an infinite amount of the cookies with no extra work or money on their part…
And I would probably suggest to them that if they were interested in profiting from their cookies they should stop giving them away for free and make them commercial instead. They might then tell me they don’t want to spend the effort and money to commercialize their cookies, or maybe they prefer it as a hobby with no obligations to customers, or maybe they tell me they have a philosophical belief that they should give their their cookies away for free for anyone to do as they please with them, including commercializing them as long as they aren’t legally responsible for anything done with the cookies which is why they handed me that legal contract explicitly stating that when they gave them to me in the first place.
How do you deal with how poorly rclone handles rate limits? It doesn't honor dropbox's retry-after header and just adds an exponential back off that, in my migrations, has resulted in a pause of days.
I've adjusted threads and the various other controls rclone offers but I still feel like I'm not see it's true potential because the second it hits a rate limit I can all but guarantee that job will have to be restarted with new settings.
I honestly haven't used it with Dropbox before, have you tried adjusting --tpslimit 12 --tpslimit-burst 0 flags? Are you creating a dedicated api key for the transfer? Rate limits may vary between Plus/Advanced forum.rclone.org is quite active you may want to post more details there.
2. do you have an example of what indexed backups would look like? Im thinking of macos time machine, where each backup only contains deltas from the last backup. Or am I completely off?
For transforms, the concept would be user friendly processing, like downcoding video & photos, compressing PDFs & text files, filtering out temporary or wasteful files . Something like AirTable for backups with a gui workflow editor with common processing jobs for backups.
For indexing, full text indexing of backups to allow for record retrieval based on keyword or date. E.g. “images in Los Angeles before 2010” or “tax records from 2015”. If possible, low resolution thumbnails of the backups to make retrieval easier.
I think #1 (transforms) would be more generally useful for cross cloud applications, and #2 is more catered toward backups
Interesting that nobody has mentioned: Warp speed Data Transfer (WDT)[1].
From the readme:
- Warp speed Data Transfer (WDT) is an embeddedable library (and command line tool) aiming to transfer data between 2 systems as fast as possible over multiple TCP paths.
- Goal: Lowest possible total transfer time - to be only hardware limited (disc or network bandwidth not latency) and as efficient as possible (low CPU/memory/resources utilization)
I prefer rsync because of its delta transfer which doesn't resend files already on the destination, saving bandwidth. This combined with rsync's ability to work over ssh lets me sync anywhere rsync runs, including the cloud. It may not be faster than rclone but it is more conserving on bandwidth.
The delta-transfer algorithm [0] is about detecting which chunks of a file differ on source and target [1], and limiting the transfer to those chunks. The savings depend on how and where they differ, and ofcourse there's tradeoffs...
You seem to be referring to the selection of candidates of files to transfer (along several possible criteria like modification time, file size or file contents using checksumming) [2]
Rsync is great. However for huge filesystems (many files and directories) with relatively less change, you'll need to think about "assisting" it somewhat (by feeding it its candidates obtained in a more efficient way, using --files-from=). For example: in a renderfarm system you would have additions of files, not really updates. Keep a list of frames that have finished rendering (in a cinematic film production this could be eg. 10h/frame), and use it to feed rsync. Otherwise you'll be spending hours for rsync to build its index (both sides) over huge filesystems, instead of transferring relatively few big and new files.
In workloads where you have many sync candidates (files) that have a majority of differing chunks, it might be worth rather disabling the delta-transfer algorithm (--whole-file) and saving on the tradeoffs.
In my case I get 8 MB/s with rsync, 80 MB/s with rclone. This is with cross-continent transfers so 200ms latency. If it was just a 2x difference I'd be fine with rsync but 10x is very hard to ignore.
Rclone can "sync" with a range of different ways to check if the existing files are the same. If no hashes are available (e.g. WebDAV) I think you can set it to check by timestamp (with a tolerance) and size.
Edit: oh I see, delta transfer only sends the changed parts of files?
However this is only really good for slow connections. If your connection is faster than about 50MB/s then the delta calculation mechanism becomes the bottleneck. On fast connections you should use the -W option for rsync which switches the delta algorithm off.
Saving on transmitted bandwidth costs is more important to me than speed of transmission for my use cases which I feel are likely to be common use cases.
The article links to a YouTube mini-review of USB enclosures from UGreen and Acasis, neither of which he loves.[1] I've been happy with the OWC 1M2 as a boot drive on a Mac Studio with Thunderbolt 5 ports.[2] I just noticed that there is an OWC 1M2 80G, based on USB4 v2.[3] I didn't know that was a thing, but I guess it's the USB cousin to Thunderbolt 5.
It turns out, fpart does just that! Fpart is a Filesystem partitioner. It helps you sort file trees and pack them into bags (called "partitions"). It is developed in C and available under the BSD license.
My go-to for fast and easy parallelization is xargs -P.
find a-bunch-of-files | xargs -P 10 do-something-with-a-file
-P max-procs
--max-procs=max-procs
Run up to max-procs processes at a time; the default is 1.
If max-procs is 0, xargs will run as many processes as
possible at a time.
Thanks! I've been using the -F{} do-something-tofile "{}" approach which is also handy for times in which the input is one pram among others. -0 is much faster.
Edit:
Looks like when doing file-by-file -F{} is still needed:
# find tmp -type f | xargs -0 ls
ls: cannot access 'tmp/b file.md'$'\n''tmp/a file.md'$'\n''tmp/c file.md'$'\n': No such file or directory
Rclone is such an elegant piece of software, reminds me of the time where most software worked well most of the time. There's few people that wouldn't benefit from it, either as a developer or end-user.
I wonder if the at least partially the reason for the speed up isn't the multi-threading, but instead that rclone maybe doesn't compress transferred data by default. That's what rsync does when using SSH, so for already compressed data (like videos for example) disabling SSH compression when invoking rsync speeds it up significantly:
Good point. Seems like I enabled it in ~/.ssh/config ages ago and did forget about it. Nonetheless, it's good to check whether it's enabled when using rsync to transfer large, already well compressed files.
IIRC rsync uses your default SSH options, so turning off compression is only needed if your default config explicitly turns it on (generally or just for that host). If sending compressible content using rsync's compression instead of SSH's is more effective when updating files because even if not sending everything it can use it to form the compression dictionary window for what does get sent (though for sending whoe files, SSH's compression may be preferable as rsync is single threaded and using SSH's compression moves that chunk of work to the SSH process).
One thing that sets rsync apart perhaps is the handling of hard links when you don't want to send both/duplicated files to the destination? Not sure if rclone can do that.
Thanks for sharing, hadn't seen it but at almost the same time he made that post I too was struggling to get decent NAS<>NAS transfer speeds with rsync. I should have thought to play more with rclone! I ended up using iSCSI but that is a lot more trouble.
>In fact, some compression modes would actually slow things down as my energy-efficient NAS is running on some slower Arm cores
Depending on the number/type of devices in the setup and usage patterns, it can be effective sometimes to have a single more powerful router and then use it directly as a hop for security or compression (or both) to a set of lower power devices. Like, I know it's not E2EE the same way to send unencrypted data to one OPNsense router, Wireguard (or Nebula or whatever tunnel you prefer) to another over the internet, and then from there to a NAS. But if the NAS is in the same physically secure rack directly attached by hardline to the router (or via isolated switch), I don't think in practice it's significantly enough less secure at the private service level to matter. If the router is a pretty important lynchpin anyone, it can be favorable to lean more heavily on that so one can go cheaper and lower power elsewhere. Not that more efficiency, hardware acceleration etc are at all bad, and conversely sometimes might make sense to have a powerful NAS/other servers and a low power router, but there are good degrees of freedom there. Handier then ever in the current crazy times where sometimes hardware that was formerly easily and cheaply available is now a king's ransom or gone and one has to improvise.
It is crazy to see how difficult google makes it for anyone to download their own pictures from google photos. Rclone used to allow you to download them, but not anymore. Only the ones uploaded by Rclone are available to download. I wish someone forced all cloud providers to allow you to download your own data. And no, google takout doesn't count. It is horrible to use.
Not just bad to use, but doesn't fully work. I've been trying to get my photos off Google Photos to backup elsewhere, but takeout misses something like 20%-30% of them.
How did you verify that takeout shorted you on 20-30% which is a huge number? This worries me as I've done some takeouts but never fully poked through them.
Yesterday I set up Rclone, and the download speed from Google Drive was so slow that caching took a long time.
After applying the method described in this article, the speed improved.
This is my mount configuration. What do you think? Is there anything that might be causing issues??
You can already perform parallel transfers quite trivially by typing "find .. | xargs -P$(nproc) -n 1 rsync ..", carefully managing sync folder sets and just being a Unix god in general like me.
It doesn't seem reasonable to expect people to use and install it, when we can just use rsync.
> You can already perform parallel transfers quite trivially by typing "find .. | xargs -P$(nproc) -n 1 rsync ..", carefully managing sync folder sets and just being a Unix god in general.
This solution sucks in a number of different ways, and is downright incorrect. I'm baffled by the frequency at which these hacky parallel commands are recommended when rclone exists, works perfectly fine, and has literally no runtime dependencies.
#1 is the fastest way to send because it keeps the buffers full in a consistent stream which allows tcp windows to grow as large as possible. 2 and 3 ( scp and rsync ) do round trip acks to the remote side which drastically slows things down, even if done in parallel.
Note that tar and rsync can handle sparse files, but scp doesn't.
Also, rsync will likely be faster if some files are already in the destination as it handles that intelligently as well as only transferring changed blocks from files.
rclone is not as good as rsync for doing ad-hoc transfers; for anything not using the filesystem, you need to set up a configuration, which adds friction. It realy is purpose built for recurring transfers rather than "I need to move X to Y just once"
zsync is better for that. zsync precalculates all the hashes and puts them in a file alongside the main one. The client downloads the hashes, compares them to what it has then downloads the parts it is missing.
With rsync, you upload hashes of what you have, then the source has to do all the hashing work to figure out what to send you. It's slightly more efficient, but If you are supporting even 10s of downloads it's a lot of work for the source.
The other option is to send just a diff, which I believe e.g. Google Chrome does. Google invented Courgette and Zucchini which partially decompile binaries then recompile them on the other end to reduce the size of diffs. These only work for exact known previous versions, though.
I wonder if the ideas of Courgette and Zucchini can be incorporated into zsync's hashes so that you get the minimal diff, but the flexibility of not having a perfect previous version to work from.
rsync does what was designed to do and the lack of scope creep is not a bad thing. There is "fpsync" - another tool on top of rsync (which was mentioned in one of the comments at article's page) that covers the parallel processing use-case: https://manpages.debian.org/bullseye/fpart/fpsync.1.en.html
The parallelism advantage of rclone is real but undersold here. rsync's single-stream design made sense when networks were the bottleneck. Now with high-bandwidth links (especially to cloud storage), the bottleneck is often the round-trip latency of per-file metadata operations.
rclone's multi-threaded transfers effectively pipeline those operations. It's the same principle as why HTTP/2 multiplexing was such a win — you stop paying the latency tax sequentially.
One thing I'd add: for local-to-local or LAN sync, rsync still often wins because the overhead of rclone's abstraction layer isn't worth it when latency is already sub-millisecond. The 4x speedup is really a story about high-latency, high-bandwidth paths where parallelism dominates.
I love rclone. I use it in place of scp/sftp frequently. My biggest complaint is that it seems to require a config file, having the ability to `rclone copy root@192.168.1.1:/tmp/foo ./` would be a game changer.
rclone is super cool, but unfortunately many of the providers it supports has such low ratelimits, that it's fairly difficult to use it to transfer much data at all.
This has been my problem. Not necessarily that the rate limits are low, many can be gotten around by using multiple users to do the work since the limits are per user, but how rclone handles those rate limits when they hit them. The exponential back off will end up making hours and days long delays that will screw a migration.
Note there is no intrinsic reason running multiple streams should be faster than one [EDIT: "at this scale"]. It almost always indicates some bottleneck in the application or TCP tuning. (Though, very fast links can overwhelm slow hardware, and ISPs might do some traffic shaping too, but this doesn't apply to local links).
SSH was never really meant to be a high performance data transfer tool, and it shows. For example, it has a hardcoded maximum receive buffer of 2MiB (separate from the TCP one), which drastically limits transfer speed over high BDP links (even a fast local link, like the 10gbps one the author has). The encryption can also be a bottleneck. hpn-ssh [1] aims to solve this issue but I'm not so sure about running an ssh fork on important systems.
1. https://github.com/rapier1/hpn-ssh
> has a hardcoded maximum receive buffer of 2MiB
For completeness, I want to add:
The 2MiB are per SSH "channel" -- the SSH protocol multiplexes multiple independent transmission channels over TCP [1], and each one has its own window size.
rsync and `cat | ssh | cat` only use a single channel, so if their counterparty is an OpenSSH sshd server, their throughput is limited by the 2MiB window limit.
rclone seems to be able to use multiple ssh channels over a single connection; I believe this is what the `--sftp-concurrency` setting controls.
Some more discussion about the 2MiB limit and links to work for upstreaming a removal of these limits can be found in my post [3].
Looking into it just now, I found that the SSH protocol itself already supports dynamically growing per-channel window sizes with `CHANNEL_WINDOW_ADJUST`, and OpenSSH seems to generally implement that. I don't fully grasp why it doesn't just use that to extend as needed.
I also found that there's an official `no-flow-control` extension with the description
> channel behaves as if all window sizes are infinite. > > This extension is intended for, but not limited to, use by file transfer applications that are only going to use one channel and for which the flow control provided by SSH is an impediment, rather than a feature.
So this looks exactly as designed for rsync. But no software implements this extension!
I wrote those things down in [4].
It is frustrating to me that we're only a ~200 line patch away from "unlimited" instead of shitty SSH transfer speeds -- for >20 years!
[1]: https://datatracker.ietf.org/doc/html/rfc4254#section-5
[2]: https://rclone.org/sftp/#sftp-concurrency
[3]: https://news.ycombinator.com/item?id=40856136
[4]: https://github.com/djmdjm/openssh-portable-wip/pull/4#issuec...
> TCP tuning
I think a lot of file transfer issues that occur outside of the corporate intranet world involve hardware that you don't fully control on (at least) one hand. In science, for example, transferring huge amounts of data over long distances is pretty common, and I've had to do this on boxes that had poor TCP buffer configurations. Being able to multiplex your streams in situations like this is invaluable and I'd love to see more open source software that does this effectively, especially if it can punch through a firewall.
In general TCP just isn't great for high performance. In the film industry we used to use a commercial product Aspera (now owned by IBM) which emulated ftp or scp but used UDP with forward error correction (instead of TCP retransmission). You could configure it to use a specific amount of bandwidth and it would just push everything else off the network to achieve it.
What does "high performance" mean here?
I get 40 Gbit/s over a single localhost TCP stream on my 10 years old laptop with iperf3.
So the TCP does not seem to be a bottleneck if 40 Gbit/s is "high" enough, which it probably is currently for most people.
I have also seen plenty situations in which TCP is faster than UDP in datacenters.
For example, on Hetzner Cloud VMs, iperf3 gets me 7 Gbit/s over TCP but only 1.5 Gbit/s over UDP. On Hetzner dedicated servers with 10 Gbit links, I get 10 Gbit/s over TCP but only 4.5 Gbit/s over UDP. But this could also be due to my use of iperf3 or its implementation.
I also suspect that TCP being a protocol whose state is inspectable by the network equipment between endpoints allows implementing higher performance, but I have not validated if that is done.
Aspera was/is designed for high latency links. Ie sending multi terabytes from london to new Zealand, or LA
For that use case, Aspera was the best tool for the job. It's designed to be fast over links that single TCP streams couldn't
You could, if you were so bold, stack up multiple TCP links and send data down those. You got the same speed, but possible not the same efficiency. It was a fucktonne cheaper to do though.
> I get 40 Gbit/s over a single localhost TCP stream on my 10 years old laptop with iperf3.
Do you mean literally just streaming data from one process to another on the same machine, without that data ever actually transiting a real network link? There's so many caveats to that test that it's basically worthless for evaluating what could happen on a real network.
Yes. Why?
To measure other overhead of what's claimed (TCP the protocol being slow), one should exclude other things that necessarily affect alternative protocols as well (e.g. latency) as much as possible, which is what this does.
It sounds like you're reasoning starting from an assumption that any claimed slowness of TCP would be something like a fixed per-packet overhead or delay that could be isolated and added back in to the result of your local testing to get a useful prediction. And it sounds like you think alternative protocols must be equally affected by latency.
But it's much more complicated than that; TCP interacts with latency and congestion and packet loss as both cause and effect. If you're testing TCP without sending traffic over real networks that have their own buffering and congestion control and packet reordering and loss, you're going to miss all of the most important dynamics affecting real-world performance. For example, you're not going to measure how multiplexing multiple data streams onto one TCP connection allows head of line blocking to drastically inflate the impact of a lost or reordered packet, because none of that happens when all you're testing is the speed at which your kernel can context-switch packets between local processes.
And all of that is without even beginning to touch on what happens to wireless networks.
Somebody made a claim that TCP isn't high performance without specifying what that means, I gave a counterexample of just how high performance TCP is picking some arbitrary notion of "high performance".
Almost like it makes the point that arguing about "high performance" is useless without saying what that means.
That said:
> you're not going to measure how multiplexing multiple data streams onto one TCP connection
Of course not: When I want to argue against "TCP is not a high performance protocol", why would I want to measure some other protocol that multiplexes connections over TCP? That is not measuring the performance of TCP.
I could conjure any protocol that requires acknowledgement from the other side for each emitted packet before sending the next, and then claim "UDP is not high performance" when running that over UDP - that doesn't make sense.
UDP by itself cannot be used to transfer files or any other kind of data with a size bigger than an IP packet.
So it is impossible to compare the performance of TCP and UDP.
UDP is used to implement various other protocols, whose performance can be compared with TCP. Any protocol implemented over UDP must have a performance better than TCP, at least in some specific scenarios, otherwise there would be no reason for its existence.
I do not know how UDP is used by iperf3, but perhaps it uses some protocol akin to TFTP, i.e. it sends a new UDP packet when the other side acknowledges the previous UDP packet. In that case the speed of iperf3 over UDP will always be inferior to that of TCP.
Sending UDP packets without acknowledgment will always be faster than for any usable transfer protocol, but the speed for this case does not provide any information about the network, but only about the speed of executing a loop in the sending computer and network-interface card.
You can transfer data without using any transfer protocol, by just sending UDP packets at maximum rate, if you accept that a fraction of the data will be lost. The fraction that is lost can be minimized, but not eliminated, by using an error-correcting code.
> perhaps it [..] sends a new UDP packet when the other side acknowledges the previous UDP packet. In that case the speed of iperf3 over UDP will always be inferior to that of TCP
It does not, otherwise it would be impossible by a factor ~100x to measure 4.5 Gbit/s as per the bandwidth-delay calculation (the ping is around the usual 0.2 ms).
With iperf3, as with many other UDP measurement tools, you set a sending rate and the other side reports how many bytes arrived.
You are right.
It is a long time since I have last used iperf3, but now that you have mentioned it I have also remembered this.
So the previous poster has misinterpreted the iperf3 results, by believing that UDP was slower, as iperf3 cannot demonstrate a speed difference between TCP and UDP, since for the former the speed is determined by the network, while for the latter the speed is determined by the "--bandwidth" iperf3 command-line option, so the poster has probably just seen some default UDP speed.
High performance means transferring files from NZ to a director's yacht in the Mediterranean with a 40Mbps satellite link and getting 40Mbps, to the point that the link is unusable for anyone else.
There's an open source implementation that does something similar but for a more specific use case: https://github.com/apernet/tcp-brutal
There's gotta be a less antisocial way though. I'd say using BBR and increasing the buffer sizes to 64 MiB does the trick in most cases.
Have you tried searching for "tcp-kind"?
Looks unmaintained.
Can we throw a bunch of AI agents at it? This sounds like a pretty tightly defined problem, much better than wasting tokens on re-inventing web browsers.
Was the torrent protocol considered at some point? Always surprised how little presence has in the industry considering how good the technology is.
If you strip out the swarm logic (ie. downloading from multiple peers), you're just left with a protocol that transfers big files via chunks, so there's no reason that'd be faster than any other sort of download manager that supports multi-thread downloads.
https://en.wikipedia.org/wiki/Download_manager
Aspera did the chunking and encryption for you, and it looked and acted like SFTP.
The cost of leaking data was/is catastrophic (as in company ending) So paying a bit of money to guarantee that your data was being sent to the right place (point to point) and couldn't leak was a worthwhile tradeoff.
For Point to point transfer torrenting is a lot higher overhead than you want. plus most clients have an anti-leaching setting, so you'd need not only a custom client, but a custom protocol as well.
The idea is sound though, have an index file with and then a list of chunks to pull over multiple TCP connections.
torrent is great for many-to-one type downloads but I assume GP is talking about single machine to single machine transfers.
So what do you use now in film industry?
I'm in a tiny part of the film industry. Bigger clients lend us licenses to Aspera and FileCatalyst when receiving files from them, but for our own trans-oceanic transfers I dug up an ancient program called Tsunami UDP and fixed it up just enough.
I suspect mostly Aspera because there are still no good alternatives.
Aspera's FASP [0] is very neat. One drawback to it is that the TCP stuff not being done the traditional way must be done on CPU. Say if one packet is missing or if packets are sent out of order, the Aspera client fixes those instead of all that being done as TCP.
As I understand it, this is also the approach of WEKA.io [1]. Another approach is RDMA [2] used by storage systems like Vast which pushes those order and resend tasks to NICs that support RDMA so that applications can read and write directly to the network instead of to system buffers.
0. https://en.wikipedia.org/wiki/Fast_and_Secure_Protocol
1. https://docs.weka.io/weka-system-overview/weka-client-and-mo...
2. https://en.wikipedia.org/wiki/Remote_direct_memory_access
FASP uses forward error correction instead of retransmission. So instead of waiting for something not to show up on the other end and sending it again, it calculates parity and transmits slightly more data up front, with enough redundancy that the receiving end is capable of reconstructing any missing bits. This is basically how all storage systems work, not just Weka. You calculate enough parity bits to be able to reconstruct the missing data when a drive fails. The more disks you have, the smaller the parity overhead is. Object storage like S3 does this on a massive scale. With a network transfer you typically only need a few percent, unless it's really lossy like Wifi, in which case standards like 802.11n are doing FEC for you to reduce retransmissions at the TCP layer.
In RDMA are the NICs able to perform the reconstruction or does that use a different mechanism to avoid CPU?
> Note there is no intrinsic reason running multiple streams should be faster than one.
The issue is the serialization of operations. There is overhead for each operation which translates into dead time between transfers.
However there are issues that can cause singular streams to underperform multiple streams in the real world once you reach a certain scale or face problems like packet loss.
Is it certain that this is the reason?
rsync's man page says "pipelining of file transfers to minimize latency costs" and https://rsync.samba.org/how-rsync-works.html says "Rsync is heavily pipelined".
If pipelining is really in rsync, there should be no "dead time between transfers".
The simple model for scp and rsync (it's likely more complex in rsync): for loop over all files. for each file, determine its metadata with fstat, then fopen and copy bytes in chunks until done. Proceed to next iteration.
I don't know what rsync does on top of that (pipelining could mean many different things), but my empirical experience is that copying 1 1 TB file is far faster than copying 1 billion 1k files (both sum to ~1 TB), and that load balancing/partitioning/parallelizing the tool when copying large numbers of small files leads to significant speedups, likely because the per-file overhead is hidden by the parallelism (in addition to dealing with individual copies stalling due to TCP or whatever else).
I guess the question is whether rsync is using multiple threads or otherwise accessing the filesystem in parallel, which I do not think it does, while tools like rclone, kopia, and aws sync all take advantage of parallelism (multiple ongoing file lookups and copies).
> I don't know what rsync does on top of that (pipelining could mean many different things), but my empirical experience is that copying 1 1 TB file is far faster than copying 1 billion 1k files (both sum to ~1 TB), and that load balancing/partitioning/parallelizing the tool when copying large numbers of small files leads to significant speedups, likely because the per-file overhead is hidden by the parallelism (in addition to dealing with individual copies stalling due to TCP or whatever else).
That's because of fast paths:
- For a large file, assuming the disk isn't fragmented to hell and beyond, there isn't much to do for rsync / the kernel: the source reads data and copies it to the network socket, the receiver copies data from the incoming network socket to the disk, the kernel just dumps it in sequence directly to the disk, that's it.
- The slightly less performant path is on a fragmented disk. Source and network still doesn't have much to do, but the kernel has a bit more work every now and then to find a contiguous block on the disk to write the data to. For spinning rust HDDs, the disk also has to do some seeking.
- Many small files? Now that's more nasty. First, the source side has to do a lot of stat(2) calls to get basic attributes of the file. For HDDs, that seeking can incur a sometimes significant latency penalty as well. Then, this information needs to be transferred to the destination, the destination has to do the same stat call again, and then the source needs to transfer the data, involving more seeking, and the destination has to write it.
- The utter worst case is when the files are plenty and small, but large enough to not fit into an inode as inline data [1]. That means two writes and thus seeks per small file. Utterly disastrous for performance.
And that's before stepping into stuff such as systems disabling write caches, soft-RAID (or the impact of RAID in general), journaling filesystems, filesystems with additional metadata...
[1] https://archive.kernel.org/oldwiki/ext4.wiki.kernel.org/inde...
> I guess the question is whether rsync is using multiple threads or otherwise accessing the filesystem in parallel
No, that is not the question. Even Wikipedia explains that rsync is single-threaded. And even if it was multithreaded "or otherwise" used concurent file IO:
The question is whether rsync _transmission_ is pipelined or not, meaning: Does it wait for 1 file to be transferred and acknowledged before sending the data of the next?
Somebody has to go check that.
If yes: Then parallel filesystem access won't matter, because a network roundtrip has brutally higher latency than reading data sequentially of an SSD.
Note that rsync on many small files is slow even within the same machine (across two physical devices), suggesting that the network roundtrip latency is not the major contributor.
The original post only mentions 3564 files and rsync spending 8 minutes on that. This just doesn't check out.
The filesystem access and general threading is the question because transmission is pipelined and not a thing "somebody has to go check". You just quoted the documentation for it.
The dead time isn't waiting for network trips between files, it's parts of the program that sometimes can't keep up with the network.
I quoted the documentation that claims _something_ is pipelined.
That is extremely vague on what that is and I also didn't check that it's true.
Both the original claim "the issue is the serialization of operations" and the counter-claim all sound like extreme guesswork or me. If you know for certain, please link the relevant code.
Otherwise somebody needs to go check what it actually does; everything else is just speculating "oh surely it's the files" and then people remember stuff that might just be plain wrong.
Speculation isn't the most useful thing, but saying "that is not the question" to valid speculation is even less useful.
I’m not sure why, but just like with scp, I’ve achieved significant speeds ups by tarring the directory first (optionally compressing it), transferring and then decompressing. Maybe because it makes the tar and submit, and the receive, untar/uncompress, happen on different threads?
One of my "goto" tools is copying files over a "tar pipe". This avoids the temporary tar file. Something like:
I've never verified this, but it feels like scp starts a new TCP connection per file. If that's the case, then scp-ing a tarred directory would be faster because you only hit the slow start once. https://www.rfc-editor.org/rfc/rfc5681#section-3.1
It's typically a disk-latency thing, as just stat-ing the many files in a directory can have significant latency implications (especially on spinning HDDs) vs opening a single file (the tar) and read-()ing that one file in memory before writing to the network.
If copying a folder with many files is slower than tarring that folder and the moving the tar (but not counting the untar) then disk latency is your bottleneck.
Not useful very often, but fast and kind of cool: You can also just netcat the whole block device if you wanted a full filesystem copy anyway. Optionally zero all empty space before using a tool like zerofree and use on-the-fly compression / decompression with lz4 or lzo. Of course, none of the block devices should be mounted, though you could probably get away with a source that's mounted read-only.
dd is not a magic tool that can deal with block devices while others can't. You can just cp myLinuxInstallDisk.iso to /dev/myUsbDrive, too.
Okay. In this case the whole operation is faster end to end. That includes the time it takes to tar and untar. Maybe those programs do something more efficient in disk access than scp and rsync?
Also handy to note that tar can handle sparse files, whereas scp doesn't.
The ideal solution to that is pipelining but it can be complex to implement.
The author tried running rsyncd demon so it's not _just_ the ssh protocol.
Great point, and even beyond that I think (based on the paths) it was just a command line invocation, with something like NFS handling all the networking.
Uhh.. I work with this stuff daily and there are a LOT of intrinsic reasons a single stream would be slower than running multiple: MPLS ECMP hashing you over a single path, a single loss event with a high BDP causing congestion control to kick in for a single flow, CPU IRQ affinity, probably many more I’m not thinking like the inner workings of NIC offloading queues.
Source: Been in big tech for roughly ten years now trying to get servers to move packets faster
Ha, it sounds like the best way to learn something is to make a confident and incorrect claim :)
> MPLS ECMP hashing you over a single path
This is kinda like the traffic shaping I was talking about though, but fair enough. It's not an inherent limitation of a single stream, just a consequence of how your network is designed.
> a single loss event with a high BDP
I thought BBR mitigates this. Even if it doesn't, I'd still count that as a TCP stack issue.
At a large enough scale I'd say you are correct that multiple streams is inherently easier to optimize throughput for. But probably not a single 1-10gb link though.
> This is kinda like the traffic shaping I was talking about though, but fair enough. It's not an inherent limitation of a single stream, just a consequence of how your network is designed.
It is. one stream gets you traffic of one path to the infrastructure. Multiple streams get you multiple and possibly also hit different servers to accelerate it even more. Just the limitation isn't hardware but "our networking device have 4 10Gbit ports instead of single 40Gbit port"
Especially if link is saturated, you'd be essentially taking n-times your "fair share" of bandwidth on link.
> SSH was never really meant to be a high performance data transfer tool, and it shows.
A practical example can be `ssh -X` vs X11 over Wireguard. The lag is obvious with the former, but X11 windows from remote clients can be indistinguishable performance-wise from those of local ones with the latter.
> It almost always indicates some bottleneck in the application or TCP tuning.
Yeah, this has been my experience with low-overhead streams as well.
Interestingly, I see a ubiquity of this "open more streams to send more data" pattern all over the place for file transfer tooling.
Recent ones that come to mind have been BackBlaze's CLI (B2) and taking a peek at Amazon's SDK for S3 uploads with Wireshark. (What do they know that we don't seem to think we know?)
It seems like they're all doing this? Which is maybe odd, because when I analyse what Plex or Netflix is doing, it's not the same? They do what you're suggesting, tune the application + TCP/UDP stack. Though that could be due to their 1-to-1 streaming use case.
There is overhead somewhere and they're trying to get past it via semi-brute-force methods (in my opinion).
I wonder if there is a serialization or loss handling problem that we could be glossing over here?
that is a different problem. For S3-esque transfers you might very well be limited by ability for target to receive X MB/s and not more and so starting parallel streams will make it faster.
I used B2 as third leg for our backups and pretty much had to give rclone more connections at once because defaults were nowhere close to saturating bandwidth
Memory and CPU are cheap (up to a point) so why not just copy/paste TCP streams. It neatly fits into multi-processing/threading as well.
When we were doing 100TB backups of storage servers we had a wrapper that run multiple rsyncs over the file system, that got throughput up to about 20gigbits a second over lan
Tuning on Linux requires root and is systemwide. I don't think BBR is even available on other systems. And you need to tune the buffer sizes of both ends too. Using multiple streams is just less of a hassle for client users. It can also fool some traffic shaping tools. Internal use is a different story.
not sure about B2 but AWS S3 SDK not assuming that people will do any tuning makes total sense
cuz in my experience no one is doing that tbh
I’ve found aws s3 it’s always been painful to get any good speed out of it unless it’s massive files you’re moving.
It’s base line tuning seems to just assume large files and does no auto scaling and it’s mostly single threaded.
Then even when tuning it’s still painfully slow, again seemly limited by its cpu processing and mostly on a single thread, highly annoying.
Especially when you’re running it on a high core, fast storage, large internet connection machine.
Just feels like there is a large amount of untapped potential in the machines…
It’s almost certainly also tuned to prevent excessive or “spiky” traffic to their service.
Note there is no intrinsic reason running multiple streams should be faster than one
If the server side scales (as cloud services do) it might end up using different end points for the parallel connections and saturate the bandwidth better. One server instance might be serving other clients as well and can't fill one particular client's pipe entirely.
Wouldn't lots of streams speed up transfers of thousands of small files?
If the application handles them serially, then yeah. But one can imagine the application opening files in threads, buffering them, and then finally sending it at full speed, so in that sense it is an application issue. If you truly have millions of small files, you're more likely to be bottlenecked by disk IO performance rather than application or network, though. My primary use case for ssh streams is zfs send, which is mostly bottlenecked by ssh itself.
It's an application issue but implementation wise it's probably way more straightforward to just open a separate network connection per thread.
Single file overheads (opening millions of tiny files whose metadata is not in the OS cache and reading them) appears to be an intrinsic reason (intrinsic to the OS, at least).
IOPs and disk read depth are common limits.
Depending on what you're doing it can be faster to leave your files in a solid archive that is less likely to be fragmented and get contiguous reads.
the majority of that will be big files. And to NVMe it is VERY fast even if you run single threaded 10Gbit should be easy
I mean isn't a single TCP connections throughput limited by the latency? Which is why in high(er) latency WAN links you generally want to open multiple connections for large file transfers.
https://wintelguy.com/wanperf.pl
Only simpler transfer protocols, like TFTP, are limited by latency.
The whole reason for the existence of TCP is to overcome the throughput limit determined by latency. On a network with negligible latency there is no need for TCP (you could just send each packet only after the previous is acknowledged, but the higher is the throughput of your network interface, the less likely is that the latency can be negligible).
However, for latency to not matter, the TCP windows must be large enough (i.e. the amount of data that is sent before an acknowledge is received, which happens after a delay caused by latency).
I use Windows very rarely today, so I do not know its current status, but until the Windows XP days it was very frequent for Windows computers to have very small default TCP window sizes, which caused low throughput on high-latency networks, so on such networks they had to be reconfigured.
On high-latency networks, opening multiple connections is just a workaround for not having appropriate network settings. However, even when your own computer is configured optimally, opening multiple connections can be a workaround against various kinds of throttling implemented either by some intermediate ISP or by the destination server, though nowadays most rate limits are applied globally, to all connections from the same IP address, in order to make this workaround ineffective.
> Note there is no intrinsic reason running multiple streams should be faster than one
Inherent reasons or no, it's been my experience across multiple protocols, applications, network connections and environments, and machines on both ends, that, _in fact_, splitting data up and operating using multiple streams is significantly faster.
So, ok, it might not be because of an "inherent reason", but we still have to deal with it in real life.
Rclone is a fantastic tool, but my favorite part of it is actually the underyling FS library. I've started baking Rclone FS into internal Go tooling and now everything transparently supports reading/writing to either local or remote storage. Really great for being able to test data analysis code locally and then running as batch jobs elsewhere.
What kind of data analysis do you run with Go and do you use an open source library for it? My experience with stats libraries in Go has been lukewarm so far.
"Rclone is a fantastic tool, but my favorite part of it is actually the underyling FS library."
Related to this is the very useful:
.. workflow that allows you to create append-only (immutable) backups.This howto is not rsync.net-specific - you can follow this recipe at any standard SSH endpoint:
https://www.rsync.net/resources/notes/2025-q4-rsync.net_tech...
RClone has been so useful over the years I built a fully managed service on top of it specifically for moving data between cloud storage providers: https://dataraven.io/
My goal is to smooth out some of the operational rough edges I've seen companies deal with when using the tool:
I hope that you sponsor the rclone project given that it’s the core of your business! I couldn’t find any indication online that you do give back to the project. I hope I’m wrong.
I'm certainly planning on sponsoring the project as soon as possible, but so far I have zero paying customers, hopefully that will change soon
first thing that popped into my mind is that your free plan is crazy generous. cut it out.
first thing that popped into mine is $30/mo for running a vm with a command is something people will now just tell LLM to do
first thing that popped into my mind is that OP did a lot of hard work and doesn't need cynical and useless comments about it.
that's just creepy and hella presumptuous
Yeah I've seen this pop up in foss a lot lately and I don't like it.
I certainly hope you give back to projects you use too.
Gifts do not confer obligation. If you give me a screwdriver and I use it to run my electrical installation service business, I don’t owe you a payment.
This idea that one must “give back” after receiving a gift freely given is simply silly.
Yes but thank-yous are always good. Making sure the project sticks around is just smart.
If your neighbor kept baking and giving you cookies, to the point where you were wrapping and reselling them at the market, don't you think you should do something for them in return?
Not if they gave me a legal document explicitly stating I didn’t need to give them anything…and I could get an infinite amount of the cookies with no extra work or money on their part…
And I would probably suggest to them that if they were interested in profiting from their cookies they should stop giving them away for free and make them commercial instead. They might then tell me they don’t want to spend the effort and money to commercialize their cookies, or maybe they prefer it as a hobby with no obligations to customers, or maybe they tell me they have a philosophical belief that they should give their their cookies away for free for anyone to do as they please with them, including commercializing them as long as they aren’t legally responsible for anything done with the cookies which is why they handed me that legal contract explicitly stating that when they gave them to me in the first place.
You may just be an annoying neighbor, have a wonderful day!
This is how capitalism works, and what happens at a large scale, even if you don’t like it.
Me too!
How do you deal with how poorly rclone handles rate limits? It doesn't honor dropbox's retry-after header and just adds an exponential back off that, in my migrations, has resulted in a pause of days.
I've adjusted threads and the various other controls rclone offers but I still feel like I'm not see it's true potential because the second it hits a rate limit I can all but guarantee that job will have to be restarted with new settings.
> doesn't honor dropbox's retry-after header
That hasn't been true for more than 8 years now.
Source: https://github.com/rclone/rclone/blob/9abf9d38c0b80094302281...
And the PR adding it: https://github.com/rclone/rclone/pull/2622
Interesting. I’ve been through 4 large transfers to Dropbox in the last 3 years and never once has it honored that header.
I honestly haven't used it with Dropbox before, have you tried adjusting --tpslimit 12 --tpslimit-burst 0 flags? Are you creating a dedicated api key for the transfer? Rate limits may vary between Plus/Advanced forum.rclone.org is quite active you may want to post more details there.
I have made a dedicated api application and I have adjusted the tps flags. I’ve scanned the forums a few times but I’ve yet to inquire there.
i had been thinking about this service for a long time, especially something supporting transforms and indexing for backups. great job spinning it up.
Thanks 1. are you thinking of something like aws data firehose transform feature? where pandas or something can run inline? https://docs.aws.amazon.com/firehose/latest/dev/data-transfo...
2. do you have an example of what indexed backups would look like? Im thinking of macos time machine, where each backup only contains deltas from the last backup. Or am I completely off?
For transforms, the concept would be user friendly processing, like downcoding video & photos, compressing PDFs & text files, filtering out temporary or wasteful files . Something like AirTable for backups with a gui workflow editor with common processing jobs for backups.
For indexing, full text indexing of backups to allow for record retrieval based on keyword or date. E.g. “images in Los Angeles before 2010” or “tax records from 2015”. If possible, low resolution thumbnails of the backups to make retrieval easier.
I think #1 (transforms) would be more generally useful for cross cloud applications, and #2 is more catered toward backups
Interesting that nobody has mentioned: Warp speed Data Transfer (WDT)[1].
From the readme:
- Warp speed Data Transfer (WDT) is an embeddedable library (and command line tool) aiming to transfer data between 2 systems as fast as possible over multiple TCP paths.
- Goal: Lowest possible total transfer time - to be only hardware limited (disc or network bandwidth not latency) and as efficient as possible (low CPU/memory/resources utilization)
1. https://github.com/facebook/wdt
I prefer rsync because of its delta transfer which doesn't resend files already on the destination, saving bandwidth. This combined with rsync's ability to work over ssh lets me sync anywhere rsync runs, including the cloud. It may not be faster than rclone but it is more conserving on bandwidth.
The delta-transfer algorithm [0] is about detecting which chunks of a file differ on source and target [1], and limiting the transfer to those chunks. The savings depend on how and where they differ, and ofcourse there's tradeoffs...
You seem to be referring to the selection of candidates of files to transfer (along several possible criteria like modification time, file size or file contents using checksumming) [2]
Rsync is great. However for huge filesystems (many files and directories) with relatively less change, you'll need to think about "assisting" it somewhat (by feeding it its candidates obtained in a more efficient way, using --files-from=). For example: in a renderfarm system you would have additions of files, not really updates. Keep a list of frames that have finished rendering (in a cinematic film production this could be eg. 10h/frame), and use it to feed rsync. Otherwise you'll be spending hours for rsync to build its index (both sides) over huge filesystems, instead of transferring relatively few big and new files.
In workloads where you have many sync candidates (files) that have a majority of differing chunks, it might be worth rather disabling the delta-transfer algorithm (--whole-file) and saving on the tradeoffs.
[0] https://www.andrew.cmu.edu/course/15-749/READINGS/required/c...
[1] https://en.wikipedia.org/wiki/Rsync#Determining_which_parts_...
[2] https://en.wikipedia.org/wiki/Rsync#Determining_which_files_...
In my case I get 8 MB/s with rsync, 80 MB/s with rclone. This is with cross-continent transfers so 200ms latency. If it was just a 2x difference I'd be fine with rsync but 10x is very hard to ignore.
For me it's about saving bandwidth which is why I use rsync.
Rclone can "sync" with a range of different ways to check if the existing files are the same. If no hashes are available (e.g. WebDAV) I think you can set it to check by timestamp (with a tolerance) and size.
Edit: oh I see, delta transfer only sends the changed parts of files?
It only sends the changed parts of files (the diffs) is my understanding which saves bandwidth.
However this is only really good for slow connections. If your connection is faster than about 50MB/s then the delta calculation mechanism becomes the bottleneck. On fast connections you should use the -W option for rsync which switches the delta algorithm off.
Saving on transmitted bandwidth costs is more important to me than speed of transmission for my use cases which I feel are likely to be common use cases.
Does rclone not do that? I thought they were specifically naming themselves similarly because they also did that.
My understanding is that rclone does not do true delta sync sending only the differing parts of files like rsync.
The article links to a YouTube mini-review of USB enclosures from UGreen and Acasis, neither of which he loves.[1] I've been happy with the OWC 1M2 as a boot drive on a Mac Studio with Thunderbolt 5 ports.[2] I just noticed that there is an OWC 1M2 80G, based on USB4 v2.[3] I didn't know that was a thing, but I guess it's the USB cousin to Thunderbolt 5.
[1] https://www.youtube.com/watch?v=gaV-O6NPWrI
[2] https://eshop.macsales.com/shop/owc-express-1m2
[3] https://eshop.macsales.com/item/OWC/US4V2EXP1M2/
rclone --multi-thread-streams allows transfers in parallel, like robocopy /MT
You can also run multiple instances of rsync, the problem seems how to efficiently divide the set of files.
> efficiently divide the set of files.
It turns out, fpart does just that! Fpart is a Filesystem partitioner. It helps you sort file trees and pack them into bags (called "partitions"). It is developed in C and available under the BSD license.
It comes with an rsync wrapper, fpsync. Now I'd like to see a benchmark of that vs rclone! via https://unix.stackexchange.com/q/189878/#688469 via https://stackoverflow.com/q/24058544/#comment93435424_255320...
https://www.fpart.org/
Sometimes find (with desired maxdepth) piped to gnu-parallel rsync is fine.
robocopy! Wow, blast from the past. Used to use it all the time when I worked in a Windows shop.
I am using robocopy right now on a project. The /MIR option is extremely useful for incrementally maintaining copies of large local directories.
My go-to for fast and easy parallelization is xargs -P.
note that one should use -print0 and -0 for safety
Thanks! I've been using the -F{} do-something-tofile "{}" approach which is also handy for times in which the input is one pram among others. -0 is much faster.
Edit: Looks like when doing file-by-file -F{} is still needed:
You have to do `find ... -print0` so find also uses \0 as the separator.
find -print0 will print the files with null bytes as separators
xargs -0 will use a null byte as separator for each argument
printf 'a\0b\0c\0' | xargs -tI{} echo “file -> {}"
Rclone is such an elegant piece of software, reminds me of the time where most software worked well most of the time. There's few people that wouldn't benefit from it, either as a developer or end-user.
I'm currently working on the GUI if you're interested: https://github.com/rclone-ui/rclone-ui
I wonder if the at least partially the reason for the speed up isn't the multi-threading, but instead that rclone maybe doesn't compress transferred data by default. That's what rsync does when using SSH, so for already compressed data (like videos for example) disabling SSH compression when invoking rsync speeds it up significantly:
Compression is off by default in OpenSSH, at least `man 5 ssh_config` says:
> Specifies whether to use compression. The argument must be yes or no (the default).
So I'm surprised you see speedups with your invocation.
Good point. Seems like I enabled it in ~/.ssh/config ages ago and did forget about it. Nonetheless, it's good to check whether it's enabled when using rsync to transfer large, already well compressed files.
IIRC rsync uses your default SSH options, so turning off compression is only needed if your default config explicitly turns it on (generally or just for that host). If sending compressible content using rsync's compression instead of SSH's is more effective when updating files because even if not sending everything it can use it to form the compression dictionary window for what does get sent (though for sending whoe files, SSH's compression may be preferable as rsync is single threaded and using SSH's compression moves that chunk of work to the SSH process).
One thing that sets rsync apart perhaps is the handling of hard links when you don't want to send both/duplicated files to the destination? Not sure if rclone can do that.
Thanks for sharing, hadn't seen it but at almost the same time he made that post I too was struggling to get decent NAS<>NAS transfer speeds with rsync. I should have thought to play more with rclone! I ended up using iSCSI but that is a lot more trouble.
>In fact, some compression modes would actually slow things down as my energy-efficient NAS is running on some slower Arm cores
Depending on the number/type of devices in the setup and usage patterns, it can be effective sometimes to have a single more powerful router and then use it directly as a hop for security or compression (or both) to a set of lower power devices. Like, I know it's not E2EE the same way to send unencrypted data to one OPNsense router, Wireguard (or Nebula or whatever tunnel you prefer) to another over the internet, and then from there to a NAS. But if the NAS is in the same physically secure rack directly attached by hardline to the router (or via isolated switch), I don't think in practice it's significantly enough less secure at the private service level to matter. If the router is a pretty important lynchpin anyone, it can be favorable to lean more heavily on that so one can go cheaper and lower power elsewhere. Not that more efficiency, hardware acceleration etc are at all bad, and conversely sometimes might make sense to have a powerful NAS/other servers and a low power router, but there are good degrees of freedom there. Handier then ever in the current crazy times where sometimes hardware that was formerly easily and cheaply available is now a king's ransom or gone and one has to improvise.
It is crazy to see how difficult google makes it for anyone to download their own pictures from google photos. Rclone used to allow you to download them, but not anymore. Only the ones uploaded by Rclone are available to download. I wish someone forced all cloud providers to allow you to download your own data. And no, google takout doesn't count. It is horrible to use.
Not just bad to use, but doesn't fully work. I've been trying to get my photos off Google Photos to backup elsewhere, but takeout misses something like 20%-30% of them.
How did you verify that takeout shorted you on 20-30% which is a huge number? This worries me as I've done some takeouts but never fully poked through them.
Honestly? It's an estimate based on spot checking a half dozen smaller albums where I could tell what was missing.
yes, that is what I meant. Once I tried to download 600GB of photos, and it crashed.
Guess I found a solution You have to export them to the drive first and then rclone where you need. Here's the full guide https://metadatafixer.com/learn/how-to-download-google-takeo...
Nice!
Yesterday I set up Rclone, and the download speed from Google Drive was so slow that caching took a long time. After applying the method described in this article, the speed improved.
This is my mount configuration. What do you think? Is there anything that might be causing issues??
rclone mount google_drive: X: ^
Do you have your own api key? Been a while, but that's the most common problem.
I set up your API key and secret. However, I couldn't verify if it was working with the API.
I have a few qualms about rclone:
You can already perform parallel transfers quite trivially by typing "find .. | xargs -P$(nproc) -n 1 rsync ..", carefully managing sync folder sets and just being a Unix god in general like me.
It doesn't seem reasonable to expect people to use and install it, when we can just use rsync.
> You can already perform parallel transfers quite trivially by typing "find .. | xargs -P$(nproc) -n 1 rsync ..", carefully managing sync folder sets and just being a Unix god in general.
This solution sucks in a number of different ways, and is downright incorrect. I'm baffled by the frequency at which these hacky parallel commands are recommended when rclone exists, works perfectly fine, and has literally no runtime dependencies.
It's a reference to a classic comment: https://news.ycombinator.com/item?id=9224
He was being sarcastic.
[dead]
for fun try this 1. tar + nc 2. rsync 3. scp
#1 is the fastest way to send because it keeps the buffers full in a consistent stream which allows tcp windows to grow as large as possible. 2 and 3 ( scp and rsync ) do round trip acks to the remote side which drastically slows things down, even if done in parallel.
Note that tar and rsync can handle sparse files, but scp doesn't.
Also, rsync will likely be faster if some files are already in the destination as it handles that intelligently as well as only transferring changed blocks from files.
rclone is not as good as rsync for doing ad-hoc transfers; for anything not using the filesystem, you need to set up a configuration, which adds friction. It realy is purpose built for recurring transfers rather than "I need to move X to Y just once"
We wrote https://github.com/chorusone/fastsync for fast ad-hoc transfers over multiple TCP streams.
I use tab-complete to navigate remote folder structures with rsync all the time, does rclone have that?
This is not a feature of rsync, but of your shell.
So the question "does rclone have that" doesn't make much sense, because it usually wouldn't be rclone implementing it.
For example, zsh does it here for rsync, which actually invokes `ssh` itself:
https://github.com/zsh-users/zsh/blob/3e72a52e27d8ce8d8be0ee...
https://github.com/zsh-users/zsh/blob/3e72a52e27d8ce8d8be0ee...
That said, some CLI tools come with tools for shells to help them implement such things. E.g. `mytool completion-helper ...`
But I don't get rclone SSH completions in zsh, as it doesn't call `_remote_files` for rclone:
https://github.com/zsh-users/zsh/blob/3e72a52e27d8ce8d8be0ee...
Why are rclone/rsync never used by default for app updates? Especially games with large assets.
zsync is better for that. zsync precalculates all the hashes and puts them in a file alongside the main one. The client downloads the hashes, compares them to what it has then downloads the parts it is missing.
With rsync, you upload hashes of what you have, then the source has to do all the hashing work to figure out what to send you. It's slightly more efficient, but If you are supporting even 10s of downloads it's a lot of work for the source.
The other option is to send just a diff, which I believe e.g. Google Chrome does. Google invented Courgette and Zucchini which partially decompile binaries then recompile them on the other end to reduce the size of diffs. These only work for exact known previous versions, though.
I wonder if the ideas of Courgette and Zucchini can be incorporated into zsync's hashes so that you get the minimal diff, but the flexibility of not having a perfect previous version to work from.
Do a CRDT but for binary executables
What’s sad to me is that rsync hasn’t been touched to fix these issues in what feels like decades.
rsync does what was designed to do and the lack of scope creep is not a bad thing. There is "fpsync" - another tool on top of rsync (which was mentioned in one of the comments at article's page) that covers the parallel processing use-case: https://manpages.debian.org/bullseye/fpart/fpsync.1.en.html
rsync was designed to synchronize directories of files from one directory to another, optionally on another host, and to do so efficiently.
It doesn’t do that.
Even before this conversation, a few weeks ago I started working on a modern replacement.
Thanks for the lms tips in the comments. Amazing!
The parallelism advantage of rclone is real but undersold here. rsync's single-stream design made sense when networks were the bottleneck. Now with high-bandwidth links (especially to cloud storage), the bottleneck is often the round-trip latency of per-file metadata operations.
rclone's multi-threaded transfers effectively pipeline those operations. It's the same principle as why HTTP/2 multiplexing was such a win — you stop paying the latency tax sequentially.
One thing I'd add: for local-to-local or LAN sync, rsync still often wins because the overhead of rclone's abstraction layer isn't worth it when latency is already sub-millisecond. The 4x speedup is really a story about high-latency, high-bandwidth paths where parallelism dominates.
I love rclone. I use it in place of scp/sftp frequently. My biggest complaint is that it seems to require a config file, having the ability to `rclone copy root@192.168.1.1:/tmp/foo ./` would be a game changer.
I'll keep saying that rclone is a fantastic and underrated piece of software.
rclone is super cool, but unfortunately many of the providers it supports has such low ratelimits, that it's fairly difficult to use it to transfer much data at all.
This has been my problem. Not necessarily that the rate limits are low, many can be gotten around by using multiple users to do the work since the limits are per user, but how rclone handles those rate limits when they hit them. The exponential back off will end up making hours and days long delays that will screw a migration.
I had to tweak some options because it was hitting ratelimit on B2 and the reaction of rclone to that was just... disabling deletes
golang concurrent IO is so accessible that even trivial IO transform scripts (e.g. compression, base64, md5sum/cksum) are very easy to multicore.
You'd be astonished at how much faster even seemingly fast local IO can go when you unblock the IO
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