TernFS – An exabyte scale, multi-region distributed filesystem(xtxmarkets.com) |
TernFS – An exabyte scale, multi-region distributed filesystem(xtxmarkets.com) |
> Is hardware agnostic and uses TCP/IP to communicate.
So no RDMA? It's very hard to make effective use of modern NVMe drives bandwidth over TCP/IP.
> A logical shard is further split into five physical instances, one leader and four followers, in a typical distributed consensus setup. The distributed consensus engine is provided by a purpose-built Raft-like implementation, which we call LogsDB
Raft-like, so not Raft, a custom algorithm? Implementing distributed consensus correctly from scratch is very hard - why not use some battle-tested implementations?
> Read/write access to the block service is provided using a simple TCP API currently implemented by a Go process. This process is hardware agnostic and uses the Go standard library to read and write blocks to a conventional local file system. We originally planned to rewrite the Go process in C++, and possibly write to block devices directly, but the idiomatic Go implementation has proven performant enough for our needs so far.
The document mentions it's designed to reach TB/s though. Which means that for an IO intensive workload, one would end up wasting a lot of drive bandwidth, and require a huge number of nodes.
Modern parallel filesystems can reach 80-90GB/s per node, using RDMA, DPDK etc.
> This is in contrast to protocols like NFS, whereby each connection is very stateful, holding resources such as open files, locks, and so on.
This is not true for NFSv3 and older, it tends to be stateless (no notion of open file).
No mention of the way this was developed and tested - does it use some formal methods, simulator, chaos engineering etc?
We can saturate the network interfaces of our flash boxes with our very simple Go block server, because it uses sendfile under the hood. It would be easy to switch to RDMA (it’s just a transport layer change) but right now we didn’t need to. We’ve had to make some difficult prioritisation decisions here.
PRs welcome!
> Implementing distributed consensus correctly from scratch is very hard - why not use some battle-tested implementations?
We’re used to building things like this, trading systems are giant distributed systems with shared state operating at millions of updates per second. We also cheated, right now there is no automatic failover enabled. Failures are rare and we will only enable that post-Jepsen.
If we used somebody else’s implementation we would never be able to do the multi-master stuff that we need to equalise latency for non-primary regions.
> This is not true for NFSv3 and older, it tends to be stateless (no notion of open file).
Even NFSv3 needs a duplicate request cache because requests are not idempotent. Idempotency of all requests is hard to achieve but rewarding.
Not to mention a Jepsen test suite, detailed CAP tradeoff explanation, etc.
There's a reason those big DFS at the FAANGs aren't really implemented anywhere else: they NEED the original authors with a big, deeply experienced infrastructure/team in house.
I think many people have said this, but "file systems" get a lot easier if you don't have to worry about overwrites, appends, truncates, etc. Anyway, always interesting to see what people come up with for their use cases.
It also does not at all preclude implementing a read-write layer on top of it, for instance with a log-structured FS design. That's however the solution to a problem these people are, it seems, not having.
But it's really just a guess, I haven't worked in this domain.
Have you seen their portfolio?
PS: Company seems legit. Impressive growth. But I still don't understand what they are doing. Provide "electronic liquidity". Well....
> TernFS should not be used for tiny files — our median file size is 2MB.
It'd be helpful to have a couple of usage examples that illustrate common operations, like creating a file or finding and reading one, right after the high-level overview section. Just to get an idea what happens at the service level in these cases.
But if we have the time it would definitely be a good addition to the docs.
It's like the engineers building them keep trying to scratch their own itch for a better filesystem that could enable seamless cross-device usage, collaboration, etc. But the engineers only get paid if they express themselves in terms of corporate desires, and corpos aren't looking to pay them to solve those hard problems. So they solve the horizontal scaling problem for the thousandth time, but only end up creating things that requires a full time engineer (or perhaps even a whole team) to use. Hooray, another centralizing "distributed" filesystem.
Have a couple of questions:
- How do you go about benchmarking throughput / latency of such a system? Curious if it's different compared to how other distributed filesystems benchmark their systems.
- Is network or storage the bottleneck for nodes (at least for throughput)?
- From my observations from RDMA-based distributed filesystems, network seems to be the case.
- Very very interesting to go down the path of writing a kernel module instead of using FUSE or writing a native client in userspace (referring to 3FS [1])
- Any crashes in production? And how do you go about tracking it down?
- What's the difference in performance between using the kernel module versus using FUSE?
I guess this is a trade-off between a file system and an object store? As in S3, ListObjects() is a heavy hitter and there can be potentially billions of objects under any prefix. Scanning only on a single instance won't be sufficient.
I wonder if a major difference is listing a prefix in object storage vs performing recursive listings in a file system?
Even in S3, performing very large lists over a prefix is slow and small files will always be slow to work with, so regular compaction and catching file names is usually worthwhile.
I haven't worked at FAANG, but is this a well-known fact? I've never heard of it. Unless they're referring to things like S3? Are these large corps running literal custom filesystem implementations?
Tectonic is Facebooks, Google's is Colossus. I'm not sure about the others.
It’s specialized knowledge, hard to do “correctly” (read posix here) but obtainable and implementable by a small team if you pick your battles right. Also supporting very specific use cases helps a lot.
It’s also pretty easy to justify as the hardware and software from vanguard tech companies is outrageously expensive. I used to develop software for a blue colored distributed filesystem.
https://cloud.google.com/blog/products/storage-data-transfer...
TernFS is Free Software. The default license for TernFS is GPL-2.0-or-later.
The protocol definitions (go/msgs/), protocol generator (go/bincodegen/) and client library (go/client/, go/core/) are licensed under Apache-2.0 with the LLVM-exception. This license combination is both permissive (similar to MIT or BSD licenses) as well as compatible with all GPL licenses. We have done this to allow people to build their own proprietary client libraries while ensuring we can also freely incorporate them into the GPL v2 licensed Linux kernel.
So much resources for producing nothing of real value. What a waste.
Great project though, appreciate open sourcing it.
At the extreme if nobody was providing this service, investors (e.g. pension funds), wouldn't be confident that they can buy/sell their assets as needed in size and at the right price... and because of that, in aggregate stocks would be worth less, and companies wouldn't be able to raise as much capital.
The theoretical model is: - You want to have efficient primary markets that allow companies to raise a lot of assets at the best possible prices - To enable efficient primary markets, investors want efficient secondary markets (so they don't need to buy and hold forever, but feel they can sell) - To enable efficient secondary markets, you need many folks that are in the business of XTX ... it just so happens that XTX is quite good at it, and so they do a lot of this work.
> At the extreme
> The theoretical model
These qualifiers would seem to belie the whole argument. Surely the volume of HFT arbitrage is some large multiple of what would be necessary to provide commercial liquidity with an acceptable spread?
No need for an underpinning, it is the underpinning.
The metadata would be crucial for performance, and given that I assume you'll want a full chain of history for every file, your metadata table will get progressively bigger every time you do any kind of metadata operation.
Plus you can only have one person write metadata at one time, so you're gonna get huge top of line blocking.
Look I like technology as much as anyone. Improbable spent $500 million on product development, and its most popular product is its grpc-web client. It didn't release any of its exotic technology. You could also go and spend that money on making $500m of games without any exotic technology, and also make it open source.
If you had 1 KiB average file size then you have quadrillions of metadata objects to quickly search and manage with fine-granularity. The kinds of operations and coordination you need to do with metadata is difficult to achieve reliably when the metadata structure itself is many PB in size. There are interesting edge cases that show up when you have to do deep paging of this metadata off of storage. Making this not slow requires unorthodox and unusual design choices that introduce a lot of complexity. Almost none of the metadata fits in memory, including many parts of conventional architectures we assume will always fit in memory.
A mere trillion objects is right around the limit of where the allocators, metadata, etc can be made to scale with heroic efforts before conventional architectures break down and things start to become deeply weird on the software design side. Storage engines need to be reliable, so avoiding that design frontier makes a lot of sense if you can avoid it.
It is possible to break this barrier but it introduces myriad interesting design and computer science problems for which there is little literature.
What are the biggest challenges in scaling metadata from a trillion to a quadrillion objects?
I initially developed it for a usecase where I needed to store billions of tiny files, and it just requires a single s3 bucket as infrastructure.
What is the motivation to use s3 as primary storage?
Main factors:
* Ceph stores both metadata and file contents using the same object store (RADOS). TernFS uses a specialized database for metadata which takes advantage of various properties of our datasets (immutable files, few moves between directories, etc.).
* While Ceph is capable of storing PBs, we currently store ~600PBs on a single TernFS deployment. Last time we checked this would be an order of magnitude more than even very large Ceph deployments.
* More generally, we wanted a system that we knew we could easily adapt to our needs and more importantly quickly fix when something went wrong, and we estimated that building out something new rather than adapting Ceph (or some other open source solution) would be less costly overall.
Their docs mention they have a custom kernel module, which I suppose is (today) shipped out of tree. Ceph is in-tree and also has a FUSE implementation.
The docs mention that TernFS also has its own S3 gateway, while RADOSGW is fully separate from CephFS.
You can't mount and access RGW S3 objects as cephfs or anything, they are completely separate (not counting things like goofys, s3fs etc.), even if both are on the same rados cluster.
Not sure if TernFS differs there, would be kind of nice to have the option of both kinds of access to the same data.
if you want performance, then you'll probably want lustre, or GPFS, or if you're rich a massive isilon system.
CAP cannot be worked around. In the event of a partition, your system is either C or A, no buts. Either the losing side of the partition refuses to process writes and usually reads as well (ensuring consistency and ensuring unavailability) or it does not refuse (ensuring availability and ensuring data corruption). There are no third options.
Well, some people say the third option is to just make sure the network is 100% reliable and a partition never occurs. That's laughable.
Or has mechanisms for tuning on a request basis what you want to prioritize: consistency or availability, and those depend on specific mechanisms for reads and writes.
If I don't see a distributed system that explains such things, then I'm assuming that they made a lot of bad assumptions.
Thanks, I haven't looked at these problems in a while.
> In the event of a partition, your system is either C or A, no buts.
Fair enough. Raft and Paxos provide well-understood tradeoffs but not a workaround.
https://www.nasdaqtrader.com/snippets/inet2.html
Message Volume 1,684,103,265
Messages per Second 1,134,640
Order Volume 871,875,595
Orders per Second 581,696
Share Volume 12,814,454,760
Executions per Second 193,350
I worked in this sector and volume of data is a real challenge, no wonder you often get custom software to handle that :)
Edit: this is more like what I was hoping for: https://aws.amazon.com/blogs/aws/amazon-fsx-for-lustre-unloc... although I wasn't looking for a file system product. Ideally a tutorial like... "Create a couple VMs, store a file on one, do XYZ, and then read it from another with this API" was what I was hoping for, or at least some first party documentation of how to use these things together.
Maybe there's something I'm missing though, and it'll just work if I give it try :)
An obvious one is bulk deletion, which is rarely fast at any scale. This may involve trillions of updates to search indexing structures, which in naive implementations could look like pointer-chasing across disk. Releasing storage to allocators has no locality because you are streaming the allocations to release off that storage in semi-random order. It is unhelpfully resistant to most scheduling-based locality optimization techniques. You also want to parallelize this as much as possible and some of these allocators will be global-ish.
The most interesting challenge to me is meta-scheduling. Cache replacement algorithms usually don't provide I/O locality at this scale so standard mitigations for cache-resistant workloads like dynamic schedule rewriting and latency-hiding are used instead. Schedulers are in the center of the hot path so you really want these to be memory-resident and fast. Their state size is loosely correlated with the number of objects, so in some extreme cases these can easily exceed available memory on large servers. You can address this by designing a "meta-scheduler" that adaptively optimizes the scheduling of scheduler state, so that the right bits are memory-resident at the right time so that the scheduler can optimally schedule its workload. It is difficult to overstate how much of a breaking change to conventional architecture this turns out to be. These add some value even if the state is memory resident but they greatly increase design complexity and make tail latencies more difficult to manage.
A more basic challenge is that you start dealing with numbers that may not be representable in 64-bits. Similarly, many popular probabilistic algorithms may not offer what you need when the number of entities is this large.
I aggressively skirted these issues for a long time before relenting. I deal more with database storage engines than filesystems, but to a first approximation "files" and "shards" are equivalent for these purposes.
Trying to have a fast globally consistent database for quadrillion items in the _same_ name space is super hard. You need to chose a tradeoff between speed, partition resistance and consistency.
You're much better off sharding into discreet logical units. Its very rare that you need a global namespace for a filesystem. For VFX where we used lustre a lot, the large namespace was a nice to have, it was more about getting a raid-0 across file servers (well object stores) to get performance.
For filesystems specifically, if you're using folders, then you don't actually need to guarantee much outside of a folder. So long as filenames are unique to that folder, you can get away with a lot of shit you can't do in a normal database. you also don't need directories to be on the same filesystem (well in linux at least) so you can also shard by using directories as a key.
The directory-key-filesystem approach is actually hilariously simple, fast scalable and reliable. If a single server/Fs goes down it only takes out that area. On the downside it does mean that you can overwhelm/get hot spots.
In 2008 when I was a youngen, 100tb filesystem that could sustain 1-3gigabytes of streaming throughput took something like 40 racks. Huge amounts of cost and power were needed to set it up and maintain it. Any kind of random IO would kneecap the performance for everyone
Now you can have a 2u server with 100tb of NVME storage and the only bottleneck is the network adaptor! not only that but its pretty cheap too.
> "But Lustre doesn't support NVMeoF directly. It uses a custom protocol."
Could you link me to this? I searched the lustre repo for nvme and didn't see anything that looked promising, but would be curious to read how this works.
> "And neither does EFA. Someone would have to modify the NVMeoF RDMA target/host drivers to support it."
To confirm, you're saying there'd need to be something like an EFA equivalent to https://kernel.googlesource.com/pub/scm/linux/kernel/git/tor... (and corresponding initiator code)?
> "EFA already supports in-kernel IB clients (that's how Lustre uses EFA today). So it's not an impossible task. It's just that no one has done it."
I think you're saying there's already in-kernel code for interfacing with EFA, because this is how lnet uses EFA? Is that https://kernel.googlesource.com/pub/scm/linux/kernel/git/tyc...? I found this but I wasn't sure if this was actually the dataplane (for lack of a better word) part of things, from what I read it sounded like most of the dataplane was implemented in userspace as a part of libfabric, but it sounds like I might be wrong.
Does this mean you can generally just pretend that EFA is a normal IB interface and have things work out? If that's the case, why doesn't NVME-of just support it naturally? Just trying to figure out how these things fit together, I appreciate your time!
In case you're curious, I have a stateful service that has an NVME backed cache over object storage and I've been wondering what it would take to make it so that we could run some proxy services that can directly read from that cache to scale out the read throughput from an instance.
Nothing, beside browsing hackernews a bit too much.
> "But Lustre doesn't support NVMeoF directly. It uses a custom protocol."
To be specific, Lustre is a parallel filesystem. Think of it like a bigger version of NFS. You format the NVMe as ext4 or ZFS and mount them as Lustre servers. Once you have an MGS, MDS, and OSS - you can mount the servers as a filesystem. Lustre won't export the NVMe to client as a block device. But you could mount individual Lustre files as a block device, if you want.
> To confirm, you're saying there'd need to be something like an EFA equivalent to https://kernel.googlesource.com/pub/scm/linux/kernel/git/tor... (and corresponding initiator code)?
Essentially, yeah.
> I think you're saying there's already in-kernel code for interfacing with EFA, because this is how lnet uses EFA?
Yes. EFA implements kernel verbs support. Normal user-space applications use user verbs i.e. https://www.kernel.org/doc/html/latest/infiniband/user_verbs.... Kernel verbs support allows kernel-space applications to also use EFA. This is currently implemented in the out-of-tree version of the EFA driver https://github.com/amzn/amzn-drivers/tree/master/kernel/linu.... Lustre interfaces with that with the driver in lnet/klnds/efalnd/. NVMeoF would need some similar glue code.
> Does this mean you can generally just pretend that EFA is a normal IB interface and have things work out? If that's the case, why doesn't NVME-of just support it naturally? Just trying to figure out how these things fit together, I appreciate your time!
There are some similarities (the EFA driver is implemented in the IB subsystem, after all). But the semantics for adding/removing ports/interfaces would be different - so it wouldn't "just work" without some changes. I don't know the full scope of the changes (I haven't dived into it too much). Although, I suspect support would look fairly similar to drivers/nvme/target/rdma.c.
> In case you're curious, I have a stateful service that has an NVME backed cache over object storage and I've been wondering what it would take to make it so that we could run some proxy services that can directly read from that cache to scale out the read throughput from an instance.
If you're looking for a scale out cache in front of s3, that's essentially Lustre/s3 integration https://docs.aws.amazon.com/fsx/latest/LustreGuide/create-dr.... It's a filesystem, so I guess it depends on how your service expects access objects.
Also note that when I say "single deployment" I mean that the full storage capacity is not subdivided in any way (i.e. there are no "zones" or "realms" or similar concepts). We wanted this to be the case after experiencing situations where we had significant overhead due to having to rebalance different storage buckets (albeit with a different piece of software, not Ceph).
If there are EB-scale Ceph deployments I'd love to hear more about them.
It would be really interesting to see larger clusters join in on their telemetry as well.
[0] https://telemetry-public.ceph.com/d/ZFYuv1qWz/telemetry?orgI...
Even smaller company's (< 500 employees) in today's big data collection age often have more than 1 PB of total data in their enterprise pool. Hosters like Digital Ocean hosts thousands of these companies.
I do think that Ceph will hit performance issues at that size and going into the EB range will likely require code changes.
My best guess would be that Hetzner, Digital Ocean and similar, maintain their own internal fork of Ceph and have customizations that tightly addresses their particular needs.
[1]: https://www.digitalocean.com/blog/why-we-chose-ceph-to-build...