10 ms·

Async I/O on Linux in databases

205 points by jtregunna 1y ago

jtregunna 1y ago
Post talks about how to use io_uring, in the context of building a "database" (a demonstration key-value cache with a write-ahead log), to maintain durability.
tlb 1y ago
The recovery process is to "only apply operations that have both intent and completion records." But then I don't see the point of logging the intent record separately. If no completion is logged, the intent is ignored. So you could log the two together. Presumably the intent record is large (containing the key-value data) while the completion record is tiny (containing just the index of the intent record). Is the point that the completion record write is guaranteed to be atomic because it fits in a disk sector, while the intent record doesn't?
- deleted 1y ago
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- ta8645 1y ago
  It's really not clear in the article. But I _think_ the gains are to be had because you can do the in-memory updating during the time that the WAL is being written to disk (rather than waiting for it to flush before proceeding). So I'm guessing the protocol as presented, is actually missing a key step: Write intent record (async) Perform operation in memory Write completion record (async) * * Wait for intent and completion to be flushed to disk * * Return success to client
  - gsliepen 1y ago
    But this makes me wonder how it works when there are concurrent requests. What if a second thread requests data that is being written to memory by the first thread? Shouldn't it also wait for both the write intent record and completion record having been flushed to disk? Otherwise you could end up with a query that returns data that after a crash won't exist anymore.
    - Manuel_D 1y ago
      It's not the write ahead log that prevents that scenario, it's transaction isolation. And note that the more permissive isolation levels offered by Postgres, for example, do allow that failure mode to occur.
      - Demiurge 1y ago
        If thats the hypothesis, it would be good to see some numbers or proof of concept. The real world performance impact seems not that obvious to predict here.
  - avinassh 1y ago
    * * Wait for intent and completion to be flushed to disk * * if you wait for both to complete, then how it can be faster than doing a single IO?
- cbzbc 1y ago
  Presumably the intent record is large (containing the key-value data) while the completion record is tiny I don't think this is necessarily the case, because the operations may have completed in a different order to how they are recorded in the intent log.
jmpman 1y ago
“Write intent record (async) Perform operation in memory Write completion record (async) Return success to client During recovery, I only apply operations that have both intent and completion records. This ensures consistency while allowing much higher throughput. “ Does this mean that a client could receive a success for a request, which if the system crashed immediately afterwards, when replayed, wouldn’t necessarily have that request recorded? How does that not violate ACID?
- JasonSage 1y ago
  As best I can tell, the author understands that the async write-ahead fails to be a guarantee where the sync one does… then turns their async write into two async writes… but there’s still no guarantee comparable to the synchronous version. So I fail to see how the two async writes are any guarantee at all. It sounds like they just happen to provide better consistency than the one async write because it forces an arbitrary amount of time to pass.
  - deleted 1y ago
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  - m11a 1y ago
    Yeah, I feel like I’m missing the point of this. The original purpose of the WAL was for recovery, so WAL entries are supposed to be flushed to disk. Seems like OP’s async approach removes that, so there’s no durability guarantee, so why even maintain a WAL to begin with?
    - nephalegm 1y ago
      Reading through the article it’s explained in the recovery process. He reads the intent log entries and the completion entries and only applies them if they both exist. So there is no guarantee that operations are committed by virtue of not being acknowledged to the application (asynchronous) the recovery replay will be consistent. I could see it would be problematic for any data where the order of operations is important, but that’s the trade off for performance. This does seem to be an improvement to ensure asynchronous IO will always result in a consistent recovery.
nromiun 1y ago
Slightly off topic but anyone knows when/if Google is going to enable io_uring for Android?
- jeffbee 1y ago
  Hopefully never. It almost seems to have been purpose-built for local privilege escalation exploits.
ozgrakkurt 1y ago
Great to see someone going into this. I wanted to do a simple LSM tree using io_uring in Zig for some time but couldn't get into it yet. I always use this approach for crash-resistance: - Append to the data (WAL) file normally. - Have a seperate small file that is like a hash + length for WAL state. - First append to WAL file. - Start fsync call on the WAL file, create a new hash/length file with different name and fsync it in parallel. - Rename the length file onto the real one for making sure it is fully atomic. - Update in-memory state to reflect the files and return from the write function call. Curious if anyone knows tradeoffs between this and doing double WAL. Maybe doing fsync on everything is too slow to maintain fast writes? I learned about append/rename approach from this article in case anyone is interested: - https://discuss.hypermode.com/t/making-badger-crash-resilient-with-alice-dgraph-blog/1685 https://discuss.hypermode.com/t/making-badger-crash-resilien... - https://research.cs.wisc.edu/adsl/Publications/alice-osdi14.pdf https://research.cs.wisc.edu/adsl/Publications/alice-osdi14....
- toolslive 1y ago
  it's possible to unify the WAL and the tree. There are some append only B-tree implementations. https://github.com/Incubaid/baardskeerder https://github.com/Incubaid/baardskeerder fe.
  - avinassh 1y ago
    There are also CoW B Trees not entirely similar, but kinda same.
    - toolslive 1y ago
      there's a whole class of persistent persistent (the repetition is intentional here) data structures. Some of them even combine performance with elegance.
tobias3 1y ago
I don't get this. How can two(+) WAL operations be faster than one (double the sync IOPS)? I think this database doesn't have durability at all.
- benjiro 1y ago
  fsync waits for the drive to report back the success write. When you do a ton of small writes, fsync becomes a bottleneck. Its a issue of context switching and pipelining with fsync. When you async write data, you do not need to wait for this confirmation. So by double writing two async requests, you are better using all your system CPU cores as they are not being stalled waiting for that I/O response. Seeing a 10x performance gain is not uncommon using a method like this. Yes, you do need to check if both records are written and then report it back to the client. But that is a non-fsync request and does not tax your system the same as fsync writes. It has literally the same durability as a fsync write. You need to take in account, that most databases are written 30, 40 ... years ago. In the time when HDDs ruled and stuff like NVME drives was a pipedream. But most DBs still work the same, and threat NVME drives like they are HDDs. Doing this above operation on a HDD, will cost you 2x the performance because you barely have like 80 to 120 IOPS/s. But a cheap NVME drive easily does 100.000 like its nothing. If you even monitored a NVME drive with a database write usage, you will noticed that those NVME drives are just underutilized. This is why you see a lot more work in trying new data storage layers being developed for Databases that better utilize NVME capabilities (and trying to bypass old HDD era bottlenecks).
  - zozbot234 1y ago
    > It has literally the same durability as a fsync write I don't think we can ensure this without knowing what fsync() maps to in the NVMe standard, and somehow replicating that. Just reading back is not enough, e.g. the hardware might be reading from a volatile cache that will be lost in a crash.
    - benjiro 1y ago
      Unless your running cheap consumer NVME drives, that is not a issue on Enterprise SSD/NVMEs as they have their own capacitors to ensure data is always written. On cheaper NVME drives, your point is valid. But we also need to add, how much at risk are you. What is the chance of a system doing funky issues, that you just happened to send X amount of confirm requests to clients, with data that never got written. For specific companies, they will not cheap out and spend tons of enterprise level of hardware. But for the rest of us? I mean, have you seen the German Hetzner, where 97% of their hardware is mostly consumer level hardware. Yes, there is a risk, but nobody complains about that risk. And frankly, everything can be a risk if you think about it. I have had EXT3 partition's corrupt on a production DB server. That is why you have replication and backups ;) TiDB, or was it another distributed DB is also not consistency guaranteed, if i remember correctly. They give for performance eventual consistency.
avinassh 1y ago
I don't get this scheme at all. The protocol violates durability, because once the client receives success from server, it should be durable. However, completion record is async, it is possible that it never completes and server crashes. During recovery, since the server applies only the operations which have both records, you will not recover a record which was successful to the client.
- benjiro 1y ago
  I think you missed the part in the middle: ----------------- So the protocol ends up becoming: Write intent record (async) Perform operation in memory Write completion record (async) Return success to client ----------------- In other words, the client only knows its a success when both wal files have been written. The goal is not to provide faster responses to the client, on the first intent record, but to ensure that the system is not stuck with I/O Waiting on fsync requests. When you write a ton of data to database, you often see that its not the core writes but the I/O > fsync that eat a ton of your resources. Cutting back on that mess, results that you can push more performance out of a write heavy server.
  - jcgrillo 1y ago
    There's no fsync in the async version, though, unless I missed it? The problem with the two WAL approach is that now none of the WAL writes are durable--you could encounter a situation where a client reads an entry on the completion WAL which upon recovery does not exist on disk. Before with the single fsynced WAL, writes were durably persisted.
  - loeg 1y ago
    No, we saw this scheme, it just doesn't work. Either of the async writes can fail after ack'ing the logical write to the client as successful (e.g., kernel crash or power failure) and then you have lost data.
    - cyanydeez 1y ago
      You can always have data loss. The intent is that when the client is told the data is saved, it doesnt happen before the garuntee. I dont know if OP achieved this, but the client isnt told "we have your data" until both of the WALs are agreeing. If the system goes down those WALs are used to rebuild data in flight. The speed up allows for decoupling synchronous disk writes that are now parallel. You are not conceptualizing what data loss means in the ACID contract between DB and Client. But you
LAC-Tech 1y ago
Great article, but I have a question: The problem with naive async I/O in a database context at least, is that you lose the durability guarantee that makes databases useful. When a client receives a success response, their expectation is the data will survive a system crash. But with async I/O, by the time you send that response, the data might still be sitting in kernel buffers, not yet written to stable storage. Shouldn't you just tie the successful response to a successful fsync? Async or sync, I'm not sure what's different here.
leentee 1y ago
First, I think the article provides false claim, the solution doesn't guarantee durability. Second, I believe good synchronous code is better than bad asynchronous code, and it's way easier to write good synchronous code than asynchronous code, especially with io_uring. Modern NVMe are fast, even with synchronous IO, enough for most applications. Before thinking about asynchronous, make sure your application use synchronous IO well.
- benjiro 1y ago
  Speaking from experience, its easy to make Postgres (for example), just trash your system usage on a lot of individual or batch inserts. The NVME drives are often extreme underutilized, and your bottleneck is the whole fsync layer. Second, the durability is the same as fsync. The client only gets reported a success, if both wall writes have been done. Its the same guarantee as fsync but you bypass the fsync bottleneck, what in turn allows for actually using the benefits of your NVME drives better (and shifting away the resource from the i/o blocking fsync). Yes, it involves more management because now you need to maintain two states, instead of one with the synchronous fsync operation. But that is the thing about parallel programming, its more complex but you get a ton of benefits from it by bypassing synchronous bottlenecks.
jorangreef 1y ago
To be clear, this is different to what we do (and why we do it) in TigerBeetle. For example, we never externalize commits without full fsync, to preserve durability [0]. Further, the motivation for why TigerBeetle has both a prepare WAL plus a header WAL is different, not performance (we get performance elsewhere, through batching) but correctness, cf. “Protocol-Aware Recovery for Consensus-Based Storage” [1]. Finally, TigerBeetle's recovery is more intricate, we do all this to survive TigerBeetle's storage fault model. You can read the actual code here [2] and Kyle Kingsbury's Jepsen report on TigerBeetle also provides an excellent overview [3]. [0] https://www.youtube.com/watch?v=tRgvaqpQPwE https://www.youtube.com/watch?v=tRgvaqpQPwE [1] https://www.usenix.org/system/files/conference/fast18/fast18-alagappan.pdf https://www.usenix.org/system/files/conference/fast18/fast18... [2] https://github.com/tigerbeetle/tigerbeetle/blob/main/src/vsr/journal.zig#L1185 https://github.com/tigerbeetle/tigerbeetle/blob/main/src/vsr... [3] https://jepsen.io/analyses/tigerbeetle-0.16.11.pdf https://jepsen.io/analyses/tigerbeetle-0.16.11.pdf
revolyssup 1y ago
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quietbritishjim 1y ago
The article claims that, when they switched to io_uring, > throughput increased by an order of magnitude almost immediately But right near the start is the real story: the sync version had > the classic fsync() call after every write to the log for durability They are not comparing performance of sync APIs vs io_uring. They're comparing using fsync vs not using fsync! They even go on to say that a problem with async API is that > you lose the durability guarantee that makes databases useful. ... the data might still be sitting in kernel buffers, not yet written to stable storage. No! That's because you stopped using fsync. It's nothing to do with your code being async. If you just removed the fsync from the sync code you'd quite possibly get a speedup of an order of magnitude too. Or if you put the fsync back in the async version (I don't know io_uring well enough to understand that but it appears to be possible with "io_uring_prep_fsync") then that would surely slide back. Would the io_uring version still be faster either way? Quite possibly, but because they made an apples-to-oranges comparison, we can't know from this article. (As other commenters have pointed out, their two-phase commit strategy also fails to provide any guarantee. There's no getting around fsync if you want to be sure that your data is really on the storage medium.)
- zozbot234 1y ago
  So OP's real point is that fsync() sucks in the context of modern hardware where thousands of I/O reqs may be in flight at any given time. We need more fine-grained mechanisms to ensure that writes are committed to permanent storage, without introducing undue serialization.
  - deleted 1y ago
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  - quietbritishjim 1y ago
    Well, there already is slightly more fine gained control: in the sync version, you can perhaps call sync write() a few times before calling fsync() once i.e. basically batch up a few writes. That does have the disadvantage that you can't easily queue new writes while waiting for the previous ones. Perhaps you could use calls to write() in another thread while the first one is waiting for fsync() for the previous batch? You could even have lots of threads doing that in parallel, but probably not the thousands that you mentioned. I don't know the nitty gritty of Linux file IO well enough to know how well that would work. As I said, I don't know anything about fsync in io_uring. Maybe that has more control? An article that did a fair comparison, by someone who actually knows what they're talking about, would be pretty interesting.
osigurdson 1y ago
I've watched the Tigerbeatle talk (youtube link in the article). This is very interesting even for those not in the space.
demaga 1y ago
I feel like writing asynchronously to a WAL defeats its purpose.
jasonthorsness 1y ago
Is the underlying NVME storage interface the kernel/drivers get to use cleaner/simpler than the Linux abstractions? Or does it get more complicated? Sometimes I wonder if certain high-performance applications would be better off running as special-purpose unikernels unburdened by interfaces designed for older generations of technology.
- loeg 1y ago
  Also an option with io_uring: https://www.usenix.org/conference/fast24/presentation/joshi https://www.usenix.org/conference/fast24/presentation/joshi (We use it at work it in a network object storage service in order to use the underlying NVMe T10-DIF[1], which isn't exposed nicely by conventional POSIX/Linux interfaces.) Ultimately, having a full, ~normal Linux stack around makes system management / orchestration easier. And programs other than our specialized storage software can still access other partitions, etc. [1]: https://en.wikipedia.org/wiki/Data_Integrity_Field https://en.wikipedia.org/wiki/Data_Integrity_Field
eatonphil 1y ago
From the title I was hoping this would be a survey of databases using io_uring, since there've been quips on the internet (here, twitter, etc) that no one uses io_uring in production. In my brief search TigerBeetle (and maybe Turso's Limbo) was the only database in production that I remember doing io_uring (by default). Some other databases had it as an option but didn't seem to default to it. If anyone else feels like doing this survey and publishing the results I'd love to see it.
jtregunna 1y ago
Update: I updated the post based on the conversation below, I wholly missed an important callout about performance, and wasn't super clear that you do need to wait for the completion record to be written before responding to the client. That was implicitly mentioned by writing the completion record coming before responding, but I made it clearer to avoid confusion. Also the dual WAL approach is worse for latency, unless you can amortize the double write over multiple async writes, so the cost paid amortizes across the batch, but when batch size is closer to 1, the cost is higher.
- gpderetta 1y ago
  How can you know that the completion record is written to disk?
- codys 1y ago
  From the update added to the post: > This is tracked through io_uring's completion queue - we only send a success response after receiving confirmation that the completion record has been persisted to stable storage. Which completion queue event(s) are you examining here? I ask because the way this is worded makes it sound like you're waiting solely for the completion queue event for the _write_ to the "completion wal". Doing that (waiting only on the "completion wal" write CQE) 1. doesn't ensure that the "intent wal" has been written (because it's a different io_uring and a different submission queue event used to do the "intent wal" write from the "completion wal" write), and 2. doesn't indicate the "intent wal" data or the "completion wal" data has made it to durable storage (one needs fsync for that, the completion queue events for writes don't make that promise. The CQE for an fsync opcode would indicate that data has made it to durable storage if the fsync has the right ordering wrt the writes and refers to the appropriate fd and data ranges. Alternatively, there are some flags that have the effect of implying an fsync following a write that could be used, but those aren't mentioned)
ptrwis 1y ago
For some background, it is now a single guy paid by Microsoft to work on implementing async direct I/O for PostgreSQL (github.com/anarazel)
lstroud 1y ago
About 10ish years ago, I ended up finding a deadlock in the Linux raid driver when turning on Oracle’s async writes with raid10 on lvm on AWS. I traced it to the ring buffers the author mentioned, but ended up having to remove lvm (since it wasn’t that necessary on this infrastructure) to get the throughput I needed.
BeeOnRope 1y ago
What is the point of the intent entry at all? It seems like operations are only durable after the completion record is written so the intent record seems to serve no purpose (unless it is say much larger).
sethev 1y ago
There's some faulty reasoning in this post. Without the code, it's hard to pin down exactly where things went wrong. These are the steps described in the post: 1. Write intent record (async) 2. Perform operation in memory 3. Write completion record (async) 4. Wait for the completion record to be written to the WAL 5. Return success to client If 4 is done correctly then 3 is not needed - it can just wait for the intent to be durable before replying to the client. Perhaps there's a small benefit to speculatively executing the operation before the WAL is committed - but I'm skeptical and my guess is that 4 is not being done correctly. The author added an update to the article: > This is tracked through io_uring's completion queue - we only send a success response after receiving confirmation that the completion record has been persisted to stable storage This makes it sound like he's submitting write operations for the completion record and then misinterpreting the completion queue for those writes as "the record is now in durable storage".
jeffbee 1y ago
What's baffling to me about this post is that anyone would believe that io_uring was even capable of speeding up this workload by 10x. Unless your profile suggests that syscall entry is taking > 90% of your CPU time, that is impossible. The only thing io_uring can do for you is reduce your syscall count, so the upper bound of its utility is whatever you are currently spending on sysenter/exit.
- loeg 1y ago
  You could also imagine it hiding write latency by allowing a very naive single-threaded application to do IOs concurrently, overlapped in time, instead of serialized. (But a threadpool would do much the same thing.)
- gpderetta 1y ago
  Io_uring could allow for better throughout by simply having multiple operations in flight and allow for better I/O scheduling. But yes, this specific case seems to be a misunderstanding in what io_uring write completion means. You would expect that they would have tested recovery by at least simulating system stops immediately after after Io completion notification. Unless they are truly using asynchronous O_SYNC writes and are just bad at explaining it.
misiek08 1y ago
1. Write intent 2. Don’t use intent write as success 3. Report success on different operation completion. While restoring: 1. Ignore all intents 2. Use only different operations with corresponding intents. I think this article introduces so much chaos that it’s like many „almost” helpful info on io_uring and finally hurts the tech. io_uring IMHO lacks clean and simple examples and here we again have some bad-explained theories instead of meat.
hardwaresofton 1y ago
Great article -- it's really subtle to see where the real perf gains came from but will try and summarize it for those who may come after: The gains are from batching and doing work in-between. io_uring does "batching at a distance", and the DB can write to memory and perform operations in between. When io_uring checks the queues (intent/operation), it will find more than one operation, and do them all at once. You don't lose durability with this setup -- you just do more speculative work (if you got the worst possible crash at the worst possible time), and if a bunch of things completed (because io_uring did them all at once) you get more confirmations you can send back faster. Latency MIGHT suffer, but throughput would (and does) increase.

205 points by jtregunna 1y ago

Post talks about how to use io_uring, in the context of building a "database" (a demonstration key-value cache with a write-ahead log), to maintain durability.

tlb 1y ago

The recovery process is to "only apply operations that have both intent and completion records." But then I don't see the point of logging the intent record separately. If no completion is logged, the intent is ignored. So you could log the two together. Presumably the intent record is large (containing the key-value data) while the completion record is tiny (containing just the index of the intent record). Is the point that the completion record write is guaranteed to be atomic because it fits in a disk sector, while the intent record doesn't?

deleted 1y ago
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ta8645 1y ago
It's really not clear in the article. But I _think_ the gains are to be had because you can do the in-memory updating during the time that the WAL is being written to disk (rather than waiting for it to flush before proceeding). So I'm guessing the protocol as presented, is actually missing a key step: Write intent record (async) Perform operation in memory Write completion record (async) * * Wait for intent and completion to be flushed to disk * * Return success to client
- gsliepen 1y ago
  But this makes me wonder how it works when there are concurrent requests. What if a second thread requests data that is being written to memory by the first thread? Shouldn't it also wait for both the write intent record and completion record having been flushed to disk? Otherwise you could end up with a query that returns data that after a crash won't exist anymore.
  - Manuel_D 1y ago
    It's not the write ahead log that prevents that scenario, it's transaction isolation. And note that the more permissive isolation levels offered by Postgres, for example, do allow that failure mode to occur.
    - Demiurge 1y ago
      If thats the hypothesis, it would be good to see some numbers or proof of concept. The real world performance impact seems not that obvious to predict here.
- avinassh 1y ago
  * * Wait for intent and completion to be flushed to disk * * if you wait for both to complete, then how it can be faster than doing a single IO?
cbzbc 1y ago
Presumably the intent record is large (containing the key-value data) while the completion record is tiny I don't think this is necessarily the case, because the operations may have completed in a different order to how they are recorded in the intent log.

jmpman 1y ago

“Write intent record (async) Perform operation in memory Write completion record (async) Return success to client During recovery, I only apply operations that have both intent and completion records. This ensures consistency while allowing much higher throughput. “ Does this mean that a client could receive a success for a request, which if the system crashed immediately afterwards, when replayed, wouldn’t necessarily have that request recorded? How does that not violate ACID?

JasonSage 1y ago
As best I can tell, the author understands that the async write-ahead fails to be a guarantee where the sync one does… then turns their async write into two async writes… but there’s still no guarantee comparable to the synchronous version. So I fail to see how the two async writes are any guarantee at all. It sounds like they just happen to provide better consistency than the one async write because it forces an arbitrary amount of time to pass.
- deleted 1y ago
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- m11a 1y ago
  Yeah, I feel like I’m missing the point of this. The original purpose of the WAL was for recovery, so WAL entries are supposed to be flushed to disk. Seems like OP’s async approach removes that, so there’s no durability guarantee, so why even maintain a WAL to begin with?
  - nephalegm 1y ago
    Reading through the article it’s explained in the recovery process. He reads the intent log entries and the completion entries and only applies them if they both exist. So there is no guarantee that operations are committed by virtue of not being acknowledged to the application (asynchronous) the recovery replay will be consistent. I could see it would be problematic for any data where the order of operations is important, but that’s the trade off for performance. This does seem to be an improvement to ensure asynchronous IO will always result in a consistent recovery.

nromiun 1y ago

Slightly off topic but anyone knows when/if Google is going to enable io_uring for Android?

jeffbee 1y ago
Hopefully never. It almost seems to have been purpose-built for local privilege escalation exploits.

ozgrakkurt 1y ago

Great to see someone going into this. I wanted to do a simple LSM tree using io_uring in Zig for some time but couldn't get into it yet. I always use this approach for crash-resistance: - Append to the data (WAL) file normally. - Have a seperate small file that is like a hash + length for WAL state. - First append to WAL file. - Start fsync call on the WAL file, create a new hash/length file with different name and fsync it in parallel. - Rename the length file onto the real one for making sure it is fully atomic. - Update in-memory state to reflect the files and return from the write function call. Curious if anyone knows tradeoffs between this and doing double WAL. Maybe doing fsync on everything is too slow to maintain fast writes? I learned about append/rename approach from this article in case anyone is interested: - https://discuss.hypermode.com/t/making-badger-crash-resilient-with-alice-dgraph-blog/1685 https://discuss.hypermode.com/t/making-badger-crash-resilien... - https://research.cs.wisc.edu/adsl/Publications/alice-osdi14.pdf https://research.cs.wisc.edu/adsl/Publications/alice-osdi14....

toolslive 1y ago
it's possible to unify the WAL and the tree. There are some append only B-tree implementations. https://github.com/Incubaid/baardskeerder https://github.com/Incubaid/baardskeerder fe.
- avinassh 1y ago
  There are also CoW B Trees not entirely similar, but kinda same.
  - toolslive 1y ago
    there's a whole class of persistent persistent (the repetition is intentional here) data structures. Some of them even combine performance with elegance.

tobias3 1y ago

I don't get this. How can two(+) WAL operations be faster than one (double the sync IOPS)? I think this database doesn't have durability at all.

benjiro 1y ago
fsync waits for the drive to report back the success write. When you do a ton of small writes, fsync becomes a bottleneck. Its a issue of context switching and pipelining with fsync. When you async write data, you do not need to wait for this confirmation. So by double writing two async requests, you are better using all your system CPU cores as they are not being stalled waiting for that I/O response. Seeing a 10x performance gain is not uncommon using a method like this. Yes, you do need to check if both records are written and then report it back to the client. But that is a non-fsync request and does not tax your system the same as fsync writes. It has literally the same durability as a fsync write. You need to take in account, that most databases are written 30, 40 ... years ago. In the time when HDDs ruled and stuff like NVME drives was a pipedream. But most DBs still work the same, and threat NVME drives like they are HDDs. Doing this above operation on a HDD, will cost you 2x the performance because you barely have like 80 to 120 IOPS/s. But a cheap NVME drive easily does 100.000 like its nothing. If you even monitored a NVME drive with a database write usage, you will noticed that those NVME drives are just underutilized. This is why you see a lot more work in trying new data storage layers being developed for Databases that better utilize NVME capabilities (and trying to bypass old HDD era bottlenecks).
- zozbot234 1y ago
  > It has literally the same durability as a fsync write I don't think we can ensure this without knowing what fsync() maps to in the NVMe standard, and somehow replicating that. Just reading back is not enough, e.g. the hardware might be reading from a volatile cache that will be lost in a crash.
  - benjiro 1y ago
    Unless your running cheap consumer NVME drives, that is not a issue on Enterprise SSD/NVMEs as they have their own capacitors to ensure data is always written. On cheaper NVME drives, your point is valid. But we also need to add, how much at risk are you. What is the chance of a system doing funky issues, that you just happened to send X amount of confirm requests to clients, with data that never got written. For specific companies, they will not cheap out and spend tons of enterprise level of hardware. But for the rest of us? I mean, have you seen the German Hetzner, where 97% of their hardware is mostly consumer level hardware. Yes, there is a risk, but nobody complains about that risk. And frankly, everything can be a risk if you think about it. I have had EXT3 partition's corrupt on a production DB server. That is why you have replication and backups ;) TiDB, or was it another distributed DB is also not consistency guaranteed, if i remember correctly. They give for performance eventual consistency.

avinassh 1y ago

I don't get this scheme at all. The protocol violates durability, because once the client receives success from server, it should be durable. However, completion record is async, it is possible that it never completes and server crashes. During recovery, since the server applies only the operations which have both records, you will not recover a record which was successful to the client.

benjiro 1y ago
I think you missed the part in the middle: ----------------- So the protocol ends up becoming: Write intent record (async) Perform operation in memory Write completion record (async) Return success to client ----------------- In other words, the client only knows its a success when both wal files have been written. The goal is not to provide faster responses to the client, on the first intent record, but to ensure that the system is not stuck with I/O Waiting on fsync requests. When you write a ton of data to database, you often see that its not the core writes but the I/O > fsync that eat a ton of your resources. Cutting back on that mess, results that you can push more performance out of a write heavy server.
- jcgrillo 1y ago
  There's no fsync in the async version, though, unless I missed it? The problem with the two WAL approach is that now none of the WAL writes are durable--you could encounter a situation where a client reads an entry on the completion WAL which upon recovery does not exist on disk. Before with the single fsynced WAL, writes were durably persisted.
- loeg 1y ago
  No, we saw this scheme, it just doesn't work. Either of the async writes can fail after ack'ing the logical write to the client as successful (e.g., kernel crash or power failure) and then you have lost data.
  - cyanydeez 1y ago
    You can always have data loss. The intent is that when the client is told the data is saved, it doesnt happen before the garuntee. I dont know if OP achieved this, but the client isnt told "we have your data" until both of the WALs are agreeing. If the system goes down those WALs are used to rebuild data in flight. The speed up allows for decoupling synchronous disk writes that are now parallel. You are not conceptualizing what data loss means in the ACID contract between DB and Client. But you

LAC-Tech 1y ago

Great article, but I have a question: The problem with naive async I/O in a database context at least, is that you lose the durability guarantee that makes databases useful. When a client receives a success response, their expectation is the data will survive a system crash. But with async I/O, by the time you send that response, the data might still be sitting in kernel buffers, not yet written to stable storage. Shouldn't you just tie the successful response to a successful fsync? Async or sync, I'm not sure what's different here.

leentee 1y ago

First, I think the article provides false claim, the solution doesn't guarantee durability. Second, I believe good synchronous code is better than bad asynchronous code, and it's way easier to write good synchronous code than asynchronous code, especially with io_uring. Modern NVMe are fast, even with synchronous IO, enough for most applications. Before thinking about asynchronous, make sure your application use synchronous IO well.

benjiro 1y ago
Speaking from experience, its easy to make Postgres (for example), just trash your system usage on a lot of individual or batch inserts. The NVME drives are often extreme underutilized, and your bottleneck is the whole fsync layer. Second, the durability is the same as fsync. The client only gets reported a success, if both wall writes have been done. Its the same guarantee as fsync but you bypass the fsync bottleneck, what in turn allows for actually using the benefits of your NVME drives better (and shifting away the resource from the i/o blocking fsync). Yes, it involves more management because now you need to maintain two states, instead of one with the synchronous fsync operation. But that is the thing about parallel programming, its more complex but you get a ton of benefits from it by bypassing synchronous bottlenecks.

jorangreef 1y ago

To be clear, this is different to what we do (and why we do it) in TigerBeetle. For example, we never externalize commits without full fsync, to preserve durability [0]. Further, the motivation for why TigerBeetle has both a prepare WAL plus a header WAL is different, not performance (we get performance elsewhere, through batching) but correctness, cf. “Protocol-Aware Recovery for Consensus-Based Storage” [1]. Finally, TigerBeetle's recovery is more intricate, we do all this to survive TigerBeetle's storage fault model. You can read the actual code here [2] and Kyle Kingsbury's Jepsen report on TigerBeetle also provides an excellent overview [3]. [0] https://www.youtube.com/watch?v=tRgvaqpQPwE https://www.youtube.com/watch?v=tRgvaqpQPwE [1] https://www.usenix.org/system/files/conference/fast18/fast18-alagappan.pdf https://www.usenix.org/system/files/conference/fast18/fast18... [2] https://github.com/tigerbeetle/tigerbeetle/blob/main/src/vsr/journal.zig#L1185 https://github.com/tigerbeetle/tigerbeetle/blob/main/src/vsr... [3] https://jepsen.io/analyses/tigerbeetle-0.16.11.pdf https://jepsen.io/analyses/tigerbeetle-0.16.11.pdf

revolyssup 1y ago

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quietbritishjim 1y ago

The article claims that, when they switched to io_uring, > throughput increased by an order of magnitude almost immediately But right near the start is the real story: the sync version had > the classic fsync() call after every write to the log for durability They are not comparing performance of sync APIs vs io_uring. They're comparing using fsync vs not using fsync! They even go on to say that a problem with async API is that > you lose the durability guarantee that makes databases useful. ... the data might still be sitting in kernel buffers, not yet written to stable storage. No! That's because you stopped using fsync. It's nothing to do with your code being async. If you just removed the fsync from the sync code you'd quite possibly get a speedup of an order of magnitude too. Or if you put the fsync back in the async version (I don't know io_uring well enough to understand that but it appears to be possible with "io_uring_prep_fsync") then that would surely slide back. Would the io_uring version still be faster either way? Quite possibly, but because they made an apples-to-oranges comparison, we can't know from this article. (As other commenters have pointed out, their two-phase commit strategy also fails to provide any guarantee. There's no getting around fsync if you want to be sure that your data is really on the storage medium.)

zozbot234 1y ago
So OP's real point is that fsync() sucks in the context of modern hardware where thousands of I/O reqs may be in flight at any given time. We need more fine-grained mechanisms to ensure that writes are committed to permanent storage, without introducing undue serialization.
- deleted 1y ago
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- quietbritishjim 1y ago
  Well, there already is slightly more fine gained control: in the sync version, you can perhaps call sync write() a few times before calling fsync() once i.e. basically batch up a few writes. That does have the disadvantage that you can't easily queue new writes while waiting for the previous ones. Perhaps you could use calls to write() in another thread while the first one is waiting for fsync() for the previous batch? You could even have lots of threads doing that in parallel, but probably not the thousands that you mentioned. I don't know the nitty gritty of Linux file IO well enough to know how well that would work. As I said, I don't know anything about fsync in io_uring. Maybe that has more control? An article that did a fair comparison, by someone who actually knows what they're talking about, would be pretty interesting.

osigurdson 1y ago

I've watched the Tigerbeatle talk (youtube link in the article). This is very interesting even for those not in the space.

demaga 1y ago

I feel like writing asynchronously to a WAL defeats its purpose.

jasonthorsness 1y ago

Is the underlying NVME storage interface the kernel/drivers get to use cleaner/simpler than the Linux abstractions? Or does it get more complicated? Sometimes I wonder if certain high-performance applications would be better off running as special-purpose unikernels unburdened by interfaces designed for older generations of technology.

loeg 1y ago
Also an option with io_uring: https://www.usenix.org/conference/fast24/presentation/joshi https://www.usenix.org/conference/fast24/presentation/joshi (We use it at work it in a network object storage service in order to use the underlying NVMe T10-DIF[1], which isn't exposed nicely by conventional POSIX/Linux interfaces.) Ultimately, having a full, ~normal Linux stack around makes system management / orchestration easier. And programs other than our specialized storage software can still access other partitions, etc. [1]: https://en.wikipedia.org/wiki/Data_Integrity_Field https://en.wikipedia.org/wiki/Data_Integrity_Field

eatonphil 1y ago

From the title I was hoping this would be a survey of databases using io_uring, since there've been quips on the internet (here, twitter, etc) that no one uses io_uring in production. In my brief search TigerBeetle (and maybe Turso's Limbo) was the only database in production that I remember doing io_uring (by default). Some other databases had it as an option but didn't seem to default to it. If anyone else feels like doing this survey and publishing the results I'd love to see it.

jtregunna 1y ago

Update: I updated the post based on the conversation below, I wholly missed an important callout about performance, and wasn't super clear that you do need to wait for the completion record to be written before responding to the client. That was implicitly mentioned by writing the completion record coming before responding, but I made it clearer to avoid confusion. Also the dual WAL approach is worse for latency, unless you can amortize the double write over multiple async writes, so the cost paid amortizes across the batch, but when batch size is closer to 1, the cost is higher.

gpderetta 1y ago
How can you know that the completion record is written to disk?
codys 1y ago
From the update added to the post: > This is tracked through io_uring's completion queue - we only send a success response after receiving confirmation that the completion record has been persisted to stable storage. Which completion queue event(s) are you examining here? I ask because the way this is worded makes it sound like you're waiting solely for the completion queue event for the _write_ to the "completion wal". Doing that (waiting only on the "completion wal" write CQE) 1. doesn't ensure that the "intent wal" has been written (because it's a different io_uring and a different submission queue event used to do the "intent wal" write from the "completion wal" write), and 2. doesn't indicate the "intent wal" data or the "completion wal" data has made it to durable storage (one needs fsync for that, the completion queue events for writes don't make that promise. The CQE for an fsync opcode would indicate that data has made it to durable storage if the fsync has the right ordering wrt the writes and refers to the appropriate fd and data ranges. Alternatively, there are some flags that have the effect of implying an fsync following a write that could be used, but those aren't mentioned)

ptrwis 1y ago

For some background, it is now a single guy paid by Microsoft to work on implementing async direct I/O for PostgreSQL (github.com/anarazel)

lstroud 1y ago

About 10ish years ago, I ended up finding a deadlock in the Linux raid driver when turning on Oracle’s async writes with raid10 on lvm on AWS. I traced it to the ring buffers the author mentioned, but ended up having to remove lvm (since it wasn’t that necessary on this infrastructure) to get the throughput I needed.

BeeOnRope 1y ago

What is the point of the intent entry at all? It seems like operations are only durable after the completion record is written so the intent record seems to serve no purpose (unless it is say much larger).

sethev 1y ago

There's some faulty reasoning in this post. Without the code, it's hard to pin down exactly where things went wrong. These are the steps described in the post: 1. Write intent record (async) 2. Perform operation in memory 3. Write completion record (async) 4. Wait for the completion record to be written to the WAL 5. Return success to client If 4 is done correctly then 3 is not needed - it can just wait for the intent to be durable before replying to the client. Perhaps there's a small benefit to speculatively executing the operation before the WAL is committed - but I'm skeptical and my guess is that 4 is not being done correctly. The author added an update to the article: > This is tracked through io_uring's completion queue - we only send a success response after receiving confirmation that the completion record has been persisted to stable storage This makes it sound like he's submitting write operations for the completion record and then misinterpreting the completion queue for those writes as "the record is now in durable storage".

jeffbee 1y ago

What's baffling to me about this post is that anyone would believe that io_uring was even capable of speeding up this workload by 10x. Unless your profile suggests that syscall entry is taking > 90% of your CPU time, that is impossible. The only thing io_uring can do for you is reduce your syscall count, so the upper bound of its utility is whatever you are currently spending on sysenter/exit.

loeg 1y ago
You could also imagine it hiding write latency by allowing a very naive single-threaded application to do IOs concurrently, overlapped in time, instead of serialized. (But a threadpool would do much the same thing.)
gpderetta 1y ago
Io_uring could allow for better throughout by simply having multiple operations in flight and allow for better I/O scheduling. But yes, this specific case seems to be a misunderstanding in what io_uring write completion means. You would expect that they would have tested recovery by at least simulating system stops immediately after after Io completion notification. Unless they are truly using asynchronous O_SYNC writes and are just bad at explaining it.

misiek08 1y ago

1. Write intent 2. Don’t use intent write as success 3. Report success on different operation completion. While restoring: 1. Ignore all intents 2. Use only different operations with corresponding intents. I think this article introduces so much chaos that it’s like many „almost” helpful info on io_uring and finally hurts the tech. io_uring IMHO lacks clean and simple examples and here we again have some bad-explained theories instead of meat.

hardwaresofton 1y ago

Great article -- it's really subtle to see where the real perf gains came from but will try and summarize it for those who may come after: The gains are from batching and doing work in-between. io_uring does "batching at a distance", and the DB can write to memory and perform operations in between. When io_uring checks the queues (intent/operation), it will find more than one operation, and do them all at once. You don't lose durability with this setup -- you just do more speculative work (if you got the worst possible crash at the worst possible time), and if a bunch of things completed (because io_uring did them all at once) you get more confirmations you can send back faster. Latency MIGHT suffer, but throughput would (and does) increase.