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.)
Suggest watching the Tigerbeatle video link in the article. There they discuss bitrot, "fsync gate", how Postgres used fsync wrong for 30 years, etc. It is very interesting even as pure entertainment.
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.
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.
Postgres claims to have some kind of commit batching, but I couldn't figure out how to turn it on.
I wanted to scrub a table by processing each row, but without holding locks, so I wanted to commit every few hundred rows, but with only ACI and not D, since I could just run the process again. I don't think Postgres supports this feature. It also seemed to be calling fsync much more than once per transaction.
The Linux RWF_DSYNC flag sets the Full Unit Access (FUA) bit in write requests. This can be used instead of fdatasync(2) in some cases. It only syncs a specific write request instead of the entire disk write cache.
> There's no getting around fsync if you want to be sure that your data is really on the storage medium.
That's not correct; io_uring supports O_DIRECT write requests just fine. Obviously bypassing the cache isn't the same as just flushing it (which is what fsync does), so there are design impacts.
But database engines are absolutely the target of io_uring's feature set and they're expected to be managing this complexity.
> But database engines are absolutely the target of io_uring's feature set and they're expected to be managing this complexity.
io_uring includes an fsync opcode (with range support). When folks talk about fsync generally here, they're not saying the io_uring is unusable, they're saying that they'd expect the fsync to be used whether it's via the io_uring opcode, the system call, or some other mechanism yet to be created.
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].
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.
“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?
> 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?
Yup. OP says "the intent record could just be sitting in a kernel buffer", but then the exact same issue applies to the completion record. So confirmation to the client cannot be issued until the completion record has been written to durable storage. Not really seeing the point of this blogpost.
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.
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.
There's not even a guarantee that the intent log flushes to disk before the completion log. You can get completions entries in the completion log that were lost in the intent log. So, no, there's no guarantee of consistent recovery.
It's interesting as a weaker safety guarantee. He is guaranteeing write integrity, so valid WAL view on restart by throwing out mismatching writes. But from an outside observation, premature signaling of completion, which would mean data loss as a client may have moved on without retries due to thinking the data was safely saved. (I was a bit confused in the completion meaning around this, so not confident.)
We hit some similar scenarios in Graphistry where we treat recieving server disk/RAM during browser uploads as writethrough caches in front of our cloud storage persistence tiers. The choice of when to signal success to the uploader is funny -- disk/RAM vs cloud storage -- and timing difference is fairly observable to the web user.
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.
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.
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.
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.
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.
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?
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
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.
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.
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.
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.
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.
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.
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).
> 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.
What mechanism can be used to check that the writes are complete if not fsync (or adjacent fdatasync)? What specific io_uring operation or system call?
> 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.
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.
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.
there's a whole class of persistent persistent (the repetition is intentional here) data structures. Some of them even combine performance with elegance.
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.
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.
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.)
Suggest watching the Tigerbeatle video link in the article. There they discuss bitrot, "fsync gate", how Postgres used fsync wrong for 30 years, etc. It is very interesting even as pure entertainment.
Thanks! Great to hear you enjoyed our talk. Most of it is simply putting the spotlight on UW-Madison’s work on storage faults.
Just to emphasize again that this blog post here is really quite different, since it does not fsync and breaks durability.
Not what we do in TigerBeetle or would recommend or encourage.
See also: https://news.ycombinator.com/item?id=44624065
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.
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.
> As I said, I don't know anything about fsync in io_uring. Maybe that has now control?
io_uring fsync has byte range support: https://man7.org/linux/man-pages/man2/io_uring_enter.2.html#...
Postgres claims to have some kind of commit batching, but I couldn't figure out how to turn it on.
I wanted to scrub a table by processing each row, but without holding locks, so I wanted to commit every few hundred rows, but with only ACI and not D, since I could just run the process again. I don't think Postgres supports this feature. It also seemed to be calling fsync much more than once per transaction.
commit_delay
https://www.postgresql.org/docs/current/runtime-config-wal.h...
Maybe I don’t understand what you’re trying to do, but you can directly control how frequently commits occur.
Chance of Postgres commit mapping 1:1 onto posix fsync or equivalent: slim.
The Linux RWF_DSYNC flag sets the Full Unit Access (FUA) bit in write requests. This can be used instead of fdatasync(2) in some cases. It only syncs a specific write request instead of the entire disk write cache.
You should prefer RWF_SYNC in case the write involves changes to the file metadata (For example, most append operations will alter the file size).
> There's no getting around fsync if you want to be sure that your data is really on the storage medium.
That's not correct; io_uring supports O_DIRECT write requests just fine. Obviously bypassing the cache isn't the same as just flushing it (which is what fsync does), so there are design impacts.
But database engines are absolutely the target of io_uring's feature set and they're expected to be managing this complexity.
> But database engines are absolutely the target of io_uring's feature set and they're expected to be managing this complexity.
io_uring includes an fsync opcode (with range support). When folks talk about fsync generally here, they're not saying the io_uring is unusable, they're saying that they'd expect the fsync to be used whether it's via the io_uring opcode, the system call, or some other mechanism yet to be created.
That's not what O_DIRECT is for. Did you mean O_SYNC ?
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
[1] https://www.usenix.org/system/files/conference/fast18/fast18...
[2] https://github.com/tigerbeetle/tigerbeetle/blob/main/src/vsr...
[3] https://jepsen.io/analyses/tigerbeetle-0.16.11.pdf
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.
“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?
> 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?
Yup. OP says "the intent record could just be sitting in a kernel buffer", but then the exact same issue applies to the completion record. So confirmation to the client cannot be issued until the completion record has been written to durable storage. Not really seeing the point of this blogpost.
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.
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?
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.
There's not even a guarantee that the intent log flushes to disk before the completion log. You can get completions entries in the completion log that were lost in the intent log. So, no, there's no guarantee of consistent recovery.
You'd be better off with a single log.
I think he says he checks for both
It's interesting as a weaker safety guarantee. He is guaranteeing write integrity, so valid WAL view on restart by throwing out mismatching writes. But from an outside observation, premature signaling of completion, which would mean data loss as a client may have moved on without retries due to thinking the data was safely saved. (I was a bit confused in the completion meaning around this, so not confident.)
We hit some similar scenarios in Graphistry where we treat recieving server disk/RAM during browser uploads as writethrough caches in front of our cloud storage persistence tiers. The choice of when to signal success to the uploader is funny -- disk/RAM vs cloud storage -- and timing difference is fairly observable to the web user.
There's no guarantee of ordering of writes within the two logs either.
This seems nightmarish to recover from.
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.
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.
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.
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
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.
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?
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:
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.
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.
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.
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.
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.
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.
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.
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.
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).
> 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.
What mechanism can be used to check that the writes are complete if not fsync (or adjacent fdatasync)? What specific io_uring operation or system call?
> 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.
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.
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.
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-resilien...
- https://research.cs.wisc.edu/adsl/Publications/alice-osdi14....
it's possible to unify the WAL and the tree. There are some append only B-tree implementations. https://github.com/Incubaid/baardskeerder fe.
There are also CoW B Trees not entirely similar, but kinda same.
there's a whole class of persistent persistent (the repetition is intentional here) data structures. Some of them even combine performance with elegance.
I've watched the Tigerbeatle talk (youtube link in the article). This is very interesting even for those not in the space.
I feel like writing asynchronously to a WAL defeats its purpose.
Slightly off topic but anyone knows when/if Google is going to enable io_uring for Android?
Hopefully never. It almost seems to have been purpose-built for local privilege escalation exploits.
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.
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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.