How memory maps (mmap) deliver faster file access in Go

It looks suspicious at 25x. Even 2.5x would be suspicious unless reading very small records.

I assume both cases have the file cached in RAM already fully, with a tiny size of 100MB. But the file read based version actually copies the data into a given buffer, which involves cache misses to get data from RAM to L1 for copying. The mmap version just returns the slice and it's discarded immediately, the actual data is not touched at all. Each record is 2 cache lines and with random indices is not prefetched. For the CPU AMD Ryzen 7 9800X3D mentioned in the repo, just reading 100 bytes from RAM to L1 should take ~100 nanos.

The benchmark compares actually getting data vs getting data location. Single digit nanos is the scale of good hash tables lookups with data in CPU caches, not actual IO. For fairness, both should use/touch the data, eg copy it.

the downside is that the go runtime doesn't expect memory reads to page fault, so you may end up with stalls/latency/under-utilization if part of your dataset is paged out (like if you have a large cdb file w/ random access patterns). Using file IO, the Go runtime could be running a different goroutine if there is a disk read, but with mmap that thread is descheduled but holding an m & p. I'm also not sure if there would be increased stop the world pauses, or if the async preemption stuff would "just work".

Section 3.2 of this paper has more details: https://db.cs.cmu.edu/papers/2022/cidr2022-p13-crotty.pdf

When I adopted mmap in klevdb [1], I saw a dramatic performance improvements. So, even as klevdb completes a write segment, it will reopen, on demand, the segment for reading with mmap (segments are basically part of write only log). With this any random reads are super fast (but of course not as fast as sequential ones).

[1] https://github.com/klev-dev/klevdb

The MmapReader is not copying the requested byte range into the buf argument, so if ever the underlying file descriptor is closed (or the file truncated out of band) any subsequent slice access will throw SIGBUS, which is really unpleasant.

It also means the latency due to pagefaults is shifted from inside mmapReader.ReadRecord() (where it would be expected) to wherever in the application the bytes are first accessed, leading to spooky unpreditactable latency spikes in what are otherwise pure functions. That inevitably leads to wild arguments about how bad GC stalls are :-)

An apples to apples comparison should be copying the bytes from the mmap buffer and returning the resulting slice.

mmap is a good crutch when you 1. don't have busy polling / async IO API available and want to do some quick & dirty preloading tricks; 2. don't want to manage the complexity of in-memory cache, especially cross-processes ones.

Obviously if you have kernel-backed async IO APIs (io_uring) and willing to dig into the deeper end (for better managed cache), you can get better performance than mmap. But in many cases, mmap is "good-enough".

This is a good article but I'm wondering what is the relationship between this website/company and varnish-cache.org, since in the article they make claims of releasing Varnish Cache, and the article wasn't written by Poul-Henning Kamp.

Just this month, I've learned the hard way that some file systems do not play well with mmap: https://github.com/mattn/go-sqlite3/issues/1355

In my case, it seems that Mac's ExFAT driver is incompatible with sqlite's WAL mode because the driver returned a memory address that is misaligned on ARM64. Most bizarre error I've encountered in years.

So, uh, mind your file systems, kids!

Is mmap still faster than fread? That might have been true in the 90s but I was wondering about current improvements.

If you have enough free memory, the file will be cached in memory anyway instead of residing on disk. Therefore both will be reading from memory, albeit through different API.

Looking for recent benchmark or view from OS developers.

The simple answer to "How do memory maps (mmap) deliver faster file access?" is "sometimes", but the blog post does give some more details.

I was suspicious of the 25× speedup claim, but it's a lot more plausible than I thought.

On this Ryzen 5 3500U running mostly at 3.667GHz (poorly controlled), reading data from an already-memory-mapped page is as fast as memcpy (about 10 gigabytes per second when not cached on one core of my laptop, which works out to 0.1 nanoseconds per byte, plus about 20 nanoseconds of overhead) while lseek+read is two system calls (590ns each) plus copying bytes into userspace (26–30ps per byte for small calls, 120ps per byte for a few megabytes). Small memcpy (from, as it happens, an mmapped page) also costs about 25ps per byte, plus about 2800ps per loop iteration, probably much of which is incrementing the loop counter and passing arguments to the memcpy function (GCC is emitting an actual call to memcpy, via the PLT).

So mmap will always be faster than lseek+read on this machine, at least if it doesn't have a page fault, but the point at which memcpy from mmap would be 25× faster than lseek+read would be where 2×590 + .028n = 25×(2.8 + .025n) = 70 + .625n. Which is to say 1110 = .597n ∴ n = 1110/.597 = 1859 bytes. At that point, memcpy from mmap should be 49ns and lseek+read should be 1232ns, which is 25× as big. You can cut that size more than in half if you use pread() instead of lseek+read, and presumably io_uring would cut it even more. If we assume that we're also taking cache misses to bring in the data from main memory in both cases, we have 2×590 + .1n = 25×(2.8 + .1n) = 70 + 2.5n, so 1110 = 2.4n ∴ n = 1110/2.4 = 462 bytes.

On the other hand, mmap will be slow if it's hitting a page fault, which sort of corresponds to the case where you could have cached the result of lseek+read in private RAM, which you could do on a smaller-than-pagesize granularity, which potentially means you could hit the slow path much less often for a given working set. And lseek+read has several possible ways to do make the I/O asynchronous, while the only way to make mmap page faults asynchronous is to hit the page faults in different threads, which is a pretty heavyweight mechanism.

On the other hand, lseek+read with a software cache is sort of using twice as much memory (one copy is in the kernel's buffer cache and another copy is in the application's software cache) so mmap could still win. And, if there are other processes writing to the data being queried, you need some way to invalidate the software cache, which can be expensive.

(On the gripping hand, if you're reading from shared memory while other processes are updating it, you're probably going to need some kind of locking or lock-free synchronization with those other processes.)

So I think a reasonably architected lseek+read (or pread) approach to the problem might be a little faster or a little slower than the mmap approach, but the gap definitely won't be 25×. But very simple applications or libraries, or libraries where many processes might be simultaneously accessing the same data, could indeed get 25× or even 256× performance improvements by letting the kernel manage the cache instead of trying to do it themselves.

wowie. mmap also dramatically improved perf for LLaMA: https://justine.lol/mmap/

Sounds interesting. Why wouldn’t the OS itself default to this behavior? Could it fall apart under load, or is it just not important enough to replace the legacy code relying on it?