Even though RavenDB 7.0 isn’t quite out of the gate yet (expect the release very soon), I want to start talking about the RavenDB 7.1 release. This release is going to represent the biggest change in RavenDB in about 7 years, and at the same time, it is expected to be a complete non-event for most people.

We care enough about performance that we have both a dedicated team to verify it as well as really talented people whose sole job is to run benchmarks and optimize RavenDB. This year, the goal was to test RavenDB’s performance when running on large machines (many hundreds of GBs or RAM, dozens of cores, etc). The idea was to find bottlenecks and remove them, and we quickly found a few.

Performance optimization is often a very repetitive process. Find a problem, figure out the root cause, and move on. It is a process full of small changes and 0.1% improvements. The key is that if you practice this routinely, you end up with real measurable results over time, since your changes compound.

Most of the time, these are the changes you celebrate:

Here you can see a 6.6% increase in the number of operations per millisecond. That is a pretty big change, especially since it affects reading documents from storage.

We were roughly two months into this process when we ran into the end of the line. The scenario in question was 95% reads / 5% writes, with the idea of seeing how quickly the system responds under load, but without stressing it to the limits. Take a look at our findings:

What you can see here is that creating a transaction is costly, andover 50% of that cost is due to contention over a lock. We didn’t notice that until we had a lot of cores to go around, since there wasn’t a sufficient number of threads actively contending for this lock. Once we started looking for it, the problem was very apparent and visible on smaller machines as well.

RavenDB creates a single transaction per request, so that means we are spending a lot of time doing essentially nothing useful. What is worse, this is actively spinning. So the more cores we have, the more CPU we’ll use and the less useful work we’ll do. Yuck!

When we looked at the root cause, the problem became pretty apparent. Take a look at the following snippet:

_txCreation.EnterReadLock(); // creating read transaction
try
{
    _cancellationTokenSource.Token.ThrowIfCancellationRequested();


    tx = new LowLevelTransaction(previous.LowLevelTransaction, transactionPersistentContext, context);


    ActiveTransactions.Add(tx);
}
finally
{
    _txCreation.ExitReadLock();
}






using (PreventNewTransactions()) // during commit of write transaction
{
    if (tx.Committed && tx.FlushedToJournal)
        Interlocked.Exchange(ref _transactionsCounter, tx.Id);


    State = tx.State;


    Journal.Applicator.OnTransactionCommitted(tx);
    ScratchBufferPool.UpdateCacheForPagerStatesOfAllScratches();
    Journal.UpdateCacheForJournalSnapshots();


    tx.OnAfterCommitWhenNewTransactionsPrevented();
}

What happens is that during the commit of a write transaction we need to make updates to a bunch of places. We also need all new read transactions to see all our changes at once. So during commit, we take a short lock to update a couple of locations. And when we create a write transaction, we hold a read lock to be able to properly read all those values safely.

This particular code (and the approach in general) dates back to the very early days of Voron:

On one hand, I look at this code and cringe internally. On the other hand, it has served us really well for a long time. Of course, on the gripping hand, that code has been around for long enough to have grown some barnacles.

Notice the OnAfterCommitWhenNewTransactionsPrevented() call we have there? It is meant to allow other operations that need to run while no read transactions are allowed, in order to maintain in-memory consistency.

Fixing this issue was both straightforward and quite complex at the same time. Instead of storing all the data in various places, I introduced the following record:

record EnvironmentStateRecord(
    Pager.State DataPagerState, 
    long TransactionId,
    ImmutableDictionary<long, PageFromScratchBuffer> ScratchPagesTable,
    TreeRootHeader Root,
    long NextPageNumber,
    (long Number, long Last4KWritePosition) Journal,
    object ClientState);

The actual details of what it does aren’t really that interesting. What is important is that we have an immutable record that is being generated by the write transaction. That record contains all the database state that we carry over between transactions. When we want to publish this state, we can just make a single atomic write to a pointer, no need for a lock.

Of course, it can’t be that simple. We needed to essentially convert large portions of the code to take their state from the environment record and provide a way for higher-level code to add their own state to the record on write.

That took about two months of hard work, I think. Not complicated work, just grinding through all of those places, figuring out the best way to represent that, and making all the necessary changes.

Unfortunately, that wasn’t the only lock convoy we found. Files inside RavenDB are held by a class called Pager (since it serves pages of data). A file (Pager) may hold multiple States, which is a fancy way of referring to a memory-mapped range we use. The Pager may hold multiple such mapping concurrently. For example, if we increase the file size, we need to map the memory again - so for a certain period of time, we have two such mappings.

A transaction may create a reference to such a state (and thus may access the mapping) for its lifetime. At the end of the transaction, we can release the state, and if it is the last reference, we’ll close the mapping.

This is implemented using the following code:

This runs for multiple Pagers for each transaction, mind you. The actual AddRef() call is very cheap, but the lock…

public void AddRef() => Interlocked.Increment(ref _refs);

As you can see from the image, this is also pretty old code. The problem was how to deal with the issue. I have to be able to keep track of the mapped memory if a transaction is using it, but at the same time, I want to dispose of it quickly if it isn’t being used.

The answer to this question was to lean on the GC. Instead of implementing AddRef() / Release(), I hold the Pager.State object and handle disposal in the finalizer. If there is a reference to it, the finalizer isn’t called, so this gives me the exact behavior I want, for free.

However… I also need to be able to dispose of this explicitly when closing the pager. A common scenario is that I’m closing the Pager and deleting the directory. If I have an outstanding reference to the file, that will fail.

Here is how I’m dealing with the issue. The Pager holds a Weak-Reference to the State and may explicitly dispose of it:

public void Dispose()
{
    foreach (WeakReference<State> state in _states)
    {
        if (state.TryGetTarget(out var v))
        {
            v.Dispose();
        }
    }
}

That also allowed me to refactor the way we deal with files in general inside Voron. The first version of Voron did everything in managed code (including significant P/Invoke and cross-platform work). At some point, we introduced a native DLL (PAL - Platform Abstraction Layer - written in C) that allowed us to have a more consistent API.

The issue was that there was no clean division of labor between the native C code and the managed code, and some responsibilities were split between them in a way that wasn’t very clean.

RavenDB is using Zig

To be rather more exact, I’m using Zig as a build system to make cross-compilation a lot easier. I initially moved everything to use Zig, but it turns out that Zig isn’t able to produce DLLs that would run properly on older versions of Windows.

At least, nothing that I did convinced Zig to give me something that wouldn’t fail to load. So we are still using MSVC to compile for Windows and then Zig to both Linux & Mac (x64 & ARM). If you have good ideas on how to solve that, I would really appreciate it.

As a final thought, this work took several months, and when I got to the end, we got a pull request with some interesting numbers (+11.3K LOC, -11.2K LOC).

I have to admit that I feel a bit bad about that for the reviewers 🙂. I was really hoping that I would get a net negative result in the number of lines changed, but that couldn’t be helped. The end result is that all of our state is now far simpler, but we didn’t actually change anything in the way we behave. We removed two frequently hit lock convoys and allowed RavenDB to be far more efficient.

How efficient? Well… that is a bit complex to answer, because we didn’t stop here. There are more things that happened in RavenDB 7.1 that I need to talk to you about. We’ll cover them in my next post.

I’m trying to reason about the behavior of this code, and I can’t decide if this is a stroke of genius or if I’m suffering from a stroke. Take a look at the code, and then I’ll discuss what I’m trying to do below:

HANDLE hFile = CreateFileA("R:/original_file.bin", 
GENERIC_READ | GENERIC_WRITE, 
FILE_SHARE_READ | FILE_SHARE_WRITE, 
NULL, 
OPEN_ALWAYS, FILE_ATTRIBUTE_NORMAL, 
NULL);
if (hFile == INVALID_HANDLE_VALUE) {
    printf("Error creating file: %d\n", GetLastError());
    exit(__LINE__);
}




HANDLE hMapFile = CreateFileMapping(hFile, NULL, 
PAGE_READWRITE, 0, 0, NULL);
if (hMapFile == NULL) {
    fprintf(stderr, "Could not create file mapping object: %x\n", GetLastError());
    exit(__LINE__);
}


char* lpMapAddress = MapViewOfFile(hMapFile, FILE_MAP_WRITE, 0, 0, 0);
if (lpMapAddress == NULL) {
    fprintf(stderr, "Could not map view of file: %x\n", GetLastError());
    exit(__LINE__);
}


for (size_t i = 2 * MB; i < 4 * MB; i++)
{
        lpMapAddress[i]++;
}


HANDLE hDirect = CreateFileA("R:/original_file.bin", 
GENERIC_READ | GENERIC_WRITE, 
FILE_SHARE_READ | FILE_SHARE_WRITE, 
NULL, 
OPEN_ALWAYS, 
FILE_ATTRIBUTE_NORMAL, 
NULL);


SetFilePointerEx(hDirect, (LARGE_INTEGER) { 6 * MB }, & fileSize, FILE_BEGIN);
for (i = 6 ; i < 10 ; i++) {
    if (!WriteFile(hDirect, lpMapAddress + i * MB, MB, &bytesWritten, NULL)) {
        fprintf(stderr, "WriteFile direct failed on iteration %d: %x\n", i, GetLastError());
        exit(__LINE__);
    }
}

The idea is pretty simple, I’m opening the same file twice. Once in buffered mode and mapping that memory for both reads & writes. The problem is that to flush the data to disk, I have to either wait for the OS, or call FlushViewOfFile() and FlushFileBuffers() to actually flush it to disk explicitly.

The problem with this approach is that FlushFileBuffers() has undesirable side effects. So I’m opening the file again, this time for unbuffered I/O. I’m writing to the memory map and then using the same mapping to write to the file itself. On Windows, that goes through a separate path (and may lose coherence with the memory map).

The idea here is that since I’m writing from the same location, I can’t lose coherence. I either get the value from the file or from the memory map, and they are both the same. At least, that is what I hope will happen.

For the purpose of discussion, I can ensure that there is no one else writing to this file while I’m abusing the system in this manner. What do you think Windows will do in this case?

I believe that when I’m writing using unbuffered I/O in this manner, I’m forcing the OS to drop the mapping and refresh from the disk. That is likely the reason why it may lose coherence, because there may be already reads that aren’t served from main memory, or something like that.

This isn’t an approach that I would actually take for production usage, but it is a damn interesting thing to speculate on. If you have any idea what will actually happen, I would love to have your input.

I would really love to have a better understanding of what is going on here!

If you format a 32 MB disk using NTFS, you’ll get the following result:

So about 10 MB are taken for NTFS metadata. I guess that makes sense, and giving up 10 MB isn’t generally a big deal these days, so I wouldn’t worry about it.

I write a 20 MB file and punch a hole in it between 6 MB and 18 MB (12 MB in total), so we have:

And in terms of disk space, we have:

The numbers match, awesome! Let’s create a new 12 MB file, like so:

And the disk is:

And now I’m running the following code, which maps the first file (with the hole punched in it) and writes 4 MB to it using memory-mapped I/O:

HANDLE hMapFile = CreateFileMapping(hFile, NULL, PAGE_READWRITE, 0, 0, NULL);
if (hMapFile == NULL) {
    fprintf(stderr, "Could not create file mapping object: %x\n", GetLastError());
    exit(__LINE__);


}


char* lpMapAddress = MapViewOfFile(hMapFile, FILE_MAP_WRITE, 0, 0, 0);
if (lpMapAddress == NULL) {
    fprintf(stderr, "Could not map view of file: %x\n", GetLastError());
    exit(__LINE__);
}


for (i = 6 * MB; i < 10 * MB; i++) {
    ((char*)lpMapAddress)[i]++;
}


if (!FlushViewOfFile(lpMapAddress, 0)) {
    fprintf(stderr, "Could not flush view of file: %x\n", GetLastError());
    exit(__LINE__);
}


if (!FlushFileBuffers(hFile)) {
        fprintf(stderr, "Could not flush file buffers: %x\n", GetLastError());
    exit(__LINE__);
}

The end for this file is:

So with the other file, we have a total of 24 MB in use on a 32 MB disk. And here is the state of the disk itself:

The problem is that there used to be 9.78 MB that were busy when we had a newly formatted disk. And now we are using at least some of that disk space for storing file data somehow.

I’m getting the same behavior when I use normal file I/O:

moveAmount.QuadPart = 6 * MB;
SetFilePointerEx(hFile, moveAmount, NULL, FILE_BEGIN);


for (i = 6 ; i < 10 ; i++) {
    if (!WriteFile(hFile, buffer, MB, &bytesWritten, NULL)) {
        fprintf(stderr, "WriteFile failed on iteration %d: %x\n", i, GetLastError());
        exit(__LINE__);
    }
}

So somehow in this sequence of operations, we get more disk space. On the other hand, if I try to write just 22 MB into a single file, it fails. See:

hFile = CreateFileA("R:/original_file.bin", GENERIC_READ | GENERIC_WRITE, 0, NULL, CREATE_ALWAYS, FILE_ATTRIBUTE_NORMAL, NULL);
if (hFile == INVALID_HANDLE_VALUE) {
    printf("Error creating file: %d\n", GetLastError());
    exit(__LINE__);
}


for (int i = 0; i < 22; i++) {
    if (!WriteFile(hFile, buffer, MB, &bytesWritten, NULL)) {
        fprintf(stderr, "WriteFile failed on iteration %d: %x\n", i, GetLastError());
        exit(__LINE__);
    }
}

You can find the full source here. I would love to understand what exactly is happening and how we suddenly get more disk space usage in this scenario.

Today I set out to figure out an answer to a very specific question. What happens at the OS level when you try to allocate disk space for a sparse file and there is no additional disk space?

Sparse files are a fairly advanced feature of file systems. They allow you to define a file whose size is 10GB, but that only takes 2GB of actual disk space. The rest is sparse (takes no disk space and on read will return just zeroes). The OS will automatically allocate additional disk space for you if you write to the sparse ranges.

This leads to an interesting question, what happens when you write to a sparse file if there is no additional disk space?

Let’s look at the problem on Linux first. We define a RAM disk with 32MB, like so:

sudo mkdir -p /mnt/ramdisk
sudo mount -t tmpfs -o size=32M tmpfs /mnt/ramdisk

And then we write the following code, which does the following (on a disk with just 32MB):

• Create a file - write 32 MB to it
• Punch a hole of 8 MB in the file (range is 12MB - 20MB)
• Create another file - write 4 MB to it (there is now only 4MB available)
• Open the original file and try to write to the range with the hole in it (requiring additional disk space allocation)

#define _GNU_SOURCE


#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <fcntl.h>
#include <linux/falloc.h>
#include <errno.h>
#include <string.h>
#include <sys/random.h>


#define MB (1024 * 1024)


void write_all(int fd, const void *buf, size_t count)
{
    size_t bytes_written = 0;
    const char *ptr = (const char *)buf;


    while (bytes_written < count)
    {
        ssize_t result = write(fd, ptr + bytes_written, count - bytes_written);


        if (result < 0)
        {
            if (errno == EINTR)
                continue;


            fprintf(stderr, "Write error: errno = %d (%s)\n", errno, strerror(errno));
            exit(EXIT_FAILURE);
        }


        if (result == 0)
        {


            fprintf(stderr, "Zero len write is bad: errno = %d (%s)\n", errno, strerror(errno));
            exit(EXIT_FAILURE);
        }


        bytes_written += result;
    }
}


int main()
{
    int fd;
    char buffer[MB];


    unlink("/mnt/ramdisk/fullfile");
    unlink("/mnt/ramdisk/anotherfile");


    getrandom(buffer, MB, 0);


    ssize_t bytes_written;


    fd = open("/mnt/ramdisk/fullfile", O_RDWR | O_CREAT | O_TRUNC, 0644);
    if (fd == -1)
    {
        fprintf(stderr, "open full file: errno = %d (%s)\n", errno, strerror(errno));
        exit(EXIT_FAILURE);
    }
    for (int i = 0; i < 32; i++)
    {
        write_all(fd, buffer, MB);
    }
    close(fd);


    fd = open("/mnt/ramdisk/fullfile", O_RDWR);
    if (fd == -1)
    {
        fprintf(stderr, "reopen full file: errno = %d (%s)\n", errno, strerror(errno));
        exit(EXIT_FAILURE);
    }


    if (fallocate(fd, FALLOC_FL_PUNCH_HOLE | FALLOC_FL_KEEP_SIZE, 12 * MB, 8 * MB) == -1)
    {
        fprintf(stderr, "fallocate failure: errno = %d (%s)\n", errno, strerror(errno));
        exit(EXIT_FAILURE);
    }
    close(fd);


    fd = open("/mnt/ramdisk/anotherfile", O_RDWR | O_CREAT | O_TRUNC, 0644);
    if (fd == -1)
    {
        fprintf(stderr, "open another file: errno = %d (%s)\n", errno, strerror(errno));
        exit(EXIT_FAILURE);
    }


    for (int i = 0; i < 4; i++)
    {
        write_all(fd, buffer, MB);
    }
    close(fd);


    // Write 8 MB to the hole in the first file
    fd = open("/mnt/ramdisk/fullfile", O_RDWR);
    if (fd == -1)
    {
        fprintf(stderr, "reopen full file 2: errno = %d (%s)\n", errno, strerror(errno));
        exit(EXIT_FAILURE);
    }


    // Seek to the start of the hole
    if (lseek(fd, 12 * MB, SEEK_SET) == -1)
    {
        fprintf(stderr, "seek full file: errno = %d (%s)\n", errno, strerror(errno));
        exit(EXIT_FAILURE);
    }
    for (int i = 0; i < 8; i++)
    {
        write_all(fd, buffer, MB);
    }
    close(fd);


    printf("Operations completed successfully.\n");
    return 0;
}

As expected, this code will fail on the 5th write (since there is no disk space to allocate in the disk). The error would be:

Write error: errno = 28 (No space left on device)

Here is what the file system reports:

$ du -h /mnt/ramdisk/*
4.0M    /mnt/ramdisk/anotherfile
28M     /mnt/ramdisk/fullfile


$ ll -h /mnt/ramdisk/
total 33M
drwxrwxrwt 2 root   root     80 Jan  9 10:43 ./
drwxr-xr-x 6 root   root   4.0K Jan  9 10:30 ../
-rw-r--r-- 1 ayende ayende 4.0M Jan  9 10:43 anotherfile
-rw-r--r-- 1 ayende ayende  32M Jan  9 10:43 fullfile

As you can see, we have a total of 32 MB of actual size reported, but ll is reporting that we actually have files bigger than that (because we have hole punching).

What would happen if we were to run this using memory-mapped I/O? Here is the code:

fd = open("/mnt/ramdisk/fullfile", O_RDWR);


char *mapped_memory = mmap(NULL, 32 * MB, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
if (mapped_memory == MAP_FAILED)
{
    fprintf(stderr, "fail mmap: errno = %d (%s)\n", errno, strerror(errno));
    exit(EXIT_FAILURE);
}


for (size_t i = (12 * MB); i < (20 * MB); i++)
{
    mapped_memory[i] = 1;
}
munmap(mapped_memory, 32 * MB);
close(fd);

This will lead to an interesting scenario. We need to allocate disk space for the memory, and we’ll do so (note that we are writing into the hole), and this code will fail with a segmentation fault.

It will fail in the loop, by the way, as part of the page fault to bring the memory in, the file system needs to allocate the disk space. If there is no such disk space, it will fail. The only way for the OS to behave in this case is to fail the write, which leads to a segmentation fault.

I also tried that on Windows. I defined a virtual disk like so:

$ diskpart
create vdisk file="D:\ramdisk.vhd" maximum=32
select vdisk file=D:\ramdisk.vhd"
attach vdisk
create partition primary
format fs=NTFS quick label=RAMDISK
assign letter=R
exit

This creates a 32MB disk and assigns it the letter R. Note that we are using NTFS, which has its own metadata, we have roughly 21MB or so of usable disk space to play with here.

Here is the Windows code that simulates the same behavior as the Linux code above:

#include <stdio.h>
#include <windows.h>


#define MB (1024 * 1024)


int main() {
    HANDLE hFile, hFile2;
    DWORD bytesWritten;
    LARGE_INTEGER fileSize, moveAmount;
    char* buffer = malloc(MB);
    
    int i;


        DeleteFileA("R:\\original_file.bin");
        DeleteFileA("R:\\another_file.bin");


    hFile = CreateFileA("R:/original_file.bin", GENERIC_READ | GENERIC_WRITE, 0, NULL, CREATE_ALWAYS, FILE_ATTRIBUTE_NORMAL, NULL);
    if (hFile == INVALID_HANDLE_VALUE) {
        printf("Error creating file: %d\n", GetLastError());
        exit(__LINE__);
    }


    for (int i = 0; i < 20; i++) {
        if (!WriteFile(hFile, buffer, MB, &bytesWritten, NULL)) {
            fprintf(stderr, "WriteFile failed on iteration %d: %x\n", i, GetLastError());
            exit(__LINE__);
        }
        if (bytesWritten != MB) {
            fprintf(stderr, "Failed to write full buffer on iteration %d\n", i);
            exit(__LINE__);
        }
    }


    FILE_ZERO_DATA_INFORMATION zeroDataInfo;
    zeroDataInfo.FileOffset.QuadPart = 6 * MB; 
    zeroDataInfo.BeyondFinalZero.QuadPart = 18 * MB; 


    if (!DeviceIoControl(hFile, FSCTL_SET_SPARSE, NULL, 0, NULL, 0, NULL, NULL) || 
        !DeviceIoControl(hFile, FSCTL_SET_ZERO_DATA, &zeroDataInfo, sizeof(zeroDataInfo), NULL, 0, NULL, NULL)) {
                printf("Error setting zero data: %d\n", GetLastError());
        exit(__LINE__);
        }




    // Create another file of size 4 MB
    hFile2 = CreateFileA("R:/another_file.bin", GENERIC_READ | GENERIC_WRITE, 0, NULL, CREATE_ALWAYS, FILE_ATTRIBUTE_NORMAL, NULL);
    if (hFile2 == INVALID_HANDLE_VALUE) {
        printf("Error creating second file: %d\n", GetLastError());
        exit(__LINE__);
    }




    for (int i = 0; i < 4; i++) {
        if (!WriteFile(hFile2, buffer, MB, &bytesWritten, NULL)) {
            fprintf(stderr, "WriteFile 2 failed on iteration %d: %x\n", i, GetLastError());
            exit(__LINE__);
        }
        if (bytesWritten != MB) {
            fprintf(stderr, "Failed to write full buffer 2 on iteration %d\n", i);
            exit(__LINE__);
        }
    }


        moveAmount.QuadPart = 12 * MB;
    SetFilePointerEx(hFile, moveAmount, NULL, FILE_BEGIN);
    for (i = 0; i < 8; i++) {
        if (!WriteFile(hFile, buffer, MB, &bytesWritten, NULL)) {
            printf("Error writing to file: %d\n", GetLastError());
            exit(__LINE__);
        }
    }


    return 0;
}

And that gives us the exact same behavior as in Linux. One of these writes will fail because there is no more disk space for it. What about when we use memory-mapped I/O?

HANDLE hMapFile = CreateFileMapping(hFile, NULL, PAGE_READWRITE, 0, 0, NULL);
if (hMapFile == NULL) {
    fprintf(stderr, "Could not create file mapping object: %x\n", GetLastError());
    exit(__LINE__);


}


char* lpMapAddress = MapViewOfFile(hMapFile, FILE_MAP_WRITE, 0, 0, 0);
if (lpMapAddress == NULL) {
    fprintf(stderr, "Could not map view of file: %x\n", GetLastError());
    exit(__LINE__);
}


for (i = 0; i < 20 * MB; i++) {
    ((char*)lpMapAddress)[i]++;
}

That results in the expected access violation:

I didn’t bother checking Mac or BSD, but I’m assuming that they behave in the same manner. I can’t conceive of anything else that they could reasonably do.

You can find my full source here.

RavenDB is meant to be a self-managing database, one that is able to take care of itself without constant hand-holding from the database administrator. That has been one of our core tenets from the get-go. Today I checked the current state of the codebase and we have roughly 500 configuration options that are available to control various aspects of RavenDB’s behavior.

These two statements are seemingly contradictory, because if we have so many configuration options, how can we even try to be self-managing? And how can a database administrator expect to juggle all of those options?

Database configuration is a really finicky topic. For example, RocksDB’s authors flat-out admit that out loud:

Even we as RocksDB developers don't fully understand the effect of each configuration change. If you want to fully optimize RocksDB for your workload, we recommend experiments and benchmarking.

And indeed, efforts were made to tune RocksDB using deep-learning models because it is that complex.

RavenDB doesn’t take that approach, tuning is something that should work out of the box, managed directly by RavenDB itself. Much of that is achieved by not doing things and carefully arranging that the environment will balance itself out in an optimal fashion. But I’ll talk about the Zen of RavenDB another time.

Today, I want to talk about why we have so many configuration options, the vast majority of which you, as a user, should neither use, care about, nor even know of.

The idea is very simple, deploying a database engine is a Big Deal, and as such, something that users are quite reluctant to do. When we hit a problem and a support call is raised, we need to provide some mechanism for the user to fix things until we can ensure that this behavior is accounted for in the default manner of RavenDB.

I treat the configuration options more as escape hatches that allow me to muddle through stuff than explicit options that an administrator is expected to monitor and manage. Some of those configuration options control whether RavenDB will utilize vectored instructions or the compression algorithm to use over the wire. If you need to touch them, it is amazing that they exist. If you have to deal with them on a regular basis, we need to go back to the drawing board.

In my previous post, I discussed how Linux will silently truncate a big write (> 2 GB) for you. That is expected by the interface of write(). The problem is that this behavior also applies when you use IO_Uring.

Take a look at the following code:

struct io_uring_sqe *sqe = io_uring_get_sqe(&ring);
if (!sqe) {
    return 1;
}
io_uring_prep_write(sqe, fd, buffer, BUFFER_SIZE, 0);
io_uring_submit(&ring);


struct io_uring_cqe *cqe;
ret = io_uring_wait_cqe(&ring, &cqe);
if (ret < 0) {
    return 2;
}

If BUFFER_SIZE is 3 GB, then this will write about 2 GB to the file. The number of bytes written is correctly reported, but the complexity this generates is huge. Consider the following function:

int32_t rvn_write_io_ring(
    void *handle,
    int32_t count,
    struct page_to_write *buffers,
    int32_t *detailed_error_code);

There is a set of buffers that I want to write, and the natural way to do that is:

int32_t rvn_write_io_ring(
    void *handle,
    int32_t count,
    struct page_to_write *buffers,
    int32_t *detailed_error_code)
{
    struct handle *handle_ptr = handle;
    for (size_t i = 0; i < count; i++)
    {
        struct io_uring_sqe *sqe = io_uring_get_sqe(
            &handle_ptr->global_state->ring);
        io_uring_prep_write(sqe,
            handle_ptr->file_fd, 
            buffers[i].ptr, 
            buffers[i].count_of_pages * VORON_PAGE_SIZE, 
            buffers[i].page_num * VORON_PAGE_SIZE
        );
    }
    return _submit_and_wait(&handle_ptr->global_state->ring,
                            count, detailed_error_code);
}


int32_t _submit_and_wait(
    struct io_uring* ring,
    int32_t count,
    int32_t* detailed_error_code)
{
    int32_t rc = io_uring_submit_and_wait(ring, count);
    if(rc < 0)
    {
        *detailed_error_code = -rc;
        return FAIL_IO_RING_SUBMIT;
    }


    struct io_uring_cqe* cqe;
    for(int i = 0; i < count; i++)
    {
        rc = io_uring_wait_cqe(ring, &cqe);
        if (rc < 0)
        {
            *detailed_error_code = -rc;
            return FAIL_IO_RING_NO_RESULT;
        }
        if(cqe->res < 0)
        {
            *detailed_error_code = -cqe->res;
            return FAIL_IO_RING_WRITE_RESULT;
        }
        io_uring_cqe_seen(ring, cqe);
    }
    return SUCCESS;
}

In other words, send all the data to the IO Ring, then wait for all those operations to complete. We verify complete success and can then move on. However, because we may have a write that is greater than 2 GB, and because the interface allows the IO Uring to write less than we thought it would, we need to handle that with retries.

After thinking about this for a while, I came up with the following implementation:

int32_t _submit_writes_to_ring(
    struct handle *handle,
    int32_t count,
    struct page_to_write *buffers,
    int32_t* detailed_error_code)
{
    struct io_uring *ring = &handle->global_state->ring;
    off_t *offsets = handle->global_state->offsets;
    memset(offsets, 0, count * sizeof(off_t));   


    while(true)
    {
        int32_t submitted = 0;


        for (size_t i = 0; i < count; i++)
        {
            off_t offset =  offsets[i];
            if(offset == buffers[i].count_of_pages * VORON_PAGE_SIZE)
                continue;


            struct io_uring_sqe *sqe = io_uring_get_sqe(ring);
            if (sqe == NULL) // the ring is full, flush it...
                break;


            io_uring_sqe_set_data(sqe, i);
            io_uring_prep_write(sqe, handle->file_fd, 
                buffers[i].ptr + offset,
                buffers[i].count_of_pages * VORON_PAGE_SIZE - offset,
                buffers[i].page_num * VORON_PAGE_SIZE + offset);


            submitted++;
        }


        if(submitted == 0)
            return SUCCESS;
        
        int32_t rc = io_uring_submit_and_wait(ring, submitted);
        if(rc < 0)
        {
            *detailed_error_code = -rc;
            return FAIL_IO_RING_SUBMIT;
        }


        struct io_uring_cqe *cqe;
        uint32_t head = 0;
        uint32_t i = 0;
        bool has_errors = false;


        io_uring_for_each_cqe(ring, head, cqe) {
            i++;
            uint64_t index = io_uring_cqe_get_data64(cqe);
            int result = cqe->res;
            if(result < 0)
            {
                has_errors = true;
                *detailed_error_code = -result;
            }
            else
            {
                offsets[index] += result;
                if(result == 0)
                {
                    // there shouldn't be a scenario where we return 0 
                    // for a write operation, we may want to retry here
                    // but figuring out if this is a single happening, of if 
                    // we need to retry this operation (_have_ retried it?) is 
                    // complex enough to treat this as an error for now.
                    has_errors = true;
                    *detailed_error_code = EIO;   
                }
            }
        }
        io_uring_cq_advance(ring, i);


        if(has_errors)
            return FAIL_IO_RING_WRITE_RESULT;
    }
}

That is a lot of code, but it is mostly because of how C works. What we do here is scan through the buffers we need to write, as well as scan through an array of offsets that store additional information for the operation.

If the offset to write doesn’t indicate that we’ve written the whole thing, we’ll submit it to the ring and keep going until we either fill the entire ring or run out of buffers to work with. The next step is to submit the work and wait for it to complete, then run through the results, check for errors, and update the offset that we wrote for the relevant buffer.

Then, we scan the buffers array again to find either partial writes that we have to complete (we didn’t write the whole buffer) or buffers that we didn’t write at all because we filled the ring. In either case, we submit the new batch of work to the ring and repeat until we run out of work.

This code assumes that we cannot have a non-error state where we write 0 bytes to the file and treats that as an error. We also assume that an error in writing to the disk is fatal, and the higher-level code will discard the entire IO_Uring if that happens.

The Windows version, by the way, is somewhat simpler. Windows explicitly limits the size of the buffer you can pass to the write() call (and its IO Ring equivalent). It also ensures that it will write the whole thing, so partial writes are not an issue there.

It is interesting to note that the code above will effectively stripe writes if you send very large buffers. Let’s assume that we send it two 4 GB buffers, like so:

The patterns of writes that will actually be executed are:

1. 1GB .. 3 GB, 10 GB .. 12 GB
2. 3 GB .. 5 GB, 12 GB .. 14 GB
3. 14 GB .. 16 GB

I can “fix” that by never issuing writes that are larger than 2 GB and issuing separate writes for each 2 GB range, but that leads to other complexities (e.g., tracking state if I split a write and hit the full ring status, etc.). At those sizes, it doesn’t actually matter in terms of efficiency or performance.

Partial writes are almost always a sign of either very large writes that were broken up or some underlying issue that is already problematic, so I don’t care that much about that scenario in general. For the vast majority of cases, this will always issue exactly one write for each buffer.

What is really interesting from my point of view, however, is how even a pretty self-contained feature can get pretty complex internally. On the other hand, this behavior allows me to push a whole bunch of work directly to the OS and have it send all of that to the disk as fast as possible.

In our scenarios, under load, we may call that with thousands to tens of thousands of pages (each 8 KB in size) spread all over the file. The buffers are actually sorted, so ideally, the kernel will be able to take advantage of that, but even if not, just reducing the number of syscalls will result in performance savings.

I previously asked what the code below does, and mentioned that it should give interesting insight into the kind of mindset and knowledge a candidate has. Take a look at the code again:

#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <fcntl.h>
#include <errno.h>
#include <sys/stat.h>


#define BUFFER_SIZE (3ULL * 1024 * 1024 * 1024) // 3GB in bytes


int main() {
    int fd;
    char *buffer;
    struct stat st;


    buffer = (char *)malloc(BUFFER_SIZE);
    if (buffer == NULL) {
        return 1;
    }


    fd = open("large_file.bin", O_WRONLY | O_CREAT | O_TRUNC, S_IRUSR | S_IWUSR);
    if (fd == -1) {
        return 2;
    }


    if (write(fd, buffer, BUFFER_SIZE) == -1) {
        return 3;
    }


    if (fsync(fd) == -1) {
        return 4;
    }


    if (close(fd) == -1) {
        return 5;
    }


    if (stat("large_file.bin", &st) == -1) {
        return 6;
    }


    printf("File size: %.2f GB\n", (double)st.st_size / (1024 * 1024 * 1024));


    free(buffer);
    return 0;
}

This program will output: File size: 2.00 GB

And it will write 2 GB of zeros to the file:

~$ head  large_file.bin  | hexdump -C
00000000  00 00 00 00 00 00 00 00  00 00 00 00 00 00 00 00  |................|
*
7ffff000

The question is why? And the answer is quite simple. Linux has a limitation of about 2 GB for writes to the disk. Any write call that attempts to write more than that will only write that much, and you’ll have to call the system again. This is not an error, mind. The write call is free to write less than the size of the buffer you passed to it.

Windows has the same limit, but it is honest about it

In Windows, all write calls accept a 32-bit int as the size of the buffer, so this limitation is clearly communicated in the API. Windows will also ensure that for files, a WriteFile call that completes successfully writes the entire buffer to the disk.

And why am I writing 2 GB of zeros? In the code above, I’m using malloc(), not calloc(), so I wouldn’t expect the values to be zero. Because this is a large allocation, malloc() calls the OS to provide us with the buffer directly, and the OS is contractually obligated to provide us with zeroed pages.

Here is a pretty simple C program, running on Linux. Can you tell me what you expect its output to be?

#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <fcntl.h>
#include <errno.h>
#include <sys/stat.h>


#define BUFFER_SIZE (3ULL * 1024 * 1024 * 1024) // 3GB in bytes


int main() {
    int fd;
    char *buffer;
    struct stat st;


    buffer = (char *)malloc(BUFFER_SIZE);
    if (buffer == NULL) {
        return 1;
    }


    fd = open("large_file.bin", O_WRONLY | O_CREAT | O_TRUNC, S_IRUSR | S_IWUSR);
    if (fd == -1) {
        return 2;
    }


    if (write(fd, buffer, BUFFER_SIZE) == -1) {
        return 3;
    }


    if (fsync(fd) == -1) {
        return 4;
    }


    if (close(fd) == -1) {
        return 5;
    }


    if (stat("large_file.bin", &st) == -1) {
        return 6;
    }


    printf("File size: %.2f GB\n", (double)st.st_size / (1024 * 1024 * 1024));


    free(buffer);
    return 0;
}

And what happens when I run:

head  large_file.bin  | hexdump -C

This shows both surprising behavior and serves as a good opening for discussion on a whole bunch of issues. In an interview setting, that can give us a lot of insight into the sort of knowledge a candidate has.

The problem was that this took time - many days or multiple weeks - for us to observe that. But we had the charts to prove that this was pretty consistent. If the RavenDB service was restarted (we did not have to restart the machine), the situation would instantly fix itself and then slowly degrade over time.

The scenario in question was performance degradation over time. The metric in question was the average request latency, and we could track a small but consistent rise in this number over the course of days and weeks. The load on the server remained pretty much constant, but the latency of the requests grew.

That the customer didn’t notice that is an interesting story on its own. RavenDB will automatically prioritize the fastest node in the cluster to be the “customer-facing” one, and it alleviated the issue to such an extent that the metrics the customer usually monitors were fine. The RavenDB Cloud team looks at the entire system, so we started the investigation long before the problem warranted users’ attention.

I hate these sorts of issues because they are really hard to figure out and subject to basically every caveat under the sun. In this case, we basically had exactly nothing to go on. The workload was pretty consistent, and I/O, memory, and CPU usage were acceptable. There was no starting point to look at.

Those are also big machines, with hundreds of GB of RAM and running heavy workloads. These machines have great disks and a lot of CPU power to spare. What is going on here?

After a long while, we got a good handle on what is actually going on. When RavenDB starts, it creates memory maps of the file it is working with. Over time, as needed, RavenDB will map, unmap, and remap as needed. A process that has been running for a long while, with many databases and indexes operating, will have a lot of work done in terms of memory mapping.

In Linux, you can inspect those details by running:

$ cat /proc/22003/smaps


600a33834000-600a3383b000 r--p 00000000 08:30 214585                     /data/ravendb/Raven.Server
Size:                 28 kB
KernelPageSize:        4 kB
MMUPageSize:           4 kB
Rss:                  28 kB
Pss:                  26 kB
Shared_Clean:          4 kB
Shared_Dirty:          0 kB
Private_Clean:        24 kB
Private_Dirty:         0 kB
Referenced:           28 kB
Anonymous:             0 kB
LazyFree:              0 kB
AnonHugePages:         0 kB
ShmemPmdMapped:        0 kB
FilePmdMapped:         0 kB
Shared_Hugetlb:        0 kB
Private_Hugetlb:       0 kB
Swap:                  0 kB
SwapPss:               0 kB
Locked:                0 kB
THPeligible:    0
VmFlags: rd mr mw me dw
600a3383b000-600a33847000 r-xp 00006000 08:30 214585                     /data/ravendb/Raven.Server
Size:                 48 kB
KernelPageSize:        4 kB
MMUPageSize:           4 kB
Rss:                  48 kB
Pss:                  46 kB
Shared_Clean:          4 kB
Shared_Dirty:          0 kB
Private_Clean:        44 kB
Private_Dirty:         0 kB
Referenced:           48 kB
Anonymous:             0 kB
LazyFree:              0 kB
AnonHugePages:         0 kB
ShmemPmdMapped:        0 kB
FilePmdMapped:         0 kB
Shared_Hugetlb:        0 kB
Private_Hugetlb:       0 kB
Swap:                  0 kB
SwapPss:               0 kB
Locked:                0 kB
THPeligible:    0
VmFlags: rd ex mr mw me dw

Here you can see the first page of entries from this file. Just starting up RavenDB (with no databases created) will generate close to 2,000 entries. The smaps virtual file can be really invaluable for figuring out certain types of problems. In the snippet above, you can see that we have some executable memory ranges mapped, for example.

The problem is that over time, memory becomes fragmented, and we may end up with an smaps file that contains tens of thousands (or even hundreds of thousands) of entries.

Here is the result of running perf top on the system, you can see that the top three items that hogs most of the resources are related to smaps accounting.

This file provides such useful information that we monitor it on a regular basis. It turns out that this can have… interesting effects. Consider that while we are running the scan through all the memory mapping, we may need to change the memory mapping for the process. That leads to contention on the kernel locks that protect the mapping, of course.

It’s expensive to generate the smaps file

Reading from /proc/[pid]/smaps is not a simple file read. It involves the kernel gathering detailed memory statistics (e.g., memory regions, page size, resident/anonymous/shared memory usage) for each virtual memory area (VMA) of the process. For large processes with many memory mappings, this can be computationally expensive as the kernel has to gather the required information every time /proc/[pid]/smaps is accessed.

When /proc/[pid]/smaps is read, the kernel needs to access memory-related structures. This may involve taking locks on certain parts of the process’s memory management system. If this is done too often or across many large processes, it could lead to contention or slow down the process itself, especially if other processes are accessing or modifying memory at the same time.

If the number of memory mappings is high, and the frequency with which we monitor is short… I hope you can see where this is going. We effectively spent so much time running over this file that we blocked other operations.

This wasn’t an issue when we just started the process, because the number of memory mappings was small, but as we worked on the system and the number of memory mappings grew… we eventually started hitting contention.

The solution was two-fold. We made sure that there is only ever a single thread that would read the information from the smaps (previously it might have been triggered from multiple locations).  We added some throttling to ensure that we aren’t hammering the kernel with requests for this file too often (returning cached information if needed) and we switched from using smaps to using smaps_rollup instead. The rollup version provides much better performance, since it deals with summary data only.

With those changes in place, we deployed to production and waited. The result was flat latency numbers and another item that the Cloud team could strike off the board successfully.

For a new feature in RavenDB, I needed to associate each transaction with a source ID. The underlying idea is that I can aggregate transactions from multiple sources in a single location, but I need to be able to distinguish between transactions from A and B.

Luckily, I had the foresight to reserve space in the Transaction Header, I had a whole 16 bytes available for me. Separately, each Voron database (the underlying storage engine that we use) has a unique Guid identifier. And a Guid is 16 bytes… so everything is pretty awesome.

There was just one issue. I needed to be able to read transactions as part of the recovery of the database, but we stored the database ID inside the database itself. I figured out that I could also put a copy of the database ID in the global file header and was able to move forward.

This is part of a much larger change, so I was going full steam ahead when I realized something pretty awful. That database Guid that I was relying on was already being used as the physical identifier of the storage as part of the way RavenDB distributes data. The reason it matters is that under certain circumstances, we may need to change that.

If we change the database ID, we lose the association with the transactions for that database, leading to a whole big mess. I started sketching out a design for figuring out that the database ID has changed, re-writing all the transactions in storage, and… a colleague said: why don’t we use another ID?

It hit me like a ton of bricks. I was using the existing database Guid because it was already there, so it seemed natural to want to reuse it. But there was no benefit in doing that. Instead, it added a lot more complexity because I was adding (many) additional responsibilities to the value that it didn’t have before.

Creating a Guid is pretty easy, after all, and I was able to dedicate one I called Journal ID to this purpose. The existing Database ID is still there, and it is completely unrelated to it. Changing the Database ID has no impact on the Journal ID, so the problem space is radically simplified.

I had to throw away heaps of complexity because of a single comment. I used the Database ID because it was there, never considering having a dedicated value for it. That single suggestion led to a better, simpler design and faster delivery.

It is funny how you can sometimes be so focused on the problem at hand, when a step back will give you a much wider view and a better path to the solution.