I intend to use this site to document my journey down the path of nerdiness (past, present, and future). I’ve been learning over the years from various sites like what I hope this one becomes, and want to give back. I have a wide variety of topics I’d like to cover. At a minimum, posting about my activities will help me document what I learned to refer back in the future. I’ll also post about projects we do ourselves around the house instead of hiring professionals, saving big $$$$ in the process. Hope you enjoy the journey with me!
Below are some topic I plan on covering (I’ve already done something with every one of these and plan on documenting it):
RTL-SDRs (receiving signals from your electric meter, ADS-B, general radio stuff)
Virtual machines and my homelab setup
Home automation / smart home (Home Assistant, Tasmota, Phillips Hue bulbs, automating various tasks throughout the house)
My mini solar setup (2x300W panels) and not-so-mini battery backup (8x272Ah LiFePO4 batteries – should yield 7ish kWh of storage)
Remote control aircraft running Arduplane with video downlink and two-way telemetry
General computer stuff (building them, what I use mine for, Hyper-V)
Home network (Ubiquiti setup, VLANs, wiring the house with CAT6, IP security cameras on Blue Iris)
Formation of my LLC if anyone wants to hear about that
screenshot of UI showing AI-generated cat using stable diffusion 1.5 via automatic1111/stable-diffusion-webui with default settings
Like most other internet-connected people, I have seen the increase in AI-generated content in recent months. ChatGPT is fun to use and I’m sure there are plenty of useful use cases for it but I’m not sure I have the imagination required to use it to it’s full potential. The AI art fad of a couple months ago was cool too. In the back of my mind, I kept thinking “where will AI take us in the next couple years”. I still don’t know the answer to that. The only “art” I am good at is pottery (thanks to high-school pottery class – I took 4 semesters of it and had a great time doing so, whole different story). But now I’m able to generate my own AI art thanks to a guide I found the other day on /g/. I am re-writing it here with screenshots and a bit more detail to try and make it more accessible to general users.
NOTE: You need a decent/recent Nvidia GPU to follow this guide. I have a RTX 2080 Super with 8GB of VRAM. There are low-memory workarounds but I haven’t tested them yet. An absolute limit is 2GB VRAM, and a GTX 7xx (Maxwell architecture) or newer GPU.
Stable Diffusion Tutorial Contents
Installing Python 3.10
Installing Git (the source control system)
Clone the Automatic1111 web UI (this is the front-end for using the various models)
Download models
Adjust memory limits & enable listening outside of localhost
First run
Launching the web UI
Generating Stable Diffusion images
Video version of this install guide
Coming soon. I always do the written guide first, then record based off the written guide. Hopefully by end of day (mountain time) Feb 24.
1 – Installing Python 3.10
This is relatively straight-forward. To check your Python version, go to a command line and enter
python --version
If you already have Python 3.10.x installed (as seen in the screenshot below), you’re good to go (minor version doesn’t matter).
Python 3.10 installed for Stable Diffusion
If not, go to the Python 3 download page and select the most recent 3.10 version. As of writing, the most recent is 3.10.10. Download the x64 installer and install. Ensure the “add python.exe to PATH” checkbox is checked. Adding python.exe to PATH means it can be called with only python at a command prompt instead of the full path, which is something like c:/users/whatever/somedirectory/moredirectories/3.10.10/python.exe.
Installing python and adding python.exe to PATH
2 – Installing Git (the source control system)
This is easier than Python – just install it – https://git-scm.com/downloads. Check for presence and version with git –version:
git installed and ready to go for Stable Diffusion
3 – Clone the Automatic1111 web UI (this is the front-end for using the various models)
With Git, clone means to download a copy of the code repository. When you clone a repo, a new directory is created in whatever directory the command is run in. Meaning that if you navigate to your desktop, and run git clone xyz, you will have a new folder on your desktop named xyz with the contents of the repository. To keep things simple, I am going to create a folder for all my Stable Diffusion stuff in the C:/ root named sd and then clone into that folder.
After the git clone completes, there will be a new directory called ‘stable-diffusion-webui’:
stable-diffusion-webui cloned and ready to download models
4 – Download models
“Models” are what actually generate the content based on provided prompts. Generally, you will want to use pre-trained models. Luckily, there are many ready to use. Training your own model is far beyond the scope of this basic installation tutorial. Training your own models generally also requires huge amounts of time crunching numbers on very powerful GPUs.
As of writing, Stable Diffusion 1.5 (SD 1.5) is the recommended model. It can be downloaded (note: this is a 7.5GB file) from huggingface here.
Take the downloaded file, and place it in the stable-diffusion-webui/models/Stable-diffusion directory and rename it to model.ckpt (it can be named anything you want but the web UI automatically attempts to load a model named ‘model.ckpt’ upon start). If you’re following along with the same directory structure as me, this file will end up at C:\sd\stable-diffusion-webui\models\Stable-diffusion\model.ckpt.
Another popular model is Deliberate. It can be downloaded (4.2GB) here. Put it in the same folder as the other model. No need to rename the 2nd (and other) models.
After downloading both models, the directory should look like this:
Stable Diffusion 1.5 (SD 1.5) and Deliberate_v11 models ready for use
5 – Adjust memory limits & enable listening outside of localhost (command line arguments)
Inside the main stable-diffusion-webui directory live a number of launcher files and helper files. Find webui-user.bat and edit it (.bat files can be right-clicked -> edit).
Add –medvram (two dashes) after the equals sign of COMMANDLINE_ARGS. If you also want the UI to listen on all IP addresses instead of just localhost (don’t do this unless you know what that means), also add –listen.
webui-user.bat after edits
@echo off
set PYTHON=
set GIT=
set VENV_DIR=
set COMMANDLINE_ARGS=--listen --medvram
call webui.bat
6 – First run
The UI tool (developed by automatic1111) will automatically download a variety of requirements upon first launch. It will take a few minutes to complete. Double-click the webui-user.bat file we just edited. It calls a few .bat files and eventually launches a Python file. The .bat files are essentially glue to stick a bunch of stuff together for the main file.
The very first thing it does is creates a Python venv (virtual environment) to keep the Stable Diffusion packages separate from your other Python packages. Then it pip installs a bunch of packages related to cuda/pytorch/numpy/etc so Python can interact with your GPU.
webui-user.bat using pip to install necessary python packages like cuda
After everything is installed and ready to go, you will see a line that says: Running on local URL: http://127.0.0.1:7860. That means the Python web server UI is running on your own computer on port 7860 (if you added –listen to the launch args, it will show 0.0.0.0:7860, which means it is listening on all IP addresses and can be accessed by external machinse).
stable-diffusion-webui launched and ready to load
7 – Launching the web UI
With the web UI server running, it can be accessed via browser on the same computer running the Python at http://127.0.0.1:7860. That link should work for you if you click it.
Note that if the Python process closes for whatever reason (you close the command window, your computer reboots, etc), you need to double-click webui-user.bat to relaunch it and it needs to be running any time you want to access the web UI.
Automatic1111 stable diffusion web UI up and running
As seen in the screenshot, there are a ton of parameters/settings. I’ll highlight a few in the next section
8 – Generating Stable Diffusion images
This is the tricky part. The prompts make or break your generation. I am still learning. The prompt is where you enter what you want to see. Negative prompt is where you enter what you don’t want to see.
Let’s start simple, with cat in the prompt. Then click generate. A very reasonable-looking cat should soon appear (typically takes a couple seconds per image):
AI-generated cat with stable diffusion 1.5 with default settings
To highlight a few of the settings/sliders:
Stable diffusion checkpoint – model selector. Note that it’ll take a bit to load a new model (the multi-GB files need to be read in their entirety and ingested).
Prompt – what you want to see
Negative prompt – what you don’t want to see
Sampling method – various methods to sample new points
Sampling steps – how many iterations to use for image generation for a single image
Width – width of image to generate (in pixels). NOTE, you need a very powerful GPU with a ton of VRAM to go much higher than the default 512
Height – height of image to generate (in pixels). Same warning applies as width
Batch count – how many images to include in a batch generation
Batch size – haven’t used yet, presumably used to specify how many batches to generate
CFG Scale – this slider tells the models how specific they need to be for the prompt. Higher is more specific. Really high values (>12ish) start to get a bit abstract. Probably want to be in the range of 3-10 for this slider.
Seed – random number generator seed. -1 means use a new seed for every image.
Some thoughts on prompt/negative prompt
From my ~24 hours using this tool, it is very clear that prompt/negative prompts are what make or break your generation. I think that your ability as a pre-AI artist would come in handy here. I am no artist so I have a hard time putting what I want to see into words. Take example prompt: valley, fairytale treehouse village covered, matte painting, highly detailed, dynamic lighting, cinematic, realism, realistic, photo real, sunset, detailed, high contrast, denoised, centered. I would’ve said “fairytale treehouse” and stopped at that. Compare the two prompts below with the more detailed prompt directly below and the basic “fairytale treehouse” prompt after that:
AI-generated “fairytale treehouse” via stable diffusion. Prompt: valley, fairytale treehouse village covered, matte painting, highly detailed, dynamic lighting, cinematic, realism, realistic, photo real, sunset, detailed, high contrast, denoised, centeredAI-generated “fairytale treehouse” via stable diffusion. Prompt: fairytale treehouse
One of these looks perfectly in place for a fantasy story. The other you could very possibly see in person in a nearby forest.
Both positive and negative can get very long very quickly. Many of the AI-generated artifacts present over the last month or two can be eliminated with negative prompt entries.
I will not pretend to know what works well vs not. Google is your friend here. I believe that “prompt engineering” will be very important in AI’s future. Google is your friend here.
Conclusion
AI-generated content is here. It will not be going away. Even if it is outlawed, the code is out there. AI will be a huge part of our future, regardless of if you want it or not. As the saying goes – pandora’s box is now open.
I figured it was worth trying. The guide this is based off made it relatively easy for me (but I do have above-average computer skill), and I wanted to make it even easier. Hopefully you found this ‘how to set up stable diffusion’ guide easy to use as well. Please let me know in the comments section if you have any questions/comments/feedback – I check at least daily!
Resources
Huge shout out to whoever wrote the guide (“all anons”) at https://rentry.org/voldy. That is essentially where this entire guide came from.
Intel Optane DC P1600X Installed in a M.2 slot for ZFS SLOG/ZIL benchmarks
As a follow-up to my last post (ZFS SLOG Performance Testing of SSDs including Intel P4800X to Samsung 850 Evo), I wanted to focus specifically on the Intel Optane devices in a slightly faster test machine. These are incredible devices, especially for the newly reduced prices on Amazon – $99 as of latest check (it was as low at $88 earlier this week) – for the P1600X 118GB M.2 form factor. Hopefully you enjoy this quick review / benchmark.
What is ZFS & SLOG/ZIL
A one-sentence summary is as follows: ZFS is a highly advanced & adaptable file system with multiple features to enhance performance, including the SLOG/ZIL (Separate Log Device/ZFS Intent Log), which essentially functions as a write cache for synchronous writes. For a more detailed write-up, see Jim Salter’s ZFS sync/async ZIL/SLOG guide.
Now to jump right into the performance.
A different (faster) test machine
I popped the P1600X into an M.2 slot of a different machine I have here at home and was very surprised at how much faster it was than in the previous test box. I know the Xeon D series isn’t exactly known for speed but people always say speed doesn’t really matter for storage. I guess being in the Major Leagues with these Optane devices means that processor speed does in fact matter. The single-thread speed of the 2678v3 isn’t a lot higher than the D-1541 (same generation Xeon) but the multi-thread is ~40% faster.
Test machine specs:
Somewhat generic 1U case with 4×3.5″ bays
AsrockRack EPC612D8
Intel Xeon E5-2678v3
2x32GB 2400 MHz (running at 2133 MHz due to v3)
Consumer Samsung NVMe boot drive (256GB)
1x Intel D3-S4610 as ZFS test-pool
2x Samsung PM853T 480GB as boot mirror for Proxmox (not used)
1x Intel Optane DC P1800X 58GB in the first M.2 slot
1x Intel Optane DC P4800X in the 2nd PCIe slot (via x16 riser)
Intel did provide the Optane devices but did not make any demands on what to write about or how to write it.
Intel Optane DC P1600X 58GB M.2
The Intel P1600X placed in the first M.2 slot of the AsrockRack EPC612D8
Intel Optane DC P4800X 375GB AIC
This drive is “face down” when installed so here’s a picture of it on my desk in its shipping tray. The other side is populated with many memory chips.
Intel P4800X in tray
TrueNAS diskinfo for Intel P1600X
I wanted to re-run this test in a machine where the M.2 slot wasn’t limited to x1 lane width. The results are far better, even for latency, which I wasn’t expecting. For 4k size, the latency is 13.6 microseconds per write. This calculates out to 71.8 kIOPS, which is great for this simple test (I believe it is QD=1). In the Xeon D-1541 machine with a x1 M.2 slot, the latencies were roughly double, and the throughput topped out at 374 MB/s. Quick comparison: spinning hard drives typically have sync write latencies in the 13 millisecond range, meaning the Optane gets writes committed 1000x faster than spinning hard drives.
The P4800X was also faster in the Xeon E5-2678v3 machine – with latencies 8-10% better and throughput up to 270MB/s faster. Makes me wonder what an even faster machine could do. The v3 stuff is getting quite old at this point, but still excellent value for homelab usage.
The 4k latency is 14.1 microseconds, which is curiously 0.5 us slower than the P1600X. Every other size was both quicker and faster.
I varied runtime (between 5 and 30s – 30s for the fast tests is limited by the single disk write speed of ~500MB/s, so I reduced the time to the default txg commit time of 5s) and iodepth (1, 4, 16, 64).
I’m just going to copy + paste (and link to) the Excel table I created since there I don’t really have a good way to do tables in my current WordPress install.
ZFS benchmark results with Intel Optane P1600X & P4800X
Conclusion
These P1600X drives are very, very quick with their 4k write latencies in the low teens. With the recent price drops, and the increasing prevalence of M.2 slots in storage devices, adding one as a SLOG is a super-cheap method to drastically improve sync write performance. I am always on eBay trying to find used enterprise drives with high performance to price ratios and I think this one tops the charts (reminder – it’s only $99 on Amazon as of writing – P1600X 118GB M.2). The write endurance is plenty high for many use cases at 1.3PB (ServeTheHome.com highlights that recycled data center SSDs rarely have more than 1PB written in their article Used enterprise SSDs: Dissecting our production SSD population). The fact that this M.2 doesn’t take a full drive bay makes it even more appealing for storage chassis with only 4 drive bays.
I’ve been meaning to type this post up for a couple months, but with a 7 month old and a 29 month old at home, a new job, and a half-renovated basement, it took a bit of a back seat.
First question you may have:
What is ZFS?
ZFS stands for ‘zettabyte file system’. It has a long history that started in the early 2000s as part of Sun Microsystem’s Solaris operating system. Since then, it has undergone a decent glow-up, broke free of Oracle/Sun licensing via a rebranding as OpenZFS (I will not pretend to know half the details of how this happened) and is now a default option in at least two somewhat mainstream operating systems (Ubuntu 20.04+ and Proxmox). It just so happens that those are the two primary Linux-based operating systems I use. It also just so happens that I use ZFS as my main filesystem for all of my Proxmox hosts, as well as my network storage systems running FreeNAS/TrueNAS core.
Why do I use ZFS?
A huge benefit of ZFS is data integrity. Long story very short, each block written to disk is written with a checksum. That checksum is checked upon reading, and if it doesn’t match, ZFS knows that the data became corrupted somewhere between writing it to disk, and reading it back (could be an issue with disk, cables, disk controller, or even a flipped bit from a cosmic ray). Luckily, ZFS has a variety of options that make this a non-issue. For anything important, I do at least a mirror, meaning two disks with the same exact data written to each. If a checksum is invalid from one disk, the other is tried. If the other checks out, the first is corrected. ZFS also run a “scrub” at regular intervals (default is usually monthly), where it will read every single block in a pool and verify the checksums for integrity. This prevents bitrot (the tendency for data to go bad if left sitting for years). I will admit, none of my data is super super important but I like to pretend it is.
Performance of ZFS
ZFS can be extremely performant, if the right hardware is used. A trade-off of the excellent data integrity is the overhead involved with checksums and all that jazz. ZFS is also a copy-on-write filesystem, which means snapshots are instant, and blocks aren’t changed – just the reference to the block. Where things get slow is when you have a pool of spinning, mechanical hard drives, and you request sync writes, meaning that ZFS will not say the write is completed until it is safely committed to disk. That takes 8-15 milliseconds for spinning disks. 8-15 milliseconds may not seem like a long time, but it is an eternity compared to CPU L1/L2/L3 cache and a tenth (don’t quote me on that) of an eternity compared to fast NVMe SSDs. To overcome to latency of mechanical disk access, loads of people think, “Oh I can just slap in this old SSD from my last build as a read/write cache and call it good”. That’s what we’ll examine today.
Benchmarking ZFS sync writes
The test system is something I picked up from r/homelabsales a few months ago for $150 in a Datto case. I figured that compared to current Synology/QNAP 4-bay NAS devices, it was 1) a steal, and 2) far more capable:
Intel Xeon D-1541 on a rebranded ASRock D1541D4U-2T8R
I stopped at 2x 4TB WD Reds instead of four so I could easily swap different SSDs into one of the other bays for benchmarking. I figure a basic 2 HDD mirror is decently close to many r/homelab setups. Of course, performance will roughly double for the base zpool if another mirror is added.
The guy on homelabsales had a bunch of similarly spec’d systems, all of which sold for very cheap – let me know if you got one!
The fio command used
fio is a standard benchmarking tool. I kept it simple and used the same script for each test. The following does random writes, with a 4k byte size to test IOPS instead of pure bandwidth/throughput, IO depth of 1, 30 second duration, and sync after each write.
Note: I set sync=always on the dataset, so I’m not specifying –fsync=1 here.
Note 2: iodepth=16 is a bit strong for home situations, but I’ve already redone this benchmark series twice so not doing it again with qd=1 for every drive. QD=1 is covered for the 3 fastest drives at the end
I will be testing a few different drives as SLOG devices (“write cache”):
Intel DC S3500 120GB
Samsung 850 Evo 500GB
HGST HUSSL4020ASS600 200GB
HGST Ultrastar SS300 HUSMM3240ASS205 400GB
Intel Optane P1600X M.2 form factor 58GB
Intel Optane P4800X AIC (PCI-e form factor) 375GB
The Intel devices were graciously provided by Intel with no expectation other than running some benchmarks on them. Apparently their Optane business is winding down, which is unfortunate because it is an amazing technology. ServeTheHome.com has confirmed the P4800X AIC form-factor is being discontinued.
A short word on “write cache” for ZFS – go read read Jim Salter’s ZFS sync/async ZIL/SLOG guide. He is a ZFS expert and has written many immensely helpful guides on it. Long story short, a SLOG is a write cache device, but only for sync writes. It needs to have consistent, low latency to function well. Old enterprise SSDs are ideal for this.
FreeBSD’s diskinfo command results
I briefly wanted to show the diskinfo results for the Intel devices:
Intel Optane P4800X 375GB
This is the single most expensive piece of computer hardware I’ve ever held in my own hands – the Intel Optane SSD DC P4800X 375GB in the AIC (PCIe) form-factor.
Bottoms out at 15 microseconds per IO for 259.6 MB/s at 4k blocksize. This is extremely fast, and is considered to be one of the best SLOG devices currently available (top 5 easily). Make no mistake, the Intel P4800X is one of the highest performing solid state drives in existence. Here’s a quote from the StorageReview.com review of the device: “For low-latency workloads, there is currently nothing that comes close to the Intel Optane SSD DC P4800X.”
There are a few form factors available, with price tags to match:
The P4800X’s little brother is no slouch. In fact, it is quicker latency-wise than any SATA and SAS SSD currently in existence. The diskinfo shows that in a slightly more capable system (Xeon E5-2678v3, 2x32GB 2133 MHz, ASRock EPC612D8), the latency is even lower than in the P4800X. The latency is an astounding 13.3 microseconds per IO at 4k size. These are now available on Amazon (as a 118GB version) for $88 as of writing – Intel Optane P1600X 118GB M.2. Note: the original version of this post had the numbers from a P1600X in a M.2 slot limited to a single PCIe lane (x1). The below numbers are from a full x4 slot.
With sync write speeds: 894 IOPS, 3.6MB/s. It is 2023 – do not use uncached mechanical hard drives for sync workloads. Median completion latency of 12 milliseconds, average of 17.8 milliseconds.
This effectively tests how fast the pool can write without consideration for sync writes. They are essentially buffered in memory (in the ZIL – ZFS intent log) and flushed every 5 seconds by default to disk.
IOPS = 48.3k, median latency = 400 microseconds, avg latency = 321 microseconds. Note that the max IOPS recorded was 288.8k, which was likely for the first few seconds as the ZIL filled up. As the ZIL started to flush to disk, the writes slowed down to keep pace with the slowness of the disks writing. This figure is the maximum this system can generate, regardless of what disks are used.
This was a very popular SSD a few years ago, with great performance per dollar when it was release. This was the 2nd SSD I ever bought (first was a Crucial M4 256GB).
IOPS = 6306, median latency = 1.9 milliseconds, avg latency = 2.5 ms, standard deviation = 1.6 ms. These are not great performance numbers. Consumer drives typically don’t deal well with high queue depth operations.
This was the first enterprise SSD I acquired for use in my homelab. I bought two, for a read/write cache on my Xpenology machine (DSM requires 2 drives to mirror for write cache). The datasheet indicates write latency of 65 us, which is pretty quick. Much faster than the 1900 us median latency of the Evo 850 in the previous section. Unfortunately for the 120GB drives, sequential writes are indicated at 135 MB/s, which is fine for gigabit filesharing. These drives (and many, many other “enterprise” SSDs) feature capacitors for power loss protection. The drive can acknowledge the writes very quick because the capacitors store enough energy for the drive to commit writes to NAND in the event of a power loss.
IOPS = 32.5k, median latency = 437 us, 5x faster than the Evo 850. Standard deviation = 0.14 ms, 10x more consistent than the Evo 850.
This is a SAS2 SSD with SLC NAND, and power loss protection. It has a rated endurance of 18 petabytes. This is effectively infinite write endurance for any home use situation (and even many enterprise/commercial use situations).
IOPS = 27.4k, median latency = 486 us, standard dev = 238 us. Very good numbers.
This is a more modern SAS3 MLC SSD, spec’d for write-intensive use cases. As far as I can tell, this drive is about as close to NVMe performance as you can get from a SAS3 interface per the datasheet specs (200k IOPS, 2050 MB/s throughput, 85 us latency max). Endurance is 10 drive writes per day for 5 years, which is 7.3 PB.
Our pool now does 43.0k IOPS (max 49.8k), with a median sync latency of 306 us, standard dev of 244 us
This is one of the drives sent to me by Intel. Optane is not NAND technology. I don’t really keep up on the details of persistent storage details, so you’ll have to look up specifics. What I do know, is the technology allows for much faster writes, in terms of latency. At the top end, the throughput isn’t as high as modern NVMe, but the latency is much quicker at low queue depths. As a reminder, all the tests so far are with queue depth = 16. At these queue depths, Optane doesn’t look as fast as it really is, unless you’re working with the P1600X’s big brother (next section).
IOPS = 39.2k (max 57.6k), median latency = 322 us, std dev = 385 us.
This is the other drive that Intel sent me. This drive is fast. Many review sites use verbiage such as “this drive has no comparison” or “this is the fastest drive we’ve ever tests”.
My first test was for 30 seconds. I realized the drive was doing it’s thing (caching sync writes) much faster than the HDD mirror could flush to disk. Regardless, the initial results are below. Note the max IOPS of 84.6k.
Recall that the default txg flush interval is 5 seconds. I used a test of 10 seconds, thinking that the SLOG will cache writes for up to 5 seconds, and for the next 5 seconds, the disks are constantly writing the cache to disk while the SLOG is still ingesting writes.
HGST SS300 – 10 second test
IOPS = 47.7k (max = 49.0k), median latency = 306 us, std dev = 125 us
IOPS = 80.7k (max = 86.3k, so might still be hitting HDD mirror limits), median latency = 172 us, std dev = 86 us. These numbers are fantastic, especially for 4k blocksize. The throughput is 331MB/s. You will have a hard time getting your hands on a faster device unless you have $xx,xxx to spend.
Where the Optane drives really shine is with low queue depth operations. I reran the tests with QD=1 (5 second test to simulate a burst of writes) for the P4800X, the P1600X, and the SS300.
P4800X queue depth = 1
IOPS = 20.2k, median latency = 45 us, std dev = 129 us.
Last test, using a P1600X as a ZFS special allocation device (special vdev)
The Intel engineer I was working with requested I demonstrate the potential of a P1600X as a ZFS special allocation device (special device/special vdev). They did not have a specific test in mind. A special device stores metadata about the files on the pool, and can additionally store small blocks (as set with the special_small_blocks property of the dataset). If you set special_small_blocks to 16k, for example, any file that is smaller than 16k will be written to the special device directly instead of the main pool disks. As you might imagine, this can really speed up transfers of small files. Note: since the special device stores all metadata about data on the pool, and potentially data itself, it is critical that it is at least a mirror (some use 3-way mirrors). If you lose the special device vdev, you lose the entire pool! Since I am just benchmarking, I do not have a mirrored special vdev. I can remove it whenever I want since the underlying pool is comprised of mirrors.
The test procedure is as follows:
Reboot machine to clear ARC
Run a command that reads metadata for every file on a dataset. The specific command prints out a distribution of file sizes, which I then used to calculate what I should use for special_small_blocks.
Note the time it took to run said command
Wipe pool
Add special device to pool
Transfer all data back to pool
Rerun command and note time difference.
The “wipe pool” and “transfer all data back to pool” steps make this test something you don’t want to repeat (as with many other ZFS things, such as changing compression type or recordsize, special device metadata is only written upon writes. it will not backfill).
Command used to for special_small_blocks calculation
find . -type f -print0 | xargs -0 ls -l | awk '{ n=int(log($5)/log(2)); if (n<10) { n=10; } size[n]++ } END { for (i in size) printf("%d %d\n", 2^i, size[i]) }' | sort -n | awk 'function human(x) { x[1]/=1024; if (x[1]>=1024) { x[2]++; human(x) } } { a[1]=$1; a[2]=0; human(a); printf("%3d%s: %6d\n", a[1],substr("kMGTEPYZ",a[2]+1,1),$2) }'
It prints out a list of sizes and how many entites are in that size bin. I threw ‘time’ in front to see how long it takes to run. I ran it once for each dataset. This is my media dataset (movies, music, tv shows, etc.)
[email protected][/mnt/test-pool/share-media]# time find . -type f -print0 | xargs -0 ls -l | awk '{ n=int(log($5)/log(2)); if (n<10) { n=10; } size[n]++ } END { for (i in size) printf("%d %d\n", 2^i, size[i]) }' | sort -n | awk 'function human(x) { x[1]/=1024; if (x[1]>=1024) { x[2]++; human(x) } } { a[1]=$1; a[2]=0; human(a); printf("%3d%s: %6d\n", a[1],substr("kMGTEPYZ",a[2]+1,1),$2) }'
1k: 14336
2k: 759
4k: 1194
8k: 4188
16k: 5609
32k: 3498
64k: 2882
128k: 552
256k: 194
512k: 103
1M: 58
2M: 6
4M: 2
8M: 14
16M: 98
32M: 253
64M: 793
128M: 1130
256M: 529
512M: 322
1G: 185
2G: 60
4G: 9
8G: 3
16G: 1
find . -type f -print0 0.15s user 2.10s system 6% cpu 37.210 total
xargs -0 ls -l 1.23s user 0.24s system 3% cpu 37.213 total
awk 0.07s user 0.02s system 0% cpu 37.213 total
sort -n 0.00s user 0.00s system 0% cpu 37.213 total
awk 0.00s user 0.00s system 0% cpu 37.212 total
37.2 seconds total to run.
And on my ‘data’ dataset (backups, general file storage, etc.):
[email protected][/mnt/test-pool/share-data]# time find . -type f -print0 | xargs -0 ls -l | awk '{ n=int(log($5)/log(2)); if (n<10) { n=10; } size[n]++ } END { for (i in size) printf("%d %d\n", 2^i, size[i]) }' | sort -n | awk 'function human(x) { x[1]/=1024; if (x[1]>=1024) { x[2]++; human(x) } } { a[1]=$1; a[2]=0; human(a); printf("%3d%s: %6d\n", a[1],substr("kMGTEPYZ",a[2]+1,1),$2) }'
1k: 106714
2k: 23595
4k: 20593
8k: 25994
16k: 20264
32k: 28506
64k: 28759
128k: 26909
256k: 20027
512k: 19708
1M: 24780
2M: 34587
4M: 20427
8M: 15888
16M: 2388
32M: 983
64M: 558
128M: 272
256M: 260
512M: 179
1G: 93
2G: 69
4G: 36
8G: 17
16G: 5
32G: 1
128G: 4
256G: 1
find . -type f -print0 0.36s user 5.46s system 3% cpu 2:43.07 total
xargs -0 ls -l 10.46s user 2.55s system 7% cpu 2:43.13 total
awk 0.78s user 0.02s system 0% cpu 2:43.13 total
sort -n 0.00s user 0.00s system 0% cpu 2:43.13 total
awk 0.00s user 0.00s system 0% cpu 2:43.13 total
2m43s. We have a baseline.
I also threw together a spreadsheet to do a cumulative sum on how much space is used by each bin. I decided to go with 16k, which should only use 0.87 GB of the 58 GB Optane P1600X for small files.
ZFS Special device size calculation spreadsheet table. Link to .xlsx file below.
Rebuilding the pool with a special allocation device
I use syncoid exclusively to synchronize ZFS pools across machines/systems. Sanoid generates the snapshots. These are great tools written by Jim Salter. I transferred with option special_small_blocks=16k so the property was set immediately. Syncoid won’t write to an empty, already created dataset. It took a while to transfer 3.7TB, even over a 10G link.
-- add 2x more WD Reds, wipe pools, recreate pools, remove p1600x as slog, re-add as special dev
[email protected][~]# zpool destroy bench-pool
zpool create -o ashift=12 test-pool mirror /dev/da0 /dev/da1 mirror /dev/da2 /dev/da3
zpool add -f test-pool special /dev/nvd1
[email protected][~]# smartctl -a /dev/nvme1
=== START OF INFORMATION SECTION ===
Model Number: INTEL SSDPEK1A058GA
Serial Number: PHOC209200KC058A
[email protected][~]# zpool status test-pool
pool: test-pool
state: ONLINE
config:
NAME STATE READ WRITE CKSUM
test-pool ONLINE 0 0 0
mirror-0 ONLINE 0 0 0
da0 ONLINE 0 0 0
da1 ONLINE 0 0 0
mirror-1 ONLINE 0 0 0
da2 ONLINE 0 0 0
da3 ONLINE 0 0 0
special
nvd1 ONLINE 0 0 0
errors: No known data errors
syncoid --no-sync-snap --recvoptions="o special_small_blocks=16k" [email protected]:big/share-media test-pool/share-media
[email protected][~]# zfs get special_small_blocks test-pool/share-media
NAME PROPERTY VALUE SOURCE
test-pool/share-media special_small_blocks 16K local
-----------------------------------------------------------------------------------------------------------------------------
-- NOTE: this zpool list was captured approximately 1 minute into the data transfer, hence why the nvd1 ALLOC is only 24.7M -
-----------------------------------------------------------------------------------------------------------------------------
[email protected][~]# zpool list -v test-pool
NAME SIZE ALLOC FREE CKPOINT EXPANDSZ FRAG CAP DEDUP HEALTH ALTROOT
test-pool 7.30T 15.1G 7.29T - - 0% 0% 1.00x ONLINE /mnt
mirror-0 3.62T 7.22G 3.62T - - 0% 0.19% - ONLINE
da0 3.64T - - - - - - - ONLINE
da1 3.64T - - - - - - - ONLINE
mirror-1 3.62T 7.87G 3.62T - - 0% 0.21% - ONLINE
da2 3.64T - - - - - - - ONLINE
da3 3.64T - - - - - - - ONLINE
special - - - - - - - - -
nvd1 54.9G 24.7M 54.5G - - 0% 0.04% - ONLINE
Special device metadata reading results
The same command as before for the ‘media’ dataset:
[email protected][/mnt/test-pool/share-media]# time find . -type f -print0 | xargs -0 ls -l | awk '{ n=int(log($5)/log(2)); if (n<10) { n=10; } size[n]++ } END { for (i in size) printf("%d %d\n", 2^i, size[i]) }' | sort -n | awk 'function human(x) { x[1]/=1024; if (x[1]>=1024) { x[2]++; human(x) } } { a[1]=$1; a[2]=0; human(a); printf("%3d%s: %6d\n", a[1],substr("kMGTEPYZ",a[2]+1,1),$2) }'
1k: 14336
2k: 759
4k: 1194
...
4G: 9
8G: 3
16G: 1
find . -type f -print0 0.14s user 1.53s system 51% cpu 3.254 total
xargs -0 ls -l 1.22s user 0.23s system 43% cpu 3.370 total
awk 0.08s user 0.00s system 2% cpu 3.370 total
sort -n 0.00s user 0.00s system 0% cpu 3.370 total
awk 0.00s user 0.00s system 0% cpu 3.369 total
And for the ‘data’ dataset:
[email protected][/mnt/test-pool/share-media]# cd ../share-data
[email protected][/mnt/test-pool/share-data]# time find . -type f -print0 | xargs -0 ls -l | awk '{ n=int(log($5)/log(2)); if (n<10) { n=10; } size[n]++ } END { for (i in size) printf("%d %d\n", 2^i, size[i]) }' | sort -n | awk 'function human(x) { x[1]/=1024; if (x[1]>=1024) { x[2]++; human(x) } } { a[1]=$1; a[2]=0; human(a); printf("%3d%s: %6d\n", a[1],substr("kMGTEPYZ",a[2]+1,1),$2) }'
1k: 106714
2k: 23595
4k: 20593
...
32G: 1
128G: 4
256G: 1
find . -type f -print0 0.24s user 3.80s system 24% cpu 16.282 total
xargs -0 ls -l 10.29s user 2.39s system 77% cpu 16.345 total
awk 0.78s user 0.02s system 4% cpu 16.345 total
sort -n 0.00s user 0.00s system 0% cpu 16.345 total
awk 0.00s user 0.00s system 0% cpu 16.345 total
To summarize the special device metadata access times (all times in seconds), it reduced the amount of time required to traverse all file metadata by 90%. That said, there was a similar reduction if I ran the command multiple times in a row without a special vdev due to ZFS’ ARC caching the metadata. It would be trivial to write a cron task that would iterate over files on a regular basis to keep that data in ARC as frequently accessed data.
share-media
share-data
no special vdev (s)
37.2
163.07
with special vdev (s)
3.25
16.28
delta (s)
-33.95
-146.79
delta %
-91.3%
-90.0%
Special device size utilization
As far as I can tell, there isn’t really a good way to calcuate how much space will be used by the special device storing metadata and such. In my case, 3.74TB of data with special_small_blocks=16k resulted in 11.9GB being used on the special device:
If you’ve made it this far, congrats. I didn’t intend to write this many words on ZFS benchmarks but here we are. Hopefully you found this data interesting/insightful and/or learned something from it. At a minimum, you should now be able to calculate how much space you need if you want to utilize a special vdev. Also special thanks to Intel for sending me Optane samples to play around with and benchmark.
Here’s a table with a summary of the results:
ZFS SLOG/ZIL benchmarks for select devices
Please let me know in the comments of this post if you’d like me to re-run any benchmarks, or have any questions/comments/concerns about the process!
Been a while since I last posted. For basically everything I do Linux-wise, I use Ubuntu. Specifically the Long Term Support versions, which are released on even years in April (April 2020, April 2022, etc.), hence the 22.04 designation of the latest LTS, Jammy. I had standardized on 20.04 (Focal) for everything, but am finding that various packages I use that I expect to be available on 20.04, aren’t. But they are present in standard, default package lists for Jammy 22.04. Last night I was creating a new VM to transfer this very blog to my shiny, new co-located server. I was going to use the Ubuntu 20.04 Proxmox Cloud Init Image Script template I wrote up a bit ago. Then I stopped and realized I should update it. So I changed 2004 everywhere in the script to 2204 and ‘focal’ to ‘jammy’, reran it, and now I have a fancy new cloud init script for Ubuntu 22.04. Note that this script does require you to install libguestfs-tools, as indicated in the previous cloud-init post.
###############
# things to double-check:
# 1. user directory
# 2. your SSH key location
# 3. which bridge you assign with the create line (currently set to vmbr100)
# 4. which storage is being utilized (script uses local-zfs)
###############
rm -f jammy-server-cloudimg-amd64.img
wget -q https://cloud-images.ubuntu.com/jammy/current/jammy-server-cloudimg-amd64.img
sudo virt-customize -a jammy-server-cloudimg-amd64.img --install qemu-guest-agent
sudo virt-customize -a jammy-server-cloudimg-amd64.img --ssh-inject root:file:/home/austin/id_rsa.pub
sudo qm destroy 9022
sudo qm create 9022 --name "ubuntu-2204-cloudinit-template" --memory 2048 --cores 2 --net0 virtio,bridge=vmbr100
sudo qm importdisk 9022 jammy-server-cloudimg-amd64.img local-zfs
sudo qm set 9022 --scsihw virtio-scsi-pci --scsi0 local-zfs:vm-9022-disk-0
sudo qm set 9022 --boot c --bootdisk scsi0
sudo qm set 9022 --ide2 local-zfs:cloudinit
sudo qm set 9022 --serial0 socket --vga serial0
sudo qm set 9022 --agent enabled=1
sudo qm template 9022
rm -f jammy-server-cloudimg-amd64.img
echo "next up, clone VM, then expand the disk"
echo "you also still need to copy ssh keys to the newly cloned VM"
Proxmox Ubuntu 22.04 cloud-init image script
I suppose for a full automation I should get user with a whoami command, assign it to a variable, and use that throughout the script. That’s a task for another day.
I also added a line in my crontab to have this script run weekly, so the image is essentially always up to date. I’ll run both 20.04 and 22.04 in parallel for a bit but anticipate that I can turn off the 20.04 job fairly soon.
# m h dom mon dow command
52 19 * * TUE sleep $(( RANDOM \% 60 )); /usr/bin/bash /home/austin/ubuntu-2004-cloud-init-create.sh >> /home/austin/ubuntu-template.log 2>&1
52 20 * * TUE sleep $(( RANDOM \% 60 )); /usr/bin/bash /home/austin/ubuntu-2204-cloud-init-create.sh >> /home/austin/ubuntu-2204-template.log 2>&1
I have finally started actual work on a “Tinder for restaurants” that I’m calling MonchMatch. I’ve been tossing the idea around for a few months but have finally made some moves with outsourcing the design. I can get the app and backend going myself, but I have zero creative talent.
If you are or know anyone who wants to work on the design, please drop me a note.
If you’re interested in being notified when MonchMatch is released, head on over to the MonchMatch site and enter your email!
