Lint your Dockerfiles with Hadolint

If you haven’t already gotten in to the habit of linting your Dockerfiles you should.  Code linting is a common practice in software development which helps find, identify and eliminate issues and bugs before they are ever able to become a problem.  One of the main benefits of linting your code is that it helps identify and eliminate nasty little bugs before they ever have a chance to become a problem.

Taking the concept of linting and appyling it to Dockerfiles gives you a quick and easy way to identify errors quickly before you ever build any Docker images.  By running your Dockerfile through a linter first, you can ensure that there aren’t any structural problems with the logic and instructions specified in your Dockerfiles.  Linting your files is fast and easy (as I will demonstrate) and getting in the habit of adding a linting step to your development workflow is often very useful because not only will the linter help identify hidden issues which you might not otherwise catch right away but it can potentially save hours of troubleshoot later on, so there is some pretty good effort to benefit ratio there.

There are serveral other Docker linting tools around:

But in my experience, these tools have either been overly complicated, don’t detect/catch as many errors and in general just don’t seem to work as well or have as much polish as Hadolint.  I may just have a skewed perspective of these tools but this was my experience when I tried them, so take my evaluation with a grain of salt.  Definitely let me know if you have experience with any of these tools, I may just need to revisit them to get a better perspective.

With that being said, Hadolint offers everything I need and is very easy to use and get started with and makes linting your Dockerfiles is trivially easy, which counts for the most points in my experience.  Another bonus of Hadolint is that the project is fairly active and the author is friendly, so if there are things you’d like to see get added, it shouldn’t be too hard to get some movement on.  You can check out the project on Github for details about how to install and run Hadolint.

Below, I will go over how to get setup and started as well as some basic usage.


If you use Mac OS X there is a brew formula for installing Hadolint.

brew update
brew install hadolint

If you are a Windows user, for now you will have to run Hadolint from within a Docker conainer.

docker run --rm -i lukasmartinelli/hadolint < Dockerfile

If you feel comfortable with the source code you can try building the code locally.  I haven’t attempted that method, so I don’t have instructions here for how to do it.  Go check out the project if you are interested.


Hadolint helps you find syntax errors and other mistakes that you may not notice in your Dockerfiles otherwise.  It’s easy to get started.  To run Hadolint run the following.

hadolint Dockerfile

If there are any issues, Hadolint will print out the rule number as well as a blurb describing what could potentially be wrong.

DL4000 Specify a maintainer of the Dockerfile
L1 DL3007 Using latest is prone to errors if the image will ever update. Pin the version explicitly to a release tag.
L3 DL3013 Pin versions in pip. Instead of `pip install <package>` use `pip install <package>==<version>`

As with any linting tool, you will definitely get some false positives at some point so just be aware of items that can potentially be ignored.  There is an issue open on Github right now to allow Hadolint to ignore certain rules, which will help eliminate some of the false positives.  For example, in the above snippet, we don’t necessarily care about the maintainer missing so it will be nice to be able to ignore that line.

Here is a complete reference for the all of the linting rules.  The wiki gives examples of what is wrong and how to fix things, which is very helpful.  Additionally, the author is welcoming ideas for additional things to check, so if you have a good idea for a linting rule open up an issue.

That’s it for now.  Good luck and happy Docker linting!

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Easy Prometheus Monitoring in Rancher

Docker monitoring and container monitoring in general is an area that has historically been difficult.  There has been a lot of movement and progress in the last year or so to beef up container monitoring tools but in my experience the tools have either been expensive or difficult to configure and complicated to use.  The combination or Rancher and Prometheus has finally given me hope.  Now it is easy easy to setup and configure a distributed monitoring solution without paying a high price.

Prometheus has recently added support for Rancher via the Rancher exporter, which is great news.  This is by far the easiest method I have discovered thus far for experimenting with Prometheus.

For those that don’t know much about Prometheus, it is an up and coming project created by engineers at Soundcloud which is hosted on Github.  Prometheus is focused on monitoring, specifically focusing on container and Docker monitoring.  Prometheus uses a polling based model for “scraping” metrics out of predefined endpoints.  The Prometheus Rancher exporter enables Prometheus to scrape Rancher server specific metrics, which are very useful to have.  To build on that, one other point worth mentioning here is that Prometheus has a very nice, flexible design built upon different client libraries in a similar way to Graphite, so adding support and instrumenting code for different types of platforms is easy to implement.  Check out the list of exporters in the Prometheus docs for idea on how to get started exporting metrics.

This post won’t cover setting up Rancher server or any of the Rancher environment since it is well documented in other places.  I won’t touch on alerting here either because I honestly haven’t had much time to dig into it much yet.  So with that said, the first step I will focus on in this post is getting Prometheus set up and running.  Luckily it is extremely easy to accomplish this using the Rancher catalog and the Prometheus template.

prometheus stack

Once Prometheus has been bootstrapped and everything is up, test it out by navigating to the Grafana home dashboard created by the bootstrap process.  Since this is a simple demo, my dashboard is located at the IP of the server using port 3000 which is the only port that should need to be publicly exposed if you are interested in sharing the Grafana dashboard.

The default Grafana credentials for this catalog template are admin/admin for the username and password, which is noted in the catalog notes found here.  The Prometheus tools ship with some nice preconfigured dashboards, so after you have things set up, it is definitely worth checking out some of them.

grafana dashboard

If you look around the dashboards you will probably notice that metrics for the Rancher server aren’t available by default.  To enable these metrics we need to configure Prometheus to connect to the Rancher API, as noted in the Rancher monitoring guide.

Navigate to http://<SERVER_IP>:8080/v1/settings/ on your Rancher server, then in the top right click edit, and then update the value there to point to the server address where InfluxDB was deployed to.

influxdb host

After this setting has been configured, restart the Rancher server container, wait a few minutes and then check Grafana.

rancher server metrics

As you can see, metrics are now flowing in the the dashboard.

Now that we have the basics configured, we can drill down in to individual containers to get a more granular view of what is happening in the environment.  This type of granularity is great because it gives a very detailed view of what exactly is going on inside our environment and gives us an easy way to share visuals with other team members.  Prometheus offers a web interface to interact with the query language and visual results, which is useful to help figure out what kinds of things to visualize in Grafana.

Navigate to the server that the Prometheus server container is deployed to on port 9090.  You should see a screen similar to the following.


There is  documentation about how to get started with using this tool, so I recommend taking a look and playing around with it yourself.  Once you find some useful metrics, visualized in the graph view, grab the query used to generate the graph and add a new dashboard to Grafana.

Prometheus offers a lot of power and flexibility and is a great tool for monitoring.  I am still very new to Prometheus but so far it looks very promising and I have to say I’m really impressed with the amount of polish and detail I was able to get in just an afternoon of experimenting.  I will be updating this post as I get more exposure to Prometheus and get more metrics and monitoring set up so stay tuned.

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Useful Vim Plugins

This post is mostly a reference for folks that are interested in adding a little bit of extra polish and functionality to the stock version of Vim.  The plugin system in Vim is a little bit confusing at first but is really powerful once you get past the initial learning curve.  I know this topic has been covered a million times but having a centralized reference for how to set up each plugin is a little bit harder to find.

Below I have highlighted a sample list of my favorite Vim plugins.  I suggest that you go try as many plugins that you can to figure out what suits your needs and workflow best.  The following plugins are the most useful to me, but certainly I don’t think will be the best for everybody so use this post as a reference to getting started with plugins and try some out to decide which ones are the best for your own environment.


This is a package manager of sorts for Vim plugins.  Vundle allows you to download, install, search and otherwise manage plugins for Vim in an easy and straight forward way.

To get started with Vundle, put the following configuration at THE VERY TOP of your vimrc.

set nocompatible              " be iMproved, required
filetype off                  " required
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#rc()
"" let Vundle manage Vundle
Bundle 'gmarik/Vundle.vim'

Then you need to clone the Vundle project in to the path specified in the vimrc from above.

git clone ~/.vim/bundle/Vundle.vim

Now you can install any other defined plugins from within Vim by  running :BundleInstall.  This should trigger Vundle to start downloading/updating its list of plugins based on your vimrc.

To install additional plugins, update your vimrc with the plugins you want to install, similar to how Vundle installs itself as shown below.

"" Example plugin
Bundle 'flazz/vim-colorschemes'

Color Schemes

Customizing the look and feel of Vim is a very personal experience.  Luckily there are a lot of options to choose from.

The vim-colorschemes plugin allows you to pick from a huge list of custom color schemes that users have put together and published.  As illustrated above you can simply add the repo to your vimrc to gain access to a large number of color options.  Then to pick one just add the following to your vimrc (after the Bundle command).

colorscheme xoria256

Next time you open up Vim you should see color output for the scheme you like.


Syntastic is a fantastic syntax highlighter and linting tool and is easily the best syntax checker I have found for Vim.  Syntastic offers support for tons of different languages and styles and even offers support for third party syntax checking plugins.

Here is how to install and configure Syntastic using Vundle.  The first step is to ddd Syntastic to your vimrc,

" Syntax highlighting
 Bundle 'scrooloose/syntastic'

There are a few basic settings that also need to get added to your vimrc to get Syntastic to work well.

" Syntastic statusline
 set statusline+=%#warningmsg#
 set statusline+=%{SyntasticStatuslineFlag()}
 set statusline+=%*
 " Sytnastic settings
 let g:syntastic_always_populate_loc_list = 1
 let g:syntastic_auto_loc_list = 1
 let g:syntastic_check_on_open = 1
 let g:syntastic_loc_list_height=5
 let g:syntastic_check_on_wq = 0
 " Better symbols
 let g:syntastic_error_symbol = 'XX'
 let g:syntastic_warning_symbol = '!!'

That’s pretty much it.  Having a syntax highlighter and automatic code linter has been a wonderful boon for productivity.  I have saved  myself so much time chasing down syntax errors and other bad code.  I definitely recommend this tool.


This plugin is an autocompletion tool that adds tab completion to Vim, giving it a really nice IDE feel.  I’ve only tested YCM out for a few weeks now but have to say it doesn’t seem to slow anything down very much at all, which is nice.  An added bonus to using YCM with Syntastic is that they work together so if there are problems with the functions entered by YCM, Syntastic will pick them up.

Here are the installation instructions for Vundle.  The first thing you will need to do is add a Vundle reference to your vimrc.

"" Autocomplete
Bundle 'Valloric/YouCompleteMe'

Then, in Vim, run :BundleInstall – this will download the git repo for YouCompleteMe.  Once the repo is downloaded you will need a few other tools installed to get things working correctly.  Check the official documentation for installation instruction for your system.  On OS X you will need to have Python, cmake, MacVim and clang support.

xcode-select --install
brew install cmake

Then, to install YouCompleteMe.

cd ~/.vim/bundle/YouCompleteMe
git submodule update --init --recursive (not needed if you use Vundle)
./ --clang-completer


Highlights pesky whitespace automatically.  This one is really useful to just have on in the background to help you catch whitespace mistakes.  I know I make a lot of mistakes with regards to missing whitespace so having this is just really nice.

To install it.

"" Whitespace highlighting
Bundle 'ntpeters/vim-better-whitespace'

That’s it.  Vundle should handle the rest.

ctrlp / nerdtree

These tools are useful for file management and traversal.  These plugins become more powersful when you work with a lot of files and move around different directories a lot.  There is some debate about whether or not to use nerdtree in favor of the built in netrw.  Nonetheless, it is still worth checking out different file browsers and see how they work.

Check out Vim Unite for a sort of hybrid file manager for fuzzy finding like ctrlp with additional functionality, like the ability to grep files from within Vim using a mapped key.

Bonus – Shellcheck

This is a shell and bash linting tool that integrates with vim and is great.  Bash is notoriously difficult to read and debug and the shellcheck tools helps out with that a lot.

Install shellcheck on your system and syntastic will automatically pick up the installation and automatically do its linting whenever you save a file.  I have been writing a lot of bash lately and the shellcheck tool has been a godsend for catching mistakes, and especially useful in Vim since it runs all the time.

By combining the powers of a good syntax highlighter and a good solid understanding of Bash you should be able to be that much more productive once you get used to having a build in to syntax and style checker for your scripts.

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xhyve vs vbox driver benchmarks for docker-machine

Getting a usable and productive dev environment working with Docker on OS X is not exactly trivial, although it is getting much better.  If you have spent any time working with docker-machine and Docker on OS X you’ve probably run across some type of roadblock to getting your dev environment working.

If you have used docker-machine you are probably familiar with the Virtualbox driver, the driver that ships as by default.  Obviously it works out of the box but if you have used Virtualbox for any amount of time you have probably discovered some of its quirks.  My biggest gripe thus far with Vbox is that their shared folders technology to sync files between the host and VM is slooooow.  In fact, I have written about my own workaround here.

I have run in to some other performance issues using VBox.  This write up is a very detailed comparison of the performance between VBox and VMWare.  The tl;dr of the post is that that the VMWare hypervisor has better performance.  To Oracle’s credit though, many of these performance issues have actually been addressed in the vbox 5.0.0 release.  So if you aren’t running on 5.x definitely make the jump.  The Docker Toolbox ships the newer release so there is no reason not to upgrade.

Making the jump to VBox 5.x may, and most likely should solve your problems but I have been curious about what other options are out there.  Recently, as of July 2015, the xhyve hypervisor project has been available on OS X.  xhyve is a port of the byhve project, which aims to bring high performance virtualization with a light footprint to OS X.  It is still very young but shows a lot of potential.

Even younger than the xhyve project itself is the xhyve driver for docker-machine.  It is so young that it is still not an officially supported driver yet, though it looks like it is well on its way.  Definitely keep an eye on the xhyve and docker-machine xhyve projects if you are looking for an alternative to either VBox or VMWare.  The xhyve docker-machine driver project has recently closed a ticket to be added to brew so it is much less complicated to get working.

Xhyve installation

I will be going over the bare minimum installation instructions to getting everything working.  If you are interested in more of the details on how to get the xhyve driver, I suggest taking a look at this awesome blog post.  The post goes in to depth on how to install and use the docker-machine xhyve driver if you are interested in a more in depth look at how to get things working.

Make sure you have brew installed first.  You will also need to have brew cask installed.  After you have brew installed you should be able to get it from the command line with the following command.

brew tap caskroom/cask

Once you have cask installed you should be able to install the remaining components.

brew update
brew install xhyve
brew cask install dockertoolbox

This might take a little bit depending on how fast your internet connection is.  After you have the toolbox installed, go grab the docker-machine xhyve driver.

brew install docker-machine-driver-xhyve

If you have the dockertoolbox installed already you might some errors in the output.  This just means there was a version conflict somewhere.  As of docker-machine version 0.5.6_1, support has been added for the xhyve driver.

There is currently a caveat to using this driver where you need to change some permissions.  This should hopefully be fixed in the future but is at least something to be aware of.

sudo chown root:wheel $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve
sudo chmod u+s $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve

You will also need to clean out your /etc/exports file if you have made changes.

sudo mv /etc/exports{,.backup} && touch /etc/exports

Then create the machine.

docker-machine create --driver xhyve --xhyve-experimental-nfs-share test

If you can interact with the Docker daemon you should be in business.

Benchmark results

The remainder of the post describes the benchmark and performance results of the VBox driver and the xhyve driver.  If you are only interested in getting the xhyve driver working then feel free to skim through the benchmarks, but be sure to take a look at the conclusion for the final verdict.

Below are the specs of the OS X machine that was used to run my benchmarks.

  • OS X 10.10.5
  • Virtualbox 5.0.12
  • xhyve 0.2.0
  • docker-machine-xyhve 0.2.2

Many of the ideas I used for the benchmark tests were taken from the post linked above.  It shows in great detail the methodology that was used to benchmark each of the drivers, which is useful because it gives some really good insight into the tools that were used and how the tests were performed.

Benchmarking on the boot2docker VM is tricky because it is mostly a read only file system and there is no package manger.  Therefore I relied on running the benchmarks inside containers, using a few different methodologies for my testing.  The first was borrowed from the simple-container-benchmarks project on Dockerhub.  This benchmark test gives a good idea of the overall write performance and CPU performance of a container running inside the VM.  For network performance I used the iperf3 image located on Dockerhub.

Below are the results of a few random runs for both the VBox driver as well as the xhyve driver.  I have left out the specific commands here as they are included in the links to each benchmark.  Use the links to each project for specific instructions on how to run the benchmarks yourself if you are interested.  The results were interesting because I was expecting the xhyve driver to outperform the VBox driver.

Virtualbox results

container benchmark results (FS write and CPU)

Client mode...
Performance benchmarks
dockerhost: tcp://
host: a8b790317264
date: Sat Jan 23 02:24:20 UTC 2016

FS write performance
1073741824 bytes (1.1 GB) copied, 2.39743 s, 448 MB/s
1073741824 bytes (1.1 GB) copied, 2.35377 s, 456 MB/s
1073741824 bytes (1.1 GB) copied, 1.9075 s, 563 MB/s
1073741824 bytes (1.1 GB) copied, 2.37838 s, 451 MB/s
1073741824 bytes (1.1 GB) copied, 2.03373 s, 528 MB/s
1073741824 bytes (1.1 GB) copied, 1.94024 s, 553 MB/s
1073741824 bytes (1.1 GB) copied, 1.99546 s, 538 MB/s
1073741824 bytes (1.1 GB) copied, 2.00287 s, 536 MB/s
1073741824 bytes (1.1 GB) copied, 1.5292 s, 702 MB/s
1073741824 bytes (1.1 GB) copied, 1.92617 s, 557 MB/s

CPU performance
268435456 bytes (268 MB) copied, 22.6775 s, 11.8 MB/s
268435456 bytes (268 MB) copied, 22.1466 s, 12.1 MB/s
268435456 bytes (268 MB) copied, 30.7552 s, 8.7 MB/s
268435456 bytes (268 MB) copied, 22.2861 s, 12.0 MB/s
268435456 bytes (268 MB) copied, 22.5571 s, 11.9 MB/s
268435456 bytes (268 MB) copied, 21.9901 s, 12.2 MB/s
268435456 bytes (268 MB) copied, 21.8232 s, 12.3 MB/s
268435456 bytes (268 MB) copied, 31.3903 s, 8.6 MB/s
268435456 bytes (268 MB) copied, 28.1219 s, 9.5 MB/s
268435456 bytes (268 MB) copied, 31.0172 s, 8.7 MB/s

System info
             total       used       free     shared    buffers     cached
Mem:       1019960     313288     706672     113104       7808     132532
Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                2
On-line CPU(s) list:   0,1
Thread(s) per core:    1
Core(s) per socket:    2
Socket(s):             1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 58
Stepping:              9
CPU MHz:               2294.770
BogoMIPS:              4589.54
Hypervisor vendor:     KVM
Virtualization type:   full
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              3072K

iperf results

Connecting to host, port 5201
[  4] local port 39476 connected to port 5201
[ ID] Interval           Transfer     Bandwidth       Retr  Cwnd
[  4]   0.00-1.00   sec  2.09 GBytes  17.9 Gbits/sec  2321   1.03 MBytes
[  4]   1.00-2.00   sec  2.46 GBytes  21.1 Gbits/sec  496    980 KBytes
[  4]   2.00-3.00   sec  2.24 GBytes  19.3 Gbits/sec  339   1.77 MBytes
[  4]   3.00-4.00   sec  2.54 GBytes  21.8 Gbits/sec  1355    389 KBytes
[  4]   4.00-5.00   sec  2.10 GBytes  18.0 Gbits/sec  106    495 KBytes
[  4]   5.00-6.00   sec  3.00 GBytes  25.7 Gbits/sec  217    411 KBytes
[  4]   6.00-7.00   sec  2.60 GBytes  22.4 Gbits/sec  440   1.72 MBytes
[  4]   7.00-8.00   sec  2.06 GBytes  17.7 Gbits/sec    0   1.72 MBytes
[  4]   8.00-9.00   sec  2.07 GBytes  17.8 Gbits/sec    0   1.72 MBytes
[  4]   9.00-10.00  sec  2.51 GBytes  21.6 Gbits/sec  876    713 KBytes
- - - - - - - - - - - - - - - - - - - - - - - - -
[ ID] Interval           Transfer     Bandwidth       Retr
[  4]   0.00-10.00  sec  23.7 GBytes  20.3 Gbits/sec  6150             sender
[  4]   0.00-10.00  sec  23.7 GBytes  20.3 Gbits/sec                  receiver

iperf Done.

xhyve results

container benchmark results (FS write and CPU)

Client mode...
Performance benchmarks
host: 2c8d9ba61eae
date: Sat Jan 23 02:08:15 UTC 2016

FS write performance
1073741824 bytes (1.1 GB) copied, 8.24671 s, 130 MB/s
1073741824 bytes (1.1 GB) copied, 5.89179 s, 182 MB/s
1073741824 bytes (1.1 GB) copied, 6.05392 s, 177 MB/s
1073741824 bytes (1.1 GB) copied, 5.37728 s, 200 MB/s
1073741824 bytes (1.1 GB) copied, 4.824 s, 223 MB/s
1073741824 bytes (1.1 GB) copied, 5.90409 s, 182 MB/s
1073741824 bytes (1.1 GB) copied, 5.22375 s, 206 MB/s
1073741824 bytes (1.1 GB) copied, 5.07298 s, 212 MB/s
1073741824 bytes (1.1 GB) copied, 5.89058 s, 182 MB/s
1073741824 bytes (1.1 GB) copied, 4.80828 s, 223 MB/s

CPU performance
268435456 bytes (268 MB) copied, 25.478 s, 10.5 MB/s
268435456 bytes (268 MB) copied, 31.3984 s, 8.5 MB/s
268435456 bytes (268 MB) copied, 24.698 s, 10.9 MB/s
268435456 bytes (268 MB) copied, 31.1973 s, 8.6 MB/s
268435456 bytes (268 MB) copied, 23.3705 s, 11.5 MB/s
268435456 bytes (268 MB) copied, 23.3973 s, 11.5 MB/s
268435456 bytes (268 MB) copied, 23.7405 s, 11.3 MB/s
268435456 bytes (268 MB) copied, 23.6118 s, 11.4 MB/s
268435456 bytes (268 MB) copied, 23.5606 s, 11.4 MB/s
268435456 bytes (268 MB) copied, 24.3341 s, 11.0 MB/s

System info
             total       used       free     shared    buffers     cached
Mem:       1020028     291632     728396      70356       6420      89824
Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                2
On-line CPU(s) list:   0,1
Thread(s) per core:    1
Core(s) per socket:    1
Socket(s):             2
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 58
Stepping:              9
CPU MHz:               2294.450
BogoMIPS:              4607.99
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              3072K

iperf results

Connecting to host, port 5201
[  4] local port 49244 connected to port 5201
[ ID] Interval           Transfer     Bandwidth       Retr  Cwnd
[  4]   0.00-1.00   sec  2.29 GBytes  19.7 Gbits/sec    0   1.90 MBytes
[  4]   1.00-2.00   sec  2.84 GBytes  24.4 Gbits/sec  567    953 KBytes
[  4]   2.00-3.00   sec  2.16 GBytes  18.6 Gbits/sec  327    667 KBytes
[  4]   3.00-4.00   sec  2.32 GBytes  19.9 Gbits/sec  166   1.52 MBytes
[  4]   4.00-5.00   sec  2.63 GBytes  22.6 Gbits/sec  565    769 KBytes
[  4]   5.00-6.00   sec  2.71 GBytes  23.3 Gbits/sec  608    583 KBytes
[  4]   6.00-7.00   sec  2.67 GBytes  22.9 Gbits/sec  217   1.40 MBytes
[  4]   7.00-8.00   sec  2.98 GBytes  25.6 Gbits/sec  782    498 KBytes
[  4]   8.00-9.00   sec  2.80 GBytes  24.0 Gbits/sec  359   1.01 MBytes
[  4]   9.00-10.00  sec  2.43 GBytes  20.9 Gbits/sec  883    467 KBytes
- - - - - - - - - - - - - - - - - - - - - - - - -
[ ID] Interval           Transfer     Bandwidth       Retr
[  4]   0.00-10.00  sec  25.8 GBytes  22.2 Gbits/sec  4474             sender
[  4]   0.00-10.00  sec  25.8 GBytes  22.2 Gbits/sec                  receiver

iperf Done.


I was on on the fence about VBox performance but the proof is in the pudding here with the test results.  The VBox driver had significantly better FS write performance (almost 2x).  CPU performance was about equal overall, and network throughput was also very similar.  I suspect CPU performance would be in favor of xhyve if these tests were run using VBox 4.x.  Regardless, equal CPU performance, similar network throughput and significantly better FS writes tip the scale in favor of the VBox driver.

As frustrating as it can be at times to use Vbox, many of its past performance issues have been fixed as of the v5.0 release.  The shared folder issue still exists but is largely taken care of by the great, easy to use tools that the Docker community has written, docker-machine-nfs is my favorite.

Surprisingly, or maybe not THAT surprisingly, xhyve actually performs worse that Virtualbox at this point.  xyhve itself is still a super young project and the docker-machine xhyve driver is still super young so there is definitely some room for growth.  That said, it was very straightforward to get xhyve and the docker-drive installed and configured, so I believe it is just a matter of time before the xhyve driver matures to a point where it can replace other drivers.  One down side of the xhyve driver is that it also suffers from the host to VM shared folder issue and the current best work around is to use the –nfs-share flag that the xhyve docker-machine driver offers.

I will definitely have my eye on the xhyve project moving forward because it looks to be a great alternative to other virtualization technologies for OS X once it reaches a point of maturity.  For now, VBox works more than sufficiently, has been around for a long time, is pretty much ubiquitous across platforms and the developers have shown that they are still actively working on improving the project with the recent 5.0 release.

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Fixing docker-machine shared folder performance issues

It is a known issue that vboxsf (Virtualbox Shared Folders) has performance problems.  This ugly fact becomes a problem if you use docker-machine with the default Virtualbox driver to mount volumes, both on Windows and OS X, especially when mounting directories with a large amount (~17k and above files).  Linux does not suffer from this performance problem since it is able to run Docker natively and does not require you to run docker-machine.

There are various issues floating around Github referencing this problem, most of which remain unresolved.

Unfortunately there is currently not a proper fix for the vboxsf performance issues on OS X and Windows.  In fact, I reached out to the Virtualbox developers around a year ago asking what the deal was and was basically told that fixing shared folder performance was not a high priority issue for their dev team.

After hearing the unsettling news, I set out to find a good way to deal with shared volumes.  I stumbled across a few different approaches to solving the problem but most of them ended up being glitchy (at the time) or overly complicated.  There is a nice write up that mentions many of the tools that I tried myself.

Having tried most of the methods out there, easily the best workaround I have found is to use NFS file shares to mount the “Users” directory using a tool called docker-machine-nfs.  It is easy to install and run and most importantly it just works out of the box, which is exactly what most folks are looking for.

Sadly this method does not work on Windows.  And as far as I can tell there is not a good workaround to this problem if you are running docker-machine on Windows.  It does sounds like some folks maybe have had some success using samba but I have not attempted to get fast volumes working on Windows so can’t say for sure what the best approach is.

To install docker-machine-nfs

curl -s |
  sudo tee /usr/local/bin/docker-machine-nfs > /dev/null && \
  sudo chmod +x /usr/local/bin/docker-machine-nfs

To run it

Make sure you already have created a docker-machine VM and verify that it is running.  Then run the following command.

docker-machine-nfs <machine-name>

And that’s pretty much it…  It requires admin access to do the NFS mounting so you might need to punch in your password, other than that you can pretty much follow along with what the output is doing.

There are a few caveats to be aware of.

I have noticed that on newer versions of docker-machine, if you run the script too quickly after creating the VM, docker-machine ends up having issues communicating with the Docker daemon.  The work around (for now) is just to wait ~30 seconds the docker-machine VM to boot fully before running the command to mount nfs.

There is also currently an issue on the docker-machine side on version 0.5.5 and above that breaks docker-machine-nfs on the first run, which is described here.  The workaround for that issue is to modify the script and place a “sleep 20” in the script, as per the comments in the issue.  The author appears to have brought the issue up with docker-machine developers so should fixed properly in the near future.

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